EPA430-R-14-003
Inventory of U.S. Greenhouse Gas
Emissions and Sinks:
1990-2012
APRIL 15,2014
U.S. Environmental Protection Agency
1200 Pennsylvania Ave., N.W.
Washington, DC 20460
U.S.A.
-------
HOW TO OBTAIN COPIES
You can electronically download this document on the U.S. EPA's homepage at
. To request free copies of this report, call
the National Service Center for Environmental Publications (NSCEP) at (800) 490-9198, or visit the web site above
and click on "order online" after selecting an edition.
All data tables of this document are available for the full time series 1990 through 2012, inclusive, at the internet site
mentioned above.
FOR FURTHER INFORMATION
Contact Mr. Leif Hockstad, Environmental Protection Agency, (202) 343-9432, hockstad.leif@epa.gov.
Or Ms. Melissa Weitz, Environmental Protection Agency, (202) 343-897, weitz.melissa@epa.gov.
For more information regarding climate change and greenhouse gas emissions, see the EPA web site at
.
Released for printing: April 15, 2014
-------
Acknowledgments
The Environmental Protection Agency would like to acknowledge the many individual and organizational
contributors to this document, without whose efforts this report would not be complete. Although the complete list
of researchers, government employees, and consultants who have provided technical and editorial support is too
long to list here, EPA's Office of Atmospheric Programs would like to thank some key contributors and reviewers
whose work has significantly improved this year's report.
Work on emissions from fuel combustion was led by Leif Hockstad. Venu Ghanta and Sarah Froman directed the
work on mobile combustion and transportation. Work on industrial process emissions was led by Mausami Desai.
Work on fugitive methane emissions from the energy sector was directed by Melissa Weitz and Gate Hight.
Calculations for the waste sector were led by Rachel Schmeltz. Tom Wirth directed work on the Agriculture, and
together with Jennifer Jenkins, directed work on the Land Use, Land-Use Change, and Forestry chapters. Work on
emissions of HFCs, PFCs, and SF6 was directed by Deborah Ottinger and Dave Godwin.
Within the EPA, other Offices also contributed data, analysis, and technical review for this report. The Office of
Transportation and Air Quality and the Office of Air Quality Planning and Standards provided analysis and review
for several of the source categories addressed in this report. The Office of Solid Waste and the Office of Research
and Development also contributed analysis and research.
The Energy Information Administration and the Department of Energy contributed invaluable data and analysis on
numerous energy-related topics. The U.S. Forest Service prepared the forest carbon inventory, and the Department
of Agriculture's Agricultural Research Service and the Natural Resource Ecology Laboratory at Colorado State
University contributed leading research on nitrous oxide and carbon fluxes from soils.
Other government agencies have contributed data as well, including the U.S. Geological Survey, the Federal
Highway Administration, the Department of Transportation, the Bureau of Transportation Statistics, the Department
of Commerce, the National Agricultural Statistics Service, the Federal Aviation Administration, and the Department
of Defense.
We would also like to thank Marian Martin Van Pelt and the full Inventory team at ICF International including
Randy Freed, Diana Pape, Robert Lanza, Toby Mandel, Lauren Pederson, Mollie Averyt, Mark Flugge, Katrin
Moffroid, Seth Greenburg, Larry O'Rourke, Deborah Harris, Leslie Chinery, Dean Gouveia, Jonathan Cohen,
Alexander Lataille, Andrew Pettit, Rachel Steele, Marybeth Riley-Gilbert, Sarah Biggar, Greg Carlock, Ben Eskin,
David Towle, Bikash Acharya, Derina Man, Bobby Renz, Rebecca Ferenchiak, Jessica Renny, Nikita Pavlenko, Jay
Creech, Thuy Phung, Cassandra Snow, Kasey Knoell, Cory Jemison, Matt Lichtash, Hemant Mallya, Tarang Mehta,
Donald Robinson, Lance LaTulipe, Andrew Shartzer, and Matthew Kelly for synthesizing this report and preparing
many of the individual analyses. Eastern Research Group, RTI International, Raven Ridge Resources, and Ruby
Canyon Engineering Inc. also provided significant analytical support.
-------
-------
The United States Environmental Protection Agency (EPA) prepares the official U.S. Inventory of Greenhouse Gas
Emissions and Sinks to comply with existing commitments under the United Nations Framework Convention on
Climate Change (UNFCCC). Under decision 3/CP.5 of the UNFCCC Conference of the Parties, national
inventories for UNFCCC Annex I parties should be provided to the UNFCCC Secretariat each year by April 15.
In an effort to engage the public and researchers across the country, the EPA has instituted an annual public review
and comment process for this document. The availability of the draft document is announced via Federal Register
Notice and is posted on the EPA web site. Copies are also mailed upon request. The public comment period is
generally limited to 30 days; however, comments received after the closure of the public comment period are
accepted and considered for the next edition of this annual report.
in
-------
-------
Table of Contents
ACKNOWLEDGMENTS I
PREFACE Ill
TABLE OF CONTENTS V
LIST OF TABLES, FIGURES, AND BOXES VIM
EXECUTIVE SUMMARY 1
ES.l. Background Information ES-2
ES.2. Recent Trends in U.S. Greenhouse Gas Emissions and Sinks ES-4
ES.3. Overview of Sector Emissions and Trends ES-17
ES.4. Other Information ES-22
1. INTRODUCTION 1-1
1.1 Background Information 1-2
1.2 Institutional Arrangements 1-10
1.3 Inventory Process 1-12
1.4 Methodology and Data Sources 1-13
1.5 Key Categories 1-14
1.6 Quality Assurance and Quality Control (QA/QC) 1-18
1.7 Uncertainty Analysis of Emission Estimates 1-20
1.8 Completeness 1-22
1.9 Organization of Report 1-22
2. TRENDS IN GREENHOUSE GAS EMISSIONS 2-1
2.1 Recent Trends in U.S. Greenhouse Gas Emissions and Sinks 2-1
2.2 Emissions by Economic Sector 2-21
2.3 Indirect Greenhouse Gas Emissions (CO, NOX, NMVOCs, and SO2) 2-31
3. ENERGY 3-1
3.1 Fossil Fuel Combustion (IPCC Source Category 1A) 3-5
3.2 Carbon Emitted from Non-Energy Uses of Fossil Fuels (TPCC Source Category 1A) 3-37
3.3 Incineration of Waste (IPCC Source Category lAla) 3-43
3.4 Coal Mining (IPCC Source Category IBla) 3-47
-------
3.5 Abandoned Underground Coal Mines (IPCC Source Category IBla) 3-51
3.6 Petroleum Systems (IPCC Source Category lB2a) 3-54
3.7 Natural Gas Systems (IPCC Source Category lB2b) 3-62
3.8 Energy Sources of Indirect Greenhouse Gas Emissions 3-74
3.9 International Bunker Fuels (IPCC Source Category 1: Memo Items) 3-75
3.10 Wood Biomass and Ethanol Consumption (IPCC Source Category 1A) 3-80
4. INDUSTRIAL PROCESSES 4-1
4.1 Cement Production (IPCC Source Category 2Al) 4-6
4.2 Lime Production (IPCC Source Category 2A2) 4-9
4.3 Other Process Uses of Carbonates (IPCC Source Category 2A3) 4-15
4.4 Soda Ash Production and Consumption (IPCC Source Category 2A4) 4-18
4.5 Glass Production (IPCC Source Category 2A7) 4-22
4.6 Ammonia Production (IPCC Source Category 2B1) 4-25
4.7 Urea Consumption for Non-Agricultural Purposes 4-28
4.8 Nitric Acid Production (IPCC Source Category 2B2) 4-31
4.9 Adipic Acid Production (IPCC Source Category 2B3) 4-34
4.10 Silicon Carbide Production (IPCC Source Category 2B4) and Consumption 4-37
4.11 Petrochemical Production (IPCC Source Category 2B5) 4-40
4.12 Titanium Dioxide Production (IPCC Source Category 2B5) 4-44
4.13 Carbon Dioxide Consumption (IPCC Source Category 2B5) 4-47
4.14 Phosphoric Acid Production (IPCC Source Category 2B5) 4-50
4.15 Iron and Steel Production (IPCC Source Category 2C1) and Metallurgical Coke Production 4-53
4.16 Ferroalloy Production (IPCC Source Category 2C2) 4-62
4.17 Aluminum Production (IPCC Source Category 2C3) 4-65
4.18 Magnesium Production and Processing (IPCC Source Category 2C4) 4-70
4.19 Zinc Production (IPCC Source Category 2C5) 4-74
4.20 Lead Production (IPCC Source Category 2C5) 4-77
4.21 HCFC-22 Production (IPCC Source Category 2E1) 4-80
4.22 Substitution of Ozone Depleting Substances (IPCC Source Category 2F) 4-82
4.23 Semiconductor Manufacture (IPCC Source Category 2F6) 4-86
4.24 Electrical Transmission and Distribution (IPCC Source Category 2F7) 4-95
4.25 Industrial Sources of Indirect Greenhouse Gases 4-102
5. SOLVENT AND OTHER PRODUCT USE 5-1
5.1 Nitrous Oxide from Product Uses (IPCC Source Category 3D) 5-1
5.2 Indirect Greenhouse Gas Emissions from Solvent Use 5-4
6. AGRICULTURE 6-1
6.1 Enteric Fermentation (IPCC Source Category 4A) 6-2
vi Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
6.2 Manure Management (IPCC Source Category 4B) 6-8
6.3 Rice Cultivation (IPCC Source Category 4C) 6-15
6.4 Agricultural Soil Management (IPCC Source Category 4D) 6-21
6.5 Field Burning of Agricultural Residues (IPCC Source Category 4F) 6-38
7. LAND USE, LAND-USE CHANGE, AND FORESTRY 7-1
7.1 Representation of the United States Land Base 7-4
7.2 Forest Land Remaining Forest Land 7-17
7.3 Land Converted to Forest Land (IPCC Source Category 5A2) 7-34
7.4 Cropland Remaining Cropland (IPCC Source Category 5B1) 7-35
7.5 Land Converted to Cropland (IPCC Source Category 5B2) 7-49
7.6 Grassland Remaining Grassland (IPCC Source Category 5C1) 7-54
7.7 Land Converted to Grassland (IPCC Source Category 5C2) 7-60
Wetlands Remaining Wetlands 7-65
7.8 Settlements Remaining Settlements 7-70
7.9 Land Converted to Settlements (IPCC Source Category 5E2) 7-77
7.10 Other (IPCC Source Category 5G) 7-77
8. WASTE 8-1
8.1 Landfills (IPCC Source Category 6A1) 8-4
8.2 Wastewater Treatment (IPCC Source Category 6B) 8-16
8.3 Waste Incineration (IPCC Source Category 6C) 8-29
8.4 Composting (IPCC Source Category 6D) 8-29
8.5 Waste Sources of Indirect Greenhouse Gases 8-32
9. OTHER 9-1
10. RECALCULATIONS AND IMPROVEMENTS 10-2
11. REFERENCES 11-1
vn
-------
List of Tables, Figures, and Boxes
Tables
Table ES-1: Global Warming Potentials (100-Year Time Horizon) Used in this Report ES-3
Table ES-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (Tg or million metric tons CO2 Eq.) ..ES-5
Table ES-3: CO2 Emissions from Fossil Fuel Combustion by Fuel Consuming End-Use Sector (Tg or million metric
tonsCO2Eq.) ES-11
Table ES-4: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector (Tg or million
metric tons CO2 Eq.) ES-17
Table ES-5: Net CO2 Flux from Land Use, Land-Use Change, and Forestry (Tg or million metric tons CO2 Eq.)..ES-
20
Table ES-6: Emissions from Land Use, Land-Use Change, and Forestry (Tg or million metric tons CO2 Eq.) ..ES-21
Table ES-7: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (Tg or million metric tons CO2 Eq.)
ES-22
Table ES-8: U.S Greenhouse Gas Emissions by Economic Sector with Electricity-Related Emissions Distributed
(Tg or million metric tons CO2 Eq.) ES-23
Table ES-9: Recent Trends in Various U.S. Data (Index 1990 = 100) ES-24
Table 1-1: Global Atmospheric Concentration, Rate of Concentration Change, and Atmospheric Lifetime (Years) of
Selected Greenhouse Gases 1-4
Table 1 -2: Global Warming Potentials and Atmospheric Lifetimes (Years) Used in this Report 1-8
Table 1-3: Comparison of 100-Year GWP values 1-9
Table 1-4: Key Categories for the United States (1990-2012) 1-15
Table 1 -5: Estimated Overall Inventory Quantitative Uncertainty (Tg CO2 Eq. and Percent) 1-21
Table 1-6: IPCC Sector Descriptions 1-22
Table 1-7: List of Annexes 1-23
Table 2-1: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (TgCO2Eq.) 2-4
Table 2-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (Gg) 2-6
Table 2-3: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector (Tg CO2 Eq.)... 2-8
Table 2-4: Emissions from Energy (Tg CO2Eq.) 2-10
Table 2-5: CO2 Emissions from Fossil Fuel Combustion by End-Use Sector (Tg CO2 Eq.) 2-12
Table 2-6: Emissions from Industrial Processes (TgCO2Eq.) 2-14
Table 2-7: N2O Emissions from Solvent and Other Product Use (Tg CO2 Eq.) 2-16
Table 2-8: Emissions from Agriculture (TgCO2Eq.) 2-17
Table 2-9: Net CO2 Flux from Land Use, Land-Use Change, and Forestry (Tg CO2 Eq.) 2-18
Table 2-10: Emissions from Land Use, Land-Use Change, and Forestry (Tg CO2 Eq.) 2-19
Table 2-11: Emissions from Waste (Tg CO2 Eq.) 2-20
Table 2-12: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (Tg CO2 Eq. and Percent of Total in
2012) 2-22
Table 2-13: Electricity Generation-Related Greenhouse Gas Emissions (Tg CO2 Eq.) 2-24
viii Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 2-14: U.S. Greenhouse Gas Emissions by Economic Sector and Gas with Electricity-Related Emissions
Distributed (Tg CO2 Eq.) and Percent of Total in 2012 2-25
Table 2-15: Transportation-Related Greenhouse Gas Emissions (Tg CO2 Eq.) 2-27
Table 2-16: Recent Trends in Various U.S. Data (Index 1990 = 100) 2-30
Table 2-17: Emissions of NOX, CO, NMVOCs, and SO2 (Gg) 2-32
Table 3-1: CO2, CH4, andN2O Emissions fromEnergy (Tg CO2 Eq.) 3-2
Table 3-2: CO2, CH4, andN2O Emissions fromEnergy (Gg) 3-3
Table 3-3: CO2, CH4, and N2O Emissions from Fossil Fuel Combustion (Tg CO2Eq.) 3-5
Table 3-4: CO2, CH4, and N2O Emissions from Fossil Fuel Combustion (Gg) 3-5
Table 3 -5: CO2 Emissions from Fossil Fuel Combustion by Fuel Type and Sector (Tg CO2 Eq.) 3-5
Table 3-6: Annual Change in CO2 Emissions and Total 2012 Emissions from Fossil Fuel Combustion for Selected
Fuels and Sectors (TgCO2Eq. and Percent) 3-6
Table 3 -7: CO2, CH4, and N2O Emissions from Fossil Fuel Combustion by Sector (Tg CO2 Eq.) 3-10
Table 3-8: CO2, CH4, and N2O Emissions from Fossil Fuel Combustion by End-Use Sector (Tg CO2 Eq.) 3-11
Table 3-9: CO2 Emissions from Stationary Fossil Fuel Combustion (Tg CO2 Eq.) 3-12
Table 3-10: CH4 Emissions from Stationary Combustion (Tg CO2 Eq.) 3-13
Table 3-11: N2O Emissions from Stationary Combustion (Tg CO2 Eq.) 3-13
Table 3-12: CO2 Emissions from Fossil Fuel Combustion in Transportation End-Use Sector (Tg CO2 Eq.) 3-19
Table 3-13: CH4 Emissions from Mobile Combustion (Tg CO2 Eq.) 3-21
Table 3-14: N2O Emissions fromMobile Combustion (Tg CO2 Eq.) 3-22
Table 3-15: Carbon Intensity from Direct Fossil Fuel Combustion by Sector (Tg CO2 Eq./QBtu) 3-27
Table 3-16: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Energy-related Fossil Fuel
Combustion by Fuel Type and Sector (Tg CO2Eq. and Percent) 3-29
Table 3-17: Tier 2 Quantitative Uncertainty Estimates for CH4 and N2O Emissions from Energy-Related Stationary
Combustion, Including Biomass (TgCO2Eq. and Percent) 3-33
Table 3-18: Tier 2 Quantitative Uncertainty Estimates for CH4 and N2O Emissions from Mobile Sources (Tg CO2
Eq. and Percent) 3-36
Table 3-19: CO2 Emissions from Non-Energy Use Fossil Fuel Consumption (Tg CO2 Eq.) 3-37
Table 3-20: Adjusted Consumption of Fossil Fuels for Non-Energy Uses (TBtu) 3-38
Table 3-21: 2012 Adjusted Non-Energy Use Fossil Fuel Consumption, Storage, and Emissions 3-39
Table 3-22: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Non-Energy Uses of Fossil Fuels
(Tg CO2 Eq. and Percent) 3-40
Table 3-23: Tier 2 Quantitative Uncertainty Estimates for Storage Factors of Non-Energy Uses of Fossil Fuels
(Percent) 3-41
Table 3-24: CO2 andN2O Emissions from the Incineration of Waste (Tg CO2Eq.) 3-43
Table 3-25: CO2 andN2O Emissions from the Incineration of Waste (Gg) 3-44
Table 3 -26: Municipal Solid Waste Generation (Metric Tons) and Percent Combusted 3-45
Table 3-27: Tier 2 Quantitative Uncertainty Estimates for CO2 and N2O from the Incineration of Waste (Tg CO2 Eq.
and Percent) 3-46
Table 3-28: CH4 Emissions from Coal Mining (Tg CO2 Eq.) 3-47
ix
-------
Table 3-29: CH4 Emissions from Coal Mining (Gg) 3-47
Table 3-30: Coal Production (Thousand Metric Tons) 3-49
Table 3-31: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Coal Mining (Tg CO2 Eq. and
Percent) 3-50
Table 3-32: CH4 Emissions from Abandoned Coal Mines (Tg CO2 Eq.) 3-51
Table 3-33: CH4 Emissions from Abandoned Coal Mines (Gg) 3-51
Table 3-34: Number of gassy abandoned mines present in U.S. basins, grouped by class according to post-
abandonment state 3-53
Table 3-35: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Abandoned Underground Coal
Mines (Tg CO2 Eq. and Percent) 3-54
Table 3-36: CH4 Emissions from Petroleum Systems (Tg CO2 Eq.) 3-56
Table 3-37: CH4 Emissions from Petroleum Systems (Gg) 3-56
Table 3-38: CO2 Emissions from Petroleum Systems (Tg CO2Eq.) 3-56
Table 3-39: CO2 Emissions from Petroleum Systems (Gg) 3-57
Table 3-40: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Petroleum Systems (Tg CO2 Eq. and
Percent) 3-58
Table 3-41: Potential Emissions from CO2 Capture and Transport (Tg CO2 Eq.) 3-61
Table 3-42: Potential Emissions from CO2 Capture and Transport (Gg) 3-62
Table 3-43: CH4 Emissions from Natural Gas Systems (Tg CO2 Eq.)a 3-63
Table 3-44: CH4Emissions from Natural Gas Systems (Gg)a 3-63
Table 3-45: Calculated Potential CH4 and Captured/Combusted CH4 from Natural Gas Systems (Tg CO2 Eq.)... 3-64
Table 3-46: Non-combustion CO2 Emissions from Natural Gas Systems (Tg CO2Eq.) 3-64
Table 3-47: Non-combustion CO2 Emissions from Natural Gas Systems (Gg) 3-64
Table 3-48: Tier 2 Quantitative Uncertainty Estimates for CH4 and Non-energy CO2 Emissions from Natural Gas
Systems (Tg CO2 Eq. and Percent) 3-67
Table 3-49: NOX, CO, and NMVOC Emissions from Energy-Related Activities (Gg) 3-74
Table 3 -50: CO2, CH4, and N2O Emissions from International Bunker Fuels (Tg CO2 Eq.) 3-76
Table 3-51: CO2, CH4 and N2O Emissions from International Bunker Fuels (Gg) 3-76
Table 3-52: Aviation CO2 and N2O Emissions for International Transport (Tg CO2 Eq.) 3-76
Table 3-53: Aviation Jet Fuel Consumption for International Transport (Million Gallons) 3-78
Table 3 -54: Marine Fuel Consumption for International Transport (Million Gallons) 3-78
Table 3-55: CO2 Emissions from Wood Consumption by End-Use Sector (Tg CO2 Eq.) 3-80
Table 3-56: CO2 Emissions from Wood Consumption by End-Use Sector (Gg) 3-80
Table 3-57: CO2 Emissions from Ethanol Consumption (Tg CO2 Eq.) 3-81
Table 3-58: CO2 Emissions from Ethanol Consumption (Gg) 3-81
Table 3-59: Woody Biomass Consumption by Sector (Trillion Btu) 3-81
Table 3-60: Ethanol Consumption by Sector (Trillion Btu) 3-81
Table 4-1: Emissions from Industrial Processes (TgCO2Eq.) 4-3
Table 4-2: Emissions from Industrial Processes (Gg) 4-4
x Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 4-3: CO2 Emissions from Cement Production (Tg CO2 Eq. and Gg) 4-7
Table 4-4: Clinker Production (Gg) 4-8
Table 4-5: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Cement Production (Tg CO2 Eq. and
Percent) 4-9
Table 4-6: CO2 Emissions from Lime Production (Tg CO2 Eq. and Gg) 4-10
Table 4-7: Potential, Recovered, and Net CO2 Emissions from Lime Production (Gg) 4-10
Table 4-8: High-Calcium- and Dolomitic-Quicklime, High-Calcium- and Dolomitic-Hydrated, and Dead-Burned-
Dolomite Lime Production (Gg) 4-12
Table 4-9: Adjusted Lime Production (Gg) 4-12
Table 4-10: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Lime Production (Tg CO2 Eq. and
Percent) in 2012 4-14
Table 4-11: CO2 Emissions from Other Process Uses of Carbonates (Tg CO2 Eq.) 4-15
Table 4-12: CO2 Emissions from Other Process Uses of Carbonates (Gg) 4-15
Table 4-13: Limestone and Dolomite Consumption (Thousand Metric Tons) 4-17
Table 4-14: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Other Process Uses of Carbonates
(Tg CO2 Eq. and Percent) 4-17
Table 4-15: CO2 Emissions from Soda Ash Production and Consumption Not Associated with Glass Manufacturing
(TgCO2Eq.) 4-19
Table 4-16: CO2 Emissions from Soda Ash Production and Consumption Not Associated with Glass Manufacturing
(Gg) 4-19
Table 4-17: Soda Ash Production and Consumption Not Associated with Glass Manufacturing (Gg) 4-20
Table 4-18: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Soda Ash Production and
Consumption (Tg CO2Eq. and Percent) 4-21
Table 4-19: CO2 Emissions from Glass Production (Tg CO2 Eq. and Gg) 4-22
Table 4-20: Limestone, Dolomite, and Soda Ash Consumption Used in Glass Production (Thousand Metric Tons) 4-
23
Table 4-21: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Glass Production (Tg CO2 Eq. and
Percent) 4-24
Table 4-22: CO2 Emissions from Ammonia Production (Tg CO2 Eq.) 4-26
Table 4-23: CO2 Emissions from Ammonia Production (Gg) 4-26
Table 4-24: Ammonia Production and Urea Production (Gg) 4-27
Table 4-25: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Ammonia Production (Tg CO2 Eq.
and Percent) 4-27
Table 4-26: CO2 Emissions from Urea Consumption for Non-Agricultural Purposes (Tg CO2 Eq.) 4-29
Table 4-27: CO2 Emissions from Urea Consumption for Non-Agricultural Purposes (Gg) 4-29
Table 4-28: Urea Production, Urea Applied as Fertilizer, Urea Imports, and Urea Exports (Gg) 4-30
Table 4-29: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Urea Consumption for Non-
Agricultural Purposes (Tg CO2Eq. and Percent) 4-30
Table 4-30: N2O Emissions from Nitric Acid Production (Tg CO2 Eq. and Gg) 4-31
Table 4-31: Nitric Acid Production (Gg) 4-32
XI
-------
Table 4-32: Tier 2 Quantitative Uncertainty Estimates for N2O Emissions from Nitric Acid Production (Tg CO2 Eq.
and Percent) 4-33
Table 4-33: N2O Emissions from Adipic Acid Production (Tg CO2 Eq. and Gg) 4-34
Table 4-34: Adipic Acid Production (Gg) 4-36
Table 4-35: Tier 2 Quantitative Uncertainty Estimates for N2O Emissions from Adipic Acid Production (Tg CO2
Eq. and Percent) 4-36
Table 4-36: CO2 and CH4 Emissions from Silicon Carbide Production and Consumption (Tg CO2 Eq.) 4-38
Table 4-37: CO2 and CH4 Emissions from Silicon Carbide Production and Consumption (Gg) 4-38
Table 4-38: Production and Consumption of Silicon Carbide (Metric Tons) 4-39
Table 4-39: Tier 2 Quantitative Uncertainty Estimates for CH4 and CO2 Emissions from Silicon Carbide Production
and Consumption (Tg CO2 Eq. and Percent) 4-39
Table 4-40: CO2 and CH4 Emissions from Petrochemical Production (Tg CO2 Eq.) 4-41
Table 4-41: CO2 and CH4 Emissions from Petrochemical Production (Gg) 4-41
Table 4-42: Production of Selected Petrochemicals (Thousand Metric Tons) 4-42
Table 4-43: Carbon Black Feedstock (Primary Feedstock) and Natural Gas Feedstock (Secondary Feedstock)
Consumption (Thousand Metric Tons) 4-42
Table 4-44: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Petrochemical Production and CO2
Emissions from Carbon Black Production (Tg CO2Eq. and Percent) 4-43
Table 4-45: CO2 Emissions from Titanium Dioxide (Tg CO2 Eq. and Gg) 4-44
Table 4-46: Titanium Dioxide Production (Gg) 4-45
Table 4-47: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Titanium Dioxide Production (Tg
CO2 Eq. and Percent) 4-46
Table 4-48: CO2 Emissions from CO2 Consumption (Tg CO2 Eq. and Gg) 4-48
Table 4-49: CO2 Production (Gg CO2) and the Percent Used for Non-EOR Applications 4-48
Table 4-50: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from CO2 Consumption (Tg CO2 Eq. and
Percent) 4-49
Table 4-51: CO2 Emissions from Phosphoric Acid Production (Tg CO2 Eq. andGg) 4-50
Table 4-52: Phosphate Rock Domestic Consumption, Exports, and Imports (Gg) 4-51
Table 4-53: Chemical Composition of Phosphate Rock (percent by weight) 4-52
Table 4-54: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Phosphoric Acid Production (Tg
CO2 Eq. and Percent) 4-52
Table 4-55: CO2 and CH4 Emissions from Metallurgical Coke Production (Tg CO2 Eq.) 4-54
Table 4-56: CO2 and CH4 Emissions from Metallurgical Coke Production (Gg) 4-54
Table 4-57: CO2 Emissions from Iron and Steel Production (Tg CO2 Eq.) 4-55
Table 4-58: CO2 Emissions from Iron and Steel Production (Gg) 4-55
Table 4-59: CH4 Emissions from Iron and Steel Production (Tg CO2 Eq.) 4-55
Table 4-60: CH4 Emissions from Iron and Steel Production (Gg) 4-55
Table 4-61: Material Carbon Contents for Metallurgical Coke Production 4-56
Table 4-62: Production and Consumption Data for the Calculation of CO2 and CH4 Emissions from Metallurgical
Coke Production (Thousand Metric Tons) 4-57
xii Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 4-63: Production and Consumption Data for the Calculation of CO2 Emissions from Metallurgical Coke
Production (million ft3) 4-57
Table 4-64: CO2 Emission Factors for Sinter Production and Direct Reduced Iron Production 4-58
Table 4-65: Material Carbon Contents for Iron and Steel Production 4-58
Table 4-66: CH4 Emission Factors for Sinter and Pig Iron Production 4-59
Table 4-67: Production and Consumption Data for the Calculation of CO2 and CH4 Emissions from Iron and Steel
Production (Thousand Metric Tons) 4-59
Table 4-68: Production and Consumption Data for the Calculation of CO2 Emissions from Iron and Steel
Production (million ft3 unless otherwise specified) 4-60
Table 4-69: Tier 2 Quantitative Uncertainty Estimates for CO2 and CH4 Emissions from Iron and Steel Production
and Metallurgical Coke Production (Tg CO2 Eq. and Percent) 4-61
Table 4-70: CO2 and CH4 Emissions from Ferroalloy Production (Tg CO2 Eq.) 4-62
Table 4-71: CO2and CH4 Emissions from Ferroalloy Production (Gg) 4-62
Table 4-72: Production of Ferroalloys (Metric Tons) 4-63
Table 4-73: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Ferroalloy Production (Tg CO2 Eq.
and Percent) 4-64
Table 4-74: CO2 Emissions from Aluminum Production (Tg CO2Eq. and Gg) 4-65
Table 4-75: PFC Emissions from Aluminum Production (Tg CO2 Eq.) 4-66
Table 4-76: PFC Emissions from Aluminum Production (Gg) 4-66
Table 4-77: Production of Primary Aluminum (Gg) 4-69
Table 4-78: Tier 2 Quantitative Uncertainty Estimates for CO2 and PFC Emissions from Aluminum Production (Tg
CO2 Eq. and Percent) 4-69
Table 4-79: SF6 Emissions from Magnesium Production and Processing (Tg CO2 Eq. and Gg) 4-70
Table 4-80: SF6 Emission Factors (kg SF6 per metric ton of magnesium) 4-71
Table 4-81: Tier 2 Quantitative Uncertainty Estimates for SF6 Emissions from Magnesium Production and
Processing (Tg CO2Eq. and Percent) 4-73
Table 4-82: Zinc Production (Metric Tons) 4-75
Table 4-83: CO2 Emissions from Zinc Production (Tg CO2 Eq. and Gg) 4-75
Table 4-84: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Zinc Production (Tg CO2 Eq. and
Percent) 4-77
Table 4-85: CO2 Emissions from Lead Production (Tg CO2 Eq. and Gg) 4-78
Table 4-86: Lead Production (Metric Tons) 4-79
Table 4-87: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Lead Production (Tg CO2 Eq. and
Percent) 4-79
Table 4-88: HFC-23 Emissions fromHCFC-22 Production (Tg CO2 Eq. and Gg) 4-80
Table 4-89: HCFC-22 Production (Gg) 4-81
Table 4-90: Quantitative Uncertainty Estimates for HFC-23 Emissions from HCFC-22 Production (Tg CO2 Eq. and
Percent) 4-82
Table 4-91: Emissions of HFCs and PFCs from ODS Substitutes (TgCO2Eq.) 4-83
Table 4-92: Emissions of HFCs and PFCs from ODS Substitution (Mg) 4-83
xill
-------
Table 4-93: Emissions of HFCs andPFCs fromODS Substitutes (TgCO2Eq.) by Sector 4-84
Table 4-94: Tier 2 Quantitative Uncertainty Estimates for HFC and PFC Emissions from ODS Substitutes (Tg CO2
Eq. and Percent) 4-86
Table 4-95: PFC, HFC, and SF6 Emissions from Semiconductor Manufacture (Tg CO2 Eq.) 4-87
Table 4-96: PFC, HFC, and SF6 Emissions from Semiconductor Manufacture (Mg) 4-87
Table 4-97: Tier 2 Quantitative Uncertainty Estimates for HFC, PFC, and SF6 Emissions from Semiconductor
Manufacture (TgCO2Eq. and Percent) 4-93
Table 4-98: SF6 Emissions from Electric Power Systems and Electrical Equipment Manufacturers (Tg CO2 Eq.).. 4-
95
Table 4-99: SF6 Emissions from Electric Power Systems and Electrical Equipment Manufacturers (Gg) 4-95
Table 4-100 Transmission Mile Coverage and Regression Coefficients for Large and Non-Large Utilities, Percent 4-
98
Table 4-101: Tier 2 Quantitative Uncertainty Estimates for SF6 Emissions from Electrical Transmission and
Distribution (Tg CO2 Eq. and Percent) 4-100
Table 4-102: 2012 Potential and Actual Emissions of HFCs, PFCs, and SF6 from Selected Sources (Tg CO2 Eq.). 4-
102
Table 4-103: NOX, CO, and NMVOC Emissions from Industrial Processes (Gg) 4-102
Table 5-1: N2O Emissions from Solvent and Other Product Use 5-1
Table 5-2: N2O Production (Gg) 5-1
Table 5-3: N2O Emissions fromN2O Product Usage (Tg CO2 Eq. and Gg) 5-2
Table 5-4: Tier 2 Quantitative Uncertainty Estimates for N2O Emissions from N2O Product Usage (Tg CO2 Eq. and
Percent) 5-3
Table 5-5: Emissions of NOX, CO, and NMVOC from Solvent Use (Gg) 5-4
Table 6-1: Emissions from Agriculture (Tg CO2Eq.) 6-2
Table 6-2: Emissions from Agriculture (Gg) 6-2
Table 6-3: CH4 Emissions fromEnteric Fermentation (Tg CO2 Eq.) 6-3
Table 6-4: CH4 Emissions from Enteric Fermentation (Gg) 6-3
Table 6-5: Quantitative Uncertainty Estimates for CH4 Emissions from Enteric Fermentation (Tg CO2 Eq. and
Percent) 6-6
Table 6-6: CH4 and N2O Emissions from Manure Management (Tg CO2Eq.) 6-9
Table 6-7: CH4 andN2O Emissions from Manure Management (Gg) 6-10
Table 6-8: Tier 2 Quantitative Uncertainty Estimates for CH4 and N2O (Direct and Indirect) Emissions from Manure
Management (Tg CO2Eq. and Percent) 6-13
Table 6-9: 2006 IPCC Implied Emission Factor Default Values Compared with Calculated Values for CH4 from
Manure Management (kg/head/year) 6-14
Table 6-10: CH4 Emissions from Rice Cultivation (Tg CO2Eq.) 6-16
Table 6-11: CH4 Emissions from Rice Cultivation (Gg) 6-16
Table 6-12: Rice Area Harvested (Hectares) 6-17
Table 6-13: Ratooned Area as Percent of Primary Growth Area 6-18
Table 6-14: Non-USDA Data Sources for Rice Harvest Information (Citation Year) 6-18
xiv Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 6-15: Non-California Seasonal Emission Factors (kg CHVha-season) 6-19
Table 6-16: California Emission Factors (kg CHVha) 6-19
Table 6-17: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Rice Cultivation (Tg CO2 Eq. and
Percent) 6-20
Table 6-18: N2O Emissions from Agricultural Soils (Tg CO2 Eq.) 6-24
Table 6-19: N2O Emissions from Agricultural Soils (Gg) 6-24
Table 6-20: Direct N2O Emissions from Agricultural Soils by Land Use Type and N Input Type (Tg CO2 Eq.)... 6-25
Table 6-21: Indirect N2O Emissions from all Land-Use Types (TgCO2Eq.) 6-25
Table 6-22: Quantitative Uncertainty Estimates of N2O Emissions from Agricultural Soil Management in 2012 (Tg
CO2 Eq. and Percent) 6-35
Table 6-23: CH4 and N2O Emissions from Field Burning of Agricultural Residues (Tg CO2 Eq.) 6-38
Table 6-24: CH4, N2O, CO, and NOX Emissions fromField Burning of Agricultural Residues (Gg) 6-38
Table 6-25: Agricultural Crop Production (Gg of Product) 6-40
Table 6-26: U.S. Average Percent Crop Area Burned by Crop (Percent) 6-41
Table 6-27: Key Assumptions for Estimating Emissions from Field Burning of Agricultural Residues 6-41
Table 6-28: Greenhouse Gas Emission Ratios and Conversion Factors 6-41
Table 6-29: Tier 2 Quantitative Uncertainty Estimates for CH4 and N2O Emissions from Field Burning of
Agricultural Residues (Tg CO2Eq. and Percent) 6-42
Table 7-1: Net CO2 Flux from Carbon Stock Changes in Land Use, Land-Use Change, and Forestry (Tg CO2 Eq.) 7-
2
Table 7-2: Net CO2 Flux from Carbon Stock Changes in Land Use, Land-Use Change, and Forestry (Tg C) 7-2
Table 7-3: Emissions from Land Use, Land-Use Change, and Forestry (TgCO2Eq.) 7-2
Table 7-4: Emissions from Land Use, Land-Use Change, and Forestry (Gg) 7-3
Table 7-5: Managed and Unmanaged Land Area by Land Use Categories for all 50 States (thousands of hectares) 7-5
Table 7-6: Land Use and Land-Use Change for the United States Managed Land Base for all 50 States (thousands of
hectares) 7-6
Table 7-7: Data sources used to determine land use and land area for the Conterminous United States, Hawaii and
Alaska 7-11
Table 7-8: Total Land Area (Hectares) by Land Use Category for United States Territories 7-16
Table 7-9: Estimated Net Annual Changes in C Stocks (Tg CO2/yr) in Forest and Harvested Wood Pools 7-21
Table 7-10: Estimated Net Annual Changes in C Stocks (Tg C/yr) in Forest and Harvested Wood Pools 7-21
Table 7-11: Estimated Forest area (1,000 ha) and C Stocks (Tg C) in Forest and Harvested Wood Pools 7-21
Table 7-12: Estimates of CO2 (Tg/yr) Emissions for the Lower 48 States and Alaska 7-24
Table 7-13: Tier 2 Quantitative Uncertainty Estimates for Net CO2 Flux from Forest Land Remaining Forest Land:
Changes in Forest C Stocks (Tg CO2 Eq. and Percent) 7-28
Table 7-14: Estimated Non-CO2 Emissions from Forest Fires (Tg CO2Eq.) for U.S. Forests 7-30
Table 7-15: Estimated Non-CO2 Emissions from Forest Fires (Gg Gas) for U.S. Forests 7-30
Table 7-16: Estimated Carbon Released from Forest Fires for U.S. Forests (Tg/yr) 7-31
Table 7-17: Tier 2 Quantitative Uncertainty Estimates of Non-CO2 Emissions from Forest Fires in Forest Land
Remaining Forest Land (Tg CO2 Eq. and Percent) 7-31
xv
-------
Table 7-18: Direct N2O Fluxes from Soils in Forest Land Remaining Forest Land (Tg CO2Eq. and GgN2O).... 7-33
Table 7-19: Quantitative Uncertainty Estimates of N2O Fluxes from Soils in Forest Land Remaining Forest Land
(Tg CO2 Eq. and Percent) 7-34
Table 7-20: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland (Tg CO2 Eq.) 7-36
Table 7-21: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland (Tg C) 7-36
Table 7-22: Tier 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Cropland
Remaining Cropland (Tg CO2Eq. and Percent) 7-42
Table 7-23: Emissions from Liming of Agricultural Soils (TgCO2Eq.) 7-43
Table 7-24: Emissions from Liming of Agricultural Soils (Tg C) 7-43
Table 7-25: Applied Minerals (Million Metric Tons) 7-45
Table 7-26: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Liming of Agricultural Soils (Tg
CO2 Eq. and Percent) 7-45
Table 7-27: CO2 Emissions from Urea Fertilization in Cropland Remaining Cropland (Tg CO2 Eq.) 7-46
Table 7-28: CO2 Emissions from Urea Fertilization in Cropland Remaining Cropland (Tg C) 7-46
Table 7-29: Applied Urea (Million Metric Tons) 7-47
Table 7-30: Quantitative Uncertainty Estimates for CO2 Emissions from Urea Fertilization (Tg CO2 Eq. and Percent)
7-48
Table 7-31: Net CO2 Flux from Soil C Stock Changes in Land Converted to Cropland by Land Use Change
Category (Tg CO2 Eq.) 7-49
Table 7-32: Net CO2 Flux from Soil C Stock Changes inland Converted to Cropland (Tg C) 7-50
Table 7-33: Tier 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Land Converted to
Cropland (Tg CO2 Eq. and Percent) 7-53
Table 7-34: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland (Tg CO2 Eq.) 7-55
Table 7-35: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland (Tg C) 7-55
Table 7-36: Tier 2 Quantitative Uncertainty Estimates for C Stock Changes Occurring Within Grassland Remaining
Grassland (Tg CO2Eq. and Percent) 7-58
Table 7-37: Net CO2 Flux from Soil C Stock Changes for Land Converted to Grassland (Tg CO2 Eq.) 7-60
Table 7-38: Net CO2 Flux from Soil C Stock Changes for Land Converted to Grassland (Tg C) 7-61
Table 7-39: Tier 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Land Converted to
Grassland (Tg CO2 Eq. and Percent) 7-64
Table 7-40: Emissions from Peatlands Remaining Peatlands (Tg CO2Eq.) 7-66
Table 7-41: Emissions from Peatlands Remaining Peatlands (Gg) 7-67
Table 7-42: Peat Production of Lower 48 States (thousand Metric Tons) 7-68
Table 7-43: Peat Production of Alaska (thousand Cubic Meters) 7-68
Table 7-44: Tier-2 Quantitative Uncertainty Estimates for CO2 Emissions from Peatlands Remaining Peatlandsl-69
Table 7-45: Net C Flux from Urban Trees (Tg CO2 Eq. and Tg C) 7-70
Table 7-46: Annual C Sequestration (Metric Tons C/yr), Tree Cover (Percent), and Annual C Sequestration per Area
of Tree Cover (kg C/m2-yr) for 50 states plus the District of Columbia 7-73
Table 7-47: Tier 2 Quantitative Uncertainty Estimates for Net C Flux from Changes in C Stocks in Urban Trees
(Tg CO2 Eq. and Percent) 7-74
xvi Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 7-48: Direct N2O Fluxes from Soils in Settlements Remaining Settlements (Tg CO2 Eq. and Gg N2O) 7-75
Table 7-49: Quantitative Uncertainty Estimates of N2O Emissions from Soils in Settlements Remaining Settlements
(Tg CO2 Eq. and Percent) 7-77
Table 7-50: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills (Tg CO2 Eq.) 7-78
Table 7-51: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills (Tg C) 7-78
Table 7-52: Moisture Contents, C Storage Factors (Proportions of Initial C Sequestered), Initial C Contents, and
Decay Rates for Yard Trimmings andFood Scraps in Landfills 7-80
Table 7-53: C Stocks in Yard Trimmings and Food Scraps in Landfills (Tg C) 7-81
Table 7-54: Tier 2 Quantitative Uncertainty Estimates for CO2 Flux from Yard Trimmings and Food Scraps in
Landfills (Tg CO2 Eq. and Percent) 7-81
Table 8-1: Emissions from Waste (Tg CO2 Eq.) 8-2
Table 8-2: Emissions from Waste (Gg) 8-2
Table 8-3: CH4 Emissions from Landfills (Tg CO2Eq.) 8-6
Table 8-4 :CH4 Emissions from Landfills (Gg) 8-6
Table 8-5: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Landfills (Tg CO2 Eq. and Percent). 8-
10
Table 8-6: Materials Discarded in the Municipal Waste Stream by Waste Type, Percent 8-14
Table 8-7: CH4 and N2O Emissions from Domestic and Industrial Wastewater Treatment (Tg CO2 Eq.) 8-17
Table 8-8: CH4 and N2O Emissions from Domestic and Industrial Wastewater Treatment (Gg) 8-17
Table 8-9: U.S. Population (Millions) and Domestic Wastewater BOD5 Produced (Gg) 8-19
Table 8-10: Domestic Wastewater CH4 Emissions from Septic and Centralized Systems (2012) 8-19
Table 8-11: Industrial Wastewater CH4 Emissions by Sector (2012) 8-20
Table 8-12: U.S. Pulp and Paper, Meat, Poultry, Vegetables, Fruits and Juices, Ethanol, and Petroleum Refining
Production (Tg) 8-20
Table 8-13: Variables Used to Calculate Percent Wastewater Treated Anaerobically by Industry (%) 8-21
Table 8-14: Wastewater Flow (m3/ton) and BOD Production (g/L) for U.S. Vegetables, Fruits, and Juices Production
8-23
Table 8-15: U.S. Population (Millions), Population Served by Biological Denitrification (Millions), Fraction of
Population Served by Wastewater Treatment (%), Available Protein (kg/person-year), Protein Consumed
(kg/person-year), and Nitrogen Removed with Sludge (Gg-N/year) 8-26
Table 8-16: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Wastewater Treatment (Tg CO2 Eq.
and Percent) 8-26
Table 8-17: CH4 and N2O Emissions from Composting (TgCO2Eq.) 8-30
Table 8-18: CH4 and N2O Emissions from Composting (Gg) 8-30
Table 8-19: U.S. Waste Composted (Gg) 8-31
Table 8-20 : Tier 1 Quantitative Uncertainty Estimates for Emissions from Composting (Tg CO2 Eq. and Percent) 8-
31
Table 8-21: Emissions of NOX, CO, and NMVOC from Waste (Gg) 8-32
Table 10-1: Revisions to U.S. Greenhouse Gas Emissions (Tg CO2 Eq.) 10-5
Table 10-2: Revisions to Annual Net CO2 Fluxes from Land Use, Land-Use Change, and Forestry (Tg CO2 Eq.) 10-7
xvil
-------
Figure ES-1: U.S. Greenhouse Gas Emissions by Gas ES-4
Figure ES-2: Annual Percent Change in U.S. Greenhouse Gas Emissions ES-5
Figure ES-3: Annual Greenhouse Gas Emissions Relative to 1990 (1990=0) ES-5
Figure ES-4: 2012 Greenhouse Gas Emissions by Gas (Percentages based on Tg CC>2 Eq.) ES-8
Figure ES-5: 2012 Sources of CCh Emissions ES-9
Figure ES-6: 2012 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type ES-10
Figure ES-7: 2012 End-Use Sector Emissions of CC>2 from Fossil Fuel Combustion ES-10
Figure ES-8: 2012 Sources of CH4 Emissions ES-13
Figure ES-9: 2012 Sources of N2O Emissions ES-15
Figure ES-10: 2012 Sources of HFCs, PFCs, and SF6 Emissions ES-16
Figure ES-11: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector ES-17
Figure ES-12: 2012 U.S. Energy Consumption by Energy Source ES-19
Figure ES-13: Emissions Allocated to Economic Sectors ES-22
Figure ES-14: Emissions with Electricity Distributed to Economic Sectors ES-24
Figure ES-15: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic Product ES-25
Figure ES-16: 2012 Key Categories ES-26
Figure 1-1: Insitutional Arrangements Diagram 1-11
Figure 1-2: U.S. QA/QC Plan Summary 1-20
Figure 2-1: U.S. Greenhouse Gas Emissions by Gas 2-1
Figure 2-2: Annual Percent Change in U.S. Greenhouse Gas Emissions 2-2
Figure 2-3: Cumulative Change in Annual U.S. Greenhouse Gas Emissions Relative to 1990 2-2
Figure 2-4: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector 2-8
Figure 2-5: 2012 Energy Chapter Greenhouse Gas Sources 2-10
Figure 2-6: 2012 U.S. Fossil Carbon Flows (TgCO2Eq.) 2-10
Figure 2-7: 2012 CCh Emissions from Fossil Fuel Combustion by Sector and Fuel Type 2-12
Figure 2-8: 2012 End-Use Sector Emissions of COa from Fossil Fuel Combustion 2-13
Figure 2-9: 2012 Industrial Processes Chapter Greenhouse Gas Sources 2-14
Figure 2-10: 2012 Agriculture Chapter Greenhouse Gas Sources 2-17
Figure 2-11: 2012 Waste Chapter Greenhouse Gas Sources 2-20
Figure 2-12: Emissions Allocated to Economic Sectors 2-21
Figure 2-13: Emissions with Electricity Distributed to Economic Sectors 2-25
Figure 2-14: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic
Product 2-31
Figure 3-1: 2012 Energy Chapter Greenhouse Gas Sources 3-1
Figure 3-2: 2012 U.S. Fossil CarbonFlows (Tg CO2Eq.) 3-2
Figure 3-3: 2012 U.S. Energy Consumption by Energy Source 3-7
xviii Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure 3-4: U.S. Energy Consumption (Quadrillion Btu) 3-8
Figure 3-5: 2012 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type 3-8
Figure 3-6: Annual Deviations from Normal Heating Degree Days for the United States (1950-2012) 3-9
Figure 3-7: Annual Deviations from Normal Cooling Degree Days for the United States (1950-2012) 3-9
Figure 3-8: Nuclear, Hydroelectric, and Wind Power Plant Capacity Factors in the United States (1990-2012).. 3-10
Figure 3-9: Electricity Generation Retail Sales by End-Use Sector 3-14
Figure 3-10: Industrial Production Indices (Index 2007=100) 3-16
Figure 3-11: Sales-Weighted Fuel Economy of New Passenger Cars and Light-Duty Trucks, 1990-2012 3-19
Figure 3-12: Sales of New Passenger Cars and Light-Duty Trucks, 1990-2012 3-19
Figure 3-13: Mobile Source CH4 and N2O Emissions 3-21
Figure 3-14: U.S. Energy Consumption and Energy-Related CO2 Emissions Per Capita and Per Dollar GDP 3-28
Figure 4-1: 2012 Industrial Processes Chapter Greenhouse Gas Sources 4-2
Figure 6-1: 2012 Agriculture Chapter Greenhouse Gas Emission Sources 6-1
Figure 6-2: Sources and Pathways of N that Result in N2O Emissions from Agricultural Soil Management 6-23
Figure 6-3: Crops, Annual Direct N2O Emissions Estimated Using the Tier 3 DAYCENT Model, 1990-2012 (Tg
CO2Eq./year) 6-26
Figure 6-4: Grasslands, Annual Direct N2O Emissions Estimated Using the Tier 3 DAYCENT Model, 1990-2012
(Tg CO2 Eq./year) 6-27
Figure 6-5: Crops, Average Annual N Losses Leading to Indirect N2O Emissions Estimated Using the Tier 3
DAYCENT Model, 1990-2012 (GgN/year) 6-27
Figure 6-6: Grasslands, Average Annual N Losses Leading to Indirect N2O Emissions Estimated Using the Tier 3
DAYCENT Model, 1990-2012 (GgN/year) 6-28
Figure 6-7: Comparison of Measured Emissions at Field Sites and Modeled Emissions Using the DAYCENT
Simulation Model and IPCC Tier 1 Approach 6-36
Figure 7-1. Percent of Total Land Area for each State in the General Land-Use Categories for 2012 7-8
Figure 7-2: Forest Sector Carbon Pools and Flows 7-19
Figure 7-3: Estimates of Net Annual Changes inC Stocks for Major C Pools 7-22
Figure 7-4: Forest Ecosystem Carbon Density Imputed from Forest Inventory Plots, Conterminous U.S., 2001-2009
7-23
Figure 7-5: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2012,
Cropland Remaining Cropland 7-37
Figure 7-6: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2012,
Cropland Remaining Cropland 7-38
Figure 7-7: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2012, Land
Converted to Cropland 7-51
Figure 7-8: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2012, Land
Converted to Cropland 7-51
Figure 7-9: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2012,
Grassland Remaining Grassland 7-56
Figure 7-10: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2012,
Grassland Remaining Grassland 7-56
xix
-------
Figure 7-11: Total Net Annual CCh Flux for Mineral Soils under Agricultural Management within States, 2012,
Land Converted to Grassland 7-62
Figure 7-12: Total Net Annual CCh Flux for Organic Soils under Agricultural Management within States, 2012,
Land Converted to Grassland 7-62
Figure 8-1: 2012 Waste Chapter Greenhouse Gas Sources 8-1
Figure 8-2: Management of Municipal Solid Waste in the United States, 2010 (BioCycle 2010) 8-13
Figure 8-3: MSW Management Trends from 1990 to 2010 (EPA 2011) 8-14
Figure 8-4: Percent of Recovered Degradable Materials from 1990 to 2010, percent (EPA
2011) 8-15
BoxES-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks ES-2
BoxES-2: Recent Trends in Various U.S. Greenhouse Gas Emissions-Related Data ES-24
BoxES- 3: Recalculations of Inventory Estimates ES-27
Box 1-1: Methodological approach for estimating and reporting U.S. emissions and sinks 1-2
Box 1-2: The IPCC Fifth Assessment Report and Global Warming Potentials 1-9
Box 1-3 :IPCC Reference Approach 1-14
Box 2-1: Methodology for Aggregating Emissions by Economic Sector 2-29
Box 2-2: Recent Trends in Various U.S. Greenhouse Gas Emissions-Related Data 2-30
Box 2-3: Sources and Effects of Sulfur Dioxide 2-33
Box 3-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks 3-4
Box 3-2: Energy Data from the Greenhouse Gas Reporting Program 3-4
Box 3 -3: Weather and Non-Fossil Energy Effects on CC>2 from Fossil Fuel Combustion Trends 3-8
Box 3-4: Uses of Greenhouse Gas Reporting Program Data and Improvements in Reporting Emissions from
Industrial Sector Fossil Fuel Combustion 3-25
Box 3-5: Carbon Intensity of U.S. Energy Consumption 3-27
Box 3-6: Carbon Dioxide Transport, Injection, and Geological Storage 3-61
Box 4-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks 4-6
Box 4-2: Potential Emission Estimates of HFCs, PFCs, and SF6 4-101
Box 6-1: Comparison of the U.S. Inventory Seasonal Emission Factors and IPCC (1996) Default Emission Factor. 6-
19
Box 6-2: Tier 1 vs. Tier 3 Approach for Estimating N2O Emissions 6-29
Box 6-3: Comparison of Tier 2 U.S. Inventory Approach and IPCC (2006) Default Approach 6-39
Box 7-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks 7-3
Box 7-2: Preliminary Estimates of Land Use in United States Territories 7-16
Box 7-3: CO2 Emissions from Forest Fires 7-23
Box 7-4: Tier 3 Approach for Soil C Stocks Compared to Tier 1 or 2 Approaches 7-39
Box 7-5: Comparison of the Tier 2 U.S. Inventory Approach and IPCC (2006) Default Approach 7-44
Box 8-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks 8-1
xx Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Box 8-2: Waste Data from the Greenhouse Gas Reporting Program 8-3
Box 8-3: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks 8-12
Box 8-4: Overview of the Waste Sector 8-13
Box 8-5: Description of a Modern, Managed Landfill 8-15
Box 8-6: Biogenic Wastes in Landfills 8-16
xxi
-------
-------
Executive Summary
An emissions inventory that identifies and quantifies a country's primary anthropogenic1 sources and sinks of
greenhouse gases is essential for addressing climate change. This inventory adheres to both (1) a comprehensive
and detailed set of methodologies for estimating sources and sinks of anthropogenic greenhouse gases, and (2) a
common and consistent mechanism that enables Parties to the United Nations Framework Convention on Climate
Change (UNFCCC) to compare the relative contribution of different emission sources and greenhouse gases to
climate change.
In 1992, the United States signed and ratified the UNFCCC. As stated in Article 2 of the UNFCCC, "The ultimate
objective of this Convention and any related legal instruments that the Conference of the Parties may adopt is to
achieve, in accordance with the relevant provisions of the Convention, stabilization of greenhouse gas
concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the
climate system. Such a level should be achieved within a time-frame sufficient to allow ecosystems to adapt
naturally to climate change, to ensure that food production is not threatened and to enable economic development to
proceed in a sustainable manner."2
Parties to the Convention, by ratifying, "shall develop, periodically update, publish and make available.. .national
inventories of anthropogenic emissions by sources and removals by sinks of all greenhouse gases not controlled by
the Montreal Protocol, using comparable methodologies.. ."3 The United States views this report as an opportunity
to fulfill these commitments.
This chapter summarizes the latest information on U.S. anthropogenic greenhouse gas emission trends from 1990
through 2012. To ensure that the U.S. emissions inventory is comparable to those of other UNFCCC Parties, the
estimates presented here were calculated using methodologies consistent with those recommended in the Revised
1996 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories
(IPCC/UNEP/OECD/IEA 1997), the IPCC Good Practice Guidance and Uncertainty Management in National
Greenhouse Gas Inventories (IPCC 2000), and the IPCC Good Practice Guidance for Land Use, Land-Use Change,
and Forestry (IPCC 2003). Additionally, the U.S. emission inventory has continued to incorporate new
methodologies and data from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006). The
use of the most recently published calculation methodologies by the IPCC, as contained in the 2006 IPCC
Guidelines, is considered to improve the rigor and accuracy of this inventory and is fully in line with the prior IPCC
guidance. The structure of this report is consistent with the UNFCCC guidelines for inventory reporting.4 For most
1 The term "anthropogenic," in this context, refers to greenhouse gas emissions and removals that are a direct result of human
activities or are the result of natural processes that have been affected by human activities (IPCC/UNEP/OECD/IEA 1997).
2 Article 2 of the Framework Convention on Climate Change published by the UNEP/WMO Information Unit on Climate
Change. See .
3 Article 4(1 Xa) of the United Nations Framework Convention on Climate Change (also identified in Article 12). Subsequent
decisions by the Conference of the Parties elaborated the role of Annex I Parties in preparing national inventories. See
.
4 See .
Executive Summary ES-1
-------
source categories, the IPCC methodologies were expanded, resulting in a more comprehensive and detailed estimate
of emissions.
Box ES-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks
In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emissions
inventories, the emissions and sinks presented in this report are organized by source and sink categories and
calculated using internationally-accepted methods provided by the IPCC.5 Additionally, the calculated emissions
and sinks in a given year for the United States are presented in a common manner in line with the UNFCCC
reporting guidelines for the reporting of inventories under this international agreement.6 The use of consistent
methods to calculate emissions and sinks by all nations providing their inventories to the UNFCCC ensures that
these reports are comparable. In this regard, U.S. emissions and sinks reported in this inventory report are
comparable to emissions and sinks reported by other countries. Emissions and sinks provided in this inventory do
not preclude alternative examinations, but rather this inventory report presents emissions and sinks in a common
format consistent with how countries are to report inventories under the UNFCCC. The report itself follows this
standardized format, and provides an explanation of the IPCC methods used to calculate emissions and sinks, and
the manner in which those calculations are conducted.
On October 30, 2009, the U.S. Environmental Protection Agency (EPA) published a rule for the mandatory
reporting of greenhouse gases (GHG) from large GHG emissions sources in the United States. Implementation of 40
CFR Part 98 is referred to as the Greenhouse Gas Reporting Program (GHGRP). 40 CFR part 98 applies to direct
greenhouse gas emitters, fossil fuel suppliers, industrial gas suppliers, and facilities that inject CO2 underground for
sequestration or other reasons.7 Reporting is at the facility level, except for certain suppliers of fossil fuels and
industrial greenhouse gases. The GHGRP dataset and the data presented in this inventory report are complementary
and, as indicated in the respective methodological and planned improvements sections in this report's chapters, EPA
is using the data, as applicable, to improve the national estimates presented in this inventory.
ES.l. Background Information
Greenhouse gases trap heat and make the planet warmer. The most important greenhouse gases directly emitted by
humans include CO2, CH4, N2O, and several other fluorine-containing halogenated substances. Although the direct
greenhouse gases CO2, CH4, and N2O occur naturally in the atmosphere, human activities have changed their
atmospheric concentrations. From the pre-industrial era (i.e., ending about 1750) to 2012, concentrations of these
greenhouse gases have increased globally by 40, 151, and 20 percent, respectively (IPCC 2007 and NOAA/ESLR
2013). This annual report estimates the total national greenhouse gas emissions and removals associated with
human activities across the United States.
Global Warming Potentials
Gases in the atmosphere can contribute to the greenhouse effect both directly and indirectly. Direct effects occur
when the gas itself absorbs radiation. Indirect radiative forcing occurs when chemical transformations of the
substance produce other greenhouse gases, when a gas influences the atmospheric lifetimes of other gases, and/or
when a gas affects atmospheric processes that alter the radiative balance of the earth (e.g., affect cloud formation or
5 See < http://www.ipcc-nggip.iges.or.jp/public/index.html>.
6 See.
7 See and .
ES-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
albedo).8 The IPCC developed the Global Warming Potential (GWP) concept to compare the ability of each
greenhouse gas to trap heat in the atmosphere relative to another gas.
The GWP of a greenhouse gas is defined as the ratio of the time-integrated radiative forcing from the instantaneous
release of 1 kilogram (kg) of a trace substance relative to that of 1 kg of a reference gas (IPCC 2001). Direct
radiative effects occur when the gas itself is a greenhouse gas. The reference gas used is CO2, and therefore GWP-
weighted emissions are measured in teragrams (or million metric tons) of CO2 equivalent (Tg CO2 Eq.).9-10 All
gases in this Executive Summary are presented in units of Tg CO2 Eq.
The UNFCCC reporting guidelines for national inventories were updated in 2006,n but continue to require the use
of GWP values from the IPCC Second Assessment Report (SAR) (IPCC 1996). This requirement ensures that
current estimates of aggregate greenhouse gas emissions for 1990 to 2012 are consistent with estimates developed
prior to the publication of the IPCC Third Assessment Report (TAR) (IPCC 2001), the IPCC Fourth Assessment
Report (AR4) (IPCC 2007) and the IPCC Fifth Assessment Report (AR5) (IPCC 2013). Therefore, to comply with
international reporting standards under the UNFCCC, official emission estimates are reported by the United States
using SAR GWP values. All estimates are provided throughout the report in both CO2 equivalents and unweighted
units. A comparison of emission values using the SAR GWP values versus the TAR, AR4 and AR5 GWP values
can be found in Chapter 1 and, in more detail, in Annex 6.1 of this report. The GWP values used in this report are
listed below in Table ES-1.
The official greenhouse gas emissions presented in this report using the SAR GWP values are the final time the SAR
GWP values will be used in the U.S. inventory. The United States and other developed countries have agreed to
submit annual inventories in 2015 and future years to the UNFCCC using GWP values from the IPCC AR4, which
will replace the current use of SAR GWP values in their annual greenhouse gas inventories.12 The use of IPCC AR4
GWP values in future year inventories will apply across the entire time series of the inventory (i.e., from 1990 to
2013 in next year's report).
Table ES-1: Global Warming Potentials (100-Year Time Horizon) Used in this Report
Gas
CO2
CH4a
N2O
HFC-23
HFC-32
HFC-125
HFC-134a
HFC-143a
HFC-152a
HFC-227ea
HFC-236fa
HFC-4310mee
CF4
C2F6
C4Fio
C«Fi4
GWP
1
21
310
11,700
650
2,800
1,300
3,800
140
2,900
6,300
1,300
6,500
9,200
7,000
7,400
8 Albedo is a measure of the Earth's reflectivity, and is defined as the fraction of the total solar radiation incident on a body that
is reflected by it.
9 Carbon comprises 12/44ths of carbon dioxide by weight.
10 One teragram is equal to 1012 grams or one million metric tons.
11 See .
12 "Revision of the UNFCCC reporting guidelines on annual inventories for Parties included in Annex I to the Convention,"
FCCC/CP/201 l/9/Add.2, Decision 6/CP 17, 15 March 2012, available at
Executive Summary ES-3
-------
SF6
23,900
Source: IPCC(1996)
a The CH4 GWP includes the direct
effects and those indirect effects due
to the production of tropospheric
ozone and stratospheric water vapor.
The indirect effect due to production
of CO2 is not included.
ES.2. Recent Trends in U.S. Greenhouse Gas
Emissions and Sinks
In 2012, total U.S. greenhouse gas emissions were 6,525.6 Tg, or million metric tons, CCh Eq. Total U.S. emissions
have increased by 4.7 percent from 1990 to 2012, and emissions decreased from 2011 to 2012 by 3.4 percent (227.4
Tg CO2 Eq.). The decrease from 2011 to 2012 was due to a decrease in the carbon intensity of fuels consumed by
power producers to generate electricity due to a decrease in the price of natural gas, a decrease in transportation
sector emissions attributed to a small increase in fuel efficiency across different transportation modes and limited
new demand for passenger transportation, and much warmer winter conditions resulting in a decreased demand for
heating fuel in the residential and commercial sectors. Since 1990, U.S. emissions have increased at an average
annual rate of 0.2 percent. Figure ES-1 through Figure ES-3 illustrate the overall trends in total U.S. emissions by
gas, annual changes, and absolute change since 1990.
Table ES-2 provides a detailed summary of U.S. greenhouse gas emissions and sinks for 1990 through 2012.
Figure ES-1: U.S. Greenhouse Gas Emissions by Gas
• MFCs, PFCs, & SF6 Nitrous Oxide
7,216 7,254 7,194 7'326 7,118
6,753
i- 526
ES-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure ES-2: Annual Percent Change in U.S. Greenhouse Gas Emissions
4%
3.2%
-3.4%
-6.4%
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Figure ES-3: Annual Greenhouse Gas Emissions Relative to 1990 (1990=0)
1,092
1,200
1,100
1,000
900
d- 800
i" 700 •
O 600 -
<-> 500
I? 400
300
200
100 -
0
-100
874
606
647 650
724
769
808
983 1'021 961
847 — • — • 385
642
520
210
287
292
sssssssss
8 S
Table ES-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (Tg or million metric
tons COz Eq.)
Gas/Source
1990
2005
2008
2009
2010
2011
2012
CO2 5,108.7 6,112.2
Fossil Fuel Combustion 4,745.1 5,752.9
Electricity Generation 1,820.8 2,402.1
Transportation 1,494.0 1,891.7|
Industrial 845.1 827.6
Residential 338.3 357.9
Commercial 219.0 223.5
U.S. Territories 27.9 50.0
Non-Energy Use of Fuels 120.8 141.0
Iron and Steel Production &
Metallurgical Coke
Production 99. si 66.7
Natural Gas Systems 37.7 30.0
Cement Production 33.3 45.9
Lime Production 11.4B 14.0
Incineration of Waste 8.0 12.5
Ammonia Production 13.0 9.2
Other Process Uses of
Carbonates 4.9 6.3
Cropland Remaining Cropland 7-lB 7-9
5,936.9
5,593.4
2,360.9
1,816.5
804.1
346.2
224.7
41.0
128.0
66.8
32.7
41.2
14.0
11.9
8.4
5.9
8.6
5,506.1
5,225.7
2,146.4
1,747.7
727.5
336.4
223.9
43.8
108.1
43.0
32.2
29.4
10.9
11.7
8.5
7.6
7.2
5,722.3
5,404.9
2,259.2
1,765.0
775.6
334.8
220.7
49.6
120.8
55.7
32.4
31.3
12.8
12.0
9.2
9.6
8.6
5,592.2
5,271.1
2,158.5
1,747.9
768.7
324.9
221.5
49.6
117.3
60.0
35.1
32.0
13.5
12.1
9.4
9.3
7.9
5,383.2
5,072.3
2,022.7
1,739.5
774.2
288.9
197.4
49.6
110.3
54.3
35.2
35.1
13.3
12.2
9.4
Executive Summary ES-5
-------
Urea Consumption for Non-
Agricultural Purposes 3.8| 3.7
Petrochemical Production 3.4B 4.3
Aluminum Production 6.8B 4.1
Soda Ash Production and
Consumption 2.7B 2.9
Carbon Dioxide Consumption 1.41 1.3
Titanium Dioxide Production 1.21 1. £
Ferroalloy Production 2.2B 1.4
Zinc Production 0.6 B 1.0
Glass Production 1-SB 1-9
Phosphoric Acid Production 1.6B 1-4
Wetlands Remaining
Wetlands l.OB 1.1
Lead Production O.sB 0.6
Petroleum Systems 0.4^ 0.3
Silicon Carbide Production
and Consumption 0.4B 0.2
Land Use, Land-Use Change,
and Forestry (Sink)"
Wood Biomass and Ethanol
Consumption11
International Bunker Fuels'
CH4
Enteric Fermentation
Natural Gas Systems
Landfills
Coal Mining
Manure Management
Petroleum Systems
Forest Land Remaining Forest
Land
Wastewater Treatment
Rice Cultivation
Stationary Combustion
Abandoned Underground Coal
Mines
Petrochemical Production
Mobile Combustion
Composting
Iron and Steel Production &
Metallurgical Coke
Production l.OB 0.7
Field Burning of Agricultural
Residues 0.3 • 0.2
Ferroalloy Production + B +1
Silicon Carbide Production
and Consumption + • +1
Incineration of Waste +B +1
International Bunker Fuels0 0.11 0.1
N20 398.6 415.8
Agricultural Soil Management 282.1 297.3
Stationary Combustion 12.3 B 20.6
Manure Management 14.4B 17.1
Mobile Combustion 44.0 36.9
Nitric Acid Production IS.lB 16.9
Forest Land Remaining Forest
Land 2.l| 7.0
(831.1) (1,030.7)
229.8
113.1
585.7
142.5
152.0
112.1
53.6
47.6
28.8
8.1
13.3
75
6.6
5.sl
3.1
2.4
1.6
4.1
3.6
4.5
2.9
1.8
1.8
1.6
1.2
1.5
1.2
1.0
0.5
0.3
3.4
2.8
3.0
2.5
1.8
1.6
1.5
0.9
1.0
1.0
1.1
0.5
0.3
4.7
3.5
2.7
2.6
2.3
1.8
1.7
1.2
1.5
1.1
1.0
0.5
0.3
4.0
3.5
3.3
2.6
1.8
1.7
1.7
1.3
1.3
1.2
0.9
0.5
0.3
5.2
3.5
3.4
2.7
1.8
1.7
1.7
1.4
1.2
1.1
0.8
0.5
0.4
0.2
254.7
114.3
606.0
147.0
151.6
114.3
63.5
51.5
28.8
8.7
13.3
7.8
6.6
5.3
2.9
1.9
1.7
0.6
0.3
0.1
423.3
319.0
21.1
17.8
25.5
16.9
7.5
0.1
0.2
0.2
250.5
106.4
596.5
146.1
142.9
115.3
67.1
50.5
29.1
5.8
13.1
7.9
6.6
5.1
2.9
1.8
1.6
0.4
0.2
0.1
412.2
316.4
20.8
17.7
22.7
14.0
5.1
265.1
117.0
585.5
144.9
134.7
109.9
69.2
51.8
29.5
4.7
13.0
9.3
6.4
5.0
3.1
1.8
1.5
0.5
0.2
0.1
409.3
310.1
22.5
17.8
20.7
16.7
4.2
268.1
111.7
578.3
143.0
133.2
107.4
59.8
52.0
30.5
14.0
12.8
7.1
6.3
4.8
3.1
1.7
1.6
0.6
0.3
0.1
417.2
307.8
21.6
18.0
18.5
15.8
11.8
0.2
(981.0) (961.6) (968.0) (980.3) (979.3)
266.8
105.8
567.3
141.0
129.9
102.8
55.8
52.9
31.7
15.3
12.8
7.4
5.7
4.7
3.1
1.7
1.6
0.6
0.3
0.1
410.1
306.6
22.0
18.0
16.5
15.3
ES-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Adipic Acid Production IS.sB 7.4
Wastewater Treatment 3.5 B 4.5
N2O from Product Uses 4.4 B 4.4
Composting 0.4B l.?|
Settlements Remaining
Settlements l.oB 1.5
Incineration of Waste O.sB 0.4
Field Burning of Agricultural
Residues O.lB 0.1
Wetlands Remaining
Wetlands +B +1
International Bunker Fuels' 0.9M 1.0
HFCs 36.9B 119.8
Substitution of Ozone
Depleting Substances'1 O.sB 103.8
HCFC-22 Production 36.4 15.£
Semiconductor Manufacture 0.2B 0.2
PFCs 20.6 5.6
Semiconductor Manufacture 2.2B 2.6
Aluminum Production 18.4B 3.0
SF6 32.6 B 14.7
Electrical Transmission and
Distribution 26.7 11.0
Magnesium Production and
Processing ^.4B 2.9
Semiconductor Manufacture O.sB 0.7
2.6
4.8
4.4
1.9
2.8
4.8
4.4
1.8
4.4
4.9
4.4
1.7
10.6
5.0
4.4
1.7
5.8
5.0
4.4
1.8
1.5
0.4
0.1
1.4
0.4
0.1
1.5
0.4
0.1
1.5
0.4
0.1
1.5
0.4
0.1
1.0
136.0
122.2
13.6
0.2
5.1
2.4
2.7
10.7
0.9
135.1
129.6
5.4
0.1
3.3
1.7
1.6
9.6
1.0
144.0
137.5
6.4
0.2
3.8
2.2
1.6
9.8
1.0
148.6
141.5
6.9
0.2
6.0
3.0
2.9
10.8
1.0
151.2
146.8
4.3
0.2
5.4
2.9
2.5
8.4
8.4
1.9
0.5
7.5
1.7
0.3
7.2
2.2
0.4
7.2
2.9
0.7
1.7
0.7
Total 6,233.2 7,253.8 7,118.1 6,662.9 6,874.7 6,753.0 6,525.6
Net Emissions (Sources and
Sinks) 5,402.1 6,223.1 6,137.1 5,701.2 5,906.7 5,772.7 5,546.3
+ Does not exceed 0.05 Tg CO2 Eq.
a Parentheses indicate negative values or sequestration. The net CCh flux total includes both emissions and
sequestration, and constitutes a net sink in the United States. Sinks are only included in net emissions total.
b Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector
totals. Net carbon fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for Land
Use, Land-Use Change, and Forestry.
c Emissions from International Bunker Fuels are not included in totals.
d Small amounts of PFC emissions also result from this source.
Note: Totals may not sum due to independent rounding.
Figure ES-4 illustrates the relative contribution of the direct greenhouse gases to total U.S. emissions in 2012. The
primary greenhouse gas emitted by human activities in the United States was CC>2, representing approximately 82.5
percent of total greenhouse gas emissions. The largest source of CCh, and of overall greenhouse gas emissions, was
fossil fuel combustion. CH4 emissions, which have decreased by 10.8 percent since 1990, resulted primarily from
enteric fermentation associated with domestic livestock, natural gas systems, and decomposition of wastes in
landfills. Agricultural soil management, manure management, mobile source fuel combustion and stationary fuel
combustion were the major sources of N2O emissions. Ozone depleting substance substitute emissions and
emissions of HFC-23 during the production of HCFC-22 were the primary contributors to aggregate HFC emissions.
PFC emissions resulted as a by-product of primary aluminum production and from semiconductor manufacturing,
while electrical transmission and distribution systems accounted for most SF6 emissions.
Executive Summary ES-7
-------
Figure ES-4: 2012 Greenhouse Gas Emissions by Gas (Percentages based on Tg COz Eq.)
6.3%
MFCs, PFCs,
&SF6
Subtotal
2.5%
Overall, from 1990 to 2012, total emissions of CO2 increased by 274.5 Tg CO2 Eq. (5.4 percent), while total
emissions of CH4 decreased by 68.4 Tg CO2Eq. (10.8 percent), and N2O increased by 11.5 Tg CO2 Eq. (2.9
percent). During the same period, aggregate weighted emissions of HFCs, PFCs, and SF6 rose by 74.8 Tg CO2 Eq.
(83.0 percent). From 1990 to 2012, HFCs increased by 114.3 Tg CO2 Eq. (309.6 percent), PFCs decreased by 15.2
Tg CO2 Eq. (73.8 percent), and SF6 decreased by 24.2 Tg CO2 Eq. (74.3 percent). Despite being emitted in smaller
quantities relative to the other principal greenhouse gases, emissions of HFCs, PFCs, and SF6 are significant because
many of these gases have extremely high global warming potentials and, in the cases of PFCs and SF6, long
atmospheric lifetimes. Conversely, U.S. greenhouse gas emissions were partly offset by carbon sequestration in
forests, trees in urban areas, agricultural soils, and landfilled yard trimmings and food scraps, which, in aggregate,
offset 15.0 percent of total emissions in 2012. The following sections describe each gas's contribution to total U.S.
greenhouse gas emissions in more detail.
Carbon Dioxide Emissions
The global carbon cycle is made up of large carbon flows and reservoirs. Billions of tons of carbon in the form of
CO2 are absorbed by oceans and living biomass (i.e., sinks) and are emitted to the atmosphere annually through
natural processes (i.e., sources). When in equilibrium, carbon fluxes among these various reservoirs are roughly
balanced. Since the Industrial Revolution (i.e., about 1750), global atmospheric concentrations of CO2 have risen
approximately 40 percent (IPCC 2007 and NOAA/ESLR 2013), principally due to the combustion of fossil fuels.
Within the United States, fossil fuel combustion accounted for 94.2 percent of CO2 emissions in 2012. Globally,
approximately 32,579 Tgof CO2 were added to the atmosphere through the combustion of fossil fuels in 2011, of
which the United States accounted for about 17 percent.13 Changes in land use and forestry practices can also emit
CO2 (e.g., through conversion of forest land to agricultural or urban use) or can act as a sink for CO2 (e.g., through
net additions to forest biomass). In addition to fossil fuel combustion, several other sources emit significant
quantities of CO2. These sources include, but are not limited to non-energy use of fuels, iron and steel production
and cement production (Figure ES-5).
13 Global CO2 emissions from fossil fuel combustion were taken from Energy Information Administration International Energy
Statistics 2011 < http://tonto.eia.doe.gov/cfapps/ipdbproject/IEDIndex3.cfm> EIA (2014).
ES-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure ES-5: 2012 Sources of COz Emissions
Fossil Fuel Combustion
Non-Energy Use of Fuels
Iron and Steel Prod, & Metallurgical Coke Prod.
Natural Gas Systems
Cement Production
Lime Production
Incineration of Waste
Ammonia Production
Other Process Uses of Carbonates
Cropland Remaining Cropland
Urea Consumption for Non-Agricultural Purposes
Petrochemical Production
Aluminum Production
Soda Ash Production and Consumption
Carbon Dioxide Consumption
Titanium Dioxide Production
Ferroalloy Production
Zinc Production
Glass Production
Phosphoric Acid Production
Wetlands Remaining Wetlands
Lead Production
Petroleum Systems
Silicon Carbide Production and Consumption
5,072
Note: Electricity generation also includes emissions of less than 0.05 Tg CChEq. from geothermal-based generation.
As the largest source of U.S. greenhouse gas emissions, CCh from fossil fuel combustion has accounted for
approximately 78 percent of GWP-weighted emissions since 1990, and is approximately 78 percent of total GWP-
weighted emissions in 2012. Emissions of CCh from fossil fuel combustion increased at an average annual rate of
0.3 percent from 1990 to 2012. The fundamental factors influencing this trend include (1) a generally growing
domestic economy over the last 23 years, (2) an overall growth in emissions from electricity generation and
transportation activities, along with (3) a general decline in the carbon intensity of fuels combusted for energy in
recent years by most sectors of the economy. Between 1990 and 2012, CCh emissions from fossil fuel combustion
increased from 4,745.1 Tg CCh Eq. to 5,072.3 Tg CCh Eq.—a 6.9 percent total increase over the twenty-three-year
period. From 2011 to 2012, these emissions decreased by 198.8 Tg CCh Eq. (3.8 percent).
Historically, changes in emissions from fossil fuel combustion have been the dominant factor affecting U.S.
emission trends. Changes in CC>2 emissions from fossil fuel combustion are influenced by many long-term and
short-term factors, including population and economic growth, energy price fluctuations, technological changes,
energy fuel choices, and seasonal temperatures. In the short term, the overall consumption of fossil fuels in the
United States fluctuates primarily in response to changes in general economic conditions, energy prices, weather,
and the availability of non-fossil alternatives. For example, in a year with increased consumption of goods and
services, low fuel prices, severe summer and winter weather conditions, nuclear plant closures, and lower
precipitation feeding hydroelectric dams, there would likely be proportionally greater fossil fuel consumption than a
year with poor economic performance, high fuel prices, mild temperatures, and increased output from nuclear and
hydroelectric plants. In the long term, energy consumption patterns respond to changes that affect the scale of
consumption (e.g., population, number of cars, and size of houses), the efficiency with which energy is used in
C02 as a Portion
of all Emissions
Executive Summary ES-9
-------
equipment (e.g., cars, power plants, steel mills, and light bulbs) and behavioral choices (e.g., walking, bicycling, or
telecommuting to work instead of driving).
Figure ES-6: 2012 COz Emissions from Fossil Fuel Combustion by Sector and Fuel Type
2,500 n
2,000
i±f 1,500 -
8
1,000
500 -
0
Relative Contribution
by Fuel Type
i
1,740
i Petroleum
i Coal
i Natural Gas
50
Figure ES-7: 2012 End-Use Sector Emissions of COz from Fossil Fuel Combustion
2,000 -i
1,500 -
d-
LJJ
8 1,000
F
500
0
• From Direct Fossil Fuel Combustion
• From Electricity Consumption
898
50
1,743
&
-1
a
2,023
The five major fuel consuming sectors contributing to CC>2 emissions from fossil fuel combustion are electricity
generation, transportation, industrial, residential, and commercial. CO2 emissions are produced by the electricity
generation sector as they consume fossil fuel to provide electricity to one of the other four sectors, or "end-use"
sectors. For the discussion below, electricity generation emissions have been distributed to each end-use sector on
the basis of each sector's share of aggregate electricity consumption. This method of distributing emissions assumes
that each end-use sector consumes electricity that is generated from the national average mix of fuels according to
their carbon intensity. Emissions from electricity generation are also addressed separately after the end-use sectors
have been discussed.
ES-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Note that emissions from U.S. territories are calculated separately due to a lack of specific consumption data for the
individual end-use sectors. Figure ES-6, Figure ES-7, and Table ES-3 summarize CCh emissions from fossil fuel
combustion by end-use sector.
Table ES-3: COz Emissions from Fossil Fuel Combustion by Fuel Consuming End-Use Sector
(Tg or million metric tons COz Eq.)
End-Use Sector
Transportation
Combustion
Electricity
Industrial
Combustion
Electricity
Residential
Combustion
Electricity
Commercial
Combustion
Electricity
U.S. Territories3
Total
Electricity Generation
1990
1,497.0
1,494.0
3.0 1
1,531.8
845.1
686.vl
931. 4 1
338. 3 1
593.0 1
757.0 1
219.0 1
538.0 I
27.9
4,745.1
1,820.8
2005
1,896.5
1,891.7 1
4.7
1,564.6 1
827.6 1
1737.0 1
1,214.7 1
357.9
856.7
1,027.2
223.5
803.7
. 50.0 1
5,752.9
2,402.1
2008
1,821.2
1,816.5
4.7
1,501.4
804.1
697.3
1,189.2
346.2
842.9
1,040.8
224.7
816.0
41.0
5,593.4
2,360.9
2009
1,752.2
1,747.7
4.5
1,329.5
727.5
602.0
1,122.9
336.4
786.5
977.4
223.9
753.5
43.8
5,225.7
2,146.4
2010
1,769.5
1,765.0
4.5
1,416.6
775.6
641.1
1,175.2
334.8
840.4
993.9
220.7
773.3
49.6
5,404.9
2,259.2
2011
1,752.1
1,747.9
4.3
1,393.6
768.7
624.9
1,115.9
324.9
791.0
959.8
221.5
738.3
49.6
5,271.1
2,158.5
2012
1,743.4
1,739.5
3.9
1,367.1
774.2
592.9
1,014.3
288.9
725.5
897.9
197.4
700.4
49.6
5,072.3
2,022.7
Note: Totals may not sum due to independent rounding. Combustion-related emissions from electricity
generation are allocated based on aggregate national electricity consumption by each end-use sector.
a Fuel consumption by U.S. territories (i.e., American Samoa, Guam, Puerto Rico, U.S. Virgin Islands, Wake
Island, and other U.S. Pacific Islands) is included in this report.
Transportation End-Use Sector. When electricity-related emissions are distributed to economic end-use sectors,
transportation activities accounted for 34.4 percent of U.S. CCh emissions from fossil fuel combustion in 2012. The
largest sources of transportation greenhouse gases in 2012 were passenger cars (43.1 percent); light duty trucks,
which include sport utility vehicles, pickup trucks, and minivans (18.4 percent), freight trucks (21.9 percent),
commercial aircraft (6.2 percent), rail (2.5 percent), and ships and boats (2.2 percent). These figures include direct
emissions from fossil fuel combustion used in transportation and emissions from non-energy use (i.e. lubricants)
used in transportation, as well as HFC emissions from mobile air conditioners and refrigerated transport allocated to
these vehicle types.
In terms of the overall trend, from 1990 to 2012, total transportation emissions rose by 18 percent due, in large part,
to increased demand for travel with limited gains in fuel efficiency over the same time period. The number of
vehicle miles traveled by light-duty motor vehicles (passenger cars and light-duty trucks) increased 35 percent from
1990 to 2012, as a result of a confluence of factors including population growth, economic growth, urban sprawl,
and low fuel prices during the beginning of this period. Almost all of the energy consumed for transportation was
supplied by petroleum-based products, with more than half being related to gasoline consumption in automobiles
and other highway vehicles. Other fuel uses, especially diesel fuel for freight trucks and jet fuel for aircraft,
accounted for the remainder. The primary driver of transportation-related emissions was CCh from fossil fuel
combustion, which increased by 16 percent from 1990 to 2012. This rise in CC>2 emissions, combined with an
increase in HFCs from close to zero emissions in 1990 to 72.9 Tg CCh Eq. in 2012, led to an increase in overall
emissions from transportation activities of 18 percent.
Industrial End-Use Sector. Industrial CO2 emissions, resulting both directly from the combustion of fossil fuels and
indirectly from the generation of electricity that is consumed by industry, accounted for 27 percent of CO2 from
fossil fuel combustion in 2012. Approximately 57 percent of these emissions resulted from direct fossil fuel
combustion to produce steam and/or heat for industrial processes. The remaining emissions resulted from
consuming electricity for motors, electric furnaces, ovens, lighting, and other applications. In contrast to the other
end-use sectors, emissions from industry have steadily declined since 1990. This decline is due to structural changes
in the U.S. economy (i.e., shifts from a manufacturing-based to a service-based economy), fuel switching, and
efficiency improvements.
Executive Summary ES-11
-------
Residential and Commercial End-Use Sectors. The residential and commercial end-use sectors accounted for 20
and 18 percent, respectively, of CC>2 emissions from fossil fuel combustion in 2012. Both sectors relied heavily on
electricity for meeting energy demands, with 72 and 78 percent, respectively, of their emissions attributable to
electricity consumption for lighting, heating, cooling, and operating appliances. The remaining emissions were due
to the consumption of natural gas and petroleum for heating and cooking. Emissions from the residential and
commercial end-use sectors have increased by 9 percent and 19 percent since 1990, respectively, due to increasing
electricity consumption for lighting, heating, air conditioning, and operating appliances.
Electricity Generation. The United States relies on electricity to meet a significant portion of its energy demands.
Electricity generators consumed 35 percent of total U.S. energy uses from fossil fuels and emitted 40 percent of the
CO2 from fossil fuel combustion in 2012. The type of fuel combusted by electricity generators has a significant
effect on their emissions. For example, some electricity is generated through non-fossil fuel options such as nuclear,
hydroelectric, or geothermal energy. Including all electricity generation modes, generators relied on coal for
approximately 39 percent their total energy requirements in2012.14 In addition, the coal used by electricity
generators accounted for 93 percent of all coal consumed for energy in the United States in 2012.15 Recently a
decrease in the carbon intensity of fuels consumed to generate electricity has occurred due to a decrease in coal
consumption, and increased natural gas consumption and other generation sources. Including all electricity
generation modes, electricity generators used natural gas for approximately 29 percent of their total energy
requirements in 2012. Across the time series, changes in electricity demand and the carbon intensity of fuels used
for electricity generation have a significant impact on CCh emissions.
Other significant CC>2 trends included the following:
• CO2 emissions from non-energy use of fossil fuels have decreased by 10.5 Tg CC>2 Eq. (8.7 percent) from
1990 through 2012. Emissions from non-energy uses of fossil fuels were 110.3 Tg CO2 Eq. in 2012, which
constituted 2.0 percent of total national €62 emissions, approximately the same proportion as in 1990.
• CO2 emissions from iron and steel production and metallurgical coke production decreased by 5.7 Tg €62
Eq. (9.5 percent) from 2011 to 2012, reversing a two-year trend of increasing emissions primarily due to
increased steel production associated with improved economic conditions. Despite this, from 1990 through
2012, emissions declined by 45.6 percent (45.5 Tg €62 Eq.). This overall decline is due to the
restructuring of the industry, technological improvements, and increased scrap utilization.
• In 2012, CO2 emissions from cement production increased by 3.0 Tg CO2 Eq. (9.5 percent) from 2011.
After decreasing in 1991 by 2.2 percent from 1990 levels, cement production emissions grew every year
through 2006 except for a slight decrease in 1997. Since 2006, emissions have fluctuated through 2012 to
the economic recession and associated decrease in demand for construction materials. Overall, from 1990
to 2012, emissions from cement production have increased by 5.3 percent, an increase of 1.8 Tg €62 Eq.
• Net CO2 uptake from Land Use, Land-Use Change, and Forestry increased by 148.2 Tg €62 Eq. (17.8
percent) from 1990 through 2012. This increase was primarily due to an increase in the rate of net carbon
accumulation in forest carbon stocks, particularly in aboveground and belowground tree biomass, and
harvested wood pools. Annual carbon accumulation in landfilled yard trimmings and food scraps slowed
over this period, while the rate of carbon accumulation in urban trees increased.
Box ES- 2: Use of ambient measurements systems for validation of emission inventories
In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emission
inventories, the emissions and sinks presented in this report are organized by source and sink categories and
calculated using internationally-accepted methods provided by the IPCC.16 Several recent studies have measured
emissions at the national or regional level (e.g., Petron 2012, Miller et al. 2013) with results that differ from EPA's
estimate of emissions. A recent study (Brandt et al. 2014) reviewed technical literature on methane emissions and
estimated methane emissions from all anthropogenic sources (e.g., livestock, oil and gas, waste emissions) to be
14 See Table 7.2b Electric Power Sector of EIA 2013.
15 See Table 6.2 Coal Consumption by Sector of EIA 2013.
16 See < http://www.ipcc-nggip.iges.or.jp/public/index.html>.
ES-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
greater than EPA's estimate. EPA has engaged with researchers on how remote sensing, ambient measurement, and
inverse modeling techniques for greenhouse gas emissions could assist in improving the understanding of inventory
estimates. An area of particular interest in EPA's outreach efforts is how these data can be used in a manner
consistent with this Inventory report's transparency on its calculation methodologies, and the ability of these
techniques to attribute emissions and removals from remote sensing to anthropogenic sources, as defined by the
IPCC for this report, versus natural sources and sinks. In working with the research community on ambient
measurement and remote sensing techniques to improve national greenhouse gas inventories, EPA relies upon
guidance from the IPCC on the use of measurements and modeling to validate emission inventories.17
Methane Emissions
Methane (CH4) is more than 20 times as effective as CO2 at trapping heat in the atmosphere (IPCC 1996). Over the
last two hundred and fifty years, the concentration of CH4 in the atmosphere increased by 151 percent (IPCC 2007).
Anthropogenic sources of CH4 include natural gas and petroleum systems, agricultural activities, landfills, coal
mining, wastewater treatment, stationary and mobile combustion, and certain industrial processes (see Figure ES-8).
Figure ES-8: 2012 Sources of CH4 Emissions
Enteric Fermentation
Natural Gas Systems
Landfills
Coal Mining
Manure Management
Petroleum Systems
Forest Land Remaining Forest Land
Wastewater Treatment
Rice Cultivation
StationaiY Combustion
Abandoned Underground Coal Mines
Petrochemical Production
Mobile Combustion
Composting
Iron and Steel Prod. & Metallurgical Coke Prod.
Field Burning of Agricultural Residues
Ferroalloy Production
Silicon Carbide Production and Consumption
Incineration of Waste
Some significant trends in U.S. emissions of CH4 include the following:
• Enteric fermentation is the largest anthropogenic source of CH4
enteric fermentation CH4 emissions were 141.0 Tg CO2 Eq. (24
CH4 as a Portion
of all Emissions
<0.5
<0.5
<0.5
< 0.5
) 25
50 75 100 125 150
Tg C02 Eq.
emissions in the United States. In 2012,
.9 percent of total CH4 emissions), which
17
See.
Executive Summary ES-13
-------
represents an increase of 3.1 Tg CO2 Eq. (2.3 percent) since 1990. This increase in emissions from 1990 to
2012 in enteric generally follows the increasing trends in cattle populations. From 1990 to 1995 emissions
increased and then decreased from 1996 to 2001, mainly due to fluctuations in beef cattle populations and
increased digestibility of feed for feedlot cattle. Emissions generally increased from 2005 to 2007, though
with a slight decrease in 2004, as both dairy and beef populations underwent increases and the literature for
dairy cow diets indicated a trend toward a decrease in feed digestibility for those years. Emissions
decreased again from 2008 to 2012 as beef cattle populations again decreased.
• Natural gas systems were the second largest anthropogenic source category of CH4 emissions in the United
States in 2012 with!29.9 Tg CO2 Eq. of CH4 emitted into the atmosphere. Those emissions have decreased
by 26.6 Tg CO2 Eq. (17.0 percent) since 1990. The decrease in CH4 emissions is largely due to the decrease
in emissions from production and distribution. The decrease in production emissions is due to increased
voluntary reductions, from activities such as replacing high bleed pneumatic devices, and the increased use
of plunger lifts for liquids unloading, and increased regulatory reductions. The decrease in distribution
emissions is due to a decrease in cast iron and unprotected steel pipelines. Emissions from field production
accounted for 32.2 percent of CH4 emissions from natural gas systems in 2012. CH4 emissions from field
production decreased by 25.2 percent from 1990 through 2012; however, the trend was not stable over the
time series-emissions from this source increased by 23.4 percent from 1990 through 2006 due primarily to
increases in hydraulically fractured well completions and workovers, and then declined by 39.4 percent
from 2006 to 2012. Reasons for the 2006-2012 trend include an increase in plunger lift use for liquids
unloading, increased voluntary reductions over that time period (including those associated with pneumatic
devices), and Reduced Emissions Completions (RECs) use for well completions and workovers with
hydraulic fracturing.
• Landfills are the third largest anthropogenic source of CH4 emissions in the United States (102.8 Tg CO2
Eq.), accounting for 18.1 percent of total CH4 emissions in 2012. From 1990 to 2012, CH4 emissions from
landfills decreased by 44.9 Tg CO2 Eq. (30.4 percent), with small increases occurring in some interim
years. This downward trend in overall emissions can be attributed to a 21 percent reduction in the amount
of decomposable materials (i.e., paper and paperboard, food scraps, and yard trimmings) discarded in MSW
landfills over the time series (EPA 2010) and an increase in the amount of landfill gas collected and
combusted,18 which has more than offset the additional CH4 emissions resulting from an increase in the
amount of municipal solid waste landfilled.
• In 2012, CH4 emissions from coal mining were 55.8 Tg CO2 Eq., a 4.0 Tg CO2 Eq. (6.7 percent) decrease
below 2011 emission levels. The overall decline of 25.2 Tg CO2 Eq. (31.1 percent) from 1990 results from
the mining of less gassy coal from underground mines and the increased use of CH4 collected from
degasification systems.
• Methane emissions from manure management increased by 68.0 percent since 1990, from 31.5 Tg CO2 Eq.
in 1990 to 52.9 Tg CO2 Eq. in 2012. The majority of this increase was from swine and dairy cow manure,
since the general trend in manure management is one of increasing use of liquid systems, which tends to
produce greater CH4 emissions. The increase in liquid systems is the combined result of a shift to larger
facilities, and to facilities in the West and Southwest, all of which tend to use liquid systems. Also, new
regulations limiting the application of manure nutrients have shifted manure management practices at
smaller dairies from daily spread to manure managed and stored on site.
Nitrous Oxide Emissions
N2O is produced by biological processes that occur in soil and water and by a variety of anthropogenic activities in
the agricultural, energy-related, industrial, and waste management fields. While total N2O emissions are much
lower than CO2 emissions, N2O is approximately 300 times more powerful than CO2 at trapping heat in the
atmosphere (IPCC 1996). Since 1750, the global atmospheric concentration of N2O has risen by approximately 20
percent (IPCC 2007). The main anthropogenic activities producing N2O in the United States are agricultural soil
18 Carbon dioxide emissions from landfills are not included specifically in summing waste sector totals. Net carbon fluxes from
changes in biogenic carbon reservoirs are accounted for in the estimates for Land Use, Land-Use Change, and Forestry.
ES-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
management, stationary fuel combustion, fuel combustion in motor vehicles, manure management and nitric acid
production (see Figure ES-9).
Figure ES-9: 2012 Sources of NzO Emissions
Agricultural Soil Management
Stationary Combustion
Manure Management
Mobile Combustion
Nitric Acid Production
Forest Land Remaining Forest Land
Adipic Acid Production
Wastewater Treatment
N2O from Product Uses
Composting
Settlements Remaining Settlements
Incineration of Waste
Reid Burning of Agricultural Residues
Wetlands Remaining Wetlands
307
10 15
Tg CO2 Eq.
20
25
Some significant trends in U.S. emissions of N2O include the following:
• Agricultural soils accounted for approximately 74.8 percent of N2O emissions and 4.7 percent of total
emissions in the United States in 2012. Estimated emissions from this source in 2012 were 306.6 Tg CO2
Eq. Annual N2O emissions from agricultural soils fluctuated between 1990 and 2012, largely as a
reflection of annual variation in weather patterns, synthetic fertilizer use, and crop production, although
overall emissions were 8.7 percent higher in 2012 than in 1990. Annual N2O emissions from agricultural
soils fluctuated between 1990 and 2012.
• N2O emissions from stationary combustion increased 9.7 Tg CO2 Eq. (79.3 percent) from 1990 through
2012. N2O emissions from this source increased primarily as a result of an increase in the number of coal
fluidized bed boilers in the electric power sector.
• In 2012, total N2O emissions from manure management were estimated to be 18.0 Tg CO2 Eq. (58 Gg); in
1990, emissions were 14.4 Tg CO2 Eq. (46 Gg). These values include both direct and indirect N2O
emissions from manure management. Nitrous oxide emissions have remained fairly steady since 1990.
Small changes in N2O emissions from individual animal groups exhibit the same trends as the animal group
populations, with the overall net effect that N2O emissions showed a 25 percent increase from 1990 to 2012
and a 0.1 percent increase from 2011 through 2012. Overall shifts toward liquid systems have driven down
the emissions per unit of nitrogen excreted.
• In 2012, N2O emissions from mobile combustion were 16.5 Tg CO2 Eq. (4.0 percent of N2O emissions).
From 1990 to 2012, N2O emissions from mobile combustion decreased by 62.4 percent. However, from
1990 to 1998 emissions increased 25.6 percent, due to control technologies that reduced NOX emissions
while increasing N2O emissions. Since 1998, newer control technologies have led to an overall decline of
38.7 Tg CO2 Eq. (70.1 percent) inN2O from this source.
• N2O emissions from adipic acid production were 5.8 Tg CO2 Eq. in 2012, and have decreased significantly
in recent years due to the widespread installation of pollution control measures. Emissions from adipic acid
production have decreased by 63.6 percent since 1990 and by 67.2 percent since a peak in 1995.
Executive Summary ES-15
-------
HFC, RFC, and SF6 Emissions
HFCs and PFCs are families of synthetic chemicals that are used as alternatives to Ozone Depleting Substances,
which are being phased out under the Montreal Protocol and Clean Air Act Amendments of 1990. HFCs and PFCs
do not deplete the stratospheric ozone layer, and are therefore acceptable alternatives under the Montreal Protocol.
These compounds, however, along with SF6, are potent greenhouse gases. In addition to having high global
warming potentials, SF6 and PFCs have extremely long atmospheric lifetimes, resulting in their essentially
irreversible accumulation in the atmosphere once emitted. Sulfur hexafluoride is the most potent greenhouse gas the
IPCC has evaluated (IPCC 1996).
Other emissive sources of these gases include HCFC-22 production, electrical transmission and distribution systems,
semiconductor manufacturing, aluminum production, and magnesium production and processing (see Figure ES-10).
Figure ES-10: 2012 Sources of HFCs, PFCs, and SF6 Emissions
Substitution of Ozone Depleting Substances I ^f ^1 147
Electrical Transmission and Distribution
HCFC-22 Production | HFCs, PFCs, and SF6 as a Portion
of all Emissions
Aluminum Production
Magnesium Production and Processing
Semiconductor Manufacture I 2-5%
1
0 10 20
Tg C02 Eq.
Some significant trends in U.S. HFC, PFC, and SF6 emissions include the following:
• Emissions resulting from the substitution of ozone depleting substances (ODS) (e.g., CFCs) have been
consistently increasing, from small amounts in 1990 to 146.8 Tg CO2 Eq. in 2012. Emissions from ODS
substitutes are both the largest and the fastest growing source of HFC, PFC, and SF6 emissions. These
emissions have been increasing as phase-out of ODS required under the Montreal Protocol came into
effect, especially after 1994, when full market penetration was made for the first generation of new
technologies featuring ODS substitutes.
• GWP-weighted PFC, HFC, and SF6 emissions from semiconductor manufacture have increased by 28
percent from 1990 to 2012, due to the rapid growth of this industry and the increasing complexity of
semiconductor products (more complex devices have a larger number of layers that require additional F-
GHG using process steps). Within that time span, emissions peaked in 1999, the initial year of the EPA's
PFC Reduction / Climate Partnership for the Semiconductor Industry, but have since declined to 3.7 Tg
CO2 Eq. in 2012 (a 48 percent decrease relative to 1999).
ES-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
• SF6 emissions from electric power transmission and distribution systems decreased by 77.5 percent (20.7
Tg CO2 Eq.) from 1990 to 2012, primarily because of higher purchase prices for SF6 and efforts by industry
to reduce emissions.
• PFC emissions from aluminum production decreased by 86.4 percent (15.9 Tg CCh Eq.) from 1990 to
2012, due to both industry emission reduction efforts and declines in domestic aluminum production.
ES.3. Overview of Sector Emissions and Trends
In accordance with the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC/UNEP/OECD/IEA 1997), and the 2003 UNFCCC Guidelines on Reporting and Review (UNFCCC 2003),
Figure ES-11 and Table ES-4 aggregate emissions and sinks by these chapters. Emissions of all gases can be
summed from each source category from IPCC guidance. Over the twenty-three-year period of 1990 to 2012, total
emissions in the Energy, Industrial Processes, and Agriculture sectors grew by 238.8 Tg CCh Eq. (4.5 percent), 18.3
Tg CO2 Eq. (5.8 percent), and 52.3 Tg CCh Eq. (11.0 percent), respectively. Emissions from the Waste and Solvent
and Other Product Use sectors decreased by 41.1 Tg CO2 Eq. (24.9 percent) and less than 0.1 Tg CO2 Eq. (0.4
percent), respectively. Over the same period, estimates of net C sequestration in the Land Use, Land-Use Change,
and Forestry (LULUCF) sector (magnitude of emissions plus CO2 flux from all LULUCF source categories)
increased by 124.1 Tg CO2 Eq. (15.2 percent).
Figure ES-11: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector
7,500
8
Industrial Processes
Agriculture
1
6,000
5,500
5,000
4,500
4,000
3,500
3,000
2,500
2,000 -
1,500 -
1,000
500
0
(500) Land Use, Land-Use Change and Forestry (si
(1,000) -f
(1,500) J
Qi-HrMro^m^or-.co
LULUCF (sources)
Q^-ifMrn^Ln^or^cQCT^Q
8888888888S
rMrMrNfNrvlrNfNfNrslrxIcN
O
fM
Note: Relatively smaller amounts of GWP-weighted emissions are also emitted from the Solvent and Other Product Use sectors
Note: Relatively smaller amounts of GWP-weighted emissions are also emitted from the Solvent and Other Product
Use sectors.
Table ES-4: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC
Sector (Tg or million metric tons COz Eq.)
Chapter/IPCC Sector
1990
2005
2008
2009
2010
2011
2012
Energy
5,260.1
6,243.5
6,071.1 5,674.6 5,860.6 5,712.9 5,498.9
Executive Summary ES-17
-------
Fossil Fuel Combustion 4,745
Natural Gas Systems 194
Non-Energy Use of Fuels 120
Coal Mining 81
Petroleum Systems 36
Stationary Combustion 19
Mobile Combustion 48
Incineration of Waste 8
Abandoned Underground Coal Mines 6
Industrial Processes 316
Substitution of Ozone Depleting
Substances 0.3
Iron and Steel Production &
Metallurgical Coke Production 100
Cement Production 33
Nitric Acid Production 18
Lime Production 11
Ammonia Production 13
Other Process Uses of Carbonates 4
Petrochemical Production 5
Electrical Transmission and
Distribution 26
Aluminum Production 25
Adipic Acid Production 15
Urea Consumption for Non-
Agricultural Purposes 3
HCFC-22 Production 36
Semiconductor Manufacture 2
Soda Ash Production and
Consumption 2
Carbon Dioxide Consumption 1
Titanium Dioxide Production 1
Magnesium Production and
Processing 5
Ferroalloy Production 2
Zinc Production 0
Glass Production 1
Phosphoric Acid Production 1
Lead Production 0
Silicon Carbide Production and
Consumption 0
Solvent and Other Product Use 4
Agriculture 473
Agricultural Soil Management 282
Enteric Fermentation 137
Manure Management 45
Rice Cultivation 7
Field Burning of Agricultural
Residues 0.4
Land Use, Land-Use Change, and
Forestry (Emissions) 13.7
Forest Land Remaining Forest Land 4.6
Cropland Remaining Cropland 7.1
Settlements Remaining Settlements 1.0
Wetlands Remaining Wetlands 1.0
Waste 165.0
Landfills 147.S
Wastewater Treatment 16.6
Composting 0.7
3.5
133.2
112.1
17.8J
33
5,593.4
184.3
128.0
63.5
29.1
27.8
27.4
12.2
5.3
335.9
5,225.7
175.2
108.1
67.1
29.5
27.4
24.5
12.0
5.1
287.8
5,404.9
167.0
120.8
69.2
29.9
28.9
22.5
12.4
5.0
324.6
5,271.1
168.3
117.3
59.8
30.9
28.0
20.2
12.5
4.8
342.9
5,072.3
165.1
110.3
55.8
32.1
27.7
18.2
12.6
4.7
334.4
122.2
67.5
41.2
16.9
14.0
8.4
5.9
6.5
8.4
7.2
2.6
4.1
13.6
3.0
2.9
1.8
1.8
1.9
1.6
1.2
1.5
1.2
0.5
0.2
4.4
543.4
319.0
147.0
69.3
7.8
0.4
27.3
16.2
8.6
1.5
1.0
136.0
114.3
18.1
3.5
129.6
43.4
29.4
14.0
10.9
8.5
7.6
5.7
7.5
4.6
2.8
3.4
5.4
2.2
2.5
1.8
1.6
1.7
1.5
0.9
1.0
1.0
0.5
0.2
4.4
538.9
316.4
146.1
68.2
7.9
0.4
20.5
10.8
7.2
1.4
1.1
136.5
115.3
17.9
3.3
137.5
56.3
31.3
16.7
12.8
9.2
9.6
6.5
7.2
4.3
4.4
4.7
6.4
2.8
2.6
2.3
1.8
2.2
1.7
1.2
1.5
1.1
0.5
0.2
4.4
534.2
310.1
144.9
69.6
9.3
0.3
20.0
8.9
8.6
1.5
1.0
131.1
109.9
17.9
3.2
141.5
60.6
32.0
15.8
13.5
9.4
9.3
6.6
7.2
6.2
10.6
4.0
6.9
3.9
2.6
1.8
1.7
2.9
1.7
1.3
1.3
1.2
0.5
0.2
4.4
528.3
307.8
143.0
70.0
7.1
0.4
36.0
25.7
7.9
1.5
0.9
128.5
107.4
17.8
3.3
54.9
35.1
15.3
13.3
9.4
8.0
6.6
6.0
5.9
5.8
5.2
4.3
3.7
2.7
1.8
1.7
1.7
1.7
1.4
1.2
1.1
0.5
0.2
4.4
526.3
306.6
141.0
70.9
7.4
0.4
37.8
28.1
7.4
1.5
0.8
124.0
102.8
17.8
3.3
Total Emissions
6,233.2
7,253.8
7,118.1 6,662.9 6,874.7 6,753.0 6,525.6
Net CO2 Flux From Land Use, Land-Use
Change and Forestry (Sinks)* (831.1)
(981.0) (961.6) (968.0) (980.3) (979.3)
ES-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Net Emissions (Sources and Sinks) 5,402.1 6,223.1 6,137.1 5,701.2 5,906.7 5,772.7 5,546.3
* The net CCh flux total includes both emissions and sequestration, and constitutes a sink in the United States. Sinks are only
included in net emissions total. Please refer to Table ES-5 for a breakout by source.
Note: Totals may not sum due to independent rounding.
Note: Parentheses indicate negative values or sequestration.
Energy
The Energy chapter contains emissions of all greenhouse gases resulting from stationary and mobile energy
activities including fuel combustion and fugitive fuel emissions. Energy-related activities, primarily fossil fuel
combustion, accounted for the vast majority of U.S. CC>2 emissions for the period of 1990 through 2012. In 2012,
approximately 82 percent of the energy consumed in the United States (on a Btu basis) was produced through the
combustion of fossil fuels. The remaining 18 percent came from other energy sources such as hydropower, biomass,
nuclear, wind, and solar energy (see Figure ES-12). Energy-related activities are also responsible for CH4 and N2O
emissions (40 percent and 9 percent of total U.S. emissions of each gas, respectively). Overall, emission sources in
the Energy chapter account for a combined 84.3 percent of total U.S. greenhouse gas emissions in 2012.
Figure ES-12: 2012 U.S. Energy Consumption by Energy Source
Renewable
Energy
9.3%
Nuclear Electric
Power
8.5%
Industrial Processes
The Industrial Processes chapter contains by-product or fugitive emissions of greenhouse gases from industrial
processes not directly related to energy activities such as fossil fuel combustion. For example, industrial processes
can chemically transform raw materials, which often release waste gases such as CCh, CH4, and N2O. These
processes include iron and steel production and metallurgical coke production, cement production, ammonia
production and urea consumption, lime production, other process uses of carbonates (e.g., flux stone, flue gas
desulfurization, and glass manufacturing), soda ash production and consumption, titanium dioxide production,
phosphoric acid production, ferroalloy production, glass production, CCh consumption, silicon carbide production
and consumption, aluminum production, petrochemical production, nitric acid production, adipic acid production,
lead production, and zinc production. Additionally, emissions from industrial processes release HFCs, PFCs, and
SF6. Overall, emission sources in the Industrial Process chapter account for 5.1 percent of U.S. greenhouse gas
emissions in 2012.
Executive Summary ES-19
-------
Solvent and Other Product Use
The Solvent and Other Product Use chapter contains greenhouse gas emissions that are produced as a by-product of
various solvent and other product uses. In the United States, emissions from N2O from product uses, the only source
of greenhouse gas emissions from this sector, accounted for less than 0.1 percent of total U.S. anthropogenic
greenhouse gas emissions on a carbon equivalent basis in 2012.
Agriculture
The Agricultural chapter contains anthropogenic emissions from agricultural activities (except fuel combustion,
which is addressed in the Energy chapter, and agricultural CO2 fluxes, which are addressed in the Land Use, Land-
Use Change, and Forestry chapter). Agricultural activities contribute directly to emissions of greenhouse gases
through a variety of processes, including the following source categories: enteric fermentation in domestic livestock,
livestock manure management, rice cultivation, agricultural soil management, and field burning of agricultural
residues. CH4 and N2O were the primary greenhouse gases emitted by agricultural activities. CH4 emissions from
enteric fermentation and manure management represented 24.9 percent and 9.3 percent of total CH4 emissions from
anthropogenic activities, respectively, in 2012. Agricultural soil management activities such as fertilizer application
and other cropping practices were the largest source of U.S. N2O emissions in 2012, accounting for 74.8 percent. In
2012, emission sources accounted for in the Agricultural chapters were responsible for 8.1 percent of total U.S.
greenhouse gas emissions.
Land Use, Land-Use Change, and Forestry
The Land Use, Land-Use Change, and Forestry chapter contains emissions of CH4 and N2O, and emissions and
removals of CO2 from forest management, other land-use activities, and land-use change. Forest management
practices, tree planting in urban areas, the management of agricultural soils, and the landfilling of yard trimmings
and food scraps resulted in a net uptake (sequestration) of C in the United States. Forests (including vegetation,
soils, and harvested wood) accounted for 88 percent of total 2012 net CO2 flux, urban trees accounted for 9 percent,
mineral and organic soil carbon stock changes accounted for 1 percent, and landfilled yard trimmings and food
scraps accounted for 1 percent of the total net flux in 2012. The net forest sequestration is a result of net forest
growth and increasing forest area, as well as a net accumulation of carbon stocks in harvested wood pools. The net
sequestration in urban forests is a result of net tree growth in these areas. In agricultural soils, mineral and organic
soils sequester approximately 4 times as much C as is emitted from these soils through liming and urea fertilization.
The mineral soil C sequestration is largely due to the conversion of cropland to permanent pastures and hay
production, a reduction in summer fallow areas in semi-arid areas, an increase in the adoption of conservation tillage
practices, and an increase in the amounts of organic fertilizers (i.e., manure and sewage sludge) applied to
agriculture lands. The landfilled yard trimmings and food scraps net sequestration is due to the long-term
accumulation of yard trimming carbon and food scraps in landfills.
Land use, land-use change, and forestry activities in 2012 resulted in a net C sequestration of 979.3 Tg CO2 Eq.
(Table ES-5). This represents an offset of 18.2 percent of total U.S. CO2 emissions, or 15.0 percent of total
greenhouse gas emissions in 2012. Between 1990 and 2012, total land use, land-use change, and forestry net C flux
resulted in a 17.8 percent increase in CO2 sequestration, primarily due to an increase in the rate of net C
accumulation in forest C stocks, particularly in aboveground and belowground tree biomass, and harvested wood
pools. Annual C accumulation in landfilled yard trimmings and food scraps slowed over this period, while the rate
of annual C accumulation increased in urban trees.
Table ES-5: Net COz Flux from Land Use, Land-Use Change, and Forestry (Tg or million metric
tons COz Eq.)
Sink Category
Forest Land Remaining Forest Land
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
1990
(704.6)
(51.9)
26.9
(9.6)
(7.3)
2005
(927.2)
(29.1)1
20.91
5.6
(8.3)
2008
(871.0)
(29.8)
16.8
6.8
(8.7)
2009
(849.4)
(29.2)
16.8
6.8
(8.7)
2010
(855.7)
(27.6)
16.8
6.7
(8.6)
2011
(867.1)
(27.5)
16.8
6.7
(8.6)
2012
(866.5)
(26.5)
16.8
6.7
(8.5)
ES-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Settlements Remaining Settlements (60.4) (80.5) (83.9) (85.0) (86.1) (87.3) (88.4)
Other (Landfilled Yard Trimmings
and Food Scraps) (24.2) (12.0) (11.2) (12.9) (13.6) (13.5) (13.0)
Total (831.1) (1,030.7) (981.0) (961.6) (968.0) (980.3) (979.3)
Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
Emissions from Land Use, Land-Use Change, and Forestry are shown in Table ES-6. Liming of agricultural soils
and urea fertilization in 2012 resulted in CCh emissions of 7.4 Tg CCh Eq. (7,381 Gg). Lands undergoing peat
extraction (i.e., Peatlands Remaining Peatlands) resulted in CO2 emissions of 0.8 Tg CO2 Eq. (830 Gg), and N2O
emissions of less than 0.1 Tg €62 Eq. The application of synthetic fertilizers to forest soils in 2012 resulted in
direct N2O emissions of 0.4 Tg €62 Eq. (1 Gg). Direct N2O emissions from fertilizer application to forest soils have
increased by 455 percent since 1990, but still account for a relatively small portion of overall emissions.
Additionally, direct N2O emissions from fertilizer application to settlement soils in 2012 accounted for 1.5 Tg €62
Eq. (5 Gg). This represents an increase of 48 percent since 1990. Forest fires in 2012 resulted in CH4 emissions of
15.3 Tg CO2 Eq. (727 Gg), and in N2O emissions of 12.5 Tg CO2 Eq. (40 Gg).
Table ES-6: Emissions from Land Use, Land-Use Change, and Forestry (Tg or million metric
tons COz Eq.)
Source Category 1990 2005 2008 2009 2010 2011 2012
C02 8.1 8.9 9.6 8.3 9.6 8.8 8.2
Cropland Remaining Cropland: Liming of
Agricultural Soils 4.?B 4.sl 5.0 3.7 4.8 3.9 3.9
Cropland Remaining Cropland: Urea Fertilization 2.4H 3.5H 3.6 3.6 3.8 4.0 3.4
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands l.ol 1.11 1.0 1.1 1.0 0.9
CH4 2.sl 8.11 8.7 5.8 4.7 14.0
Forest Land Remaining Forest Land:
ForestFires 2.5B 8.l| 8.7 5.8 4.7 14.0 15.3
N20 3.ll 8.4l 9.0 6.5 5.7 13.3 14.3
Forest Land Remaining Forest Land:
ForestFires 2.ol 6.6M 7.1 4.7 3.9 11.4 12.5
Forest Land Remaining Forest Land:
Forest Soils O.ll 0.4l 0.4 0.4 0.4 0.4 0.4
Settlements Remaining Settlements:
Settlement Soils l.oB l.sB 1.5 1.4 1.5 1.5 1.5
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands + + + + + + +
Total 13.7 25.5 27.3 20.5 20.0 36.0 37.8
+ Less than 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Waste
The Waste chapter contains emissions from waste management activities (except incineration of waste, which is
addressed in the Energy chapter). Landfills were the largest source of anthropogenic greenhouse gas emissions in
the Waste chapter, accounting for 82.9 percent of this chapter's emissions, and 18.1 percent of total U.S. CH4
emissions.19 Additionally, wastewater treatment accounts for 14.3 percent of Waste emissions, 2.2 percent of U.S.
CH4 emissions, and 1.2 percent of U.S. N2O emissions. Emissions of CH4 and N2O from composting are also
accounted for in this chapter, generating emissions of 1.6 Tg €62 Eq. and 1.8 Tg €62 Eq., respectively. Overall,
19 Landfills also store carbon, due to incomplete degradation of organic materials such as wood products and yard trimmings, as
described in the Land-Use, Land-Use Change, and Forestry chapter of the Inventory report.
Executive Summary ES-21
-------
emission sources accounted for in the Waste chapter generated 1.9 percent of total U.S. greenhouse gas emissions in
2012.
ES.4. Other Information
Emissions by Economic Sector
Throughout the Inventory of U.S. Greenhouse Gas Emissions and Sinks report, emission estimates are grouped into
six sectors (i.e., chapters) defined by the IPCC: Energy; Industrial Processes; Solvent Use; Agriculture; Land Use,
Land-Use Change, and Forestry; and Waste. While it is important to use this characterization for consistency with
UNFCCC reporting guidelines, it is also useful to allocate emissions into more commonly used sectoral categories.
This section reports emissions by the following economic sectors: Residential, Commercial, Industry,
Transportation, Electricity Generation, Agriculture, and U.S. Territories.
Table ES-7 summarizes emissions from each of these sectors, and Figure ES-13 shows the trend in emissions by
sector from 1990 to 2012.
Figure ES-13: Emissions Allocated to Economic Sectors
2,500
2,000 •
s
Q
1,000
500
Electric
Power Industiy
Transportation
Industry
~ Agriculture
m Commercial (Red)
Residential (Blue)
OCTlCT»Cri
-------
Note: Totals may not sum due to independent rounding. Emissions include CCh, CELi, N2O, HFCs, PFCs, and SFe.
See Table 2-12 for more detailed data.
Using this categorization, emissions from electricity generation accounted for the largest portion (32 percent) of
U.S. greenhouse gas emissions in 2012. Transportation activities, in aggregate, accounted for the second largest
portion (28 percent), while emissions from industry accounted for the third largest portion (20 percent) of U.S.
greenhouse gas emissions in 2012. In contrast to electricity generation and transportation, emissions from industry
have in general declined over the past decade. The long-term decline in these emissions has been due to structural
changes in the U.S. economy (i.e., shifts from a manufacturing-based to a service-based economy), fuel switching,
and energy efficiency improvements. The remaining 21 percent of U.S. greenhouse gas emissions were contributed
by, in order of importance, the agriculture, commercial, and residential sectors, plus emissions from U.S. Territories.
Activities related to agriculture accounted for 9 percent of U.S. emissions; unlike other economic sectors,
agricultural sector emissions were dominated by N2O emissions from agricultural soil management and CH4
emissions from enteric fermentation. The commercial and residential sectors each accounted for 5 percent of
emissions and U.S. Territories accounted for 1 percent of emissions; emissions from these sectors primarily
consisted of CO2 emissions from fossil fuel combustion. CO2 was also emitted and sequestered by a variety of
activities related to forest management practices, tree planting in urban areas, the management of agricultural soils,
and landfilling of yard trimmings.
Electricity is ultimately consumed in the economic sectors described above. Table ES-8 presents greenhouse gas
emissions from economic sectors with emissions related to electricity generation distributed into end-use categories
(i.e., emissions from electricity generation are allocated to the economic sectors in which the electricity is
consumed). To distribute electricity emissions among end-use sectors, emissions from the source categories
assigned to electricity generation were allocated to the residential, commercial, industry, transportation, and
agriculture economic sectors according to retail sales of electricity.20 These source categories include CO2 from
fossil fuel combustion and the use of limestone and dolomite for flue gas desulfurization, CO2 and N2O from
incineration of waste, CH4 and N2O from stationary sources, and SF6 from electrical transmission and distribution
systems.
When emissions from electricity are distributed among these sectors, industrial activities and transportation account
for the largest shares of U.S. greenhouse gas emissions (each with 28 percent) in 2012. The residential and
commercial sectors contributed the next largest shares of total U.S. greenhouse gas emissions in 2012. Emissions
from these sectors increase substantially when emissions from electricity are included, due to their relatively large
share of electricity consumption (e.g., lighting, appliances, etc.). In all sectors except agriculture, CO2 accounts for
more than 80 percent of greenhouse gas emissions, primarily from the combustion of fossil fuels. Figure ES-14
shows the trend in these emissions by sector from 1990 to 2012.
Table ES-8: U.S Greenhouse Gas Emissions by Economic Sector with Electricity-Related
Emissions Distributed (Tg or million metric tons COz Eq.)
Implied Sectors
Industry
Transportation
Commercial
Residential
Agriculture
U.S. Territories
Total Emissions
Land Use, Land-Use Change,
and Forestry (Sinks)
Net Emissions (Sources and
Sinks)
1990
2,173.9
1,556.3
936.7B
953. ll
579.4
33.7
6,233.2
(831.1)
5,402.1
2005
2,093.7
2,022.0
1,188.6
1,243.5
647.7
58.2
7,253.8
(1,030.7)
6,223.1
2008
2,009.0
1,939.9
1,209.3
1,222.9
687.1
49.8
7,118.1
(981.0)
6,137.1
2009
1,766.0
1,866.9
1,149.6
1,159.2
673.1
47.9
6,662.9
(961.6)
5,701.2
2010
1,885.4
1,880.9
1,164.7
1,216.5
669.3
58.0
6,874.7
(968.0)
5,906.7
2011
1,869.2
1,856.4
1,131.1
1,160.1
678.2
57.9
6,753.0
(980.3)
5,772.7
2012
1,821.2
1,841.0
1,067.5
1,061.7
676.3
57.9
6,525.6
(979.3)
5,546.3
See Table 2-14 for more detailed data.
20 Emissions were not distributed to U.S. territories, since the electricity generation sector only includes emissions related to the
generation of electricity in the 50 states and the District of Columbia.
Executive Summary ES-23
-------
Figure ES-14: Emissions with Electricity Distributed to Economic Sectors
2,500
2,000
d- 1,500
LU
8
1,000
500
0
Industry (Green)
Transportation
(Purple)
Residential (Blue)
Commercial (Red)
' Agriculture
Box ES- 2: Recent Trends in Various U.S. Greenhouse Gas Emissions-Related Data
Total emissions can be compared to other economic and social indices to highlight changes over time. These
comparisons include: (1) emissions per unit of aggregate energy consumption, because energy-related activities are
the largest sources of emissions; (2) emissions per unit of fossil fuel consumption, because almost all energy-related
emissions involve the combustion of fossil fuels; (3) emissions per unit of electricity consumption, because the
electric power industry—utilities and nonutilities combined—was the largest source of U.S. greenhouse gas
emissions in 2012; (4) emissions per unit of total gross domestic product as a measure of national economic activity;
and (5) emissions per capita.
Table ES-9 provides data on various statistics related to U.S. greenhouse gas emissions normalized to 1990 as a
baseline year. Greenhouse gas emissions in the United States have grown at an average annual rate of 0.2 percent
since 1990. This rate is slightly slower than that for total energy and for fossil fuel consumption, and much slower
than that for electricity consumption, overall gross domestic product and national population (see Figure ES-15).
Table ES-9: Recent Trends in Various U.S. Data (Index 1990 = 100)
Variable
1990
Greenhouse Gas Emissions''
Energy Consumption b
Fossil Fuel Consumption b
Electricity Consumption b
GDPC
Population d
Avg. Annual
2008 2009 2010 2011 2012 Growth Rate
108
116
111
137
168
125
105
113
108
135
173
125
0.2%
0.6%
0.4%
1.4%
2.5%
1.0%
a GWP-weighted values
b Energy content-weighted values (EIA 2013)
c Gross Domestic Product in chained 2009 dollars (BEA 2013)
d U.S. Census Bureau (2013)
ES-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure ES-15: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic
Product
8
I
£
175
165
155 -
145
135
125
115
105
95
85
75
65
55
Real GDP
Population
Emissions
per capita
Emissions
per $GDP
S
01
Source: BEA (2013), U.S. Census Bureau (2013), and emission estimates in this report.
Key Categories
The IPCC Good Practice Guidance (IPCC 2000) defines a key category as a "[source or sink category] that is
prioritized within the national inventory system because its estimate has a significant influence on a country's total
inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions, or both."21
By definition, key categories are sources or sinks that have the greatest contribution to the absolute overall level of
national emissions in any of the years covered by the time series. In addition, when an entire time series of emission
estimates is prepared, a thorough investigation of key categories must also account for the influence of trends of
individual source and sink categories. Finally, a qualitative evaluation of key categories should be performed, in
order to capture any key categories that were not identified in either of the quantitative analyses.
Figure ES-16 presents 2012 emission estimates for the key categories as defined by a level analysis (i.e., the
contribution of each source or sink category to the total inventory level). The UNFCCC reporting guidelines request
that key category analyses be reported at an appropriate level of disaggregation, which may lead to source and sink
category names which differ from those used elsewhere in the inventory report. For more information regarding key
categories, see section 1.5 and Annex 1.
21 See Chapter 7 "Methodological Choice and Recalculation" in IPCC (2000). .
Executive Summary ES-25
-------
Figure ES-16: 2012 Key Categories
CO2 Emissions from Stationary Combustion - Coal - Elec. Gen.
C02 Emissions from Mobile Combustion: Road
CO2 Emissions from Stationary Combustion - Gas - Elec. Gen.
C02 Emissions from Stationary Combustion - Gas - Industrial
C02 Emissions from Stationary Combustion - Oil - Industrial
Direct N2O Emissions from Agricultural Soil Management
CO2 Emissions from Stationary Combustion - Gas - Residential
CO2 Emissions from Stationary Combustion - Gas - Commercial
CO2 Emissions from Mobile Combustion: Aviation
CH4 Emissions from Enteric Fermentation
Emissions from Substitutes for Ozone Depleting Substances
CH4 Emissions from Natural Gas Systems
C02 Emissions from Non-Energy Use of Fuels
CH4 Emissions from Landfills
C02 Emissions from Mobile Combustion: Other
C02 Emissions from Stationary Combustion - Coal - Industrial
C02 Emissions from Stationary Combustion - Oil - Residential
Fugitive Emissions from Coal Mining
CO2 Emissions from Iron and Steel Prod. & Metallurgical Coke Prod.
CH4 Emissions from Manure Management
C02 Emissions from Mobile Combustion: Marine
Indirect N20 Emissions from Applied Nitrogen
C02 Emissions from Stationary Combustion - Oil - U.S. Territories
CO2 Emissions from Stationary Combustion - Oil - Commercial
CH4 Emissions from Petroleum Systems
CO2 Emissions from Natural Gas Systems
Non-C02 Emissions from Stationary Combustion - Elec. Gen.
Key Categories as a Portion of All Emissions
0 200 400 600
800 1,000 1,200 1,400 1,600 1,800
Tg C02 Eq.
Note: For a complete discussion of the key category analysis, see Annex 1. Blue bars indicate a Tier 1 level assessment key
category. Gray bars indicate a Tier 2 level assessment key category.
Quality Assurance and Quality Control (QA/QC)
The United States seeks to continually improve the quality, transparency, and credibility of the Inventory of U.S.
Greenhouse Gas Emissions and Sinks. To assist in these efforts, the United States implemented a systematic
approach to QA/QC. While QA/QC has always been an integral part of the U. S. national system for inventory
development, the procedures followed for the current inventory have been formalized in accordance with the
QA/QC plan and the UNFCCC reporting guidelines.
Uncertainty Analysis of Emission Estimates
While the current U.S. emissions inventory provides a solid foundation for the development of a more detailed and
comprehensive national inventory, there are uncertainties associated with the emission estimates. Some of the
current estimates, such as those for CCh emissions from energy-related activities and cement processing, are
considered to have low uncertainties. For some other categories of emissions, however, a lack of data or an
incomplete understanding of how emissions are generated increases the uncertainty associated with the estimates
presented. Acquiring a better understanding of the uncertainty associated with inventory estimates is an important
step in helping to prioritize future work and improve the overall quality of the Inventory. Recognizing the benefit of
conducting an uncertainty analysis, the UNFCCC reporting guidelines follow the recommendations of the IPCC
ES-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Good Practice Guidance (IPCC 2000) and require that countries provide single estimates of uncertainty for source
and sink categories.
Currently, a qualitative discussion of uncertainty is presented for all source and sink categories. Within the
discussion of each emission source, specific factors affecting the uncertainty surrounding the estimates are
discussed. Most sources also contain a quantitative uncertainty assessment, in accordance with UNFCCC reporting
guidelines.
Box ES- 3: Recalculations of Inventory Estimates
Each year, emission and sink estimates are recalculated and revised for all years in the Inventory of U.S. Greenhouse
Gas Emissions and Sinks, as attempts are made to improve both the analyses themselves, through the use of better
methods or data, and the overall usefulness of the report. In this effort, the United States follows the 2006 IPCC
Guidelines (IPCC 2006), which states, "Both methodological changes and refinements over time are an essential
part of improving inventory quality. It is good practice to change or refine methods" when: available data have
changed; the previously used method is not consistent with the IPCC guidelines for that category; a category has
become key; the previously used method is insufficient to reflect mitigation activities in a transparent manner; the
capacity for inventory preparation has increased; new inventory methods become available; and for correction of
errors." In general, recalculations are made to the U.S. greenhouse gas emission estimates either to incorporate new
methodologies or, most commonly, to update recent historical data.
In each Inventory report, the results of all methodology changes and historical data updates are presented in the
"Recalculations and Improvements" chapter; detailed descriptions of each recalculation are contained within each
source's description contained in the report, if applicable. In general, when methodological changes have been
implemented, the entire time series (in the case of the most recent inventory report, 1990 through 2012) has been
recalculated to reflect the change, per the 2006 IPCC Guidelines (IPCC 2006). Changes in historical data are
generally the result of changes in statistical data supplied by other agencies. References for the data are provided for
additional information.
Executive Summary ES-27
-------
-------
1. Introduction
This report presents estimates by the United States government of U.S. anthropogenic greenhouse gas emissions and
sinks for the years 1990 through 2012. A summary of these estimates is provided in Table 2-1 and Table 2-2 by gas
and source category in the Trends in Greenhouse Gas Emissions chapter. The emission estimates in these tables are
presented on both a full molecular mass basis and on a Global Warming Potential (GWP) weighted basis in order to
show the relative contribution of each gas to global average radiative forcing. This report also discusses the
methods and data used to calculate these emission estimates.
In 1992, the United States signed and ratified the United Nations Framework Convention on Climate Change
(UNFCCC). As stated in Article 2 of the UNFCCC, "The ultimate objective of this Convention and any related
legal instruments that the Conference of the Parties may adopt is to achieve, in accordance with the relevant
provisions of the Convention, stabilization of greenhouse gas concentrations in the atmosphere at a level that would
prevent dangerous anthropogenic interference with the climate system. Such a level should be achieved within a
time-frame sufficient to allow ecosystems to adapt naturally to climate change, to ensure that food production is not
threatened and to enable economic development to proceed in a sustainable manner."22'23
Parties to the Convention, by ratifying, "shall develop, periodically update, publish and make available.. .national
inventories of anthropogenic emissions by sources and removals by sinks of all greenhouse gases not controlled by
the Montreal Protocol, using comparable methodologies.. ,"24 The United States views this report as an opportunity
to fulfill these commitments under the UNFCCC.
In 1988, preceding the creation of the UNFCCC, the World Meteorological Organization (WMO) and the United
Nations Environment Programme (UNEP) jointly established the Intergovernmental Panel on Climate Change
(IPCC). The role of the IPCC is to assess on a comprehensive, objective, open and transparent basis the scientific,
technical and socio-economic information relevant to understanding the scientific basis of risk of human-induced
climate change, its potential impacts and options for adaptation and mitigation (IPCC 2003). Under Working Group
1 of the IPCC, nearly 140 scientists and national experts from more than thirty countries collaborated in the creation
of the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC/UNEP/OECD/IEA 1997) to
ensure that the emission inventories submitted to the UNFCCC are consistent and comparable between nations. The
IPCC accepted the Revised 1996 IPCC Guidelines at its Twelfth Session (Mexico City, September 11-13, 1996).
This report presents information in accordance with these guidelines. In addition, this Inventory is in accordance
with the IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories and
the IPCC Good Practice Guidance for Land Use, Land-Use Change, and Forestry, which further expanded upon the
methodologies in the Revised 1996 IPCC Guidelines. The IPCC has also accepted the 2006 Guidelines for National
Greenhouse Gas Inventories (IPCC 2006) at its Twenty-Fifth Session (Mauritius, April 2006). The 2006 IPCC
22 The term "anthropogenic," in this context, refers to greenhouse gas emissions and removals that are a direct result of human
activities or are the result of natural processes that have been affected by human activities (IPCC/UNEP/OECD/IEA 1997).
23 Article 2 of the Framework Convention on Climate Change published by the UNEP/WMO Information Unit on Climate
Change. See . (UNEP/WMO 2000)
24 Article 4(l)(a) of the United Nations Framework Convention on Climate Change (also identified in Article 12). Subsequent
decisions by the Conference of the Parties elaborated the role of Annex I Parties in preparing national inventories. See
.
Introduction 1-1
-------
Guidelines build on the previous bodies of work and includes new sources and gases "... as well as updates to the
previously published methods whenever scientific and technical knowledge have improved since the previous
guidelines were issued." Many of the methodological improvements presented in the 2006 Guidelines have been
adopted in this Inventory.
Overall, this inventory of anthropogenic greenhouse gas emissions provides a common and consistent mechanism
through which Parties to the UNFCCC can estimate emissions and compare the relative contribution of individual
sources, gases, and nations to climate change. The inventory provides a national estimate of sources and sinks for
the United States, including all states and U.S. territories.25 The structure of this report is consistent with the current
UNFCCC Guidelines on Annual Inventories (UNFCCC 2006).
Box 1-1: Methodological approach for estimating and reporting U.S. emissions and sinks
In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emissions
inventories, the emissions and sinks presented in this report are organized by source and sink categories and
calculated using internationally-accepted methods provided by the IPCC.26 Additionally, the calculated emissions
and sinks in a given year for the United States are presented in a common manner in line with the UNFCCC
reporting guidelines for the reporting of inventories under this international agreement.27 The use of consistent
methods to calculate emissions and sinks by all nations providing their inventories to the UNFCCC ensures that
these reports are comparable. In this regard, U.S. emissions and sinks reported in this inventory report are
comparable to emissions and sinks reported by other countries. Emissions and sinks provided in this inventory do
not preclude alternative examinations, but rather this inventory report presents emissions and sinks in a common
format consistent with how countries are to report inventories under the UNFCCC. The report itself follows this
standardized format, and provides an explanation of the IPCC methods used to calculate emissions and sinks, and
the manner in which those calculations are conducted.
On October 30, 2009, the U.S. Environmental Protection Agency (EPA) published a rule for the mandatory
reporting of greenhouse gases (GHG) from large GHG emissions sources in the United States. Implementation of 40
CFR Part 98 is referred to as the Greenhouse Gas Reporting Program (GHGRP). 40 CFR Part 98 applies to direct
greenhouse gas emitters, fossil fuel suppliers, industrial gas suppliers, and facilities that inject CO2 underground for
sequestration or other reasons28. Reporting is at the facility level, except for certain suppliers of fossil fuels and
industrial greenhouse gases. The GHGRP dataset and the data presented in this Inventory report are complementary
and, as indicated in the respective planned improvements sections in this report's chapters, EPA is analyzing the
data for use, as applicable, to improve the national estimates presented in this Inventory.
1.1 Background Information
Science
For over the past 200 years, the burning of fossil fuels such as coal and oil, deforestation, and other sources have
caused the concentrations of heat-trapping "greenhouse gases" to increase significantly in our atmosphere. These
25 U.S. Territories include American Samoa, Guam, Puerto Rico, U.S. Virgin Islands, Wake Island, and other U.S. Pacific
Islands.
26 See .
27 See.
28 See and .
1-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
gases absorb some of the energy being radiated from the surface of the earth and trap it in the atmosphere,
essentially acting like a blanket that makes the earth's surface warmer than it would be otherwise.
Greenhouse gases are necessary to life as we know it, because without them the planet's surface would be about 60
°F cooler than present. But, as the concentrations of these gases continue to increase in the atmosphere, the Earth's
temperature is climbing above past levels. According to NOAA and NASA data, the Earth's average surface
temperature has increased by about 1.2 to 1.4 °F since 1900. The ten warmest years on record (since 1850) have all
occurred in the past 15 years (EPA 2013). Most of the warming in recent decades is very likely the result of human
activities. Other aspects of the climate are also changing such as rainfall patterns, snow and ice cover, and sea level.
If greenhouse gases continue to increase, climate models predict that the average temperature at the Earth's surface
could increase from 2.0 to 11.5 °F above 1990 levels by the end of this century (IPCC 2007). Scientists are certain
that human activities are changing the composition of the atmosphere, and that increasing the concentration of
greenhouse gases will change the planet's climate. However, they are not sure by how much it will change, at what
rate it will change, or what the exact effects will be.29
Although the Earth's atmosphere consists mainly of oxygen and nitrogen, neither plays a significant role in
enhancing the greenhouse effect because both are essentially transparent to terrestrial radiation. The greenhouse
effect is primarily a function of the concentration of water vapor, carbon dioxide (CO2), and other trace gases in the
atmosphere that absorb the terrestrial radiation leaving the surface of the Earth (IPCC 2001). Changes in the
atmospheric concentrations of these greenhouse gases can alter the balance of energy transfers between the
atmosphere, space, land, and the oceans.30 A gauge of these changes is called radiative forcing, which is a measure
of the influence a factor has in altering the balance of incoming and outgoing energy in the Earth-atmosphere system
(IPCC 2001). Holding everything else constant, increases in greenhouse gas concentrations in the atmosphere will
produce positive radiative forcing (i.e., a net increase in the absorption of energy by the Earth).
Climate change can be driven by changes in the atmospheric concentrations of a number ofradiatively
active gases and aerosols. We have clear evidence that human activities have affected concentrations,
distributions and life cycles of these gases (IPCC 1996).
Naturally occurring greenhouse gases include water vapor, CO2, methane (CH4), nitrous oxide (N2O), and ozone
(Os). Several classes of halogenated substances that contain fluorine, chlorine, or bromine are also greenhouse
gases, but they are, for the most part, solely a product of industrial activities. Chlorofluorocarbons (CFCs) and
hydrochlorofluorocarbons (HCFCs) are halocarbons that contain chlorine, while halocarbons that contain bromine
are referred to as bromofluorocarbons (i.e., halons). As stratospheric ozone depleting substances, CFCs, HCFCs,
and halons are covered under the Montreal Protocol on Substances that Deplete the Ozone Layer. The UNFCCC
defers to this earlier international treaty. Consequently, Parties to the UNFCCC are not required to include these
gases in national greenhouse gas inventories.31 Some other fluorine-containing halogenated substances—
hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6)—do not deplete stratospheric
ozone but are potent greenhouse gases. These latter substances are addressed by the UNFCCC and accounted for in
national greenhouse gas inventories.
There are also several gases that, although they do not have a commonly agreed upon direct radiative forcing effect,
do influence the global radiation budget. These tropospheric gases include carbon monoxide (CO), nitrogen dioxide
(NO2), sulfur dioxide (SO2), and tropospheric (ground level) ozone (Os). Tropospheric ozone is formed by two
precursor pollutants, volatile organic compounds (VOCs) and nitrogen oxides (NOX) in the presence of ultraviolet
light (sunlight). Aerosols are extremely small particles or liquid droplets that are often composed of sulfur
compounds, carbonaceous combustion products, crustal materials and other human induced pollutants. They can
29 For more information see .
30 For more on the science of climate change, see NRC (2001).
31 Emissions estimates of CFCs, HCFCs, halons and other ozone-depleting substances are included in this document for
informational purposes.
Introduction 1-3
-------
affect the absorptive characteristics of the atmosphere. Comparatively, however, the level of scientific
understanding of aerosols is still very low (IPCC 2001).
CO2, CH4, and N2O are continuously emitted to and removed from the atmosphere by natural processes on Earth.
Anthropogenic activities, however, can cause additional quantities of these and other greenhouse gases to be emitted
or sequestered, thereby changing their global average atmospheric concentrations. Natural activities such as
respiration by plants or animals and seasonal cycles of plant growth and decay are examples of processes that only
cycle carbon or nitrogen between the atmosphere and organic biomass. Such processes, except when directly or
indirectly perturbed out of equilibrium by anthropogenic activities, generally do not alter average atmospheric
greenhouse gas concentrations over decadal timeframes. Climatic changes resulting from anthropogenic activities,
however, could have positive or negative feedback effects on these natural systems. Atmospheric concentrations of
these gases, along with their rates of growth and atmospheric lifetimes, are presented in Table 1-1.
Table 1-1: Global Atmospheric Concentration, Rate of Concentration Change, and
Atmospheric Lifetime (Years) of Selected Greenhouse Gases
Atmospheric Variable
Pre-industrial atmospheric
concentration
Atmospheric concentration
Rate of concentration change
Atmospheric lifetime (years)
C02
280 ppm
391 ppm
1 .4 ppm/yr
See footnote*
CH4
0.700 ppm
1.758-1. 874 ppma
0.005 ppm/yrb
12e
N20
0.270 ppm
0.323-0.324 ppma
0.26%/yr
114e
SF6
Oppt
7.09-7.47 ppt
Linear0
3,200
CF4
40 ppt
74 ppt
Linear0
>50,000
Source: Pre-industrial atmospheric concentrations and rate of concentration changes for all gases are from IPCC (2007). The
current atmospheric concentration for CCh is from NOAA/ESRL (2013).
a The range is the annual arithmetic averages from a mid-latitude Northern-Hemisphere site and a mid-latitude Southern-
Hemisphere site for 2011 (CDIAC 2013).
b The growth rate for atmospheric CH4 decreased from over 10 ppb/yr in the 1980s to nearly zero in the early 2000s; recently, the
growth rate has been about 5 ppb/yr.
c IPCC (2007) identifies the rate of concentration change for SFe and CF4 as linear.
d For a given amount of carbon dioxide emitted, some fraction of the atmospheric increase in concentration is quickly absorbed by
the oceans and terrestrial vegetation, some fraction of the atmospheric increase will only slowly decrease over a number of years,
and a small portion of the increase will remain for many centuries or more.
e This lifetime has been defined as an "adjustment time" that takes into account the indirect effect of the gas on its own residence
time.
A brief description of each greenhouse gas, its sources, and its role in the atmosphere is given below. The following
section then explains the concept of GWPs, which are assigned to individual gases as a measure of their relative
average global radiative forcing effect.
Water Vapor (H2O). Overall, the most abundant and dominant greenhouse gas in the atmosphere is water vapor.
Water vapor is neither long-lived nor well mixed in the atmosphere, varying spatially from 0 to 2 percent (IPCC
1996). In addition, atmospheric water can exist in several physical states including gaseous, liquid, and solid.
Human activities are not believed to affect directly the average global concentration of water vapor, but, the
radiative forcing produced by the increased concentrations of other greenhouse gases may indirectly affect the
hydrologic cycle. While a warmer atmosphere has an increased water holding capacity, increased concentrations of
water vapor affect the formation of clouds, which can both absorb and reflect solar and terrestrial radiation. Aircraft
contrails, which consist of water vapor and other aircraft emittants, are similar to clouds in their radiative forcing
effects (IPCC 1999).
Carbon Dioxide (CO2). In nature, carbon is cycled between various atmospheric, oceanic, land biotic, marine biotic,
and mineral reservoirs. The largest fluxes occur between the atmosphere and terrestrial biota, and between the
atmosphere and surface water of the oceans. In the atmosphere, carbon predominantly exists in its oxidized form as
CO2. Atmospheric CO2 is part of this global carbon cycle, and therefore its fate is a complex function of
geochemical and biological processes. CO2 concentrations in the atmosphere increased from approximately 280
parts per million by volume (ppmv) in pre-industrial times to 391 ppmv in 2012, a 39.6 percent increase (IPCC 2007
1-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
and NOAA/ESRL 2013).32>33 The IPCC definitively states that "the present atmospheric CO2 increase is caused by
anthropogenic emissions of CO2" (IPCC 2001). The predominant source of anthropogenic CO2 emissions is the
combustion of fossil fuels. Forest clearing, other biomass burning, and some non-energy production processes (e.g.,
cement production) also emit notable quantities of CO2. In its Fifth Assessment Report, the IPCC stated "it is
extremely likely that more than half of the observed increase in global average surface temperature from 1951 to
2010 was caused by the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings
together," of which CO2 is the most important (IPCC 2013).
Methane (CH4). CH4 is primarily produced through anaerobic decomposition of organic matter in biological
systems. Agricultural processes such as wetland rice cultivation, enteric fermentation in animals, and the
decomposition of animal wastes emit CH4, as does the decomposition of municipal solid wastes. CH4 is also
emitted during the production and distribution of natural gas and petroleum, and is released as a by-product of coal
mining and incomplete fossil fuel combustion. Atmospheric concentrations of CH4 have increased by about 151
percent since 1750, from a pre-industrial value of about 700 ppb to 1,758-1,874 ppb in 2012,34 although the rate of
increase has been declining. The IPCC has estimated that slightly more than half of the current CH4 flux to the
atmosphere is anthropogenic, from human activities such as agriculture, fossil fuel use, and waste disposal (IPCC
2007).
CH4 is removed from the atmosphere through a reaction with the hydroxyl radical (OH) and is ultimately converted
to CO2. Minor removal processes also include reaction with chlorine in the marine boundary layer, a soil sink, and
stratospheric reactions. Increasing emissions of CH4 reduce the concentration of OH, a feedback that may increase
the atmospheric lifetime of CH4 (IPCC 2001).
Nitrous Oxide (N2O). Anthropogenic sources of N2O emissions include agricultural soils, especially production of
nitrogen-fixing crops and forages, the use of synthetic and manure fertilizers, and manure deposition by livestock;
fossil fuel combustion, especially from mobile combustion; adipic (nylon) and nitric acid production; wastewater
treatment and waste incineration; and biomass burning. The atmospheric concentration of N2O has increased by 20
percent since 1750, from a pre-industrial value of about 270 ppb to 323-324 ppb in 2012,35 a concentration that has
not been exceeded during the last thousand years. N2O is primarily removed from the atmosphere by the photolytic
action of sunlight in the stratosphere (IPCC 2007).
Ozone (O3). Ozone is present in both the upper stratosphere,36 where it shields the Earth from harmful levels of
ultraviolet radiation, and at lower concentrations in the troposphere,37 where it is the main component of
anthropogenic photochemical "smog." During the last two decades, emissions of anthropogenic chlorine and
bromine-containing halocarbons, such as CFCs, have depleted stratospheric ozone concentrations. This loss of
ozone in the stratosphere has resulted in negative radiative forcing, representing an indirect effect of anthropogenic
emissions of chlorine and bromine compounds (IPCC 1996). The depletion of stratospheric ozone and its radiative
forcing was expected to reach a maximum in about 2000 before starting to recover.
The past increase in tropospheric ozone, which is also a greenhouse gas, is estimated to provide the third largest
increase in direct radiative forcing since the pre-industrial era, behind CO2 and CH4. Tropospheric ozone is
32 The pre-industrial period is considered as the time preceding the year 1750 (IPCC 2001).
33 Carbon dioxide concentrations during the last 1,000 years of the pre-industrial period (i.e., 750-1750), a time of relative
climate stability, fluctuated by about +10 ppmv around 280 ppmv (IPCC 2001).
34 The range is the annual arithmetic averages from a mid-latitude Northern-Hemisphere site and a mid-latitude Southern-
Hemisphere site for October 2011 through September 2012 (ERSL 2013).
35 The range is the annual arithmetic averages from a mid-latitude Northern-Hemisphere site and a mid-latitude Southern-
Hemisphere site for October 2011 through September 2012 (ERSL 2013).
36 The stratosphere is the layer from the troposphere up to roughly 50 kilometers. In the lower regions the temperature is nearly
constant but in the upper layer the temperature increases rapidly because of sunlight absorption by the ozone layer. The ozone-
layer is the part of the stratosphere from 19 kilometers up to 48 kilometers where the concentration of ozone reaches up to 10
parts per million.
37 The troposphere is the layer from the ground up to 11 kilometers near the poles and up to 16 kilometers in equatorial regions
(i.e., the lowest layer of the atmosphere where people live). It contains roughly 80 percent of the mass of all gases in the
atmosphere and is the site for most weather processes, including most of the water vapor and clouds.
Introduction 1-5
-------
produced from complex chemical reactions of volatile organic compounds mixing with NOX in the presence of
sunlight. The tropospheric concentrations of ozone and these other pollutants are short-lived and, therefore,
spatially variable (IPCC 2001).
Halocarbons, Perfluorocarbons, and Sulfur Hexafluoride. Halocarbons are, for the most part, man-made chemicals
that have both direct and indirect radiative forcing effects. Halocarbons that contain chlorine (CFCs, HCFCs,
methyl chloroform, and carbon tetrachloride) and bromine (halons, methyl bromide, and hydrobromofluorocarbons
HFCs) result in stratospheric ozone depletion and are therefore controlled under the Montreal Protocol on
Substances that Deplete the Ozone Layer. Although CFCs and HCFCs include potent global warming gases, their
net radiative forcing effect on the atmosphere is reduced because they cause stratospheric ozone depletion, which
itself is an important greenhouse gas in addition to shielding the Earth from harmful levels of ultraviolet radiation.
Under the Montreal Protocol, the United States phased out the production and importation of halons by 1994 and of
CFCs by 1996. Under the Copenhagen Amendments to the Protocol, a cap was placed on the production and
importation of HCFCs by non-Article 538 countries beginning in 1996, and then followed by a complete phase-out
by the year 2030. While ozone depleting gases covered under the Montreal Protocol and its Amendments are not
covered by the UNFCCC, they are reported in this inventory under Annex 6.2 of this report for informational
purposes.
HFCs, PFCs, and SF6 are not ozone depleting substances, and therefore are not covered under the Montreal Protocol.
They are, however, powerful greenhouse gases. HFCs are primarily used as replacements for ozone depleting
substances but also emitted as a by-product of the HCFC-22 manufacturing process. Currently, they have a small
aggregate radiative forcing impact, but it is anticipated that their contribution to overall radiative forcing will
increase (IPCC 2001). PFCs and SF6 are predominantly emitted from various industrial processes including
aluminum smelting, semiconductor manufacturing, electric power transmission and distribution, and magnesium
casting. Currently, the radiative forcing impact of PFCs and SF6 is also small, but they have a significant growth
rate, extremely long atmospheric lifetimes, and are strong absorbers of infrared radiation, and therefore have the
potential to influence climate far into the future (IPCC 2001).
Carbon Monoxide. Carbon monoxide has an indirect radiative forcing effect by elevating concentrations of CH4 and
tropospheric ozone through chemical reactions with other atmospheric constituents (e.g., the hydroxyl radical, OH)
that would otherwise assist in destroying CH4 and tropospheric ozone. Carbon monoxide is created when carbon-
containing fuels are burned incompletely. Through natural processes in the atmosphere, it is eventually oxidized to
CO2. Carbon monoxide concentrations are both short-lived in the atmosphere and spatially variable.
Nitrogen Oxides (NO,). The primary climate change effects of nitrogen oxides (i.e., NO and NO2) are indirect and
result from their role in promoting the formation of ozone in the troposphere and, to a lesser degree, lower
stratosphere, where they have positive radiative forcing effects.39 Additionally, NOX emissions from aircraft are
also likely to decrease CH4 concentrations, thus having a negative radiative forcing effect (IPCC 1999). Nitrogen
oxides are created from lightning, soil microbial activity, biomass burning (both natural and anthropogenic fires)
fuel combustion, and, in the stratosphere, from the photo-degradation of N2O. Concentrations of NOX are both
relatively short-lived in the atmosphere and spatially variable.
Nonmethane Volatile Organic Compounds (NMVOCs). Non-CH4 volatile organic compounds include substances
such as propane, butane, and ethane. These compounds participate, along with NOX, in the formation of
tropospheric ozone and other photochemical oxidants. NMVOCs are emitted primarily from transportation and
industrial processes, as well as biomass burning and non-industrial consumption of organic solvents. Concentrations
of NMVOCs tend to be both short-lived in the atmosphere and spatially variable.
Aerosols. Aerosols are extremely small particles or liquid droplets found in the atmosphere. They can be produced
by natural events such as dust storms and volcanic activity, or by anthropogenic processes such as fuel combustion
and biomass burning. Aerosols affect radiative forcing differently than greenhouse gases, and their radiative effects
38 Article 5 of the Montreal Protocol covers several groups of countries, especially developing countries, with low consumption
rates of ozone depleting substances. Developing countries with per capita consumption of less than 0.3 kg of certain ozone
depleting substances (weighted by their ozone depleting potential) receive financial assistance and a grace period often
additional years in the phase-out of ozone depleting substances.
39 NOX emissions injected higher in the stratosphere, primarily from fuel combustion emissions from high altitude supersonic
aircraft, can lead to stratospheric ozone depletion.
1-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
occur through direct and indirect mechanisms: directly by scattering and absorbing solar radiation; and indirectly by
increasing droplet counts that modify the formation, precipitation efficiency, and radiative properties of clouds.
Aerosols are removed from the atmosphere relatively rapidly by precipitation. Because aerosols generally have
short atmospheric lifetimes, and have concentrations and compositions that vary regionally, spatially, and
temporally, their contributions to radiative forcing are difficult to quantify (IPCC 2001).
The indirect radiative forcing from aerosols is typically divided into two effects. The first effect involves decreased
droplet size and increased droplet concentration resulting from an increase in airborne aerosols. The second effect
involves an increase in the water content and lifetime of clouds due to the effect of reduced droplet size on
precipitation efficiency (IPCC 2001). Recent research has placed a greater focus on the second indirect radiative
forcing effect of aerosols.
Various categories of aerosols exist, including naturally produced aerosols such as soil dust, sea salt, biogenic
aerosols, sulfates, and volcanic aerosols, and anthropogenically manufactured aerosols such as industrial dust and
carbonaceous40 aerosols (e.g., black carbon, organic carbon) from transportation, coal combustion, cement
manufacturing, waste incineration, and biomass burning.
The net effect of aerosols on radiative forcing is believed to be negative (i.e., net cooling effect on the climate),
although because they remain in the atmosphere for only days to weeks, their concentrations respond rapidly to
changes in emissions.41 Locally, the negative radiative forcing effects of aerosols can offset the positive forcing of
greenhouse gases (IPCC 1996). "However, the aerosol effects do not cancel the global-scale effects of the much
longer-lived greenhouse gases, and significant climate changes can still result" (IPCC 1996).
The IPCC's Third Assessment Report notes that "the indirect radiative effect of aerosols is now understood to also
encompass effects on ice and mixed-phase clouds, but the magnitude of any such indirect effect is not known,
although it is likely to be positive" (IPCC 2001). Additionally, current research suggests that another constituent of
aerosols, black carbon, has a positive radiative forcing, and that its presence "in the atmosphere above highly
reflective surfaces such as snow and ice, or clouds, may cause a significant positive radiative forcing" (IPCC 2007).
The primary anthropogenic emission sources of black carbon include diesel exhaust and open biomass burning.
Global Warming Potentials
A global warming potential is a quantified measure of the globally averaged relative radiative forcing impacts of a
particular greenhouse gas (see Table 1-2). It is defined as the ratio of the time-integrated radiative forcing from the
instantaneous release of 1 kilogram (kg) of a trace substance relative to that of 1 kg of a reference gas (IPCC 2001).
Direct radiative effects occur when the gas itself absorbs radiation. Indirect radiative forcing occurs when chemical
transformations involving the original gas produce a gas or gases that are greenhouse gases, or when a gas
influences other radiatively important processes such as the atmospheric lifetimes of other gases. The reference gas
used is CO2, and therefore GWP-weighted emissions are measured in teragrams of CO2 equivalent (Tg CO2 Eq.).42
The relationship between gigagrams (Gg) of a gas and Tg CO2 Eq. can be expressed as follows:
/ m \
Tg CO2 Eq = (Gg of gas ) x (GWP) x
where,
Tg CO2 Eq. = Teragrams of CO2 Equivalent
Gg = Gigagrams (equivalent to a thousand metric tons)
GWP = Global Warming Potential
40 Carbonaceous aerosols are aerosols that are comprised mainly of organic substances and forms of black carbon (or soot)
(IPCC 2001).
41 Volcanic activity can inject significant quantities of aerosol producing sulfur dioxide and other sulfur compounds into the
stratosphere, which can result in a longer negative forcing effect (i.e., a few years) (IPCC 1996).
42 Carbon comprises 12/44ths of carbon dioxide by weight.
Introduction 1-7
-------
Tg = Teragrams
GWP values allow for a comparison of the impacts of emissions and reductions of different gases. According to the
IPCC, GWPs typically have an uncertainty of ±35 percent. Parties to the UNFCCC have also agreed to use GWPs
based upon a 100-year time horizon, although other time horizon values are available.
Greenhouse gas emissions and removals should be presented on a gas-by-gas basis in units of mass... In
addition, consistent with decision 2/CP.3, Parties should report aggregate emissions and removals of
greenhouse gases, expressed in CO2 equivalent terms at summary inventory level, using GWP values
provided by the IPCC in its Second Assessment Report... based on the effects of greenhouse gases over a
100-year time horizon.43
Greenhouse gases with relatively long atmospheric lifetimes (e.g., CO2, CH4, N2O, HFCs, PFCs, and SF6) tend to be
evenly distributed throughout the atmosphere, and consequently global average concentrations can be determined.
The short-lived gases such as water vapor, carbon monoxide, tropospheric ozone, ozone precursors (e.g., NOX, and
NMVOCs), and tropospheric aerosols (e.g., SCh products and carbonaceous particles), however, vary regionally,
and consequently it is difficult to quantify their global radiative forcing impacts. Parties to the UNFCCC have not
agreed upon GWP values for these gases that are short-lived and spatially inhomogeneous in the atmosphere.
Table 1-2: Global Warming Potentials and Atmospheric Lifetimes (Years) Used in this Report
Gas
CO2
CH4b
N2O
HFC-23
HFC-32
HFC-125
HFC-134a
HFC-143a
HFC-152a
HFC-227ea
HFC-236fa
HFC-4310mee
CF4
C2Fe
C4Fio
C6Fi4
SF6
Atmospheric Lifetime
*
12±3
120
264
5.6
32.6
14.6
48.3
1.5
36.5
209
17.1
50,000
10,000
2,600
3,200
3,200
GWPa
1
21
310
11,700
650
2,800
1,300
3,800
140
2,900
6,300
1,300
6,500
9,200
7,000
7,400
23,900
Source: (IPCC 1996)
* For a given amount of carbon dioxide emitted, some fraction of the
atmospheric increase in concentration is quickly absorbed by the oceans
and terrestrial vegetation, some fraction of the atmospheric increase will
only slowly decrease over a number of years, and a small portion of the
increase will remain for many centuries or more.
a 100-year time horizon
b The GWP of CH4 includes the direct effects and those indirect effects
due to the production of tropospheric ozone and stratospheric water
vapor. The indirect effect due to the production of CO2 is not included.
43 Framework Convention on Climate Change; ; 1 November 2002; Report of the
Conference of the Parties at its eighth session; held at New Delhi from 23 October to 1 November 2002; Addendum; Part One:
Action taken by the Conference of the Parties at its eighth session; Decision -/CP.8; Communications from Parties included in
Annex I to the Convention: Guidelines for the Preparation of National Communications by Parties Included in Annex I to the
Convention, Part 1: UNFCCC reporting guidelines on annual inventories; p. 7. (UNFCCC 2003)
1-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Box 1-2: The IPCC Fifth Assessment Report and Global Warming Potentials
In 2013, the IPCC published its Fifth Assessment Report (AR5), which provided an updated and more
comprehensive scientific assessment of climate change. Within the AR5 report, the GWP values of several gases
were revised relative to previous IPCC reports, namely the IPCC Second Assessment Report (SAR) (IPCC 1996),
the IPCC Third Assessment Report (TAR) (IPCC 2001), and the IPCC's Fourth Assessment Report (AR4) (IPCC
2007). Although the SAR GWP values are used throughout this report, consistent with UNFCCC reporting
requirements, it is interesting to review the changes to the GWP values and the impact improved understanding has
on the total GWP-weighted emissions of the United States. In the AR5, the IPCC has applied an improved
calculation of CCh radiative forcing and an improved CCh response function in presenting updated GWP values.
Additionally, the atmospheric lifetimes of some gases have been recalculated, and updated background
concentrations were used. In addition, the values for radiative forcing and lifetimes have been recalculated for a
variety of halocarbons, which were not presented in the SAR. Table 1-3 presents the new GWP values, relative to
those presented in the SAR and using the 100-year time horizon common to UNFCCC reporting.
Table 1-3: Comparison of 100-Year GWP values
Gas
C02
CH4a
N2O
HFC-23
HFC-32
HFC-125
HFC-134a
HFC-143a
HFC-152a
HFC-227ea
HFC-236fa
HFC-4310mee
CF4
C2F6
C4Fio
C6Fi4
SF6
SAR
1
21
310
11,700
650
2,800
1,300
3,800
140
2,900
6,300
1,300
6,500
9,200
7,000
7,400
23,900
TAR
1
23
296
12,000
550
3,400
1,300
4,300
120
3,500
9,400
1,500
5,700
11,900
8,600
9,000
22,200
AR4
1
25
298
14,800
675
3,500
1,430
4,470
124
3,220
9,810
1,640
7,390
12,200
8,860
9,300
22,800
AR5b
1
28
265
12,400
677
3,170
1,300
4,800
138
3,350
8,060
1,650
6,630
11,100
9,200
7,910
23,500
Change from SAR
TAR
NC
2
(14)
300
(100)
600
NC
500
(20)
600
3,100
200
(800)
2,700
1,600
1,600
(1,700)
AR4
NC
4
(12)
3,100
25
700
130
670
(16)
320
3,510
340
890
3,000
1,860
1,900
(1,100)
AR5
NC
7
(45)
700
27
370
NC
1,000
(2)
450
1,760
350
130
1,900
2,200
510
(400)
Source: (IPCC 2013, IPCC 2007, IPCC 2001, IPCC 1996)
NC (No Change)
Note: Parentheses indicate negative values.
a The GWP of CELi includes the direct effects and those indirect effects due to the production of tropospheric ozone and
stratospheric water vapor. The indirect effect due to the production of CO2 is not included.
b The GWPs presented here are the ones most consistent with the methodology used in the AR4 report. The AR5 report has
also calculated GWPs (not shown here) where climate-carbon feedbacks have been included for the non-CO2 gases in order
to be consistent with the approach used in calculating the CO2 lifetime. Additionally, the AR5 reported separate values for
fossil versus biogenic methane in order to account for the CO2 oxidation product.
To comply with international reporting standards under the UNFCCC, official emission estimates are reported by
the United States using SAR GWP values. The UNFCCC reporting guidelines for national inventories44 were
updated in 2002 but continue to require the use of GWPs from the SAR so that current estimates of aggregate
greenhouse gas emissions for 1990 through 2012 are consistent and comparable with estimates developed prior to
the publication of the TAR, AR4 and AR5. All estimates provided throughout this report are also presented in
unweighted units. For informational purposes, emission estimates that use the updated GWPs are presented in detail
44
See.
Introduction 1-9
-------
in Annex 6.1 of this report. It should be noted that the official greenhouse gas emissions presented in this report
using the SAR GWP values are the final time the SAR GWP values will be used in the U.S. Inventory. The United
States and other developed countries to the UNFCCC have agreed to submit annual inventories in 2015 and future
years to the UNFCCC using GWP values from the IPCC AR4, which will replace the current use of SAR GWP
values in their annual greenhouse gas inventories.45
1.2
The U.S. Environmental Protection Agency (EPA), in cooperation with other U.S. government agencies, prepares
the Inventory of U.S. Greenhouse Gas Emissions and Sinks. A wide range of agencies and individuals are involved
in supplying data to, reviewing, or preparing portions of the U.S. Inventory—including federal and state government
authorities, research and academic institutions, industry associations, and private consultants.
Within EPA, the Office of Atmospheric Programs (OAP) is the lead office responsible for the emission calculations
provided in the Inventory, as well as the completion of the National Inventory Report and the Common Reporting
Format tables. The Office of Transportation and Air Quality (OTAQ) is also involved in calculating emissions for
the Inventory. While the U.S. Department of State officially submits the annual Inventory to the UNFCCC, EPA's
OAP serves as the focal point for technical questions and comments on the U.S. Inventory. The staff of OAP and
OTAQ coordinates the annual methodological choice, activity data collection, and emission calculations at the
individual source category level. Within OAP, an inventory coordinator compiles the entire Inventory into the
proper reporting format for submission to the UNFCCC, and is responsible for the collection and consistency of
cross-cutting issues in the Inventory.
Several other government agencies contribute to the collection and analysis of the underlying activity data used in
the Inventory calculations. Formal relationships exist between EPA and other U.S. agencies that provide official
data for use in the Inventory. The U.S. Department of Energy's Energy Information Administration provides
national fuel consumption data and the U. S. Department of Defense provides military fuel consumption and bunker
fuels. Informal relationships also exist with other U.S. agencies to provide activity data for use in EPA's emission
calculations. These include: the U.S. Department of Agriculture, the U.S. Geological Survey, the Federal Highway
Administration, the Department of Transportation, the Bureau of Transportation Statistics, the Department of
Commerce, the National Agricultural Statistics Service, and the Federal Aviation Administration. Academic and
research centers also provide activity data and calculations to EPA, as well as individual companies participating in
voluntary outreach efforts with EPA. Finally, the U.S. Department of State officially submits the Inventory to the
UNFCCC each April diagrams the institutional arrangements.
45 "Revision of the UNFCCC reporting guidelines on annual inventories for Parties included in Annex I to the Convention,'
FCCC/CP/201 l/9/Add.2, Decision 6/CP 17, 15 March 2012, available at
.
1-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure 1-1: Insitutional Arrangements Diagram
Introduction 1-11
-------
1.3 Inventory Process
EPA has a decentralized approach to preparing the annual U.S. Inventory, which consists of a National Inventory
Report (NIR) and Common Reporting Format (CRF) tables. The Inventory coordinator at EPA is responsible for
compiling all emission estimates and ensuring consistency and quality throughout the NIR and CRF tables.
Emission calculations for individual sources are the responsibility of individual source leads, who are most familiar
with each source category and the unique characteristics of its emissions profile. The individual source leads
determine the most appropriate methodology and collect the best activity data to use in the emission calculations,
based upon their expertise in the source category, as well as coordinating with researchers and contractors familiar
with the sources. A multi-stage process for collecting information from the individual source leads and producing
the Inventory is undertaken annually to compile all information and data.
Methodology Development, Data Collection, and Emissions
and Sink Estimation
Source leads at EPA collect input data and, as necessary, evaluate or develop the estimation methodology for the
individual source categories. For most source categories, the methodology for the previous year is applied to the
new "current" year of the Inventory, and inventory analysts collect any new data or update data that have changed
from the previous year. If estimates for a new source category are being developed for the first time, or if the
methodology is changing for an existing source category (e.g., the United States is implementing a higher Tiered
approach for that source category), then the source category lead will develop a new methodology, gather the most
appropriate activity data and emission factors (or in some cases direct emission measurements) for the entire time
series, and conduct a special source-specific peer review process involving relevant experts from industry,
government, and universities.
Once the methodology is in place and the data are collected, the individual source leads calculate emissions and sink
estimates. The source leads then update or create the relevant text and accompanying annexes for the Inventory.
Source leads are also responsible for completing the relevant sectoral background tables of the Common Reporting
Format, conducting quality assurance and quality control (QA/QC) checks, and uncertainty analyses.
Summary Spreadsheet Compilation and Data Storage
The inventory coordinator at EPA collects the source categories' descriptive text and Annexes, and also aggregates
the emission estimates into a summary spreadsheet that links the individual source category spreadsheets together.
This summary sheet contains all of the essential data in one central location, in formats commonly used in the
Inventory document. In addition to the data from each source category, national trend and related data are also
gathered in the summary sheet for use in the Executive Summary, Introduction, and Recent Trends sections of the
Inventory report. Electronic copies of each year's summary spreadsheet, which contains all the emission and sink
estimates for the United States, are kept on a central server at EPA under the jurisdiction of the Inventory
coordinator.
National Inventory Report Preparation
The NIR is compiled from the sections developed by each individual source lead. In addition, the inventory
coordinator prepares a brief overview of each chapter that summarizes the emissions from all sources discussed in
the chapters. The inventory coordinator then carries out a key category analysis for the Inventory, consistent with
the IPCC Good Practice Guidance, IPCC Good Practice Guidance for Land Use, Land Use Change and Forestry,
and in accordance with the reporting requirements of the UNFCCC. Also at this time, the Introduction, Executive
Summary, and Recent Trends sections are drafted, to reflect the trends for the most recent year of the current
Inventory. The analysis of trends necessitates gathering supplemental data, including weather and temperature
conditions, economic activity and gross domestic product, population, atmospheric conditions, and the annual
1-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
consumption of electricity, energy, and fossil fuels. Changes in these data are used to explain the trends observed in
greenhouse gas emissions in the United States. Furthermore, specific factors that affect individual sectors are
researched and discussed. Many of the factors that affect emissions are included in the Inventory document as
separate analyses or side discussions in boxes within the text. Text boxes are also created to examine the data
aggregated in different ways than in the remainder of the document, such as a focus on transportation activities or
emissions from electricity generation. The document is prepared to match the specification of the UNFCCC
reporting guidelines for National Inventory Reports.
Common Reporting Format Table Compilation
The CRF tables are compiled from individual tables completed by each individual source lead, which contain source
emissions and activity data. The inventory coordinator integrates the source data into the UNFCCC's "CRF
Reporter" for the United States, assuring consistency across all sectoral tables. The summary reports for emissions,
methods, and emission factors used, the overview tables for completeness and quality of estimates, the recalculation
tables, the notation key completion tables, and the emission trends tables are then completed by the inventory
coordinator. Internal automated quality checks on the CRF Reporter, as well as reviews by the source leads, are
completed for the entire time series of CRF tables before submission.
QA/QC and Uncertainty
QA/QC and uncertainty analyses are supervised by the QA/QC and Uncertainty coordinators, who have general
oversight over the implementation of the QA/QC plan and the overall uncertainty analysis for the Inventory (see
sections on QA/QC and Uncertainty, below). These coordinators work closely with the source leads to ensure that a
consistent QA/QC plan and uncertainty analysis is implemented across all inventory sources. The inventory QA/QC
plan, detailed in a following section, is consistent with the quality assurance procedures outlined by EPA and IPCC.
Expert and Public Review Periods
During the Expert Review period, a first draft of the document is sent to a select list of technical experts outside of
EPA. The purpose of the Expert Review is to encourage feedback on the methodological and data sources used in
the current Inventory, especially for sources which have experienced any changes since the previous Inventory.
Once comments are received and addressed, a second draft of the document is released for public review by
publishing a notice in the U.S. Federal Register and posting the document on the EPA Web site. The Public Review
period allows for a 30 day comment period and is open to the entire U.S. public.
Final Submittal to UNFCCC and Document Printing
After the final revisions to incorporate any comments from the Expert Review and Public Review periods, EPA
prepares the final National Inventory Report and the accompanying Common Reporting Format Reporter database.
The U.S. Department of State sends the official submission of the U.S. Inventory to the UNFCCC. The document is
then formatted for printing, posted online, printed by the U.S. Government Printing Office, and made available for
the public.
1.4 Methodology and Data Sources
Emissions of greenhouse gases from various source and sink categories have been estimated using methodologies
that are consistent with the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC/UNEP/OECD/IEA 1997). In addition, the United States references the additional guidance provided in the
IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (IPCC 2000),
the IPCC Good Practice Guidance for Land Use, Land-Use Change, and Forestry (IPCC 2003), and the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories (IPCC 2006). To the extent possible, the present report relies
Introduction 1-13
-------
on published activity and emission factor data. Depending on the emission source category, activity data can
include fuel consumption or deliveries, vehicle-miles traveled, raw material processed, etc. Emission factors are
factors that relate quantities of emissions to an activity.
The IPCC methodologies provided in the Revised 1996IPCC Guidelines represent baseline methodologies for a
variety of source categories, and many of these methodologies continue to be improved and refined as new research
and data become available. In this regard, the U.S. has implemented many methodological improvements published
in the IPCC 2006 Guidelines. The use of the most recently published calculation methodologies by the IPCC, as
contained in the 2006 IPCC Guidelines, is fully in line with the IPCC Good Practice Guidance for methodological
choice to improve rigor and accuracy. In addition, the improvements in using the latest methodological guidance
from the IPCC have been recognized by the UNFCCC's Subsidiary Body for Scientific and Technological Advice in
the conclusions of its 30th Session46. Numerous U.S. inventory experts were involved in the development of the
2006 IPCC Guidelines, and their expertise has provided this latest guidance from the IPCC with the most
appropriate calculation methods that are then used in this inventory. This report uses the IPCC methodologies when
applicable, and supplements them with other available country-specific methodologies and data where possible.
Choices made regarding the methodologies and data sources used are provided in conjunction with the discussion of
each source category in the main body of the report. Complete documentation is provided in the annexes on the
detailed methodologies and data sources utilized in the calculation of each source category.
Box 1-3: IPCC Reference Approach
The UNFCCC reporting guidelines require countries to complete a "top-down" reference approach for estimating
CO2 emissions from fossil fuel combustion in addition to their "bottom-up" sectoral methodology. This estimation
method uses alternative methodologies and different data sources than those contained in that section of the Energy
chapter. The reference approach estimates fossil fuel consumption by adjusting national aggregate fuel production
data for imports, exports, and stock changes rather than relying on end-user consumption surveys (see Annex 4 of
this report). The reference approach assumes that once carbon-based fuels are brought into a national economy, they
are either saved in some way (e.g., stored in products, kept in fuel stocks, or left unoxidized in ash) or combusted,
and therefore the carbon in them is oxidized and released into the atmosphere. Accounting for actual consumption
of fuels at the sectoral or sub-national level is not required.
1.5 Key Categories
The IPCC's Good Practice Guidance (IPCC 2000) defines a key category as a "[source or sink category] that is
prioritized within the national inventory system because its estimate has a significant influence on a country's total
inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions, or both."47
By definition, key categories include those sources that have the greatest contribution to the absolute level of
national emissions. In addition, when an entire time series of emission estimates is prepared, a thorough
investigation of key categories must also account for the influence of trends and uncertainties of individual source
and sink categories. This analysis culls out source and sink categories that diverge from the overall trend in national
46 These Subsidiary Body for Scientific and Technological Advice (SBSTA) conclusions state, "The SBSTA acknowledged that
the 2006 IPCC Guidelines contain the most recent scientific methodologies available to estimate emissions by sources and
removals by sinks of greenhouse gases (GHGs) not controlled by the Montreal Protocol, and recognized that Parties have gained
experience with the 2006 IPCC Guidelines. The SBSTA also acknowledged that the information contained in the 2006 IPCC
Guidelines enables Parties to further improve the quality of their GHG inventories." See
.
47 See Chapter 7 "Methodological Choice and Recalculation" in IPCC (2000). See .
1-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
emissions. Finally, a qualitative evaluation of key categories is performed to capture any categories that were not
identified in any of the quantitative analyses.
A Tier 1 approach, as defined in the IPCC Good Practice Guidance (IPCC 2000), was implemented to identify the
key categories for the United States. This analysis was performed twice; one analysis included sources and sinks
from the Land Use, Land-Use Change, and Forestry (LULUCF) sector, the other analysis did not include the
LULUCF categories. Following the Tier 1 approach, a Tier 2 approach, as defined in the i (IPCC 2000), was then
implemented to identify any additional key categories not already identified in the Tier 1 assessment. This analysis,
which includes each source category's uncertainty assessments (or proxies) in its calculations, was also performed
twice to include or exclude LULUCF categories.
In addition to conducting Tier 1 and 2 level and trend assessments, a qualitative assessment of the source categories,
as described in the IPCC Good Practice Guidance (IPCC 2000), was conducted to capture any key categories that
were not identified by either quantitative method. One additional key category, international bunker fuels, was
identified using this qualitative assessment. International bunker fuels are fuels consumed for aviation or marine
international transport activities, and emissions from these fuels are reported separately from totals in accordance
with IPCC guidelines. If these emissions were included in the totals, bunker fuels would qualify as a key category
according to the Tier 1 approach. The amount of uncertainty associated with estimation of emissions from
international bunker fuels also supports the qualification of this source category as key, because it would qualify
bunker fuels as a key category according to the Tier 2 approach. Table 1-4 presents the key categories for the United
States (including and excluding LULUCF categories) using emissions and uncertainty data in this report, and ranked
according to their sector and global warming potential-weighted emissions in 2012. The table also indicates the
criteria used in identifying these categories (i.e., level, trend, Tier 1, Tier 2, and/or qualitative assessments). Annex
1 of this report provides additional information regarding the key categories in the United States and the
methodologies used to identify them.
Table 1-4: Key Categories for the United States (1990-2012)
IPCC Source
Categories
Gas
Tierl
Level Trend Level Trend
Without Without With With
LULUCF LULUCF LULUCF LULUCF
Tier 2
Level Trend Level Trend
Without Without With With
LULUCF LULUCF LULUCF LULUCF
Quala
2012
Emissions
(Tg C02
Eq.)
Energy
CO2 Emissions from
Stationary
Combustion - Coal -
ElGctncitv CjGn.Gr3.ti on
CO2 Emissions from
Mobile Combustion:
Road
CO2 Emissions from
Stationary
Combustion - Gas -
Electricity Generation
CO2 Emissions from
Stationary
Combustion - Gas -
Industrial
CO2 Emissions from
Stationary
Combustion - Oil -
Industrial
CO2 Emissions from
Stationary
Combustion - Gas -
Residential
C02
C02
CO2
C02
C02
CO2
• • • •
• • • •
• • • •
• • • •
| |
........ |
• • •
• • • •
• • • •
• •
• • • •
• •
1,511.2
1,469.8
492.2
434.7
265.2
224.8
Introduction 1-15
-------
CO2 Emissions from
Stationary
Combustion - Gas -
Commercial
CO2 Emissions from
Mobile Combustion:
Aviation
CO2 Emissions from
Non-Energy Use of
Fuels
CO2 Emissions from
Mobile Combustion:
Other
CO2 Emissions from
Stationary
Combustion - Coal -
Industrial
CO2 Emissions from
Stationary
Combustion - Oil -
Residential
CO2 Emissions from
Stationary
Combustion - Oil -
U.S. Territories
CO2 Emissions from
Mobile Combustion:
Marine
CO2 Emissions from
Stationary
Combustion - Oil -
Commercial
CO2 Emissions from
Natural Gas Systems
CO2 Emissions from
Stationary
Combustion - Oil -
Electricity Generation
CO2 Emissions from
Stationary
Combustion - Coal -
Commercial
CO2 Emissions from
Stationary
Combustion - Coal -
Residential
Fugitive Emissions
from Natural Gas
Systems
Fugitive Emissions
from Coal Mining
Fugitive Emissions
from Petroleum
Systems
Non-CO2 Emissions
from Stationary
Combustion -
Residential
Non-CO2 Emissions
from Stationary
Combustion -
Electricity Generation
18.3
1-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
N2O Emissions from
Mobile Combustion:
Road
Non-CO2 Emissions
from Stationary
Combustion -
Industrial
International Bunker
Fuels0
N2O
N20
Several
12.6
2.5
Industrial Processes
CO2 Emissions from
Iron and Steel
Production &
Metallurgical Coke
Production
CO2 Emissions from
Cement Production
N2O Emissions from
Adipic Acid
Production
Emissions from
Substitutes for Ozone
Depleting Substances
SFe Emissions from
Electrical
Transmission and
Distribution
HFC-23 Emissions
from HCFC-22
Production
PFC Emissions from
Aluminum Production
C02
CO2
N20
HiGW
P
HiGW
P
HiGW
P
HiGW
P
Agriculture
CELi Emissions from
Enteric Fermentation
CH4
141.0
CH4 Emissions from
Manure Management
CH4
52.9
Direct N2O Emissions
from Agricultural Soil
Management
Indirect N2O
Emissions from
Applied Nitrogen
N20
N20
260.9
45.7
Waste
CH4 Emissions from
Landfills
CH4
• • • •
• • • •
102.8
Land Use, Land Use Change, and Forestry
CO2 Emissions from
Land Converted to
Cropland
CO2
CO2 Emissions from
Grassland Remaining
Grassland
CO2 Emissions from
Landfilled Yard
Trimmings and Food
Scraps
CO2 Emissions from
Cropland Remaining
Cropland
C02
CO2
C02
(13.2)
Introduction 1-17
-------
CO2 Emissions from
Urban Trees
CO2 Emissions from
Changes in Forest
Carbon Stocks
CH4 Emissions from
Forest Fires
N2O Emissions from
Forest Fires
C02
CO2
CH4
N20
(88.4)
(866.5)
15.3
12.5
Subtotal Without LULUCF
Total Emissions Without LULUCF
6,324.6
6,487.8
Percent of Total Without LULUCF
Subtotal With LULUCF
Total Emissions With LULUCF
97%
5,379.1
5,546.3
Percent of Total With LULUCF
97%
a Qualitative criteria.
b Emissions from this source not included in totals.
Note: Parentheses indicate negative values (or sequestration).
1.6 Quality Assurance and Quality Control
(QA/QC)
As part of efforts to achieve its stated goals for inventory quality, transparency, and credibility, the United States has
developed a quality assurance and quality control plan designed to check, document and improve the quality of its
inventory over time. QA/QC activities on the Inventory are undertaken within the framework of the U.S. QA/QC
plan, Quality Assurance/Quality Control and Uncertainty Management Plan for the U.S. Greenhouse Gas Inventory:
Procedures Manual for QA/QC and Uncertainty Analysis.
Key attributes of the QA/QC plan are summarized in Figure 1-2. These attributes include:
• Procedures and Forms: detailed and specific systems that serve to standardize the process of documenting
and archiving information, as well as to guide the implementation of QA/QC and the analysis of
uncertainty
• Implementation of Procedures: application of QA/QC procedures throughout the whole inventory
development process from initial data collection, through preparation of the emission estimates, to
publication of the Inventory
• Quality Assurance: expert and public reviews for both the inventory estimates and the Inventory report
(which is the primary vehicle for disseminating the results of the inventory development process)
• Quality Control: consideration of secondary data and source-specific checks (Tier 2 QC) in parallel and
coordination with the uncertainty assessment; the development of protocols and templates, which provides
for more structured communication and integration with the suppliers of secondary information
• Tier 1 (general) and Tier 2 (source-specific) Checks: quality controls and checks, as recommended by
IPCC Good Practice Guidance
1-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
• Record Keeping: provisions to track which procedures have been followed, the results of the QA/QC,
uncertainty analysis, and feedback mechanisms for corrective action based on the results of the
investigations which provide for continual data quality improvement and guided research efforts
• Multi-Year Implementation: a schedule for coordinating the application of QA/QC procedures across
multiple years
• Interaction and Coordination: promoting communication within the EPA, across Federal agencies and
departments, state government programs, and research institutions and consulting firms involved in
supplying data or preparing estimates for the Inventory. The QA/QC Management Plan itself is intended to
be revised and reflect new information that becomes available as the program develops, methods are
improved, or additional supporting documents become necessary.
In addition, based on the national QA/QC plan for the Inventory, source-specific QA/QC plans have been developed
for a number of sources. These plans follow the procedures outlined in the national QA/QC plan, tailoring the
procedures to the specific text and spreadsheets of the individual sources. For each greenhouse gas emissions source
or sink included in this Inventory, a minimum of a Tier 1 QA/QC analysis has been undertaken. Where QA/QC
activities for a particular source go beyond the minimum Tier 1 level, further explanation is provided within the
respective source category text.
The quality control activities described in the U.S. QA/QC plan occur throughout the inventory process; QA/QC is
not separate from, but is an integral part of, preparing the inventory. Quality control—in the form of both good
practices (such as documentation procedures) and checks on whether good practices and procedures are being
followed—is applied at every stage of inventory development and document preparation. In addition, quality
assurance occurs at two stages—an expert review and a public review. While both phases can significantly
contribute to inventory quality, the public review phase is also essential for promoting the openness of the inventory
development process and the transparency of the inventory data and methods.
The QA/QC plan guides the process of ensuring inventory quality by describing data and methodology checks,
developing processes governing peer review and public comments, and developing guidance on conducting an
analysis of the uncertainty surrounding the emission estimates. The QA/QC procedures also include feedback loops
and provide for corrective actions that are designed to improve the inventory estimates over time.
Introduction 1-19
-------
Figure 1-2: U.S. QA/QC Plan Summary
-J
>.
TO
c
<
O
c
fl)
>
C
in
ro
C
,,
a
§
• Obtain data in electronic
format (if possible)
• Review spreadsheet
construction
• Avoid hardwiring
Use data validation
Protect cells
• Develop automatic
checkers for:
Outliers, negative
values, or missing
data
Variabletvpes
match values
Time series
consistency
• Maintain tracking tab for
• Contact reports for non-
electronic communications
• Provide cell referencesfor
primary data elements
• Obtain copies of all data
sources
* List and location of any
working/external
spreadsheets
• Document assumptions
• Clearly label parameters,
units, and conversion
factors
• Review spreadsheet
integrity
• Equations
• Units
• Inputs and output
• Develop automated
checkers for:
• Input ranges
• Calculations
• Emission aggregation
status of gathering 1
efforts b^ \— tL I
• Check input datafor
transcription errors
• Inspect automatic
checkers
• Identify spreadsheet
modifications that could
provide additional
QA/QC checks
1 Check citations in
spreadsheet andtext for
accuracy and style
1 Check reference docket for
new citations
• Review documentation for
any data/ methodology
changes
|
• Reproduce calculations
• Reviewtime series
consistency
• Review changes in
data/consistency with IPCC
methodology
Common starting
versionsforeach
inventoryyear
Utilize unalterable
summary tab foreach
source spreadsheet for
linkingtoamaster
summary spreadsheet
Follow strictversion
control procedures
Document QA/QC
procedures
Data Gathering
Data Documentation CalculatingEmissions
Cross-Cutting
Coordination
1.7 Uncertainty Analysis of Emission Estimates
Uncertainty estimates are an essential element of a complete and transparent emissions inventory. Uncertainty
information is not intended to dispute the validity of the inventory estimates, but to help prioritize efforts to improve
the accuracy of future inventories and guide future decisions on methodological choice. While the U.S. Inventory
calculates its emission estimates with the highest possible accuracy, uncertainties are associated to a varying degree
with the development of emission estimates for any inventory. Some of the current estimates, such as those for CC>2
emissions from energy-related activities, are considered to have minimal uncertainty associated with them. For
some other categories of emissions, however, a lack of data or an incomplete understanding of how emissions are
generated increases the uncertainty surrounding the estimates presented. The UNFCCC reporting guidelines follow
the recommendation in the 1996 IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997) and require that countries
provide single point estimates for each gas and emission or removal source category. Within the discussion of each
emission source, specific factors affecting the uncertainty associated with the estimates are discussed.
Additional research in the following areas could help reduce uncertainty in the U.S. Inventory:
• Incorporating excluded emission sources. Quantitative estimates for some of the sources and sinks of
greenhouse gas emissions are not available at this time. In particular, emissions from some land-use
activities and industrial processes are not included in the inventory either because data are incomplete or
because methodologies do not exist for estimating emissions from these source categories. See Annex 5 of
this report for a discussion of the sources of greenhouse gas emissions and sinks excluded from this report.
1-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
• Improving the accuracy of emission factors. Further research is needed in some cases to improve the
accuracy of emission factors used to calculate emissions from a variety of sources. For example, the
accuracy of current emission factors applied to CH4 and N2O emissions from stationary and mobile
combustion is highly uncertain.
• Collecting detailed activity data. Although methodologies exist for estimating emissions for some sources,
problems arise in obtaining activity data at a level of detail in which aggregate emission factors can be
applied. For example, the ability to estimate emissions of SF6 from electrical transmission and distribution
is limited due to a lack of activity data regarding national SF6 consumption or average equipment leak
rates.
The overall uncertainty estimate for the U.S. greenhouse gas emissions inventory was developed using the IPCC
Tier 2 uncertainty estimation methodology. Estimates of quantitative uncertainty for the overall greenhouse gas
emissions inventory are shown below, in Table 1-5.
The IPCC provides good practice guidance on two approaches—Tier 1 and Tier 2—to estimating uncertainty for
individual source categories. Tier 2 uncertainty analysis, employing the Monte Carlo Stochastic Simulation
technique, was applied wherever data and resources permitted; further explanation is provided within the respective
source category text and in Annex 7. Consistent with the IPCC Good Practice Guidance (IPCC 2000), over a multi-
year timeframe, the United States expects to continue to improve the uncertainty estimates presented in this report.
Table 1-5: Estimated Overall Inventory Quantitative Uncertainty (Tg COz Eq. and Percent)
2012 Emission
Estimate3
Gas (Tg CO2 Eq.)
C02
CH4e
N2Oe
PFC, HFC & SFee
Total
Net Emissions (Sources and
Sinks)
5,382.8
567.3
410.1
161.9
6,522.0
5,542.7
Uncertainty Range Relative to Emission Standard
Estimate1" Mean0 Deviation0
(Tg COz Eq.) (%) (Tg CO2 Eq.)
Lower
Bound"1
5,265.2
512.7
378.0
161.3
6,448.3
5,419.9
Upper
Bound"1
5,629.5
670.9
540.2
182.4
6,873.0
5,940.5
Lower
Bound
-2%
-10%
-8%
0%
-1%
-2%
Upper
Bound
5%
18%
32%
13%
5%
7%
5,448
586
452
172
6,658
5,681
93
40
41
5
109
134
Notes:
a Emission estimates reported in this table correspond to emissions from only those source categories for which quantitative
uncertainty was performed this year. Thus the totals reported in this table exclude approximately 3.6 Tg CCh Eq. of emissions
for which quantitative uncertainty was not assessed. Hence, these emission estimates do not match the final total U.S.
greenhouse gas emission estimates presented in this Inventory.
b The lower and upper bounds for emission estimates correspond to a 95 percent confidence interval, with the lower bound
corresponding to 2.5th percentile and the upper bound corresponding to 97.5th percentile.
c Mean value indicates the arithmetic average of the simulated emission estimates; standard deviation indicates the extent of
deviation of the simulated values from the mean.
d The lower and upper bound emission estimates for the sub-source categories do not sum to total emissions because the low and
high estimates for total emissions were calculated separately through simulations.
e The overall uncertainty estimates did not take into account the uncertainty in the GWP values for CH4, N2O and high GWP
gases used in the inventory emission calculations for 2011.
Emissions calculated for the U.S. Inventory reflect current best estimates; in some cases, however, estimates are
based on approximate methodologies, assumptions, and incomplete data. As new information becomes available in
the future, the United States will continue to improve and revise its emission estimates. See Annex 7 of this report
for further details on the U.S. process for estimating uncertainty associated with the emission estimates and for a
more detailed discussion of the limitations of the current analysis and plans for improvement. Annex 7 also includes
details on the uncertainty analysis performed for selected source categories.
Introduction 1-21
-------
1.8 Completeness
This report, along with its accompanying CRF tables, serves as a thorough assessment of the anthropogenic sources
and sinks of greenhouse gas emissions for the United States for the time series 1990 through 2012. Although this
report is intended to be comprehensive, certain sources have been identified which were excluded from the estimates
presented for various reasons. Generally speaking, sources not accounted for in this inventory are excluded due to
data limitations or a lack of thorough understanding of the emission process. The United States is continually
working to improve upon the understanding of such sources and seeking to find the data required to estimate related
emissions. As such improvements are implemented, new emission sources are quantified and included in the
Inventory. For a complete list of sources not included, see Annex 5 of this report.
1.9 Organization of Report
In accordance with the Revised 1996IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC/UNEP/OECD/IEA 1997), and the 2006 UNFCCC Guidelines on Reporting and Review (UNFCCC 2006),
this Inventory of U.S. Greenhouse Gas Emissions and Sinks is segregated into six sector-specific chapters, listed
below in Table 1-6. In addition, chapters on Trends in Greenhouse Gas Emissions and Other information to be
considered as part of the U.S. Inventory submission are included.
Table 1-6: IPCC Sector Descriptions
Chapter/EPCC Sector Activities Included
Energy Emissions of all greenhouse gases resulting
from stationary and mobile energy activities
including fuel combustion and fugitive fuel
emissions.
Industrial Processes By-product or fugitive emissions of greenhouse
gases from industrial processes not directly
related to energy activities such as fossil fuel
combustion.
Solvent and Other Product Emissions, of primarily NMVOCs, resulting
Use from the use of solvents and N2O from product
uses.
Agriculture Anthropogenic emissions from agricultural
activities except fuel combustion, which is
addressed under Energy.
Land Use, Land-Use Emissions and removals of CCh, CELi, and N2O
Change, and Forestry from forest management, other land-use
activities, and land-use change.
Waste Emissions from waste management activities.
Source: (IPCC/UNEP/OECD/IEA 1997)
Within each chapter, emissions are identified by the anthropogenic activity that is the source or sink of the
greenhouse gas emissions being estimated (e.g., coal mining). Overall, the following organizational structure is
consistently applied throughout this report:
ChaptGlYIPCC SGCtOT Overview of emission trends for each IPCC defined sector
Source CStegory. Description of source pathway and emission trends.
Methodology: Description of analytical methods employed to produce emission estimates and
identification of data references, primarily for activity data and emission factors.
Uncertainty: A discussion and quantification of the uncertainty in emission estimates and a
discussion of time-series consistency.
1-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
QA/QC and Verification: A discussion on steps taken to QA/QC and verify the emission
estimates, where beyond the overall U.S. QA/QC plan, and any key findings.
Recalculations: A discussion of any data or methodological changes that necessitate a
recalculation of previous years' emission estimates, and the impact of the recalculation on the
emission estimates, if applicable.
Planned Improvements: A discussion on any source-specific planned improvements, if
applicable.
Special attention is given to CCh from fossil fuel combustion relative to other sources because of its share of
emissions and its dominant influence on emission trends. For example, each energy consuming end-use sector (i.e..
residential, commercial, industrial, and transportation), as well as the electricity generation sector, is described
individually. Additional information for certain source categories and other topics is also provided in several
Annexes listed in Table 1-7.
Table 1-7: List of Annexes
ANNEX 1 Key Category Analysis
ANNEX 2 Methodology and Data for Estimating CCh Emissions from Fossil Fuel Combustion
2.1. Methodology for Estimating Emissions of CCh from Fossil Fuel Combustion
2.2. Methodology for Estimating the Carbon Content of Fossil Fuels
2.3. Methodology for Estimating Carbon Emitted from Non-Energy Uses of Fossil Fuels
ANNEX 3 Methodological Descriptions for Additional Source or Sink Categories
3.1. Methodology for Estimating Emissions of CH/i, N2O, and Indirect Greenhouse Gases from Stationary
Combustion
3.2. Methodology for Estimating Emissions of CELi, N2O, and Indirect Greenhouse Gases from Mobile
Combustion and Methodology for and Supplemental Information on Transportation-Related Greenhouse Gas
Emissions
3.3. Methodology for Estimating Emissions from Commercial Aircraft Jet Fuel Consumption
3.4. Methodology for Estimating CELi Emissions from Coal Mining
3.5. Methodology for Estimating CELi Emissions from Natural Gas Systems
3.6. Methodology for Estimating CELi and CCh Emissions from Petroleum Systems
3.7. Methodology for Estimating CCh and N2O Emissions from Incineration of Waste
3.8. Methodology for Estimating Emissions from International Bunker Fuels used by the U.S. Military
3.9. Methodology for Estimating HFC and PFC Emissions from Substitution of Ozone Depleting Substances
3.10. Methodology for Estimating CELi Emissions from Enteric Fermentation
3.11. Methodology for Estimating CELi and N2O Emissions from Manure Management
3.12. Methodology for Estimating N2O Emissions and Soil Organic C Stock Changes from Agricultural Soil
Management (Cropland and Grassland)
3.13. Methodology for Estimating Net Carbon Stock Changes in Forest Lands Remaining Forest Lands
3.14. Methodology for Estimating CELi Emissions from Landfills
ANNEX 4 IPCC Reference Approach for Estimating CO2 Emissions from Fossil Fuel Combustion
ANNEX 5 Assessment of the Sources and Sinks of Greenhouse Gas Emissions Not Included
ANNEX 6 Additional Information
6.1. Global Warming Potential Values
6.2. Ozone Depleting Substance Emissions
6.3. Sulfur Dioxide Emissions
6.4. Complete List of Source Categories
6.5. Constants, Units, and Conversions
6.6. Abbreviations
6.7. Chemical Formulas
ANNEX 7 Uncertainty
7.1. Overview
7.2. Methodology and Results
7.3. Planned Improvements
ANNEX 8 QA/QC Procedures
8.1. Background
8.2. Purpose
8.3. Assessment Factors
Introduction 1-23
-------
-------
2. Trends in Greenhouse Gas Emissions
2.1 Recent Trends in U.S. Greenhouse Gas
Emissions and Sinks
In 2012, total U.S. greenhouse gas emissions were 6,525.6 Tg or million metric tons CCh Eq. Total U.S. emissions
have increased by 4.7 percent from 1990 to 2012, and emissions decreased from 201 1 to 2012 by 3.4 percent (227.4
Tg CO2 Eq.). The decrease from 201 1 to 2012 was due to a decrease in the carbon intensity of fuels consumed to
generate electricity due to a decrease in coal consumption, with increased natural gas consumption. Additionally,
relatively mild winter conditions, especially in regions of the United States where electricity is an important heating
fuel, resulted in an overall decrease in electricity demand in most sectors. Since 1990, U.S. emissions have increased
at an average annual rate of 0.2 percent.
Figure 2-1: U.S. Greenhouse Gas Emissions by Gas
8,000 -i
7,000 -
6,000
5,000 -
• MFCs, PFCs, & SF6 Nitrous Oxide
• Methane
c,., 6443 6,520 6,613 £
6,233 6,208 6'311 _ —
• Carbon Dioxide
6,8SO 6,883 6,957
6,753
6,526
Trends 2-1
-------
Figure 2-2: Annual Percent Change in U.S. Greenhouse Gas Emissions
4% -i
2% -
3.4%
3.2%
-3.4%
-6.4%
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Figure 2-3: Cumulative Change in Annual U.S. Greenhouse Gas Emissions Relative to 1990
r-j"
LU
t4
o
o
en
h-
1,200 -i
1,100
1,000
900
800
700
600
500
400 •
300
200
100
0 -
-100 J
1,092
874
885
606
647 650
724
642
520
287
210
292
-25
77
rN
en
01
Ol
Ol
O
£3
I § §
CM fM fM
CO
o
o
OJ
As the largest contributor to U.S. greenhouse gas emissions, carbon dioxide (CCh) from fossil fuel combustion has
accounted for approximately 78 percent of global warming potential (GWP) weighted emissions for the entire time
series since 1990, from 76 percent of total GWP-weighted emissions in 1990 to 78 percent in 2012. Emissions from
this source category grew by 6.9 percent (327.2 Tg CO2 Eq.) from 1990 to 2012 and were responsible for most of
the increase in national emissions during this period. From 2011 to 2012, these emissions decreased by 3.8 percent
(198.8 Tg CO2 Eq.). Historically, changes in emissions from fossil fuel combustion have been the dominant factor
affecting U.S. emission trends.
Changes in CC>2 emissions from fossil fuel combustion are influenced by many long-term and short-term factors,
including population and economic growth, energy price fluctuations, technological changes, and seasonal
temperatures. On an annual basis, the overall consumption of fossil fuels in the United States fluctuates primarily in
response to changes in general economic conditions, energy prices, weather, and the availability of non-fossil
alternatives. For example, in a year with increased consumption of goods and services, low fuel prices, severe
summer and winter weather conditions, nuclear plant closures, and lower precipitation feeding hydroelectric dams,
there would likely be proportionally greater fossil fuel consumption than in a year with poor economic performance,
high fuel prices, mild temperatures, and increased output from nuclear and hydroelectric plants.
In the longer-term, energy consumption patterns respond to changes that affect the scale of consumption (e.g.,
population, number of cars, and size of houses), the efficiency with which energy is used in equipment (e.g., cars,
power plants, steel mills, and light bulbs) and behavioral choices (e.g., walking, bicycling, or telecommuting to work
instead of driving).
Energy-related CCh emissions also depend on the type of fuel or energy consumed and its carbon (C) intensity.
Producing a unit of heat or electricity using natural gas instead of coal, for example, can reduce the CCh emissions
because of the lower C content of natural gas.
2-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
A brief discussion of the year to year variability in fuel combustion emissions is provided below, beginning with
2008.
From 2008 to 2009, CCh from fossil fuel combustion emissions experienced a decrease of 6.6 percent, the greatest
decrease of any year over the course of the twenty three-year period from 1990 to 2012. Various factors contributed
to this decrease in emissions. The continued economic downturn resulted in a 2.8 percent decrease in GDP,
decreased industrial production and manufacturing output, and a decrease in energy consumption across all sectors.
In 2009, the price of coal used to generate electricity increased, while the price of natural gas used to generate
electricity decreased significantly. As a result, natural gas was used for a greater share of electricity generation in
2009 than 2008, and coal was used for a smaller share. The fuel switching from coal to natural gas and additional
electricity generation from other energy sources in 2009, which included a 6.3 percent increase in hydropower
generation from the previous year, resulted in a decrease in carbon intensity, and in turn, a decrease in emissions
from electricity generation. From 2008 to 2009, industrial sector emissions decreased significantly as a result of a
decrease in output from energy-intensive industries, namely in nonmetallic mineral and primary metal industries.
The residential and commercial sectors only experienced minor decreases in emissions as summer and winter
weather conditions were less energy-intensive from 2008 to 2009, and the price of electricity only increased slightly.
Heating degree days decreased slightly and cooling degree days decreased by 3.5 percent from 2008 to 2009.
From 2009 to 2010, CCh emissions from fossil fuel combustion increased by 3.4 percent, which represents one of
the largest annual increases in CC>2 emissions from fossil fuel combustion for the twenty three-year period from
1990 to 2012.48 This increase is primarily due to an increase in economic output 2009 to 2010, and increased
industrial production and manufacturing output (FRB 2013). Carbon dioxide emissions from fossil fuel combustion
in the industrial sector increased by 6.6 percent, including increased emissions from the combustion of fuel oil,
natural gas and coal. Overall, coal consumption increased by 5.8 percent, the largest annual increase in coal
consumption for the twenty three-year period between 1990 and 2012. In 2010, weather conditions remained fairly
constant in the winter and were much hotter in the summer compared to 2009, as heating degree days decreased
slightly by 0.5 percent and cooling degree days increased by 16.8 percent to their highest levels in the twenty three-
year period from 1990 to 2012. As a result of the more energy-intensive summer weather conditions, electricity
sales to the residential and commercial end-use sectors in 2010 increased approximately 6.0 percent and 1.8 percent,
respectively.
From 2010 to 2011, CC>2 emissions from fossil fuel combustion decreased by 2.5 percent. This decrease is a result of
multiple factors including: (1) a decrease in the carbon intensity of fuels consumed to generate electricity due to a
decrease in coal consumption, with increased natural gas consumption and a significant increase in hydropower
used; (2) a decrease in transportation-related energy consumption due to higher fuel costs, improvements in fuel
efficiency, and a reduction in miles traveled; and (3) relatively mild winter conditions resulting in an overall
decrease in energy demand in most sectors. Changing fuel prices played a role in the decreasing emissions. A
significant increase in the price of motor gasoline in the transportation sector was a major factor leading to a
decrease in energy consumption by 1.0 percent. In addition, an increase in the price of coal and a concurrent
decrease in natural gas prices led to a 5.7 percent decrease and a 2.5 percent increase in fuel consumption of these
fuels by electric generators. This change in fuel prices also reduced the carbon intensity of fuels used to produce
electricity in 2011, further contributing to the decrease in fossil fuel combustion emissions.
From 2011 to 2012, CO2 emissions from fossil fuel combustion decreased by 3.8 percent, with emissions from fossil
fuel combustion at their lowest level since 1995. This decrease from 2011 to 2012 is primarily a result of the
decrease in the carbon intensity of fuels used to generate electricity due to a slight increase in the price of coal, and a
significant decrease in the price of natural gas. The consumption of coal used to generate electricity decreased by
12.3 percent, while consumption of natural gas for electricity generation increased by 20.4 percent. Also, emissions
declined in the transportation sector largely due to a small increase in fuel efficiency across different transportation
modes and limited new demand for passenger transportation. In 2012, weather conditions remained fairly constant in
the summer and were much warmer in the winter compared to 2011, as cooling degree days only increased by 2.2
percent while heating degree days decreased 12.8 percent. This decrease in heating degree days resulted in a
decreased demand for heating fuel in the residential and commercial sector, which had a decrease in natural gas
consumption of 11.7 and 8.0 percent, respectively.
48 This increase also represents the largest absolute and percentage increase since 1988 (EIA 201 la).
Trends 2-3
-------
Overall, from 1990 to 2012, total emissions of CO2 increased by 274.5 Tg CO2 Eq. (5.4 percent), while total
emissions of CH4 decreased by 68.4 Tg €62Eq. (10.8 percent), and total emissions of N2O increased 11.5 Tg CC>2
Eq. (2.9 percent). During the same period, aggregate weighted emissions of HFCs, PFCs, and SF6 rose by 74.8 Tg
CO2 Eq. (83.0 percent). Despite being emitted in smaller quantities relative to the other principal greenhouse gases,
emissions of HFCs, PFCs, and SF6 are significant because many of them have extremely high GWPs and, in the
cases of PFCs and SF6, long atmospheric lifetimes. Conversely, U.S. greenhouse gas emissions were partly offset
by C sequestration in managed forests, trees in urban areas, agricultural soils, and landfilled yard trimmings. These
were estimated to offset 15.0 percent of total emissions in 2012.
Table 2-1 summarizes emissions and sinks from all U.S. anthropogenic sources in weighted units of Tg CC>2 Eq.,
while unweighted gas emissions and sinks in gigagrams (Gg) are provided in Table 2-2.
Table 2-1: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (Tg COz Eq.)
Gas/Source
CO2
Fossil Fuel Combustion
Electricity Generation
Transportation
Industrial
Residential
Commercial
U.S. Territories
Non-Energy Use of Fuels
5
4
1
1
199
,108
.745.
,820,
,494,
845,
338,
219,
27,
120,
'0
7
.1
.8
.0
.1
.3
.0
.9
.8
6
5
2
1
2005
,112.2
,752.91
,402. ll
,891.71
827.6 B
357.9(
223. 5l
so. om
141. om
2008
5,936.9
5,593.4
2,360.9
1,816.5
804.1
346.2
224.7
41.0
128.0
2009
5,506.1
5,225.7
2,146.4
1,747.7
727.5
336.4
223.9
43.8
108.1
2010
5,722.3
5,404.9
2,259.2
1,765.0
775.6
334.8
220.7
49.6
120.8
2011
5,592.2
5,271.1
2,158.5
1,747.9
768.7
324.9
221.5
49.6
117.3
2012
5,383.2
5,072.3
2,022.7
1,739.5
774.2
288.9
197.4
49.6
110.3
Iron and Steel Production & Metallurgical Coke
Production 99.8 66.7
Natural Gas Systems 37.7 30.0
Cement Production 33.3 45.9
Lime Production 11.4 14.0
Incineration of Waste 8.oB 12.5
Ammonia Production 13.ol 9.2
Other Process Uses of Carbonates 4.91 6.3
Cropland Remaining Cropland 7.lH 7.9
Urea Consumption for Non-Agricultural Purposes 3.sB 3.7
Petrochemical Production 3.4B 4.3
Aluminum Production 6.81 4.1
Soda Ash Production and Consumption 2.7B 2.9
Carbon Dioxide Consumption 1.4 B 1.3
Titanium Dioxide Production 1.2 B 1 • &
Ferroalloy Production 2.2B 1.4
Zinc Production 0.6B 1.0
Glass Production 1.5 B 1.9
Phosphoric Acid Production 1.6B 1-4
Wetlands Remaining Wetlands 1.0 B 1.1
Lead Production O.sB 0.6
Petroleum Systems 0.4B 0.3
Silicon Carbide Production and Consumption 0.4B 0.2
Land Use, Land-Use Change, and Forestry (Sink)" (831.1) (1,030.7)
Wood Biomass and Ethanol Consumption11 219.4 229.8
International Bunker Fuels0 103.5 113.1
CH4 635.7 585.7
Enteric Fermentation 137.9 142.5
Natural Gas Systems 156.4 152.0
Landfills 147.8 112.1
Coal Mining 81.1 53.6
Manure Management 31.5 47.6
Petroleum Systems 35.8 28.8
Forest Land Remaining Forest Land 2.5 B 8.1
Wastewater Treatment 13.2B 13.3
66.8
32.7
41.2
14.0
11.9
8.4
5.9
8.6
4.1
3.6
4.5
2.9
1.8
1.8
1.6
1.2
1.5
1.2
1.0
0.5
0.3
0.2
(981.0)
254.7
114.3
606.0
147.0
151.6
114.3
63.5
51.5
28.8
8.7
13.3
43.0
32.2
29.4
10.9
11.7
8.5
7.6
7.2
3.4
2.8
3.0
2.5
1.8
1.6
1.5
0.9
1.0
1.0
1.1
0.5
0.3
0.1
(961.6)
250.5
106.4
596.5
146.1
142.9
115.3
67.1
50.5
29.1
5.8
13.1
55.7
32.4
31.3
12.8
12.0
9.2
9.6
8.6
4.7
3.5
2.7
2.6
2.3
1.8
1.7
1.2
1.5
1.1
1.0
0.5
0.3
0.2
(968.0)
265.1
117.0
585.5
144.9
134.7
109.9
69.2
51.8
29.5
4.7
13.0
60.0
35.1
32.0
13.5
12.1
9.4
9.3
7.9
4.0
3.5
3.3
2.6
1.8
1.7
1.7
1.3
1.3
1.2
0.9
0.5
0.3
0.2
(980.3)
268.1
111.7
578.3
143.0
133.2
107.4
59.8
52.0
30.5
14.0
12.8
54.3
35.2
35.1
13.3
12.2
9.4
8.0
7.4
5.2
3.5
3.4
2.7
1.8
1.7
1.7
1.4
1.2
1.1
0.8
0.5
0.4
0.2
(979.3)
266.8
105.8
567.3
141.0
129.9
102.8
55.8
52.9
31.7
15.3
12.8
2-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Rice Cultivation
Stationary Combustion
Abandoned Underground Coal Mines
Petrochemical Production
Mobile Combustion
Composting
Iron and Steel Production & Metallurgical Coke
Production
Field Burning of Agricultural Residues
Ferroalloy Production
Silicon Carbide Production and Consumption
Incineration of Waste
International Bunker Fuels'
N2O
Agricultural Soil Management
Stationary Combustion
Manure Management
Mobile Combustion
Nitric Acid Production
Forest Land Remaining Forest Land
Adipic Acid Production
Wastewater Treatment
N2O from Product Uses
Composting
Settlements Remaining Settlements
Incineration of Waste
Field Burning of Agricultural Residues
Wetlands Remaining Wetlands
International Bunker Fuels0
HFCs
Substitution of Ozone Depleting Substances'1
HCFC-22 Production
Semiconductor Manufacture
PFCs
Semiconductor Manufacture
Aluminum Production
SF6
Electrical Transmission and Distribution
Magnesium Production and Processing
Semiconductor Manufacture
7.8
6.6
5.3
2.9
1.9
1.7
0.6
0.3
1.0
136.0
122.2
13.6
0.2
5.1
2.4
2.7
10.7
8.4
1.9
0.5
7.9
6.6
5.1
2.9
1.8
1.6
0.4
0.2
9.3
6.4
5.0
3.1
1.8
1.5
0.5
0.2
7.1
6.3
4.8
3.1
1.7
1.6
0.6
0.3
0.9
135.1
129.6
5.4
0.1
3.3
1.7
1.6
9.6
7.5
1.7
0.3
1.0
144.0
137.5
6.4
0.2
3.8
2.2
1.6
9.8
7.2
2.2
0.4
1.0
148.6
141.5
6.9
0.2
6.0
3.0
2.9
10.8
7.2
2.9
0.7
7.4
5.7
4.7
3.1
1.7
1.6
0.6
0.3
0.1
423.3
319.0
21.1
17.8
25.5
16.9
7.5
2.6
4.8
4.4
1.9
1.5
0.4
0.1
0.1
412.2
316.4
20.8
17.7
22.7
14.0
5.1
2.8
4.8
4.4
1.8
1.4
0.4
0.1
0.1
409.3
310.1
22.5
17.8
20.7
16.7
4.2
4.4
4.9
4.4
1.7
1.5
0.4
0.1
0.1
417.2
307.8
21.6
18.0
18.5
15.8
11.8
10.6
5.0
4.4
1.7
1.5
0.4
0.1
0.1
410.1
306.6
22.0
18.0
16.5
15.3
12.8
5.8
5.0
4.4
1.8
1.5
0.4
0.1
1.0
151.2
146.8
4.3
0.2
5.4
2.9
2.5
8.4
6.0
1.7
0.7
Total
Net Emissions (Sources and Sinks)
6,233.2 7,253.8 7,118.1 6,662.9 6,874.7 6,753.0 6,525.6
5,402.1 6,223.1 6,137.1 5,701.2 5,906.7 5,772.7 5,546.3
+ Does not exceed 0.05 Tg CO2 Eq.
a The net CCh flux total includes both emissions and sequestration, and constitutes a sink in the United States. Sinks are only
included in net emissions total. Parentheses indicate negative values or sequestration.
b Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals. Net
carbon fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for Land Use, Land-Use Change, and
Forestry.
c Emissions from International Bunker Fuels are not included in totals.
d Small amounts of PFC emissions also result from this source.
Note: Totals may not sum due to independent rounding.
Trends 2-5
-------
Table 2-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (Gg)
Gas/Source
C02
Fossil Fuel Combustion
Electricity Generation
Transportation
Industrial
Residential
Commercial
U.S. Territories
Non-Energy Use of Fuels
Iron and Steel Production &
Metallurgical Coke Production
Natural Gas Systems
Cement Production
Lime Production
Incineration of Waste
Ammonia Production
Other Process Uses of Carbonates
Cropland Remaining Cropland
1990
5,108,723
4,745,067
1,820,818
1,493,968
845,089
338,347
218,9631
27,8821
120,842
99,781
37,705
33,278 1
11,420!
7,972l
13,047
4,907l
7,084!
2005
6,112,227
5,752,860
2,402,143
1,891,744
827,600
357,903
223,511
49,960 1
140,997
66,666 1
29,98sl
45,9101
13,99ol
12,4541
9,196l
6,339l
7,854l
2008
5,936,945
5,593,424
2,360,920
1,816,472
804,121
346,237
224,715
40,959
127,997
66,822
32,707
41,161
13,992
11,867
8,414
5,885
8,638
2009
5,506,116
5,225,717
2,146,415
1,747,674
727,505
336,363
223,941
43,818
108,115
43,029
32,234
29,432
10,914
11,672
8,454
7,583
7,224
2010
5,722,330
5,404,903
2,259,190
1,765,025
775,574
334,828
220,669
49,615
120,827
55,746
32,362
31,256
12,834
12,033
9,188
9,560
8,563
2011
5,592,162
5,271,097
2,158,481
1,747,879
768,715
324,928
221,519
49,576
117,313
60,008
35,082
32,010
13,471
12,142
9,428
9,335
7,864
2012
5,383,214
5,072,271
2,022,679
1,739,536
774,161
288,883
197,431
49,582
110,313
54,319
35,232
35,051
13,318
12,195
9,366
7,997
7,381
Urea Consumption for Non-
Agricultural Purposes
Petrochemical Production
Aluminum Production
Soda Ash Production and
Consumption
Carbon Dioxide Consumption
Titanium Dioxide Production
Ferroalloy Production
Zinc Production
Glass Production
Phosphoric Acid Production
Wetlands Remaining Wetlands
Lead Production
Petroleum Systems
Silicon Carbide Production and
Consumption
Land Use, Land-Use Change, and
Forestry (Sink)"
Wood Biomass and Ethanol
Consumption11
International Bunker Fuels'
CH4
Enteric Fermentation
Natural Gas Systems
Landfills
Coal Mining
Manure Management
Petroleum Systems
Forest Land Remaining Forest
Wastewater Treatment
Rice Cultivation
Stationary Combustion
Abandoned Underground Coal
Mines
Petrochemical Production
Mobile Combustion
3,784
3,429
6,831
2,741
2,152
632
1,535
1,586
1,033
516
394
375
3,653
4,330
4,142
2,868
1,321
1,755
1,392
1,030
1,9281
1,396
1,079
553
306
219
(831,108) (1,030,713)
219,413
103,463
30,272
6,566
7,450
7,036
3,860
1,499
1,704
119
626
366
1
288
108
218
264
150
ml
4,065
3,572
4,477
3,427
2,833
3,009
4,728
3,455
2,722
3,999
3,505
3,292
175
145
181
170
253
137
92
244
138
87
237
146
85
231
148
82
5,243
3,505
3,439
2,865
1,780
1,809
1,599
1,159
1,523
1,177
992
547
300
2,488
1,784
1,648
1,469
943
1,045
1,016
1,089
525
320
2,612
2,253
1,769
1,663
1,182
1,481
1,130
1,010
542
332
2,624
1,843
1,729
1,663
1,286
1,299
1,199
919
538
347
2,672
1,815
1,742
1,663
1,422
1,247
1,101
830
527
406
158
(981,016) (961,619) (968,010) (980,310) (979,305)
229,844
113,139
27,893
6,785 1
7,240 1
5,339l
2,552l
2,265 1
l,374l
386 1
635
358
315
254, 672
114,342
28,857
6,999
7,218
5,444
3,026
2,452
1,372
416
635
370
317
250,491
106,410
28,406
6,956
6,806
5,492
3,194
2,403
1,388
275
623
378
316
265,110
116,992
27,882
6,898
6,413
5,234
3,293
2,466
1,407
225
619
444
304
268,064
111,660
27,538
6,809
6,343
5,112
2,849
2,478
1,453
664
611
339
302
266,831
105,805
27,013
6,714
6,186
4,897
2,658
2,519
1,511
727
608
351
271
226
147
81
2-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Composting 15 • 75 • 80 75 73 75 76
Iron and Steel Production &
Metallurgical Coke Production 461 341 31 17 25 28 29
Field Burning of Agricultural
Residues 13! 9! 13 12 H 12 12
Ferroalloy Production 11 +B + + + + +
Silicon Carbide Production and
Consumption 11 +B + + + + +
Incineration of Waste +B +B + + + + +
International Bunker Fuels' 71 5 65 6 5 4
N20 1,286 1,341 1,365 1,330 1,320 1,346 1,323
Agricultural Soil Management 91oB 959B 1,029 1,021 1,000 993 989
Stationary Combustion 401 66 68 67 73 70 71
Manure Management 461 55 57 57 57 58 58
Mobile Combustion 142^ 119 82 73 67 60 53
Nitric Acid Production 591 55 54 45 54 51 49
Forest Land Remaining Forest
Land 7| 22 24 16 14 38 41
Adipic Acid Production 511 24 8 9 14 34 19
Wastewater Treatment 111 14 15 16 16 16 16
N2O from Product Uses 141 14 14 14 14 14 14
Composting 11 6 66 5 6 6
Settlements Remaining Settlements 31 5 55 5 5 5
Incineration of Waste 2! 1 11 1 1 1
Field Burning of Agricultural
Residues +B + + + + + +
Wetlands Remaining Wetlands +B + ++ + + +
International Bunker Fuels0 3M 3 33 3 3 3
HFCs M M M M M M M
Substitution of Ozone Depleting
Substances* M M M M M M M
HCFC-22 Production 3M 1 1 + 1 1 +
Semiconductor Manufacture +B + ++ + + +
PFCs M M M M M M M
Semiconductor Manufacture M M M M M M M
Aluminum Production M M M M M M M
SF6 lll++ + + +
Electrical Transmission and
Distribution 11 + + + + + +
Magnesium Production and
Processing +B + ++ + + +
Semiconductor Manufacture + + + + + + +_
+ Does not exceed 0.5 Gg.
M Mixture of multiple gases
a The net CCh flux total includes both emissions and sequestration, and constitutes a sink in the United States. Sinks are only included
in net emissions total. Parentheses indicate negative values or sequestration.
b Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals. Net carbon
fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for Land Use, Land-Use Change, and Forestry
0 Emissions from International Bunker Fuels are not included in totals.
d Small amounts of PFC emissions also result from this source.
Note: Totals may not sum due to independent rounding.
Emissions of all gases can be summed from each source category into a set of six sectors defined by the
Intergovernmental Panel on Climate Change (IPCC). Over the twenty three-year period of 1990 to 2012, total
emissions in the Energy, Industrial Processes, and Agriculture sectors grew by 238.8 Tg CCh Eq. (4.5 percent), 18.3
Tg CO2 Eq. (5.8 percent), and 52.3 Tg CCh Eq. (11.0 percent), respectively. Emissions from the Waste and Solvent
and Other Product Use sectors decreased by 41.1 Tg CO2 Eq. (24.9 percent) and less than 0.1 Tg CO2 Eq. (0.4
percent), respectively. Over the same period, estimates of net C sequestration in the Land Use, Land-Use Change,
and Forestry sector increased by 124.1 Tg CO2 Eq. (15.2 percent).
Trends 2-7
-------
Figure 2-4: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector
8
7,500
7,000
6,500
6,000
5,500
5,000
4,500
4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0
(500)
(1,000)
(1,500)
Industrial Processes
Agriculture
i-
Waste
LULUCF (sources)
Lari
Use, Land-Use Change and Foreshy (si
Note: Relatively smaller amounts of GWP-weighted emissions are also emitted from the Solvent and Other Product
Use sectors.
Table 2-3: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC
Sector (Tg COz Eq.)
Chapter/IPCC Sector
1990
2005
2008
2009
2010
2011
2012
Energy
Fossil Fuel Combustion
Natural Gas Systems
Non-Energy Use of Fuels
Coal Mining
Petroleum Systems
Stationary Combustion
Mobile Combustion
Incineration of Waste
Abandoned Underground Coal Mines
Industrial Processes
Substitution of Ozone Depleting
Substances
Iron and Steel Production &
Metallurgical Coke Production
Cement Production
Nitric Acid Production
Lime Production
Ammonia Production
Other Process Uses of Carbonates
Petrochemical Production
Electrical Transmission and
Distribution
Aluminum Production
Adipic Acid Production
Urea Consumption for Non-
Agricultural Purposes
HCFC-22 Production
Semiconductor Manufacture
Soda Ash Production and
Consumption
0.3
100.7
333
18.2
11.4
13.0
4.9
26.7
253
15.8
3.8
36.4
2.9
2.7
6,243.5
5,752.9
182.0
141.0
53.6
29.1
27.2
39.3
12.9
5.5
334.9
103.8
67.41
45.9J
16.9
14.0
«
1
n.ol
71
1
37
15.8J
1
2.9
6,071.1
5,593.4
184.3
128.0
63.5
29.1
27.8
27.4
12.2
5.3
335.9
122.2
67.5
41.2
16.9
14.0
8.4
5.9
6.5
8.4
7.2
2.6
4.1
13.6
3.0
2.9
5,674.6
5,225.7
175.2
108.1
67.1
29.5
27.4
24.5
12.0
5.1
287.8
129.6
43.4
29.4
14.0
10.9
8.5
7.6
5.7
7.5
4.6
2.8
3.4
5.4
2.2
2.5
5,860.6
5,404.9
167.0
120.8
69.2
29.9
28.9
22.5
12.4
5.0
324.6
137.5
56.3
31.3
16.7
12.8
9.2
9.6
6.5
7.2
4.3
4.4
4.7
6.4
2.8
2.6
5,712.9
5,271.1
168.3
117.3
59.8
30.9
28.0
20.2
12.5
4.8
342.9
141.5
60.6
32.0
15.8
13.5
9.4
9.3
6.6
7.2
6.2
10.6
4.0
6.9
3.9
2.6
5,498.9
5,072.3
165.1
110.3
55.8
32.1
111
18.2
12.6
4.7
334.4
146.8
54.9
35.1
15.3
13.3
9.4
8.0
6.6
6.0
5.9
5.8
5.2
4.3
3.7
2.7
2-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Carbon Dioxide Consumption 1.4
Titanium Dioxide Production 1.2
Magnesium Production and
Processing 5.4
Ferroalloy Production 2.2
Zinc Production 0.6
Glass Production 1.5
Phosphoric Acid Production 1.6
Lead Production 0.5
Silicon Carbide Production and
Consumption 0.4
Solvent and Other Product Use 4.4
Agriculture 473.9
Agricultural Soil Management 282.1
Enteric Fermentation 137.9
Manure Management 45.£
Rice Cultivation 7.7
Field Burning of Agricultural
Residues 0.4
Land Use, Land-Use Change, and
Forestry (Emissions) 13.7
Forest Land Remaining Forest Land 4.6
Cropland Remaining Cropland 7.1
Settlements Remaining Settlements 1.0
Wetlands Remaining Wetlands 1.0
Waste 165.0
Landfills 147.8
Wastewater Treatment 16.6
Composting 0.7
1.8
2.9|
1.4
1.0
1.9
1.4
0.6|
0.2
4.4
512.2
297.3
142.5
64.6
7.5
0.3
25.5
15.1
7.9
1.5
1.1
133.2
112.1
17.8
3.3
112.1
17.sl
1.8
1.8
1.9
1.6
1.2
1.5
1.2
0.5
0.2
4.4
543.4
319.0
147.0
69.3
7.8
0.4
27.3
16.2
8.6
1.5
1.0
136.0
114.3
18.1
3.5
1.8
1.6
1.7
1.5
0.9
1.0
1.0
0.5
0.2
4.4
538.9
316.4
146.1
68.2
7.9
0.4
20.5
10.8
7.2
1.4
1.1
136.5
115.3
17.9
3.3
2.3
1.8
2.2
1.7
1.2
1.5
1.1
0.5
0.2
4.4
534.2
310.1
144.9
69.6
9.3
0.3
20.0
8.9
8.6
1.5
1.0
131.1
109.9
17.9
3.2
1.8
1.7
2.9
1.7
1.3
1.3
1.2
0.5
0.2
4.4
528.3
307.8
143.0
70.0
7.1
0.4
36.0
25.7
7.9
1.5
0.9
128.5
107.4
17.8
3.3
1.8
1.7
1.7
1.7
1.4
1.2
1.1
0.5
0.2
4.4
526.3
306.6
141.0
70.9
7.4
0.4
37.8
28.1
7.4
1.5
0.8
124.0
102.8
17.8
3.3
Total Emissions
6,233.2
7,253.8
7,118.1 6,662.9 6,874.7 6,753.0 6,525.6
Net CO2 Flux From Land Use, Land-Use
Change and Forestry (Sinks)" (831.1) (1,030.7)
(981.0) (961.6) (968.0) (980.3) (979.3)
Net Emissions (Sources and Sinks)
5,402.1
6,223.1
6,137.1 5,701.2 5,906.7 5,772.7 5,546.3
a The net CCh flux total includes both emissions and sequestration, and constitutes a sink in the United States. Sinks are only
included in net emissions total. Please refer to Table 2-9 for a breakout by source.
Note: Totals may not sum due to independent rounding.
Note: Parentheses indicate negative values or sequestration.
Energy
Energy-related activities, primarily fossil fuel combustion, accounted for the vast majority of U.S. CCh emissions for
the period of 1990 through 2012. In 2012, approximately 82 percent of the energy consumed in the United States
(on a Btu basis) was produced through the combustion of fossil fuels. The remaining 18 percent came from other
energy sources such as hydropower, biomass, nuclear, wind, and solar energy (see Figure 2-5 and Figure 2-6). A
discussion of specific trends related to CCh as well as other greenhouse gas emissions from energy consumption is
presented in the Energy chapter. Energy-related activities are also responsible for CH4 and N2O emissions (40
percent and 9 percent of total U.S. emissions of each gas, respectively). Table 2-4 presents greenhouse gas
emissions from the Energy chapter, by source and gas.
Trends 2-9
-------
Figure 2-5: 2012 Energy Chapter Greenhouse Gas Sources
Fossil Fuel Combustion
Natural Gas Systems
Non-Energy Use of Fuels
Coal Mining
Petroleum Systems
Stationary Combustion
Mobile Combustion
Incineration of Waste
Abandoned Underground Coal Mines
Energy as a Portion
of all Emissions
5,072
50
100
Tg C02 Eq.
150
200
Figure 2-6: 2012 U.S. Fossil Carbon Flows (Tg COz Eq.)
NEU Emissions 13
Coal Emissions
1,606
IEU Emissions 5
Natural Gas Emissions
1,356
NEU Emissions 53
Non-Energy Use
Carbon Sequestered
Fossil Fuel
Non-Energy Consumption
Use Imports U'S-
26 Territoiies
50
Note: Totals may not sum due to independent rounding.
The "Balancing Item' above accounts for the statistical imbalancf
and unknowns in the reported data sets combined here,
NEU = Non-Energy Use
NG = Natural Gas
Table 2-4: Emissions from Energy (Tg COz Eq.)
Gas/Source
C02
Fossil Fuel Combustion
Electricity Generation
Transportation
Industrial
Residential
Commercial
U.S. Territories
Non-Energy Use of Fuels
Natural Gas Systems
1990
4,912.0
4,745.1
1,820.8
1,494.0
845.1
338.3
219.0
27.9
120.8
37.7
2005
5,936.6
5,752.9
2,402.1
1,891.7
827.6
357.9
223.5
50.0
141.0
30.0
2008
5,766.3
5,593.4
2,360,
1,816
804
346
224
41
128
32
,9
,5
.1
,2
,7
,0
,0
.7
2009
5,378.1
5,225.7
2,146.4
1,747.7
727.5
336.4
223.9
43.8
108.1
32.2
2010
5,570.5
5,404,
2,259
1,765
775
334
220
49
120
32
,9
,2
,0
,6
,8
,7
,6
,8
,4
2011
5,436.
5,271
2,158,
0
,1
,5
1,747.9
768.7
324.9
221,
49
117
35
,5
,6
,3
,1
2012
5,230.4
5,072.3
2,022.7
1,739.5
774.2
288.9
197.4
49.6
110.3
35.2
2-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Incineration of Waste
Petroleum Systems
Biomass - Wood"
International Bunker Fuelsb
Biomass - Ethanol"
CH4
Natural Gas Systems
Coal Mining
Petroleum Systems
Stationary Combustion
Abandoned Underground Coal
Mines
Mobile Combustion
Incineration of Waste
International Bunker Fuelsb
N2O
Stationary Combustion
Mobile Combustion
Incineration of Waste
International Bunker Fuelsb
Total
8.0
0.4l
215.2M
103.5M
4.2
291.4
156.4
81.1
35.8 1
7.5 1
6.0 1
4.6
56.8 1
12.31
44.0 I
0.5 1
0.9
5,260.1
12.5 I
0.3 1
206.9m
113.m
22.9
249.0
152.0
53.6
28.8 1
6.6
5.5
2.4
+ 1
0.1 1
57.9 1
20.6 1
36.9 1
0.4 1
1.0
6,243.5
11.9
0.3
199.9
114.3
54.7
257.8
151.6
63.5
28.8
6.6
5.3
1.9
+
0.1
47.0
21.1
25.5
0.4
1.0
6,071.1
11.7
0.3
188.2
106.4
62.3
252.8
142.9
67.1
29.1
6.6
5.1
1.8
+
0.1
43.8
20.8
22.7
0.4
0.9
5,674.6
12.0
0.3
192.5
117.0
72.6
246.5
134.7
69.2
29.5
6.4
5.0
1.8
+
0.1
43.6
22.5
20.7
0.4
1.0
5,860.6
12.1
0.3
195.2
111.7
72.9
236.5
133.2
59.8
30.5
6.3
4.8
1.7
+
0.1
40.5
21.6
18.5
0.4
1.0
5,712.9
12.2
0.4
194.0
105.8
72.8
229.6
129.9
55.8
31.7
5.7
4.7
1.7
+
0.1
38.9
22.0
16.5
0.4
1.0
5,498.9
+ Does not exceed 0.05 Tg CO2 Eq.
a Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals. Net
carbon fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for Land Use, Land-Use Change,
and Forestry
b Emissions from International Bunker Fuels are not included in totals.
Note: Totals may not sum due to independent rounding.
Carbon dioxide emissions from fossil fuel combustion are presented in Table 2-5 based on the underlying U.S.
energy consumer data collected by EIA. Estimates of CO2 emissions from fossil fuel combustion are calculated from
these EIA "end-use sectors" based on total consumption and appropriate fuel properties (any additional analysis and
refinement of the EIA data is further explained in the Energy chapter of this report). EIA's fuel consumption data for
the electric power sector comprises electricity -only and combined-heat-and-power (CHP) plants within the NAICS
22 category whose primary business is to sell electricity, or electricity and heat, to the public (nonutility power
producers can be included in this sector as long as they meet they electric power sector definition). EIA statistics for
the industrial sector include fossil fuel consumption that occurs in the fields of manufacturing, agriculture, mining,
and construction. EIA's fuel consumption data for the transportation sector consists of all vehicles whose primary
purpose is transporting people and/or goods from one physical location to another. EIA's fuel consumption data for
the industrial sector consists of all facilities and equipment used for producing, processing, or assembling goods
(EIA includes generators that produce electricity and/or useful thermal output primarily to support on-site industrial
activities in this sector). EIA's fuel consumption data for the residential sector consists of living quarters for private
households. EIA's fuel consumption data for the commercial sector consists of service-providing facilities and
equipment from private and public organizations and businesses (EIA includes generators that produce electricity
and/or useful thermal output primarily to support the activities at commercial establishments in this sector). Table
2-5 and Figure 2-7 summarize CCh emissions from fossil fuel combustion by end-use sector. Figure 2-8 further
describes the total emissions from fossil fuel combustion, separated by end-use sector, including CH4, and N2O in
addition to
Trends 2-11
-------
Table 2-5: COz Emissions from Fossil Fuel Combustion by End-Use Sector (Tg COz Eq.)
End-Use Sector
Transportation
Combustion
Electricity
Industrial
Combustion
Electricity
Residential
Combustion
Electricity
Commercial
Combustion
Electricity
U.S. Territories3
1990
1,497.0
1,494.0
3.0
1,531.8
845.1
686.7
931.4
338.3
593.0
757.0
219.0
538.0
27.9
2005
1,896.5
1,891.7
4.7
1,564.6
827.6
737.0
1,214.7
357.9
856.7
1,027.2
223.5
803.7
50.0
2008
2009
2010
2011
1,821.2
1,816.5
4.7
1,501.4
804.1
697.3
1,189.2
346.2
842.9
1,040.8
224.7
816.0
41.0
1,752.2
1,747.7
4.5
1,329.5
727.5
602.0
1,122.9
336.4
786.5
977.4
223.9
753.5
43.8
1,769.5
1,765.0
4.5
1,416.6
775.6
641.1
1,175.2
334.8
840.4
993.9
220.7
773.3
49.6
1,752.1
1,747.9
4.3
1,393.6
768.7
624.9
1,115.9
324.9
791.0
959.8
221.5
738.3
49.6
2012
1,743.4
1,739.5
3.9
1,367.1
774.2
592.9
1,014.3
288.9
725.5
897.9
197.4
700.4
49.6
Total
4,745.1
5,752.9
5,593.4 5,225.7 5,404.9 5,271.1 5,072.3
Electricity Generation
1,820.8
2,402.1
2,360.9 2,146.4 2,259.2 2,158.5 2,022.7
Note: Totals may not sum due to independent rounding. Combustion-related emissions from electricity
generation are allocated based on aggregate national electricity consumption by each end-use sector.
a Fuel consumption by U.S. Territories (i.e., American Samoa, Guam, Puerto Rico, U.S. Virgin Islands,
Wake Island, and other U.S. Pacific Islands) is included in this report.
Figure 2-7: 2012 COz Emissions from Fossil Fuel Combustion by Sector and Fuel Type
iff
8
H
2,500 -|
2,000 -
1,500 -
1,000 -
500
n
R^ldtivO
byF
™
50
1,740
2,023
2-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure 2-8: 2012 End-Use Sector Emissions of COz from Fossil Fuel Combustion
2,000
i From Direct Fossil Fuel Combustion
1,743
From Electricity Consumption
The main driver of emissions in the Energy sector is CC>2 from fossil fuel combustion. Electricity generation is the
largest emitter of CCh, and electricity generators consumed 35 percent of U.S. energy from fossil fuels and emitted
40 percent of the CC>2 from fossil fuel combustion in 2012. Electricity generation emissions can also be allocated to
the end-use sectors that are consuming that electricity, as presented in Table 2-5. The transportation end-use sector
accounted for 1,743.4 Tg CCh Eq. in 2012 or approximately 34 percent of total CCh emissions from fossil fuel
combustion. The industrial end-use sector accounted for 27 percent of CC>2 emissions from fossil fuel combustion.
The residential and commercial end-use sectors accounted for 20 and 18 percent, respectively, of CO2 emissions
from fossil fuel combustion. Both of these end-use sectors were heavily reliant on electricity for meeting energy
needs, with electricity consumption for lighting, heating, air conditioning, and operating appliances contributing 72
and 78 percent of emissions from the residential and commercial end-use sectors, respectively. Significant trends in
emissions from energy source categories over the twenty three-year period from 1990 through 2012 included the
following:
• Total CO2 emissions from fossil fuel combustion increased from 4,745.1 Tg CO2 Eq. in 1990 to 5,072.3 Tg
CO2 Eq. in 2012—a 6.9 percent total increase over the twenty three-year period. From 2011 to 2012, these
emissions decreased by 198.8 Tg €62 Eq. (3.8 percent).
• CH4 emissions from natural gas systems were the second largest anthropogenic source of CH4 emissions in
the United States with 129.9 Tg €62 Eq. emitted into the atmosphere in 2012; emissions have decreased by
26.6 Tg CO2 Eq. (17.0 percent) since 1990.
• CO2 emissions from non-energy use of fossil fuels decreased by 10.5 Tg CO2 Eq. (8.7 percent) from 1990
through 2012. Emissions from non-energy uses of fossil fuels were 110.3 Tg CO2 Eq. in 2012, which
constituted 2.0 percent of total national €62 emissions.
• N2O emissions from stationary combustion increased by 9.7 Tg €62 Eq. (79.3 percent) from 1990 through
2012. N2O emissions from this source increased primarily as a result of an increase in the number of coal
fluidized bed boilers in the electric power sector.
• CO2 emissions from incineration of waste (12.2 Tg CO2 Eq. in 2012) increased by 4.2 Tg CO2 Eq. (53.0
percent) from 1990 through 2012, as the volume of plastics and other fossil carbon-containing materials in
municipal solid waste grew.
The decrease in €62 emissions from fossil fuel combustion in 2012 was a result of multiple factors including: (1) a
decrease in the carbon intensity of fuels consumed by power producers to generate electricity due to significant
decrease in the price of natural gas compared to the slight increase in the price of coal; (2) a decrease in
transportation sector emissions attributed to a small increase in fuel efficiency across different transportation modes
and limited new demand for passenger transportation; and (3) much warmer winter conditions resulting in a
decreased demand for heating fuel in the residential and commercial sectors.
Trends 2-13
-------
Industrial Processes
Greenhouse gas emissions are produced as the by-products of many non-energy-related industrial activities. For
example, industrial processes can chemically transform raw materials, which often release waste gases such as
CH4, and N2O. These processes include iron and steel production and metallurgical coke production, cement
production, ammonia production, urea consumption, lime production, other process uses of carbonates (e.g., flux
stone, flue gas desulfurization, and glass manufacturing), soda ash production and consumption, titanium dioxide
production, phosphoric acid production, ferroalloy production, CO2 consumption, silicon carbide production and
consumption, aluminum production, petrochemical production, nitric acid production, adipic acid production, lead
production, and zinc production (see Figure 2-9). Industrial processes also release HFCs, PFCs and SF6. In addition
to their use as ODS substitutes, HFCs, PFCs, SF6, and other fluorinated compounds are employed and emitted by a
number of other industrial sources in the United States. These industries include aluminum production, HCFC-22
production, semiconductor manufacture, electric power transmission and distribution, and magnesium metal
production and processing. Table 2-6 presents greenhouse gas emissions from industrial processes by source
category.
Figure 2-9: 2012 Industrial Processes Chapter Greenhouse Gas Sources
Substitution of Ozone Depleting Substances
Iron and Steel Prod. & Metallurgical Coke Prod.
Cement Production
Nitric Acid Production
Lime Production
Ammonia Production
Other Process Uses of Carbonates
Electrical Transmission and Distribution
Aluminum Production
Adipic Acid Production
Urea Consumption for Non-Agricultural Purposes
HCFC-22 Production
Semiconductor Manufacture
Soda Ash Production and Consumption
Carbon Dioxide Consumption
Titanium Dioxide Production
Magnesium Production and Processing
Ferroalloy Production
Zinc Production
Glass Production
Phosphoric Acid Production
Lead Production
Silicon Carbide Production and Consumption
147
Industrial Processes as a Poition
of all Emissions
5.1%
<0.5
10 20 30 40
Tg C02 Eq.
50
60
70
Table 2-6: Emissions from Industrial Processes (Tg COz Eq.)
Gas/Source
1990
2005
2008 2009 2010 2011 2012
C02
Iron and Steel Production & Metallurgical Coke
Production
188.6 166.7 161.0 119.7 142.3 147.4 144.6
99.8 66.7 66.8 43.0 55.7 60.0 54.3
2-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Iron and Steel Production
Metallurgical Coke Production
Cement Production
Lime Production
Ammonia Production
Other Process Uses of Carbonates
Urea Consumption for Non-Agricultural
Purposes
Petrochemical Production
Aluminum Production
Soda Ash Production and Consumption
Carbon Dioxide Consumption
Titanium Dioxide Production
Ferroalloy Production
Zinc Production
Glass Production
Phosphoric Acid Production
Lead Production
Silicon Carbide Production and Consumption
CH4
Petrochemical Production
Iron and Steel Production & Metallurgical Coke
Iron and Steel Production
Metallurgical Coke Production
Ferroalloy Production
Silicon Carbide Production and Consumption
N2O
Nitric Acid Production
Adipic Acid Production
HFCs
Substitution of Ozone Depleting Substances*
HCFC-22 Production
Semiconductor Manufacture
PFCs
Semiconductor Manufacture
Aluminum Production
SF6
Electrical Transmission and Distribution
Magnesium Production and Processing
Semiconductor Manufacture
Total
97.3
2.5
33.3
11.4
13.0
4.9
3.8
3.4
6.8
2.7
1.4
1.2
2.2
0.6
1.5
1.6
0.5
0.4
3.3
2.3
1.0
1.0
+
+
+
64.6 64.5
2.0M 2.3
45.9 41.2
14.0 14.0
9.2| 8.4
6.3
3.7
4.3
4.1
2.9
1.3
1.8
1.4
1.0
1.9
1.4
0.6
0.2
3.9
3.1
0.7
0.7
+
+
+
5.9
4.1
3.6
4.5
2.9
1.8
1.8
1.6
1.2
1.5
1.2
0.5
0.2
3.6
2.9
0.6
0.6
+
+
+
34.0 24.4 • 19.4
18.2 16. 9l 16.9
15.8 7.4 • 2.6
36.9 119.8 136.0
0.3 103.8 122.2
36.4 15.8 13.6
0.2 0.2 0.2
20.6 5.6 1 5.1
2.2 2.6M 2.4
18.4 3.oB 2.7
32.6 14.7 1 10.7
26.7 11.0 8.4
5.4 2.9i 1.9
0.5 0.7 0.5
316.1 334.9 335.9
42.1
1.0
29.4
10.9
8.5
7.6
3.4
2.8
3.0
2.5
1.8
1.6
1.5
0.9
1.0
1.0
0.5
0.1
3.3
2.9
0.4
0.4
+
+
+
16.8
14.0
2.8
135.1
129.6
5.4
0.1
3.3
1.7
1.6
9.6
7.5
1.7
0.3
287.8
53.7
2.1
31.3
12.8
9.2
9.6
4.7
3.5
2.7
2.6
2.3
1.8
1.7
1.2
1.5
1.1
0.5
0.2
3.6
3.1
0.5
0.5
+
+
+
21.1
16.7
4.4
144.0
137.5
6.4
0.2
3.8
2.2
1.6
9.8
7.2
2.2
0.4
324.6
58.6
1.4
32.0
13.5
9.4
9.3
4.0
3.5
3.3
2.6
1.8
1.7
1.7
1.3
1.3
1.2
0.5
0.2
3.7
3.1
0.6
0.6
+
+
+
26.5
15.8
10.6
148.6
141.5
6.9
0.2
6.0
3.0
2.9
10.8
7.2
2.9
0.7
342.9
53.8
0.5
35.1
13.3
9.4
8.0
5.2
3.5
3.4
2.7
1.8
1.7
1.7
1.4
1.2
1.1
0.5
0.2
3.7
3.1
0.6
0.6
+
+
+
21.0
15.3
5.8
151.2
146.8
4.3
0.2
5.4
2.9
2.5
8.4
6.0
1.7
0.7
334.4
+ Does not exceed 0.05 Tg CO2 Eq.
a Small amounts of PFC emissions also result from
this source.
Overall, emissions from the Industrial Processes sector increased by 5.8 percent from 1990 to 2012. Significant
trends in emissions from industrial processes source categories over the twenty three-year period from 1990 through
2012 included the following:
• HFC emissions from ODS substitutes have been increasing from small amounts in 1990 to 146.8 Tg CO2
Eq. in 2012. This increase results from efforts to phase out CFCs and other ODS' in the United States. In
the short term, this trend is expected to continue, and will likely accelerate over the next decade as
HCFCs—which are interim substitutes in many applications—are phased out under the provisions of the
Copenhagen Amendments to the Montreal Protocol.
• Combined CO2 and CH4 emissions from iron and steel production and metallurgical coke production
decreased by 9.3 percent to 54.9 Tg CO2 Eq. from 2011 to 2012, and have declined overall by 45.8 Tg CO2
Trends 2-15
-------
Eq. (45.5 percent) from 1990 through 2012, due to restructuring of the industry, technological
improvements, and increased scrap steel utilization.
• CO2 emissions from ammonia production (9.4 Tg CO2 Eq. in 2012) decreased by 3.7 Tg CO2 Eq. (28.2
percent) since 1990. Ammonia production relies on natural gas as both a feedstock and a fuel, and as such,
market fluctuations and volatility in natural gas prices affect the production of ammonia. Urea consumption
for non-agricultural purposes (5.2 Tg CO2 Eq. in 2012) increased by 1.5 Tg CO2 Eq. (38.6 percent) since
1990.
• N2O emissions from adipic acid production were 5.8 Tg CO2 Eq. in 2012, and have decreased significantly
since 1990 due to both the widespread installation of pollution control measures in the late 1990s and plant
idling in the late 2000s. Emissions from adipic acid production have decreased by 63.6 percent since 1990
and by 67.2 percent since a peak in 1995.
• PFC emissions from aluminum production decreased by 86.4 percent (15.9 Tg CO2 Eq.) from 1990 to
2012, due to both industry emission reduction efforts and lower domestic aluminum production.
Greenhouse gas emissions are produced as a by-product of various solvent and other product uses. In the United
States, N2O Emissions from Product Uses, the only source of greenhouse gas emissions from this sector, accounted
for 4.4 Tg CO2 Eq., or less than 0.1 percent of total U.S. greenhouse gas emissions in 2012 (see Table 2-7).
Table 2-7: N2O Emissions from Solvent and Other Product Use (Tg COz Eq.)
Gas/Source
N20
N2O from Product Uses
Total
1990
4.4
4.4
4.4
2005
4.4
4.4
4.4
2008
4.4
4.4
4.4
2009
4.4
4.4
4.4
2010
4.4
4.4
4.4
2011
4.4
4.4
4.4
2012
4.4
4.4
4.4
In 2012, N2O emissions from product uses constituted 1.1 percent of U.S. N2O emissions. From 1990 to 2012,
emissions from this source category decreased by 0.4 percent, though slight increases occurred in intermediate
years.
Agricultural activities contribute directly to emissions of greenhouse gases through a variety of processes, including
the following source categories: enteric fermentation in domestic livestock, livestock manure management, rice
cultivation, agricultural soil management, and field burning of agricultural residues.
In 2012, agricultural activities were responsible for emissions of 526.3 Tg CO2 Eq., or 8.1 percent of total U.S.
greenhouse gas emissions. CH4 and N2O were the primary greenhouse gases emitted by agricultural activities. CH4
emissions from enteric fermentation and manure management represented about 24.9 percent and 9.3 percent of total
CH4 emissions from anthropogenic activities, respectively, in 2012. Agricultural soil management activities, such as
fertilizer use and other cropping practices, were the largest source of U.S. N2O emissions in 2012, accounting for
74.8 percent.
2-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure 2-10: 2012 Agriculture Chapter Greenhouse Gas Sources
Agricultural Soil Management
Enteric Fermentation
Manure Management
Rice Cultivation
Reid Burning of Agricultural Residues
307
Agriculture as a Portion of all Emissions
8.1%
<0.5
50
100
150
Tg CO2 Eq.
Table 2-8: Emissions from Agriculture (Tg COz Eq.)
Gas/Source
CH4
Enteric Fermentation
Manure Management
Rice Cultivation
Field Burning of Agricultural
Residues
N2O
Agricultural Soil Management
Manure Management
Field Burning of Agricultural
Residues
Total
1990
177.3
137.91
296.6
282.1 1
14.41
0.1
473.9
2005
197.7
142.5 !
47.6 1
75
0.2 •
314.5 1
297.31
17.1 !
0.1
512.2
2008
206.5
147.0
51.5
7.8
0.3
336.9
319.0
17.8
0.1
543.4
2009
204.7
146.1
50.5
7.9
0.2
334.2
316.4
17.7
0.1
538.9
2010
206.2
144.9
51.8
9.3
0.2
327.9
310.1
17.8
0.1
534.2
2011
202.4
143.0
52.0
7.1
0.3
325.8
307.8
18.0
0.1
528.3
2012
201.5
141.0
52.9
7.4
0.3
324.7
306.6
18.0
0.1
526.3
Note: Totals may not sum due to independent rounding.
Some significant trends in U.S. emissions from Agriculture source categories include the following:
• Agricultural soils produced approximately 74.8 percent of N2O emissions in the United States in 2012.
Estimated emissions from this source in 2012 were 306.6 Tg CCh Eq. Annual N2O emissions from
agricultural soils fluctuated between 1990 and 2012, although overall emissions were 8.7 percent higher in
2012 than in 1990. Year-to-year fluctuations are largely a reflection of annual variation in weather
patterns, synthetic fertilizer use, and crop production.
• Enteric fermentation was the largest source of CH4 emissions in the United States in 2012, at 141.0 Tg CO2
Eq. Generally, from 1990 to 1995 emissions increased and then decreased from 1996 to 2004. These
trends were mainly due to fluctuations in beef cattle populations and increased digestibility of feed for
feedlot cattle. Emissions generally increased from 2005 to 2007, as both dairy and beef populations
underwent increases and the literature for dairy cow diets indicated a trend toward a decrease in feed
digestibility for those years. Emissions decreased again from 2008 to 2012 as beef cattle populations again
decreased. Regarding trends in other animals, during the timeframe of this analysis, populations of sheep
have decreased 53 percent while horse populations have nearly doubled, with each annual increase ranging
from about 2 to 9 percent. Goat and swine populations have increased 25 percent and 23 percent,
respectively, during this timeframe, though with some slight annual decreases. The population of American
bison almost tripled, while mules and asses have increased by more than a factor of six.
Trends 2-17
-------
• Overall, emissions from manure management increased 54.7 percent between 1990 and 2012. This
encompassed an increase of 68.0 percent for CH4, from 31.5 Tg CO2 Eq. in 1990 to 52.9 TgCO2Eq. in
2012; and an increase of 25.5 percent for N2O, from 14.4 Tg CO2 Eq. in 1990 to 18.0 Tg CO2 Eq. in 2012.
The majority of the increase observed in CH4 resulted from swine and dairy cow manure, where emissions
increased 53 and 116 percent, respectively, from 1990 to 2012. From 2011 to 2012, there was a 1.7 percent
increase in total CH4 emissions, mainly due to minor shifts in the animal populations and the resultant
effects on manure management system allocations.
Land Use, Land-Use Change, and Forestry
When humans alter the terrestrial biosphere through land use, changes in land use, and land management practices,
they also alter the background carbon fluxes between biomass, soils, and the atmosphere. Forest management
practices, tree planting in urban areas, the management of agricultural soils, and the landfilling of yard trimmings
and food scraps have resulted in an uptake (sequestration) of carbon in the United States, which offset about 15.0
percent of total U.S. greenhouse gas emissions in 2012. Forests (including vegetation, soils, and harvested wood)
accounted for approximately 88 percent of total 2012 net CO2 flux, urban trees accounted for 9 percent, mineral and
organic soil carbon stock changes accounted for 1 percent, and landfilled yard trimmings and food scraps accounted
for 1 percent of the total net flux in 2012. The net forest sequestration is a result of net forest growth, increasing
forest area, and a net accumulation of carbon stocks in harvested wood pools. The net sequestration in urban forests
is a result of net tree growth and increased urban forest size. In agricultural soils, mineral and organic soils
sequester approximately 4 times as much C as is emitted from these soils through liming and urea fertilization. The
mineral soil C sequestration is largely due to the conversion of cropland to hay production fields, the limited use of
bare-summer fallow areas in semi-arid areas, and an increase in the adoption of conservation tillage practices. The
landfilled yard trimmings and food scraps net sequestration is due to the long-term accumulation of yard trimming
and food scraps carbon in landfills.
Land use, land-use change, and forestry activities in 2012 resulted in a net C sequestration of 979.3 Tg CO2 Eq.
(267.1 Tg C) (Table 2-9). This represents an offset of approximately 18.2 percent of total U.S. CO2 emissions, or
15.0 percent of total greenhouse gas emissions in 2012. Between 1990 and 2012, total land use, land-use change,
and forestry net C flux resulted in a 17.8 percent increase in CO2 sequestration, primarily due to an increase in the
rate of net C accumulation in forest C stocks, particularly in aboveground and belowground tree biomass, and
harvested wood pools.
Table 2-9: Net COz Flux from Land Use, Land-Use Change, and Forestry (Tg COz Eq.)
Sink Category
Forest Land Remaining Forest Land
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Settlements Remaining Settlements
Other (Landfilled Yard Trimmings and
Food Scraps)
Total
1990
(704.6)
(51.9)1
26.91
(9.6)
(7.3)1
(60.4)1
(24.2)H
(831.1)
2005
(927.2)
(29.1)
20.9B
5.6
(8.3)1
(80.5)1
(12.0)
(1,030.7)
2008
(871.0)
(29.8)
16.8
6.8
(8.7)
(83.9)
(11.2)
(981.0)
2009
(849.4)
(29.2)
16.8
6.8
(8.7)
(85.0)
(12.9)
(961.6)
2010
(855.7)
(27.6)
16.8
6.7
(8.6)
(86.1)
(13.6)
(968.0)
2011
(867.1)
(27.5)
16.8
6.7
(8.6)
(87.3)
(13.5)
(980.3)
2012
(866.5)
(26.5)
16.8
6.7
(8.5)
(88.4)
(13.0)
(979.3)
Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
Land use, land-use change, and forestry source categories also resulted in emissions of CO2, CH4, and N2O that are
not included in the net CO2 flux estimates presented in Table 2-9. The application of crushed limestone and
dolomite to managed land (i.e., soil liming) and urea fertilization resulted in CO2 emissions of 7.4 Tg CO2 Eq. in
2012, an increase of about 4.2 percent relative to 1990. Lands undergoing peat extraction resulted in CO2 emissions
of 0.8 Tg CO2 Eq. and N2O emissions of less than 0.1 Tg CO2 Eq. N2O emissions from the application of synthetic
fertilizers to forest soils have increased from 0.1 Tg CO2 Eq. in 1990 to 0.4 Tg CO2 Eq. in 2012. Settlement soils in
2012 resulted in direct N2O emissions of 1.5 Tg CO2 Eq., a 48.2 percent increase relative to 1990. Emissions from
forest fires in 2012 resulted in CH4 emissions of 15.3 Tg CO2 Eq., and in N2O emissions of 12.5 Tg CO2 Eq. (Table
2-10).
2-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 2-10: Emissions from Land Use, Land-Use Change, and Forestry (Tg COz Eq.)
Source Category 1990 2005 2008 2009 2010 2011 2012
C02 8.1 8.9 9.6 8.3 9.6 8.8 8.2
Cropland Remaining Cropland: Liming of Agricultural
Soils
Cropland Remaining Cropland: Urea Fertilization
Wetlands Remaining Wetlands: Peatlands Remaining
Peatlands
CH4
Forest Land Remaining Forest Land: Forest Fires
N2O
Forest Land Remaining Forest Land: Forest Fires
Settlements Remaining Settlements: Settlement Soils
Forest Land Remaining Forest Land: Forest Soils
Wetlands Remaining Wetlands: Peatlands Remaining
Peatlands + + + + + + +
Total 13.7 25.5 27.3 20.5 20.0 36.0 37.8
+ Less than 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Other significant trends from 1990 to 2012 in emissions from land use, land-use change, and forestry source
categories include:
• Net C sequestration by forest land (i.e., carbon stock accumulation in the five carbon pools) has increased
by approximately 23 percent. This is primarily due to increased forest management and the effects of
previous reforestation. The increase in intensive forest management resulted in higher growth rates and
higher biomass density. The tree planting and conservation efforts of the 1970s and 1980s continue to have
a significant impact on sequestration rates. Finally, the forested area in the United States increased over the
past twenty three-years, although only at an average rate of 0.2 percent per year.
• Net sequestration of C by urban trees has increased by 46.3 percent over the period from 1990 to 2012.
This is primarily due to an increase in urbanized land area in the United States.
• Annual C sequestration in landfilled yard trimmings and food scraps has decreased by 46.1 percent since
1990. Food scrap generation has grown by 53 percent since 1990, and though the proportion of food scraps
discarded in landfills has decreased slightly from 82 percent in 1990 to 78 percent in 2012, the tonnage
disposed in landfills has increased considerably (by 46 percent). Overall, the decrease in the landfill
disposal rate of yard trimmings has more than compensated for the increase in food scrap disposal in
landfills.
Waste
Waste management and treatment activities are sources of greenhouse gas emissions (see Figure 2-11). In 2012,
landfills were the third largest source of U.S. anthropogenic CH4 emissions, accounting for 18.1 percent of total U.S.
CH4 emissions.49 Additionally, wastewater treatment accounts for 14.3 percent of Waste emissions, 2.2 percent of
U.S. CH4 emissions, and 1.2 percent of N2O emissions. Emissions of CH4 and N2O from composting grew from
1990 to 2012, and resulted in emissions of 3.3 Tg CO2 Eq. in 2012. A summary of greenhouse gas emissions from
the Waste chapter is presented in Table 2-11.
49 Landfills also store carbon, due to incomplete degradation of organic materials such as wood products and yard trimmings, as
described in the Land Use, Land-Use Change, and Forestry chapter.
Trends 2-19
-------
Figure 2-11: 2012 Waste Chapter Greenhouse Gas Sources
Landfills
Waste water Treatment
Composting
Waste as a Portion of all Emissions
1.9%
100
120
Overall, in 2012, waste activities generated emissions of 124.0 Tg CO2 Eq., or 1.9 percent of total U.S. greenhouse
gas emissions.
Table 2-11: Emissions from Waste (Tg COz Eq.)
Gas/Source
CH4
Landfills
Wastewater Treatment
Composting
N2O
Wastewater Treatment
Composting
Total
1990
161.2
147.8B
13.2
0.3
3.8
3.5
0.4
165.0
2005 •
127.0
112.1
13.3
1
1.7
133.2
2008
129.3
114.3
13.3
1.7
6.6
4.8
1.9
136.0
2009
130.0
115.3
13.1
1.6
6.6
4.8
1.8
136.5
2010
124.5
109.9
13.0
1.5
6.6
4.9
1.7
131.1
2011
121.8
107.4
12.8
1.6
6.7
5.0
1.7
128.5
2012
117.2
102.8
12.8
1.6
6.8
5.0
1.8
124.0
Note: Totals may not sum due to independent rounding.
Some significant trends in U.S. emissions from waste source categories include the following:
• From 1990 to 2012, net CH4 emissions from landfills decreased by 44.9 Tg CO2 Eq. (30.4 percent), with
small increases occurring in interim years. This downward trend in overall emissions is the result of
increases in the amount of landfill gas collected and combusted as well as reduction in the amount of
decomposable materials (i.e., paper and paperboard, food scraps, and yard trimmings) discarded in MSW
landfills over the time series,50 which has more than offset the additional CH4 emissions resulting from an
increase in the amount of municipal solid waste landfilled.
• Combined CH4 and N2O emissions from composting have generally increased since 1990, from 0.7 Tg CO2
Eq. to 3.3 Tg CO2 Eq. in 2012, which represents slightly less than a five-fold increase over the time series.
The growth in composting since the 1990s is attributable to primarily two factors: (1) steady growth in
population and residential housing, and (2) the enactment of legislation by state and local governments that
discouraged the disposal of yard trimmings in landfills.
• From 1990 to 2012, CH4 and N2O emissions from wastewater treatment decreased by 0.4 Tg CO2 Eq. (3.0
percent) and increased by 1.6 Tg CO2 Eq. (45.4 percent), respectively. Methane emissions from domestic
50 The CO2 produced from combusted landfill CH4 at landfills is not counted in national inventories as it is considered part of the
natural C cycle of decomposition.
2-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
wastewater treatment have decreased since 1997 due to decreasing percentages of wastewater being treated
in anaerobic systems, including reduced use of on-site septic systems and central anaerobic treatment
systems. Nitrous oxide emissions from wastewater treatment processes gradually increased across the time
series as a result of increasing U.S. population and protein consumption.
2.2 Emissions by Economic Sector
Throughout this report, emission estimates are grouped into six sectors (i.e., chapters) defined by the IPCC and
detailed above: Energy; Industrial Processes; Solvent and Other Product Use; Agriculture; Land Use, Land-Use
Change, and Forestry; and Waste. While it is important to use this characterization for consistency with UNFCCC
reporting guidelines, it is also useful to allocate emissions into more commonly used sectoral categories. This
section reports emissions by the following U.S. economic sectors: residential, commercial, industry, transportation,
electricity generation, and agriculture, as well as U.S. territories.
Using this categorization, emissions from electricity generation accounted for the largest portion (32 percent) of
U.S. greenhouse gas emissions in 2012. Transportation activities, in aggregate, accounted for the second largest
portion (28 percent). Emissions from industry accounted for about 20 percent of U.S. greenhouse gas emissions in
2012. In contrast to electricity generation and transportation, emissions from industry have in general declined over
the past decade. The long-term decline in these emissions has been due to structural changes in the U.S. economy
(i.e., shifts from a manufacturing-based to a service-based economy), fuel switching, and efficiency improvements.
The remaining 21 percent of U.S. greenhouse gas emissions were contributed by the residential, agriculture, and
commercial sectors, plus emissions from U.S. territories. The residential sector accounted for 5 percent, and
primarily consisted of CCh emissions from fossil fuel combustion. Activities related to agriculture accounted for
roughly 9 percent of U.S. emissions; unlike other economic sectors, agricultural sector emissions were dominated by
N2O emissions from agricultural soil management and CH4 emissions from enteric fermentation, rather than CC>2
from fossil fuel combustion. The commercial sector accounted for roughly 5 percent of emissions, while U.S.
territories accounted for less than 1 percent. Carbon dioxide was also emitted and sequestered (in the form of C) by a
variety of activities related to forest management practices, tree planting in urban areas, the management of
agricultural soils, and landfilling of yard trimmings.
Table 2-12 presents a detailed breakdown of emissions from each of these economic sectors by source category, as
they are defined in this report. Figure 2-12 shows the trend in emissions by sector from 1990 to 2012.
Figure 2-12: Emissions Allocated to Economic Sectors
2,500 1
2,000
ri- 1,500
LJJ
1,000
500
I]
Electric
Power Industi~y
• Agriculture
, Commercial (Red)
Residential (Blue)
^» &>
Cn
cnooooooooooooo
-
Trends 2-21
-------
Table 2-12: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (Tg COz Eq. and
Percent of Total in 2012)
Sector/Source
Electric Power Industry
CO2 from Fossil Fuel Combustion
Stationary Combustion
Incineration of Waste
Electrical Transmission and Distribution
Other Process Uses of Carbonates
Transportation
CO2 from Fossil Fuel Combustion
Substitution of Ozone Depleting
Substances
Mobile Combustion
Non-Energy Use of Fuels
Industry
CO2 from Fossil Fuel Combustion
Natural Gas Systems
Non-Energy Use of Fuels
Coal Mining
Iron and Steel Production
Cement Production
Petroleum Systems
Substitution of Ozone Depleting
Substances
Nitric Acid Production
Lime Production
Ammonia Production
Petrochemical Production
Aluminum Production
Adipic Acid Production
Urea Consumption for Non-Agricultural
Purposes
Abandoned Underground Coal Mines
N2O from Product Uses
HCFC-22 Production
Other Process Uses of Carbonates
Semiconductor Manufacture
Stationary Combustion
Soda Ash Production and Consumption
Carbon Dioxide Consumption
Titanium Dioxide Production
Magnesium Production and Processing
Ferroalloy Production
Zinc Production
Mobile Combustion
Glass Production
Phosphoric Acid Production
Lead Production
Silicon Carbide Production and
Consumption
Agriculture
N2O from Agricultural Soil Management
Enteric Fermentation
Manure Management
CO2 from Fossil Fuel Combustion
CELi and N2O from Forest Fires
Rice Cultivation
1990
1,866.1
1,820.8
26.7
2.sl
1,553.2
1,494.0
...
11.8
1,531.5
814.0
194.2B
103.31
81.1
100.7!
33.3
36.2
+
18.2
11.4
13.0
5.7
25.3
15.8
3.8
6.0
4.4
36.4
2
4.9
2.7
1.4
1.2
5.4
2.2
().(>•
0.9
1.5
1.6
0.5
0,
518.1
282. ll
137.9B
45. sl
31.0
4.5
7.7|
2005
2,445.7
2,402.1
16.5
12.9
11.0
3.2l
2,017.2
1,891.7
77.8
37.5
10.2
1,407.5
780.8
182.ol
122.8 1
53.6
67.4
45.9
29.1 1
::
16.91
14.0
9.2
7.5
7.1
1
3.7
5.5
4.4
15.8
3.2
3.5
4.7
2.9
1.3
1.8
2.9
1.4
i.ol
1.3
0.6
0.2
583.6
297.3 1
142. 5 1
64.6 •
46.8
14.7
7.5
2008
2,401.8
2,360.9
17.3
12.2
8.4
2.9
1,935.2
1,816.5
83.6
25.6
9.5
1,371.5
758.7
184.3
109.8
63.5
67.5
41.2
29.1
8.5
16.9
14.0
8.4
6.5
7.2
2.6
4.1
5.3
4.4
13.6
2.9
3.0
4.2
2.9
1.8
1.8
1.9
1.6
1.2
1.3
1.5
1.2
0.5
0.2
615.3
319.0
147.0
69.3
45.4
15.9
7.8
2009
2,187.0
2,146.4
17.2
12.0
7.5
3.8
1,862.4
1,747.7
83.5
22.7
8.5
1,220.5
680.8
175.2
95.6
67.1
43.4
29.4
29.5
10.9
14.0
10.9
8.5
5.7
4.6
2.8
3.4
5.1
4.4
5.4
3.8
2.2
3.7
2.5
1.8
1.6
1.7
1.5
0.9
1.3
1.0
1.0
0.5
0.2
605.3
316.4
146.1
68.2
46.7
10.5
7.9
2010
2,302.5
2,259.2
18.9
12.4
7.2
4.8
1,876.4
1,765.0
81.3
20.6
9.5
1,300.5
727.9
167.0
103.2
69.2
56.3
31.3
29.9
13.5
16.7
12.8
9.2
6.5
4.3
4.4
4.7
5.0
4.4
6.4
4.8
2.8
4.0
2.6
2.3
1.8
2.2
1.7
1.2
1.4
1.5
1.1
0.5
0.2
600.9
310.1
144.9
69.6
47.6
8.6
9.3
2011
2,200.9
2,158.5
18.0
12.5
7.2
4.7
1,852.1
1,747.9
76.9
18.3
9.0
1,297.5
719.3
168.3
100.1
59.8
60.6
32.0
30.9
15.0
15.8
13.5
9.4
6.6
6.2
10.6
4.0
4.8
4.4
6.9
4.7
3.9
3.9
2.6
1.8
1.7
2.9
1.7
1.3
1.4
1.3
1.2
0.5
0.2
612.7
307.8
143.0
70.0
49.4
25.3
7.1
2012
2,064.0
2,022.7
18.8
12.6
6.0
4.0
1,837.0
1,739.5
72.9
16.3
8.3
1,278.4
723.2
165.1
93.9
55.8
54.9
35.1
32.1
16.4
15.3
13.3
9.4
6.6
5.9
5.8
5.2
4.7
4.4
4.3
4.0
3.7
3.7
2.7
1.8
1.7
1.7
1.7
1.4
1.4
1.2
1.1
0.5
0.2
614.1
306.6
141.0
70.9
51.0
27.7
7.4
Percent3
31.6%
31.0%
0.3%
0.2%
0.1%
0.1%
28.2%
26.7%
1.1%
0.2%
0.1%
19.6%
11.1%
2.5%
1.4%
0.9%
0.8%
0.5%
0.5%
0.3%
0.2%
0.2%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
+
+
+
+
+
+
+
+
+
+
+
9.4%
4.7%
2.2%
1.1%
0.8%
0.4%
0.1%
2-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Liming of Agricultural Soils
Urea Fertilization
CO2 and N2O from Managed Peatlands
Mobile Combustion
N2O from Forest Soils
Field Burning of Agricultural Residues
Stationary Combustion
Commercial
CO2 from Fossil Fuel Combustion
Landfills
Substitution of Ozone Depleting
Substances
Wastewater Treatment
Human Sewage
Composting
Stationary Combustion
Residential
CO2 from Fossil Fuel Combustion
Substitution of Ozone Depleting
Substances
Stationary Combustion
Settlement Soil Fertilization
U.S. Territories
CO2 from Fossil Fuel Combustion
Non-Energy Use of Fuels
Stationary Combustion
Total Emissions
-••
i.ol
0.3
0.1
0.4
+
385.3
219.0B
147.81
+ 1
13.2
1.3
345.4
338.31
s
!.()•
33.7
27. 9 •
5.7
0.1
6,233.2
4.3
3.5
1.1
0.5
0.4
0.3
+
370.4
223.5
112.1
12.3
13.3
4.5
3.3
1.3
371.3
357.9
7.3
4.6
1.5
58.2
50.0
8.1
0.2
7,253.8
Sinks
CO2 Flux from Forests
Urban Trees
Landfilled Yard Trimmings and Food
Scraps
CO2 Flux from Agricultural Soil Carbon
Stocks
Net Emissions
(831.1) I (1,030.7)
(704.6) (927.2)
(60.4m (80.5)
5.0
3.6
1.0
0.5
0.4
0.4
379.2
224.7
114.3
17.2
13.3
4.8
3.5
1.3
365.4
346.2
12.9
4.8
1.5
49.8
41.0
8.7
0.2
7,118.1
(981.0)
(871.0)
(83.9)
3.7
3.6
1.1
0.5
0.4
0.4
381.9
223.9
115.3
20.1
13.1
4.8
3.3
1.3
357.9
336.4
15.1
5.0
1.4
47.9
43.8
3.9
0.2
6,662.9
(961.6)
(849.4)
(85.0)
4.8
3.8
1.0
0.5
0.4
0.3
376.6
220.7
109.9
23.6
13.0
4.9
3.2
1.3
360.0
334.8
19.1
4.5
1.5
58.0
49.6
8.2
0.2
6,874.7
(968.0)
(855.7)
(86.1)
3.9
4.0
0.9
0.5
0.4
0.4
378.3
221.5
107.4
27.0
12.8
5.0
3.3
1.2
353.6
324.9
22.6
4.5
1.5
57.9
49.6
8.2
0.2
6,753.0
(980.3)
(867.1)
(87.3)
3.9
3.4
0.8
0.54
0.4
0.4
352.7
197.4
102.8
30.3
12.8
5.0
3.3
1.1
321.4
288.9
27.2
3.9
1.5
57.9
49.6
8.2
0.2
6,525.6
(979.3)
(866.5)
(88.4)
0.1%
0.1%
+
+
+
+
5.4%
3.0%
1.6%
0.5%
0.2%
0.1%
0.1%
+
4.9%
4.4%
0.4%
0.1%
+
0.9%
0.8%
0.1%
+
100.0%
-15.0%
-13.3%
-1.4%
(24.2)
(41.9)
5,402.1
(12.0
6,223.1
(11.2) (12.9) (13.6) (13.5) (13.0) -0.2%
(14.9) (14.3) (12.7) (12.5) (11.4) -0.2%
6,137.1 5,701.2 5,906.7 5,772.7 5,546.3 85.0%
Note: Includes all emissions of CO2, CELi, N2O, HFCs, PFCs, and SFe. Parentheses indicate negative values or sequestration. Totals may not
sum due to independent rounding.
+ Does not exceed 0.05 Tg CO2 Eq. or 0.05 percent.
a Percent of total emissions for year 2012.
b Includes the effects of net additions to stocks of carbon stored in harvested wood products.
Emissions with Electricity Distributed to Economic Sectors
It can also be useful to view greenhouse gas emissions from economic sectors with emissions related to electricity
generation distributed into end-use categories (i.e., emissions from electricity generation are allocated to the
economic sectors in which the electricity is consumed). The generation, transmission, and distribution of electricity,
which is the largest economic sector in the United States, accounted for 32 percent of total U.S. greenhouse gas
emissions in 2012. Emissions increased by 11 percent since 1990, as electricity demand grew and fossil fuels
remained the dominant energy source for generation. Electricity generation-related emissions decreased from 2011
to 2012 by 6.2 percent, primarily due to decreased CCh emissions from fossil fuel combustion. Electricity sales to
the residential and commercial end-use sectors in 2012 decreased approximately 3.4 percent and 0.1 percent,
respectively. The trend in the residential and commercial sectors can largely be attributed to milder, less energy-
intensive winter conditions compared to 2011. Electricity sales to the industrial sector in 2012 decreased by
approximately 0.6 percent. Overall, in 2012, the amount of electricity generated (inkWh) decreased by 1.5 percent
from the previous year. As a result, CCh emissions from the electric power sector decreased by 6.2 percent as the
consumption of coal and petroleum for electricity generation decreased by 12.3 percent and 27.6 percent,
Trends 2-23
-------
respectively, in 2012 and the consumption of natural gas for electricity generation, increased by 20.4 percent. Table
2-13 provides a detailed summary of emissions from electricity generation-related activities.
Table 2-13: Electricity Generation-Related Greenhouse Gas Emissions (Tg COz Eq.)
Gas/Fuel Type or Source
CO2
Fossil Fuel Combustion
Coal
Natural Gas
Petroleum
Geothermal
Incineration of Waste
Other Process Uses of
Carbonates
CH4
Stationary Combustion*
Incineration of Waste
N2O
Stationary Combustion*
Incineration of Waste
SF6
Electrical Transmission and
Distribution
Total
1990
1,831.2
1,820.8
1,547.6
775j|
97. 5 B
0.4
8.0
2.5
0.3
0.3
+ 1
7.8
7.4
0.5
26.7
26.71
1,866.1
2005
2,417.8
2,402. ll
1,983.8
318.8
99.2
0.4M
12.5M
3.2|
0.5 •
0.5l
+
16.4
16. ol
0.4l
11. Ill
11. oH
2,445.7
2008
2,375.7
2,360.9
1,959.4
361.9
39.2
0.4
11.9
2.9
0.5
0.5
+
17.2
16.8
0.4
8.4
8.4
2,401.8
2009
2,161.9
2,146.4
1,740.9
372.2
33.0
0.4
11.7
3.8
0.4
0.4
+
17.2
16.8
0.4
7.5
7.5
2,187.0
2010
2,276.0
2,259.2
1,827.6
399.0
32.2
0.4
12.0
4.8
0.5
0.5
+
18.8
18.5
0.4
7.2
7.2
2,302.5
2011
2,175.3
2,158.5
1,722.7
408.8
26.6
0.4
12.1
4.7
0.4
0.4
+
18.0
17.6
0.4
7.2
7.2
2,200.9
2012
2,038.9
2,022.7
1,511.2
492.2
18.8
0.4
12.2
4.0
0.5
0.5
+
18.6
18.3
0.4
6.0
6.0
2,064.0
Note: Totals may not sum due to independent rounding.
* Includes only stationary combustion emissions related to the generation of electricity.
+ Does not exceed 0.05 Tg CO2 Eq.
To distribute electricity emissions among economic end-use sectors, emissions from the source categories assigned
to the electricity generation sector were allocated to the residential, commercial, industry, transportation, and
agriculture economic sectors according to each economic sector's share of retail sales of electricity consumption
(EIA2011, Duffield 2006). These source categories include CO2 from Fossil Fuel Combustion, CH4 and N2O from
Stationary Combustion, Incineration of Waste, Other Process Uses of Carbonates, and SF6 from Electrical
Transmission and Distribution Systems. Note that only 50 percent of the Other Process Uses of Carbonates
emissions were associated with electricity generation and distributed as described; the remainder of Other Process
Uses of Carbonates emissions were attributed to the industrial processes economic end-use sector.51
When emissions from electricity are distributed among these sectors, transportation activities account for the largest
share of total U.S. greenhouse gas emissions (28.2 percent), followed closely by emissions from industry (27.9
percent). Emissions from the residential and commercial sectors also increase substantially when emissions from
electricity are included. In all sectors except agriculture, CO2 accounts for more than 80 percent of greenhouse gas
emissions, primarily from the combustion of fossil fuels.
Table 2-14 presents a detailed breakdown of emissions from each of these economic sectors, with emissions from
electricity generation distributed to them. Figure 2-13 shows the trend in these emissions by sector from 1990 to
2012.
51 Emissions were not distributed to U.S. territories, since the electricity generation sector only includes emissions related to the
generation of electricity in the 50 states and the District of Columbia.
2-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure 2-13: Emissions with Electricity Distributed to Economic Sectors
2,500
2,000 -
o- 1,500
LU
8
p 1,000 -
500 -
0
Industry (Green)
Transportation
(Purple)
Residential (Red)
Commercial (Blue)
'Agriculture
.-I CM
Ol (Ti
CTI en
s
QQ er.
m (T>
O^ O^
Table 2-14: U.S. Greenhouse Gas Emissions by Economic Sector and Gas with Electricity-
Related Emissions Distributed (Tg COz Eq.) and Percent of Total in 2012
Sector/Gas
Industry
Direct Emissions
C02
CH4
N20
HFCs, PFCs, and
SF6
Electricity-Related
C02
CH4
N20
SF6
Transportation
Direct Emissions
C02
CH4
N20
HFCsb
Electricity-Related
CO2
CH4
N2O
SF6
Commercial
Direct Emissions
CO2
CH4
N2O
HFCs
Electricity-Related
C02
CH4
N20
SF6
Residential
Direct Emissions
1990
2,173.9
1,531.5
1,141.6
284. 3l
42.4
63.2
642.4
630.41
•
9.2
1,556.3
1,553.2
1,505.8
4.4l
43.0
+
3.1
3.1
,
+
936.7
385.3
219.ol
162.1 1
4.2
+
551.41
541. ll
Oil
2.3
7.9H
953.1
345.4
2005
2,093.7
1,407.5
1,097.4
245. 6l
33.ol
31.6
686.2
678.41
li
3.1
2,022.0
2,017.2
1,901.9
2.1
35.4
77.8
4.8
4.8
,
+
1,188.6
370.4
223. 5 1
128.ol
6.6
12.3
818.3
808.91
0.2
5.5
3.7
1,243.5
371.3
2008
2,009.0
1,371.5
1,059.5
254.4
27.8
29.8
637.5
630.6
0.1
4.6
2.2
1,939.9
1,935.2
1,826.0
1.6
24.0
83.6
4.8
4.7
+
+
+
1,209.3
379.2
224.7
130.3
7.0
17.2
830.2
821.2
0.2
5.9
2.9
1,222.9
365.4
2009
1,766.0
1,220.5
925.0
248.9
24.8
21.8
545.5
539.3
0.1
4.3
1.9
1,866.9
1,862.4
1,756.2
1.5
21.2
83.5
4.6
4.5
+
+
+
1,149.6
381.9
223.9
130.9
6.9
20.1
767.7
758.9
0.2
6.0
2.6
1,159.2
357.9
2010
1,885.4
1,300.5
1,001.3
243.4
29.4
26.3
584.9
578.2
0.1
4.8
1.8
1,880.9
1,876.4
1,774.5
1.5
19.1
81.3
4.6
4.5
+
+
+
1,164.7
376.6
220.7
125.4
6.9
23.6
788.1
779.0
0.2
6.4
2.5
1,216.5
360.0
2011
1,869.2
1,297.5
997.6
233.6
34.7
31.6
571.7
565.1
0.1
4.7
1.9
1,856.4
1,852.1
1,756.9
1.4
16.9
76.9
4.3
4.3
+
+
+
1,131.1
378.3
221.5
122.7
7.1
27.0
752.8
744.1
0.1
6.1
2.5
1,160.1
353.6
2012 ]
1,821.2
1,278.4
993.3
227.3
29.1
28.7
542.8
536.2
0.1
4.9
1.6
1,841.0
1,837.0
1,747.8
1.4
14.9
72.9
3.9
3.9
+
+
+
1,067.5
352.7
197.4
118.0
7.1
30.3
714.8
706.0
0.2
6.5
2.1
1,061.7
321.4
Percent3
27.9%
19.6%
15.2%
3.5%
0.4%
0.4%
8.3%
8.2%
0.1%
+
28.2%
28.2%
26.8%
0.0%
0.2%
1.1%
0.1%
0.1%
+
+
+
16.4%
5.4%
3.0%
1.8%
0.1%
0.5%
11.0%
10.8%
0.1%
+
16.3%
4.9%
Trends 2-25
-------
CO2
CH4
N2O
HFCs
Electricity-Related
C02
CH4
N20
SF6
Agriculture
Direct Emissions
CO2
CH4
N2O
Electricity-Related
CO2
CH4
N2O
SF6
U.S. Territories
Total
338.3
4.6
2.1
0.3
607.8
596.4
0.1
2.6
8.7
579.4
518.1
39.2
179.91
298. 9l
61.41
60.2
I
0.3
<>.';•
33.7
6,233.2
357.9H
•
3.6i
2.4
862.31
0.2
5.8
3.9
647.7
583.6
55.7B
206.0 1
__._
7,253.8
346.2
3.8
2.4
12.9
857.5
848.2
0.2
6.1
3.0
687.1
615.3
55.1
215.4
344.8
71.8
71.0
+
0.5
0.3
49.8
7,118.1
336.4
4.0
2.4
15.1
801.4
792.2
0.2
6.3
2.8
673.1
605.3
55.0
210.6
339.7
67.9
67.1
+
0.5
0.2
47.9
6,662.9
334.8
3.6
2.4
19.1
856.5
846.7
0.2
7.0
2.7
669.3
600.9
57.2
211.1
332.5
68.4
67.7
+
0.6
0.2
58.0
6,874.7
324.9
3.6
2.4
22.6
806.5
797.1
0.2
6.6
2.6
678.2
612.7
58.2
216.5
338.0
65.5
64.7
+
0.5
0.2
57.9
6,753.0
288.9
3.1
2.2
27.2
740.3
731.3
0.2
6.7
2.2
676.3
614.1
59.2
217.0
338.0
62.2
61.5
+
0.6
0.2
57.9
6,525.6
4.4%
+
+
0.4%
11.3%
11.2%
+
0.1%
+
10.4%
9.4%
0.9%
3.3%
5.2%
1.0%
0.9%
+
+
+
0.9%
100.0%
Note: Emissions from electricity generation are allocated based on aggregate electricity consumption in each end-use
sector.
Totals may not sum due to independent rounding.
+ Does not exceed 0.05 Tg CO2 Eq. or 0.05 percent.
a Percent of total emissions for year 2012.
b Includes primarily HFC-134a.
Industry
The industrial end-use sector includes CC>2 emissions from fossil fuel combustion from all manufacturing facilities,
in aggregate. This sector also includes emissions that are produced as a by-product of the non-energy-related
industrial process activities. The variety of activities producing these non-energy-related emissions includes
methane emissions from petroleum and natural gas systems, fugitive CH4 emissions from coal mining, by-product
CO2 emissions from cement manufacture, and HFC, PFC, and SF6 by-product emissions from semiconductor
manufacture, to name a few. Since 1990, industrial sector emissions have declined. The decline has occurred both
in direct emissions and indirect emissions associated with electricity use. However, the decline in direct emissions
has been sharper. In theory, emissions from the industrial end-use sector should be highly correlated with economic
growth and industrial output, but heating of industrial buildings and agricultural energy consumption are also
affected by weather conditions. In addition, structural changes within the U.S. economy that lead to shifts in
industrial output away from energy-intensive manufacturing products to less energy-intensive products (e.g., from
steel to computer equipment) also have a significant effect on industrial emissions.
Transportation
When electricity-related emissions are distributed to economic end-use sectors, transportation activities accounted
for 28 percent of U.S. greenhouse gas emissions in 2012. The largest sources of transportation greenhouse gases in
2012 were passenger cars (43.1 percent), light duty trucks, which include sport utility vehicles, pickup trucks, and
minivans (18.4 percent), freight trucks (21.9 percent), commercial aircraft (6.2 percent), rail (2.5 percent), and ships
and boats (2.2 percent). These figures include direct emissions from fossil fuel combustion used in transportation
and emissions from non-energy use (i.e. lubricants) used in transportation, as well as HFC emissions from mobile air
conditioners and refrigerated transport allocated to these vehicle types.
Although average fuel economy over this period increased slightly due primarily to the retirement of older vehicles,
average fuel economy among new vehicles sold annually gradually declined from 1990 to 2004. The decline in new
vehicle fuel economy between 1990 and 2004 reflected the increasing market share of light duty trucks, which grew
2-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
from about one-fifth of new vehicle sales in the 1970s to slightly over half of the market by 2004. Increasing fuel
prices have since decreased overall light duty truck sales, and average new vehicle fuel economy has improved since
2005 as the market share of passenger cars increased. Over the 1990s through early this decade, growth in vehicle
travel substantially outweighed improvements in vehicle fuel economy; however, the rate of Vehicle Miles Traveled
(VMT) growth slowed considerably starting in 2005 (and declined rapidly in 2008) while average vehicle fuel
economy increased. In 2012, VMT increased by 0.6 percent. Additionally, consumption of diesel fuel has
continued to increase recently, due in part to an increase in commercial activity and freight trucking as a result of the
economic recovery. Table 2-15 provides a detailed summary of greenhouse gas emissions from transportation-
related activities with electricity-related emissions included in the totals.
In terms of the overall trend, from 1990 to 2012, transportation emissions rose by 18 percent due, in large part, to
increased demand for travel with limited gains in fuel efficiency over the same time period. The number of vehicle
miles traveled by light-duty motor vehicles (passenger cars and light-duty trucks) increased 35 percent from 1990 to
2012, as a result of a confluence of factors including population growth, economic growth, urban sprawl, and low
fuel prices during the beginning of this period.
From 2008 to 2009, CO2 emissions from the transportation end-use sector declined 0.5 percent. The decrease in
emissions could largely be attributed to decreased economic activity in 2009 and an associated decline in the
demand for transportation. Modes such as medium- and heavy-duty trucks were significantly impacted by the
decline in freight transport. From 2009 to 2012, CO2 emissions from the transportation end-use sector stabilized
even as economic activity rebounded slightly.
Almost all of the energy consumed for transportation was supplied by petroleum-based products, with more than
half being related to gasoline consumption in automobiles and other highway vehicles. Other fuel uses, especially
diesel fuel for freight trucks and jet fuel for aircraft, accounted for the remainder. The primary driver of
transportation-related emissions was CO2 from fossil fuel combustion, which increased by 16 percent from 1990 to
2012. This rise in CO2 emissions, combined with an increase in HFCs from close to zero emissions in 1990 to 72.9
Tg CO2 Eq. in 2012, led to an increase in overall emissions from transportation activities of 18 percent.
Table 2-15: Transportation-Related Greenhouse Gas Emissions (Tg COz Eq.)
Gas/Vehicle
1990
2005
Passenger Cars
C02
CH4
N2O
HFCs
Light-Duty Trucks
C02
CH4
N2O
HFCs
Medium- and Heavy-Duty
Trucks
C02
CH4
N2O
HFCs
Buses
C02
CH4
N2O
HFCs
Motorcycles
C02
CH4
N2O
Commercial Aircraft3
2008
2009
2010
2011
2012
712.6
662.3
1.1
17.8
31.4
553.1
505.9
0.7
13.7
32.8
408.4
396.0
0.1
1.1
11.1
12.11
ll.S
1.7
1.6
134.0
817.9
769.3
1.0
14.7
32.9
354.8
312.8
0.3
5.2
36.4
811.5
766.0
0.9
12.4
32.1
359.9
317.4
0.3
5.2
37.0
805.8
763.7
0.9
10.9
30.4
359.1
317.6
0.3
4.7
36.5
798.0
760.1
0.8
9.4
27.6
343.1
303.8
0.3
4.1
34.9
793.8
759.8
0.8
8.0
25.2
338.4
301.2
0.3
3.6
33.3
427.0
413.9
0.1
1.4
11.6
17.4
17.0
389.2
376.3
0.2
1.1
11.6
16.5
16.1
402.9
390.0
0.1
1.1
11.6
16.3
15.9
402.4
389.6
0.1
1.0
11.7
17.5
17.0
403.4
390.6
0.1
0.9
11.7
18.6
18.2
0.4
4.5
4.4
128.5
0.4
4.3
4.2
120.7
0.4
3.8
3.8
114.4
0.4
3.7
3.7
115.7
0.4
4.3
4.2
114.4
Trends 2-27
-------
C02 109.9 132.7 127.3 119.5 113.3 114.6 113.3
CH4 +•
N2O l.ll 1.3M 1.2 1.1 1.1 1.1 1.1
Other Aircraft" 78.3 59.71 48.2 36.8 40.5 34.2 32.1
C02 77.5 59.1 47.8 36.4 40.1 33.9 31.8
CH4 0.11 +• + + + + +
N2O 0.7| O.eB 0.4 0.3 0.4 0.3 0.3
Ships and Boatsc 45. ll 45.2 45.9 39.3 45.3 47.0 40.8
C02 44.5 44.5 45.2 38.7 44.6 46.3 40.1
CH4 +•+•+ + + + +
N2O 0.6M 0.6M 0.6 0.5 0.6 0.7 0.6
HFCs +• + + + + + +
Rail 39.0 53.0 50.7 43.4 46.3 48.0 46.9
C02 38.5 50.3 47.9 40.7 43.5 45.3 44.1
CH4 O.ll O.ll 0.1 0.1 0.1 0.1 0.1
N2O O.sl 0.4| 0.4 0.3 0.3 0.4 0.3
HFCs +• 2.2l 2.3 2.3 2.3 2.3 2.3
Other Emissions from
Electricity Generation"1 0.11 0
Pipelines6 36.0 32.2 35.6 36.7 37.1 37.8 40.1
C02 36.0 32.2 35.6 36.7 37.1 37.8 40.1
Lubricants 11.8 10.2 9.5 8.5 9.5 9.0 8.3
CO2 11.8 10.2 9.5 8.5 9.5 9.0 8.3
Total Transportation 1,556.4 2,022.0 1,939.9 1,866.9 1,880.9 1,856.4 1,841.0
International Bunker Fuel/ 104.5 114.3 115.5 107.5 118.2 112.8 106.9
Note: Totals may not sum due to independent rounding. Passenger cars and light-duty trucks include vehicles
typically used for personal travel and less than 8,500 Ibs; medium- and heavy-duty trucks include vehicles larger than
8,500 Ibs. HFC emissions primarily reflect HFC-134a.
+ Does not exceed 0.05 Tg CO2 Eq.
a Consists of emissions from jet fuel consumed by domestic operations of commercial aircraft (no bunkers).
b Consists of emissions from jet fuel and aviation gasoline consumption by general aviation and military aircraft.
0 Fluctuations in emission estimates are associated with fluctuations in reported fuel consumption, and may reflect
data collection problems.
d Other emissions from electricity generation are a result of waste incineration (as the majority of municipal solid
waste is combusted in "trash-to-steam" electricity generation plants), electrical transmission and distribution, and a
portion of Other Process Uses of Carbonates (from pollution control equipment installed in electricity generation
plants).
e CO2 estimates reflect natural gas used to power pipelines, but not electricity. While the operation of pipelines
produces CH4 and N2O, these emissions are not directly attributed to pipelines in the US Inventory.
f Emissions from International Bunker Fuels include emissions from both civilian and military activities; these
emissions are not included in the transportation totals.
Commercial
The commercial sector is heavily reliant on electricity for meeting energy needs, with electricity consumption for
lighting, heating, air conditioning, and operating appliances. The remaining emissions were largely due to the direct
consumption of natural gas and petroleum products, primarily for heating and cooking needs. Energy-related
emissions from the residential and commercial sectors have generally been increasing since 1990, and are often
correlated with short-term fluctuations in energy consumption caused by weather conditions, rather than prevailing
economic conditions. Landfills and wastewater treatment are included in this sector, with landfill emissions
decreasing since 1990 and wastewater treatment emissions increasing slightly.
Residential
The residential sector is heavily reliant on electricity for meeting energy needs, with electricity consumption for
lighting, heating, air conditioning, and operating appliances. The remaining emissions were largely due to the direct
2-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
consumption of natural gas and petroleum products, primarily for heating and cooking needs. Emissions from the
residential sectors have generally been increasing since 1990, and are often correlated with short-term fluctuations in
energy consumption caused by weather conditions, rather than prevailing economic conditions. In the long-term,
this sector is also affected by population growth, regional migration trends, and changes in housing and building
attributes (e.g., size and insulation).
Agriculture
The agriculture sector includes a variety of processes, including enteric fermentation in domestic livestock, livestock
manure management, and agricultural soil management. In 2012, agricultural soil management was the largest
source of N2O emissions, and enteric fermentation was the second largest source of CH4 emissions in the United
States. This sector also includes small amounts of CO2 emissions from fossil fuel combustion by motorized farm
equipment like tractors. The agriculture sector is less reliant on electricity than the other sectors.
Box 2-1: Methodology for Aggregating Emissions by Economic Sector
In presenting the Economic Sectors in the annual Inventory of U.S. Greenhouse Gas Emissions and Sinks, the
Inventory expands upon the standard IPCC sectors common for UNFCCC reporting. Discussing greenhouse gas
emissions relevant to U.S.-specific sectors improves communication of the report's findings.
In the Electricity Generation economic sector, CO2 emissions from the combustion of fossil fuels included in the
EIA electric utility fuel consuming sector are apportioned to this economic sector. Stationary combustion emissions
of CH4 and N2O are also based on the EIA electric utility sector. Additional sources include CO2, CH4) and N2O
from waste incineration, as the majority of municipal solid waste is combusted in "trash-to-steam" electricity
generation plants. The Electricity Generation economic sector also includes SF6 from Electrical Transmission and
Distribution, and a portion of CO2 from Other Process Uses of Carbonates (from pollution control equipment
installed in electricity generation plants).
In the Transportation economic sector, the CO2 emissions from the combustion of fossil fuels included in the EIA
transportation fuel consuming sector are apportioned to this economic sector (additional analyses and refinement of
the EIA data is further explained in the Energy chapter of this report). Additional emissions are apportioned from
the CH4 and N2O from Mobile Combustion, based on the EIA transportation sector. Substitutes of Ozone Depleting
Substitutes are apportioned based on their specific end-uses within the source category, with emissions from
transportation refrigeration/air-conditioning systems to this economic sector. Finally, CO2 emissions from Non-
Energy Uses of Fossil Fuels identified as lubricants for transportation vehicles are included in the Transportation
economic sector.
For the Industry economic sector, the CO2 emissions from the combustion of fossil fuels included in the EIA
industrial fuel consuming sector, minus the agricultural use of fuel explained below, are apportioned to this
economic sector. Stationary and mobile combustion emissions of CH4 and N2O are also based on the EIA industrial
sector, minus emissions apportioned to the Agriculture economic sector described below. Substitutes of Ozone
Depleting Substitutes are apportioned based on their specific end-uses within the source category, with most
emissions falling within the Industry economic sector (minus emissions from the other economic sectors).
Additionally, all process-related emissions from sources with methods considered within the IPCC Industrial
Process guidance have been apportioned to this economic sector. This includes the process-related emissions (i.e.,
emissions from the actual process to make the material, not from fuels to power the plant) from such activities as
Cement Production, Iron and Steel Production and Metallurgical Coke Production, and Ammonia Production.
Additionally, fugitive emissions from energy production sources, such as Natural Gas Systems, Coal Mining, and
Petroleum Systems are included in the Industry economic sector. A portion of CO2 from Other Process Uses of
Carbonates (from pollution control equipment installed in large industrial facilities) are also included in the Industry
economic sector. Finally, all remaining CO2 emissions from Non-Energy Uses of Fossil Fuels are assumed to be
industrial in nature (besides the lubricants for transportation vehicles specified above), and are attributed to the
Industry economic sector.
As agriculture equipment is included in EIA's industrial fuel consuming sector surveys, additional data is used to
extract the fuel used by agricultural equipment, to allow for accurate reporting in the Agriculture economic sector
Trends 2-29
-------
from all sources of emissions, such as motorized farming equipment. Energy consumption estimates are obtained
from Department of Agriculture survey data, in combination with separate EIA fuel sales reports. This
supplementary data is used to apportion CO2 emissions from fossil fuel combustion, and CH4 and N2O emissions
from stationary and mobile combustion (all data is removed from the Industrial economic sector, to avoid double-
counting). The other emission sources included in this economic sector are intuitive for the agriculture sectors, such
as N2O emissions from Agricultural Soils, CH4 from Enteric Fermentation (i.e., exhalation from the digestive tracts
of domesticated animals), CH4 and N2O from Manure Management, CH4 from Rice Cultivation, CO2 emissions
from Liming of Agricultural Soils and Urea Application, and CH4 and N2O from Forest Fires. N2O emissions from
the Application of Fertilizers to tree plantations (termed "forest land" by the IPCC) are also included in the
Agriculture economic sector.
The Residential economic sector includes the CO2 emissions from the combustion of fossil fuels reported for the
EIA residential sector. Stationary combustion emissions of CH4 and N2O are also based on the EIA residential fuel
consuming sector. Substitutes of Ozone Depleting Substitutes are apportioned based on their specific end-uses
within the source category, with emissions from residential air-conditioning systems to this economic sector. N2O
emissions from the Application of Fertilizers to developed land (termed "settlements" by the IPCC) are also
included in the Residential economic sector.
The Commercial economic sector includes the CO2 emissions from the combustion of fossil fuels reported in the
EIA commercial fuel consuming sector data. Stationary combustion emissions of CH4 and N2O are also based on the
EIA commercial sector. Substitutes of Ozone Depleting Substitutes are apportioned based on their specific end-uses
within the source category, with emissions from commercial refrigeration/air-conditioning systems to this economic
sector. Public works sources including direct CH4 from Landfills and CH4 and N2O from Wastewater Treatment and
Composting are included in this economic sector.
Box 2-2: Recent Trends in Various U.S. Greenhouse Gas Emissions-Related Data
Total emissions can be compared to other economic and social indices to highlight changes over time. These
comparisons include: (1) emissions per unit of aggregate energy consumption, because energy-related activities are
the largest sources of emissions; (2) emissions per unit of fossil fuel consumption, because almost all energy-related
emissions involve the combustion of fossil fuels; (3) emissions per unit of electricity consumption, because the
electric power industry—utilities and non-utilities combined—was the largest source of U.S. greenhouse gas
emissions in 2012; (4) emissions per unit of total gross domestic product as a measure of national economic activity;
or (5) emissions per capita.
Table 2-16 provides data on various statistics related to U.S. greenhouse gas emissions normalized to 1990 as a
baseline year. Greenhouse gas emissions in the United States have grown at an average annual rate of 0.2 percent
since 1990. This rate is slightly faster than that for total energy consumption and slightly slower than growth in
national population since 1990 and much slower than that for electricity consumption and overall gross domestic
product, respectively. Total U.S. greenhouse gas emissions are growing at a rate similar to that of fossil fuel
consumption since 1990 (see Table 2-16).
Table 2-16: Recent Trends in Various U.S. Data (Index 1990 = 100)
Chapter/IPCC Sector
Greenhouse Gas Emissions b
Energy Consumption c
Fossil Fuel Consumption c
Electricity Consumption c
GDPd
Population e
1990
100
100
100
100
100
100
2005
116
119
119
134
159
118
2008
114
118
116
136
166
122
2009
107
113
109
131
161
123
2010
110
117
113
137
165
124
2011
108
116
111
137
168
125
2012
105
113
108
135
173
125
Growth3
0.2%
0.6%
0.4%
1.4%
2.5%
1.0%
a Average annual growth rate
b GWP-weighted values
c Energy-content-weighted values (EIA 2014)
d Gross Domestic Product in chained 2005 dollars (BEA 2013)
e U.S. Census Bureau (2013)
2-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure 2-14: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic
Product
8
I
175
165 -
155
145
135
125
115 -\
105
95 H
85
75
65 H
55
Real GDP
Population
Emissions
per capita
Emissions
per $GDP
tTi (Ti (T* {T* {T* (T*
O1OOOOOOOOOOOOO
fMfMfMfMfMfMfMfMfNfNfNrslrsJ
Source: BEA (2011), U.S. Census Bureau (2011), and emission estimates in this report.
2.3 Indirect Greenhouse Gas Emissions (CO,
NOx, NMVOCs, and SO2)
The reporting requirements of the UNFCCC52 request that information be provided on indirect greenhouse gases,
which include CO, NOX, NMVOCs, and SO2. These gases do not have a direct global warming effect, but indirectly
affect terrestrial radiation absorption by influencing the formation and destruction of tropospheric and stratospheric
ozone, or, in the case of SO2, by affecting the absorptive characteristics of the atmosphere. Additionally, some of
these gases may react with other chemical compounds in the atmosphere to form compounds that are greenhouse
gases. Carbon monoxide is produced when carbon-containing fuels are combusted incompletely. Nitrogen oxides
(i.e., NO and NO2) are created by lightning, fires, fossil fuel combustion, and in the stratosphere from N2O. Non-
CH4 volatile organic compounds—which include hundreds of organic compounds that participate in atmospheric
chemical reactions (i.e., propane, butane, xylene, toluene, ethane, and many others)—are emitted primarily from
transportation, industrial processes, and non-industrial consumption of organic solvents. In the United States, SO2 is
primarily emitted from coal combustion for electric power generation and the metals industry. Sulfur-containing
compounds emitted into the atmosphere tend to exert a negative radiative forcing (i.e., cooling) and therefore are
discussed separately.
One important indirect climate change effect of NMVOCs and NOX is their role as precursors for tropospheric ozone
formation. They can also alter the atmospheric lifetimes of other greenhouse gases. Another example of indirect
greenhouse gas formation into greenhouse gases is CO's interaction with the hydroxyl radical—the major
atmospheric sink for CH4 emissions—to form CO2. Therefore, increased atmospheric concentrations of CO limit
the number of hydroxyl molecules (OH) available to destroy CH4.
52
See.
Trends 2-31
-------
Since 1970, the United States has published estimates of emissions of CO, NOX, NMVOCs, and SO2 (EPA 2013),53
which are regulated under the Clean Air Act. Table 2-17 shows that fuel combustion accounts for the majority of
emissions of these indirect greenhouse gases. Industrial processes—such as the manufacture of chemical and allied
products, metals processing, and industrial uses of solvents—are also significant sources of CO, NOX, and
NMVOCs.
Table 2-17: Emissions of NOX, CO, NMVOCs, and SOz (Gg)
Gas/Activity
NOx
Mobile Fossil Fuel Combustion
Stationary Fossil Fuel Combustion
Oil and Gas Activities
Forest Land Remaining Forest Land
Industrial Processes
Waste Combustion
Agricultural Burning
Waste
Solvent Use
CO
Mobile Fossil Fuel Combustion
Forest Land Remaining Forest Land
Stationary Fossil Fuel Combustion
Industrial Processes
Waste Combustion
Oil and Gas Activities
Agricultural Burning
Waste
Solvent Use
NMVOCs
Mobile Fossil Fuel Combustion
Solvent Use
Oil and Gas Activities
Industrial Processes
Stationary Fossil Fuel Combustion
Waste Combustion
Waste
Agricultural Burning
SO2
Stationary Fossil Fuel Combustion
Industrial Processes
Oil and Gas Activities
Mobile Fossil Fuel Combustion
Waste Combustion
Waste
Solvent Use
Agricultural Burning
1990
21,782
10,862
10,023
139
76
591
82
8
1
132,748
119,360
2,711
5,000
4,125
978
302
268
1
5
20,930
10,932
5,216
554
2,422
912
222
673
NA
20,935
18,407
1,307
390
793
38
NA
2005
17,366
10
5
74
58
8
4
1
,250
,847
317
246
566
128
6
2
3
,956
,062
,783
,644
,553
1,402
318
184
7
2
13,080
5
3
1
13
11
,667
,851
510
,982
715
241
114
NA
,180
,529
829
180
616
25
1
NA
2008
14,440
8,481
4,698
386
266
510
85
8
2
4
62,582
46,003
9,481
3,959
1,376
1,244
238
270
6
6
11,878
5,059
2,992
1,580
1,548
530
114
54
NA
9,350
8,289
690
135
217
18
1
1
NA
2009
13
,395
7,809
4
52
39
6
4
1
1
11
4
2
1
1
8
7
,365
464
175
488
81
8
1
3
,618
,219
,250
,036
,326
,164
366
247
5
5
,545
,652
,838
,806
,544
553
103
49
NA
,236
,208
656
125
228
17
1
1
NA
2010
12,579
7,307
4,031
543
144
466
77
8
1
2
51,807
39,468
5,124
4,112
1,277
1,085
493
241
5
3
11,563
4,596
2,684
2,032
1,540
576
92
44
NA
7,029
6,128
622
115
147
16
NA
2011
12,574
7,214
3,787
621
424
444
73
8
1
1
59,918
37,486
15,125
4,188
1,232
1,005
621
255
5
1
11,164
4,118
2,531
2,257
1,538
602
81
38
NA
5,898
5,048
621
105
109
14
NA
2012
11,882
6,732
3,538
621
464
444
73
8
1
1
61,343
37,486
16,553
4,188
1,232
1,005
621
253
5
1
10,971
3,925
2,531
2,257
1,538
602
81
38
NA
4,739
3,895
621
105
103
14
NA
Source: (EPA 2013) except for estimates from Field Burning of Agricultural Residues.
NA (Not Available)
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.5 Gg.
53 NOX and CO emission estimates from Field Burning of Agricultural Residues were estimated separately, and therefore not
taken from EPA (2013).
2-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Box 2-3: Sources and Effects of Sulfur Dioxide
Sulfur dioxide (SCh) emitted into the atmosphere through natural and anthropogenic processes affects the earth's
radiative budget through its photochemical transformation into sulfate aerosols that can (1) scatter radiation from the
sun back to space, thereby reducing the radiation reaching the earth's surface; (2) affect cloud formation; and (3)
affect atmospheric chemical composition (e.g., by providing surfaces for heterogeneous chemical reactions). The
indirect effect of sulfur-derived aerosols on radiative forcing can be considered in two parts. The first indirect effect
is the aerosols' tendency to decrease water droplet size and increase water droplet concentration in the atmosphere.
The second indirect effect is the tendency of the reduction in cloud droplet size to affect precipitation by increasing
cloud lifetime and thickness. Although still highly uncertain, the radiative forcing estimates from both the first and
the second indirect effect are believed to be negative, as is the combined radiative forcing of the two (IPCC 2001).
However, because SCh is short-lived and unevenly distributed in the atmosphere, its radiative forcing impacts are
highly uncertain.
Sulfur dioxide is also a major contributor to the formation of regional haze, which can cause significant increases in
acute and chronic respiratory diseases. Once SO2 is emitted, it is chemically transformed in the atmosphere and
returns to the earth as the primary source of acid rain. Because of these harmful effects, the United States has
regulated SCh emissions in the Clean Air Act.
Electricity generation is the largest anthropogenic source of SC>2 emissions in the United States, accounting for 63.3
percent in 2012. Coal combustion contributes nearly all of those emissions (approximately 92 percent). Sulfur
dioxide emissions have decreased in recent years, primarily as a result of electric power generators switching from
high-sulfur to low-sulfur coal and installing flue gas desulfurization equipment.
Trends 2-33
-------
-------
3. Energy
Energy-related activities were the primary sources of U.S. anthropogenic greenhouse gas emissions, accounting for
84.3 percent of total greenhouse gas emissions on a carbon dioxide (CCh) equivalent basis in 2012.54 This included
97, 40, and 9 percent of the nation's CCh, methane (CH4), and nitrous oxide (N2O) emissions, respectively. Energy-
related CO2 emissions alone constituted 80.2 percent of national emissions from all sources on a CCh equivalent
basis, while the non-CCh emissions from energy-related activities represented a much smaller portion of total
national emissions (4.1 percent collectively).
Emissions from fossil fuel combustion comprise the vast majority of energy-related emissions, with CC>2 being the
primary gas emitted (see Figure 3-1). Globally, approximately 32,579 Tg of CO2 were added to the atmosphere
through the combustion of fossil fuels in 2011, of which the United States accounted for approximately 17
percent.55 Due to their relative importance, fossil fuel combustion-related CCh emissions are considered separately,
and in more detail than other energy-related emissions (see Figure 3-2). Fossil fuel combustion also emits CH4 and
N2O. Stationary combustion of fossil fuels was the second largest source of N2O emissions in the United States and
mobile fossil fuel combustion was the fourth largest source.
Figure 3-1: 2012 Energy Chapter Greenhouse Gas Sources
Fossil Fuel Combustion
Natural Gas Systems
Non-Energy Use of Fuels
Coal Mining
Petroleum Systems
Stationary Combustion
Mobile Combustion
Incineration of Waste
Abandoned Underground Coal Mines
Energy as a Portion
of all Emissions
5,072
50 100
Tg C02 Eq.
150
200
54 Estimates are presented in units of teragrams of carbon dioxide equivalent (Tg CCh Eq.), which weight each gas by its global
warming potential, or GWP, value. See section on global warming potentials in the Executive Summary.
55 Global CO2 emissions from fossil fuel combustion were taken from Energy Information Administration International Energy
Statistics 2012 < http://tonto.eia.doe.gov/cfapps/ipdbproject/IEDIndex3.cfm> EIA (2012).
Energy 3-1
-------
Figure 3-2: 2012 U.S. Fossil Carbon Flows (Tg COz Eq.)
NEU Emissions 13
Coal Emissions
1,606
Natural Gas Fmissions
1.356
Non-Energy Use
Carbon Sequestered
Fossil Fuel
Non-Energy Consumption
Use Imports u-s-
26 Territories
50
Note: Totals may not sum due to independent rounding.
The "Balancing Item" above accounts for the statistical imbalances
and unknowns in the reported data sets combined here.
NEU - Non Energy Use
NG-Natural Gas
Energy-related activities other than fuel combustion, such as the production, transmission, storage, and distribution
of fossil fuels, also emit greenhouse gases. These emissions consist primarily of fugitive CH4 from natural gas
systems, petroleum systems, and coal mining. Table 3-1 summarizes emissions from the Energy sector in units of
teragrams (or million metric tons) of CC>2 equivalents (Tg CCh Eq.), while unweighted gas emissions in gigagrams
(Gg) are provided in Table 3-2. Overall, emissions due to energy-related activities were 5,498.9 Tg CCh Eq. in
2012, an increase of 4.5 percent since 1990.
Table 3-1: COz, CH4, and N2O Emissions from Energy (Tg COz Eq.)
Gas/Source
CO2
Fossil Fuel Combustion
Electricity Generation
Transportation
Industrial
Residential
Commercial
U.S. Territories
Non-Energy Use of Fuels
Natural Gas Systems
Incineration of Waste
Petroleum Systems
Biomass - Wood"
International Bunker Fuels"
Biomass - Ethanol"
CH4
Natural Gas Systems
Coal Mining
Petroleum Systems
Stationary Combustion
Abandoned Underground Coal
Mines
Mobile Combustion
Incineration of Waste
International Bunker Fuels"
2005
5,936.6
5,752.9
2,402.1
1,891.7
827.6
357.9
223.5
50.0
141.0
30.0
12.5 1
0.3 1
206.9
113.1
22.9
249.0
152.0
53.6 1
28.8 1
6.6
5.5 1
2.4 1
2008
5,766.3
5,593.4
2,360.9
1,816.5
804.1
346.2
224.7
41.0
128.0
32.7
11.9
0.3
199.9
114.3
54.7
257.8
151.6
63.5
28.8
6.6
5.3
1.9
2009
5,378.1
5,225.7
2,146.4
1,747.7
727.5
336.4
223.9
43.8
108.1
32.2
11.7
0.3
188.2
106.4
62.3
252.8
142.9
67.1
29.1
6.6
5.1
1.8
2010
5,570.5
5,404.9
2,259.2
1,765.0
775.6
334.8
220.7
49.6
120.8
32.4
12.0
0.3
192.5
117.0
72.6
246.5
134.7
69.2
29.5
6.4
5.0
1.8
2011
5,436.0
5,271.1
2,158.5
1,747.9
768.7
324.9
221.5
49.6
117.3
35.1
12.1
0.3
195.2
111.7
72.9
236.5
133.2
59.8
30.5
6.3
4.8
1.7
2012
5,230.4
5,072.3
2,022.7
1,739.5
774.2
288.9
197.4
49.6
110.3
35.2
12.2
0.4
194.0
105.8
72.8
229.6
129.9
55.8
31.7
5.7
4.7
1.7
0.1
0.1
0.1
0.1
0.1
0.1
3-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
N20
Stationary Combustion
Mobile Combustion
Incineration of Waste
International Bunker Fuels"
Total
56.8
12.3 1
44.0 1
0.5 1
0.9
5,260.1
57.9
20.6
36.9
0.4
1 1.0
6,243.5
47.0
21.1
25.5
0.4
1.0
6,071.1
43.8
20.8
22.7
0.4
0.9
5,674.6
43.6
22.5
20.7
0.4
1.0
5,860.6
40.5
21.6
18.5
0.4
1.0
5,712.9
38.9
22.0
16.5
0.4
1.0
5,498.9
+ Does not exceed 0.05 Tg CO2 Eq.
a These values are presented for informational purposes only, in line with IPCC methodological guidance and UNFCCC reporting
obligations, and are not included in the specific energy sector contribution to the totals, and are already accounted for elsewhere.
Note: Totals may not sum due to independent rounding.
Table 3-2: COz, CH4, and NzO Emissions from Energy (Gg)
Gas/Source
C02
Fossil Fuel Combustion
Non-Energy Use of Fuels
Natural Gas Systems
Incineration of Waste
Petroleum Systems
Biomass -Wood"
International Bunker Fuels"
Biomass - Ethanol"
CH4
Natural Gas Systems
Coal Mining
Petroleum Systems
Stationary Combustion
Abandoned Underground
Coal Mines
Mobile Combustion
Incineration of Waste
International Bunker Fuels"
N2O
Stationary Combustion
Mobile Combustion
Incineration of Waste
International Bunker Fuels"
1990
4,911,980
4,745,067
120,842 1
37,705
7,972
394 1
215,186
103,463
4,227 \
13,875
7,450
3,860
1,704
355
288 1
218 1
+
1
183
40 1
142 1
2
3
2005
5,936,605
5,752,860
140,997
29,988 1
12,454 1
306
206,901
113,139
22,943 \
11,858
7,240 1
12,552 1
1,374
315
264 1
113
+ 1
1
187
66 1
119 1
1
3
2008
5,766,294
5,593,424
127,997
32,707
11,867
300
199,932
114,342
54,739
12,278
7,218
3,026
1,372
317
253
92
+
6
151
68
82
1
3
2009
5,378,059
5,225,717
108,115
32,234
11,672
320
188,220
106,410
62,272
12,037
6,806
3,194
1,388
316
244
87
+
5
141
67
73
1
3
2010
5,570,456
5,404,903
120,827
32,362
12,033
332
192,462
116,992
72,647
11,739
6,413
3,293
1,407
304
237
85
+
6
141
73
67
1
3
2011
5,435,980
5,271,097
117,313
35,082
12,142
347
195,182
111,660
72,881
11,260
6,343
2,849
1,453
302
231
82
+
5
131
70
60
1
3
2012
5,230,417
5,072,271
110,313
35,232
12,195
406
194,003
105,805
72,827
10,933
6,186
2,658
1,511
271
226
81
+
4
125
71
53
1
3
+ Does not exceed 0.05 Tg CO2 Eq.
a These values are presented for informational purposes only, in line with IPCC methodological guidance and UNFCCC reporting
obligations, and are not included in the specific energy sector contribution to the totals, and are already accounted for elsewhere.
Note: Totals may not sum due to independent rounding.
In this chapter the methodological guidance was primarily taken from the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories. The use of the most recently published calculation methodologies by the IPCC, as
contained in the 2006 IPCC Guidelines, is fully in line with the IPCC Good Practice Guidance for methodological
choice to improve rigor and accuracy. In addition, the improvements in using the latest methodological guidance
from the IPCC has been recognized by the UNFCCC's Subsidiary Body for Scientific and Technological Advice in
the conclusions of its 30th Session, Numerous U.S. inventory experts were involved in the development of the 2006
IPCC Guidelines, and their expertise has provided this latest guidance from the IPCC with the most appropriate
calculation methods that are then used in this chapter.56
56 These Subsidiary Body for Scientific and Technological Advice (SBSTA) conclusions state, "The SBSTA acknowledged that
the 2006 IPCC Guidelines contain the most recent scientific methodologies available to estimate emissions by sources and
removals by sinks of greenhouse gases (GHGs) not controlled by the Montreal Protocol, and recognized that Parties have gained
experience with the 2006 IPCC Guidelines. The SBSTA also acknowledged that the information contained in the 2006 IPCC
Energy 3-3
-------
Box 3-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks
In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emission
inventories, the emissions and sinks presented in this report and this chapter, are organized by source and sink
categories and calculated using internationally-accepted methods provided by the Intergovernmental Panel on
Climate Change (IPCC). Additionally, the calculated emissions and sinks in a given year for the United States are
presented in a common manner in line with the UNFCCC reporting guidelines for the reporting of inventories under
this international agreement. The use of consistent methods to calculate emissions and sinks by all nations
providing their inventories to the UNFCCC ensures that these reports are comparable. In this regard, U.S. emissions
and sinks reported in this inventory report are comparable to emissions and sinks reported by other countries.
Emissions and sinks provided in this inventory do not preclude alternative examinations, but rather this inventory
presents emissions and sinks in a common format consistent with how countries are to report inventories under the
UNFCCC. The report itself, and this chapter, follows this standardized format, and provides an explanation of the
IPCC methods used to calculate emissions and sinks, and the manner in which those calculations are conducted.
Box 3-2: Energy Data from the Greenhouse Gas Reporting Program
On October 30, 2009, the U.S. Environmental Protection Agency (EPA) published a rule for the mandatory
reporting of greenhouse gases (GHG) from large GHG emissions sources in the United States. Implementation of 40
CFR Part 98 is referred to as the Greenhouse Gas Reporting Program (GHGRP). 40 CFR Part 98 applies to direct
greenhouse gas emitters, fossil fuel suppliers, industrial gas suppliers, and facilities that inject CO2 underground for
sequestration or other reasons. Reporting is at the facility level, except for certain suppliers of fossil fuels and
industrial greenhouse gases. 40 CFR part 98 requires reporting by 41 industrial categories. Data reporting by
affected facilities included the reporting of emissions from fuel combustion at that affected facility. In general, the
threshold for reporting is 25,000 metric tons or more of CO2 Eq. per year.
The GHGRP dataset and the data presented in this inventory report are complementary and, as indicated in the
respective planned improvements sections for source categories in this chapter, EPA is analyzing how to use
facility-level GHGRP data to improve the national estimates presented in this Inventory (see, also, Box 3-4). Most
methodologies used in EPA's GHGRP are consistent with IPCC, though for EPA's GHGRP, facilities collect
detailed information specific to their operations according to detailed measurement standards, which may differ with
the more aggregated data collected for the inventory to estimate total, national U.S. emissions. It should be noted
that the definitions and provisions for reporting fuel types in EPA's GHGRP may differ from those used in the
inventory in meeting the UNFCCC reporting guidelines. In line with the UNFCCC reporting guidelines, the
inventory report is a comprehensive accounting of all emissions from fuel types identified in the IPCC guidelines
and provides a separate reporting of emissions from biomass. Further information on the reporting categorizations in
EPA's GHGRP and specific data caveats associated with monitoring methods in EPA's GHGRP has been provided
on the GHGRP website.
EPA presents the data collected by its GHGRP through a data publication tool that allows data to be viewed in
several formats including maps, tables, charts and graphs for individual facilities or groups of facilities.
Guidelines enables Parties to further improve the quality of their GHG inventories." See
3-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
3.1 Fossil Fuel Combustion (IPCC Source
Category 1A)
Emissions from the combustion of fossil fuels for energy include the gases CO2, CH4, and N2O. Given that CO2 is
the primary gas emitted from fossil fuel combustion and represents the largest share of U.S. total emissions, CO2
emissions from fossil fuel combustion are discussed at the beginning of this section. Following that is a discussion
of emissions of all three gases from fossil fuel combustion presented by sectoral breakdowns. Methodologies for
estimating CO2 from fossil fuel combustion also differ from the estimation of CH4 and N2O emissions from
stationary combustion and mobile combustion. Thus, three separate descriptions of methodologies, uncertainties,
recalculations, and planned improvements are provided at the end of this section. Total CO2, CH4, and N2O
emissions from fossil fuel combustion are presented in Table 3-3 and Table 3-4.
Table 3-3: COz, Cm, and NzO Emissions from Fossil Fuel Combustion (Tg COz Eq.)
Gas
CO2
CH4
N2O
Total
1990
4,745.1
12.0
56.3
4,813.4
2005
5,752.9
9.0
57.5
5,819.4
2008
5,593.4
8.6
46.6
5,648.6
2009
5,225.7
8.5
43.5
5,277.7
2010
5,404.9
8.2
43.3
5,456.3
2011
5,271.1
8.1
40.2
5,319.3
2012
5,072.3
7.4
38.5
5,118.2
Table 3-4: COz, Cm, and NzO Emissions from Fossil Fuel Combustion (Gg)
Gas
CO2
CH4
N2O
1990
4,745,067
574
182
2005
5,752,860
429
186
2008
5,593,424
409
150
2009
5,225,717
404
140
2010
5,404,903
389
140
2011
5,271,097
385
130
2012
5,072,271
352
124
Note: Totals may not sum due to independent rounding
CO2 from Fossil Fuel Combustion
CO2 is the primary gas emitted from fossil fuel combustion and represents the largest share of U.S. total greenhouse
gas emissions. CO2 emissions from fossil fuel combustion are presented in Table 3-5. In 2012, CO2 emissions from
fossil fuel combustion decreased by 3.8 percent relative to the previous year. The decrease in CO2 emissions from
fossil fuel combustion was a result of multiple factors including: (1) a decrease in the carbon intensity of fuels
consumed by power producers to generate electricity due to a significant decrease in the price of natural gas
compared to the slight increase in the price of coal; (2) a decrease in transportation sector emissions attributed to a
small increase in fuel efficiency across different transportation modes and limited new demand for passenger
transportation; and (3) much warmer winter conditions resulting in a decreased demand for heating fuel in the
residential and commercial sectors. In 2012, CO2 emissions from fossil fuel combustion were 5,072.3 Tg CO2 Eq.,
or 6.9 percent above emissions in 1990 (see Table 3-5).57
Table 3-5: COz Emissions from Fossil Fuel Combustion by Fuel Type and Sector (Tg COz Eq.)
Fuel/Sector
Coal
Residential
Commercial
Industrial
Transportation
Electricity Generation
1990
1,718.4
3.0
12.0
155.3
NE
1,547.6
2005
2,112.3
0.8
9.3
115.3
NE
1,983.8
2008
2,072.8
+
7.6
102.4
NE
1,959.4
2009
1,834.2
+
6.9
83.0
NE
1,740.9
2010
1,927.7
+
6.6
90.1
NE
1,827.6
2011
1,813.9
+
5.8
82.0
NE
1,722.7
2012
1,593.0
+
4.1
74.3
NE
1,511.2
57 An additional discussion of fossil fuel emission trends is presented in the Trends in U.S. Greenhouse Gas Emissions Chapter.
Energy 3-5
-------
U.S. Territories
Natural Gas
Residential
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
Petroleum
Residential
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
Geothermal*
Total
0.6
1,000.3
238.0
142.1
408.9
36.0
175.3
NO
2,025.9
97.4
64.9 1
280.9
1,457.9
97.5
27.2 1
0.4
4,745.1
3.0
1,166.7 1
262.2
162.9
388.5 1
33.1
318.8 1
2,473.5
94.9
51.3
323.8 1
1,858.7
99.2
45.7
0.4
5,752.9
3.4
1,238.1
1265.5
171.1
401.3
36.7
361.9
1.6
2,282.1
80.7
46.0
300.4
1,779.8
39.2
36.0
• 0.4
5,593.4
3.4
1,216.9
258.8
168.9
377.6
37.9
372.2
1.5
2,174.2
77.5
48.1
266.8
1,709.8
33.0
39.0
0.4
5,225.7
3.4
1,272.1
258.6
167.7
407.2
38.1
399.0
1.5
2,204.8
76.3
46.4
278.3
1,726.9
32.2
44.7
0.4
5,404.9
3.4
1,291.5
254.7
170.5
417.3
38.9
408.8
1.4
2,165.3
70.3
45.2
269.4
1,709.0
26.6
44.7
0.4
5,271.1
3.4
1,351.2
224.8
156.9
434.7
41.2
492.2
1.4
2,127.6
64.1
36.4
265.2
1,698.3
18.8
44.7
0.4
5,072.3
+ Does not exceed 0.05 Tg CO2 Eq.
NE (Not estimated)
NO (Not occurring)
* Although not technically a fossil fuel, geothermal energy-related CO2 emissions are included for
reporting purposes.
Note: Totals may not sum due to independent rounding.
Trends in CCh emissions from fossil fuel combustion are influenced by many long-term and short-term factors. On
a year-to-year basis, the overall demand for fossil fuels in the United States and other countries generally fluctuates
in response to changes in general economic conditions, energy prices, weather, and the availability of non-fossil
alternatives. For example, in a year with increased consumption of goods and services, low fuel prices, severe
summer and winter weather conditions, nuclear plant closures, and lower precipitation feeding hydroelectric dams,
there would likely be proportionally greater fossil fuel consumption than a year with poor economic performance,
high fuel prices, mild temperatures, and increased output from nuclear and hydroelectric plants.
Longer-term changes in energy consumption patterns, however, tend to be more a function of aggregate societal
trends that affect the scale of consumption (e.g., population, number of cars, size of houses, and number of houses),
the efficiency with which energy is used in equipment (e.g., cars, power plants, steel mills, and light bulbs), and
social planning and consumer behavior (e.g., walking, bicycling, or telecommuting to work instead of driving).
CO2 emissions also depend on the source of energy and its carbon (C) intensity. The amount of C in fuels varies
significantly by fuel type. For example, coal contains the highest amount of C per unit of useful energy. Petroleum
has roughly 75 percent of the C per unit of energy as coal, and natural gas has only about 55 percent.58 Table 3-6
shows annual changes in emissions during the last five years for coal, petroleum, and natural gas in selected sectors.
Table 3-6: Annual Change in COz Emissions and Total 2012 Emissions from Fossil Fuel
Combustion for Selected Fuels and Sectors (Tg COz Eq. and Percent)
Sector Fuel Type
Electricity Generation Coal
Electricity Generation Natural Gas
Electricity Generation Petroleum
Transportation* Petroleum
Residential Natural Gas
Commercial Natural Gas
Industrial Coal
Industrial Natural Gas
All Sectors" All Fuels"
2008 to 2009
-218.5 -11.2%
10.3 2.8%
-6.3 -15.9%
-70.0 -3.9%
-6.7 -2.5%
-2.2 -1.3%
-19.3 -18.9%
-23.7 -5.9%
-367.7 -6.6%
2009 to 2010
86.7 5.0%
26.8 7.2%
-0.8 -2.3%
17.1 1.0%
-0.3 -0.1%
-1.2 -0.7%
7.0 8.5%
29.6 7.8%
179.2 3.4%
2010 to 2011
-104.9 -5.7%
9.8 2.5%
-5.6 -17.4%
-17.9 -1.0%
-3.9 -1.5%
2.7 1.6%
-8.1 -9.0%
10.1 2.5%
-133.8 -2.5%
2011 to 2012
-211.5 -12.3%
83.5 20.4%
-7.8 -29.3%
-10.7 -0.6%
-29.8 -11.7%
-13.6 -8.0%
-7.7 -9.4%
17.3 4.2%
-198.8 -3.8%
Total 2012
1,511.2
492.2
18.8
1,698.3
224.8
156.9
74.3
434.7
5,072.3
1 Excludes emissions from International Bunker Fuels.
b Includes fuels and sectors not shown in table.
58 Based on national aggregate carbon content of all coal, natural gas, and petroleum fuels combusted in the United States.
3-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
In the United States, 82 percent of the energy consumed in 2012 was produced through the combustion of fossil
fuels such as coal, natural gas, and petroleum (see Figure 3-3 and Figure 3-4). The remaining portion was supplied
by nuclear electric power (8 percent) and by a variety of renewable energy sources (9 percent), primarily
hydroelectric power and biofuels (EIA 2014).59 Specifically, petroleum supplied the largest share of domestic
energy demands, accounting for 36 percent of total U.S. energy consumption in 2012. Natural gas and coal
followed in order of energy demand importance, accounting for approximately 27 percent and 18 percent of total
U.S. energy consumption, respectively. Petroleum was consumed primarily in the transportation end-use sector and
the vast majority of coal was used in electricity generation. Natural gas was broadly consumed in all end-use sectors
except transportation (see Figure 3-5) (EIA 2014).
Figure 3-3: 2012 U.S. Energy Consumption by Energy Source
Renewable
Energy
9.3%
Nuclear Electric
Power
8.5%
59 Renewable energy, as defined in EIA's energy statistics, includes the following energy sources: hydroelectric power,
geothermal energy, biofuels, solar energy, and wind energy.
Energy 3-7
-------
Figure 3-4: U.S. Energy Consumption (Quadrillion Btu)
120 n
100 -
C
-B
Q.
un
c
o
u
60 -
40
20
Total Energy
Renewable & Nuclear
O *-< fM
CT^ CTi Oi
^ C^ ^
O i-< (N
Figure 3-5: 2012 COz Emissions from Fossil Fuel Combustion by Sector and Fuel Type
2,500 1
2,000
iS" 1,500
6"
£ 1,000 i
500
0
Petroleum
i Coal
i Natural Gas
1,740
2,023
50
Fossil fuels are generally combusted for the purpose of producing energy for useful heat and work. During the
combustion process, the C stored in the fuels is oxidized and emitted as CO2 and smaller amounts of other gases,
including CH4, CO, and NMVOCs.60 These other C containing non-CCh gases are emitted as a byproduct of
incomplete fuel combustion, but are, for the most part, eventually oxidized to CC>2 in the atmosphere. Therefore, it
is assumed all of the C in fossil fuels used to produce energy is eventually converted to atmospheric CCh.
Box 3-3: Weather and Non-Fossil Energy Effects on CO2 from Fossil Fuel Combustion Trends
In 2012, weather conditions, and a very warm first quarter of the year in particular, caused a significant decrease in
energy demand for heating fuels and is reflected in the decreased residential emissions during the early part of the
year (EIA 2014). The United States in 2012 also experienced a warmer summer compared to 2011, as heating
degree days decreased (12.8 percent) and cooling degree days increased by 2.2 percent. This slight increase in
cooling degree days led to only a minor increase in electricity demand to cool homes. However the warmer winter
60 See the sections entitled Stationary Combustion and Mobile Combustion in this chapter for information on non-CCh gas
emissions from fossil fuel combustion.
3-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
conditions also resulted in a significant decrease in the amount of energy required for heating, with heating degree
days in the United States 18.0 percent below normal (see Figure 3-6). Summer conditions were slightly warmer in
2012 compared to 2011, and summer temperatures were much warmer than normal, with cooling degree days 24.4
percent above normal (see Figure 3-7) (EIA 2014).61
Figure 3-6: Annual Deviations from Normal Heating Degree Days for the United States
(1950-2012)
Normal
(4,524 Heating Degree Days)
i/im
Lrvi/iv£>v£ii£tu3u}r*.r*vr-.r^r^
OrN^-kDCOOfNTr^OOOrNjTrvDOOOlN
Figure 3-7: Annual Deviations from Normal Cooling Degree Days for the United States
(1950-2012)
x -4=
* •? -10
-20
Normal
(1,242 Cooling Degree Days)
Although no new U.S. nuclear power plants have been constructed in recent years, the utilization (i.e., capacity
factors)62 of existing plants in 2012 remained high at 86 percent. Electricity output by hydroelectric power plants
decreased in 2012 by approximately 14 percent. In recent years, the wind power sector has been showing strong
growth, such that, on the margin, it is becoming a relatively important electricity source. Electricity generated by
nuclear plants in 2012 provided more than twice as much of the energy consumed in the United States as
hydroelectric plants (EIA 2013a). Nuclear, hydroelectric, and wind power capacity factors since 1990 are shown in
Figure 3-8.
61 Degree days are relative measurements of outdoor air temperature. Heating degree days are deviations of the mean daily
temperature below 65° F, while cooling degree days are deviations of the mean daily temperature above 65° F. Heating degree
days have a considerably greater effect on energy demand and related emissions than do cooling degree days. Excludes Alaska
and Hawaii. Normals are based on data from 1971 through 2000. The variation in these normals during this time period was +10
percent and +14 percent for heating and cooling degree days, respectively (99 percent confidence interval).
62 The capacity factor equals generation divided by net summer capacity. Summer capacity is defined as "The maximum output
that generating equipment can supply to system load, as demonstrated by a multi-hour test, at the time of summer peak demand
(period of June 1 through September 30)." Data for both the generation and net summer capacity are from EIA (2013a).
Energy 3-9
-------
Figure 3-8: Nuclear, Hydroelectric, and Wind Power Plant Capacity Factors in the United
States (1990-2012)
100 -i
90 -
80 -
70 -
€ 60 •
S.
£• 50 -
1-J
30
20 -
10 -
0
Nuclear
Wind
Ssi$Sgls8888888S
Fossil Fuel Combustion Emissions by Sector
In addition to the CCh emitted from fossil fuel combustion, CH4 and N2O are emitted from stationary and mobile
combustion as well. Table 3-7 provides an overview of the CCh, CH4, and N2O emissions from fossil fuel
combustion by sector.
Table 3-7: COz, CH4, and NzO Emissions from Fossil Fuel Combustion by Sector (Tg COz Eq.)
End-Use Sector
Electricity Generation
CO2
CH4
N2O
Transportation
CO2
CH4
N20
Industrial
C02
CH4
N20
Residential
C02
CH4
N2O
Commercial
CO2
CH4
N2O
U.S. Territories*
Total
1990
1,828.5
1,820.8
0.3
7.4
1,542.6
1,494.0
4.6
44.0 1
850.0
845.1
1.6
3.3
344.1
338.3
4.6 1
1.1
220.2
219.0
0.9
0.4 1
28.0
4,813.4
2005
2,418.6
2,402.1
0.5
16.0
1,931.0
1,891.7
2.4
36.9 1
832.3
827.6
1.5
3.2
362.5
357.9
224.8
223.5
0.9
0.4 1
50.2
5,819.4
2008
2,378.2
2,360.9
0.5
16.9
1,843.9
1,816.5
1.9
25.5
808.4
804.1
1.4
2.9
351.0
346.2
3.8
1.0
226.0
224.7
0.9
0.3
41.1
5,648.6
2009
2,163.7
2,146.4
0.4
16.8
1,772.2
1,747.7
1.8
22.7
731.2
727.5
1.2
2.5
341.3
336.4
4.0
1.0
225.2
223.9
0.9
0.3
44.0
5,277.7
2010
2,278.1
2,259.2
0.5
18.5
1,787.5
1,765.0
1.8
20.7
779.6
775.6
1.3
2.7
339.4
334.8
3.6
0.9
221.9
220.7
0.9
0.3
49.8
5,456.3
2011
2,176.6
2,158.5
0.4
17.6
1,768.1
1,747.9
1.7
18.5
772.7
768.7
1.3
2.7
329.5
324.9
3.6
0.9
222.8
221.5
0.9
0.3
49.8
5,319.3
2012
2,041.5
2,022.7
0.5
18.3
1,757.8
1,739.5
1.7
16.5
777.9
774.2
1.2
2.5
292.8
288.9
3.1
0.8
198.5
197.4
0.8
0.3
49.8
5,118.2
Note: Totals may not sum due to independent rounding. Emissions from fossil fuel combustion by
electricity generation are allocated based on aggregate national electricity consumption by each
end-use sector.
* U.S. Territories are not apportioned by sector, and emissions are total greenhouse gas emissions
from all fuel combustion sources.
3-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Other than CO2, gases emitted from stationary combustion include the greenhouse gases CH4 and N2O and the
indirect greenhouse gases NOX, CO, and NMVOCs.63 Methane and N2O emissions from stationary combustion
sources depend upon fuel characteristics, size and vintage, along with combustion technology, pollution control
equipment, ambient environmental conditions, and operation and maintenance practices. N2O emissions from
stationary combustion are closely related to air-fuel mixes and combustion temperatures, as well as the
characteristics of any pollution control equipment that is employed. Methane emissions from stationary combustion
are primarily a function of the CH4 content of the fuel and combustion efficiency.
Mobile combustion produces greenhouse gases other than CO2, including CH4, N2O, and indirect greenhouse gases
including NOX, CO, and NMVOCs. As with stationary combustion, N2O and NOX emissions from mobile
combustion are closely related to fuel characteristics, air-fuel mixes, combustion temperatures, and the use of
pollution control equipment. N2O from mobile sources, in particular, can be formed by the catalytic processes used
to control NOX, CO, and hydrocarbon emissions. Carbon monoxide emissions from mobile combustion are
significantly affected by combustion efficiency and the presence of post-combustion emission controls. Carbon
monoxide emissions are highest when air-fuel mixtures have less oxygen than required for complete combustion.
These emissions occur especially in idle, low speed, and cold start conditions. Methane and NMVOC emissions
from motor vehicles are a function of the CH4 content of the motor fuel, the amount of hydrocarbons passing
uncombusted through the engine, and any post-combustion control of hydrocarbon emissions (such as catalytic
converters).
An alternative method of presenting combustion emissions is to allocate emissions associated with electricity
generation to the sectors in which it is used. Four end-use sectors were defined: industrial, transportation,
residential, and commercial. In the table below, electricity generation emissions have been distributed to each end-
use sector based upon the sector's share of national electricity consumption, with the exception of CH4 and N2O
from transportation.64 Emissions from U.S. territories are also calculated separately due to a lack of end-use-specific
consumption data. This method assumes that emissions from combustion sources are distributed across the four end-
use sectors based on the ratio of electricity consumption in that sector. The results of this alternative method are
presented in Table 3-8.
Table 3-8: COz, CH4, and NzO Emissions from Fossil Fuel Combustion by End-Use Sector (Tg
COz Eq.)
End-Use Sector
1990
2005
2008
2009
2010
2011
2012
Transportation
C02
CH4
N20
Industrial
C02
CH4
N20
Residential
CO2
CH4
N2O
Commercial
CO2
CH4
N2O
U.S. Territories*
1,545.6
1,497.0
4.6
44.0
1,539.6
1,531.8
1.7
6.1
939.6
931.4
4.7
3.5
760.5
757.0
1.0
2.6
28.0
1,935.8
1,896.5
2.4
36.9
1,574.3
1,564.6
1.7
8.1
1,225.1
1,214.7
3.8
6.7
1,034.0
1,027.2
1.1
5.7
50.2
1,848.6
1,821.2
1.9
25.5
1,510.8
1,501.4
1.5
7.9
1,200.1
1,189.2
4.0
7.0
1,048.0
1,040.8
1.1
6.2
41.1
1,776.7
1,752.2
1.8
22.7
1,338.0
1,329.5
1.3
7.2
1,134.2
1,122.9
4.1
7.2
984.8
977.4
1.1
6.3
44.0
1,792.0
1,769.5
1.8
20.7
1,426.0
1,416.6
1.4
7.9
1,186.8
1,175.2
3.8
7.8
1,001.7
993.9
1.1
6.7
49.8
1,772.4
1,752.1
1.7
18.5
1,402.8
1,393.6
1.4
7.8
1,127.1
1,115.9
3.8
7.4
967.3
959.8
1.1
6.4
49.8
1,761.7
1,743.4
1.7
16.6
1,376.3
1,367.1
1.4
7.9
1,025.0
1,014.3
3.3
7.4
905.4
897.9
1.0
6.6
49.8
Total
4,813.4
5,819.4
5,648.6 5,277.7 5,456.3 5,319.3 5,118.2
Note: Totals may not sum due to independent rounding. Emissions from fossil fuel combustion by
electricity generation are allocated based on aggregate national electricity consumption by each end
sector.
-use
63 Sulfur dioxide (SO2) emissions from stationary combustion are addressed in Annex 6.3.
64 Separate calculations were performed for transportation-related CH4 and N2O. The methodology used to calculate these
emissions are discussed in the mobile combustion section.
Energy 3-11
-------
* U.S. Territories are not apportioned by sector, and emissions are total greenhouse gas emissions from all
fuel combustion sources.
Stationary Combustion
The direct combustion of fuels by stationary sources in the electricity generation, industrial, commercial, and
residential sectors represent the greatest share of U.S. greenhouse gas emissions. Table 3-9 presents CO2 emissions
from fossil fuel combustion by stationary sources. The CO2 emitted is closely linked to the type of fuel being
combusted in each sector (see Methodology section for CO2 from fossil fuel combustion). Other than CO2, gases
emitted from stationary combustion include the greenhouse gases CH4 and N2O. Table 3-10 and Table 3-11 present
CH4 and N2O emissions from the combustion of fuels in stationary sources.65 Methane and N2O emissions from
stationary combustion sources depend upon fuel characteristics, combustion technology, pollution control
equipment, ambient environmental conditions, and operation and maintenance practices. N2O emissions from
stationary combustion are closely related to air-fuel mixes and combustion temperatures, as well as the
characteristics of any pollution control equipment that is employed. Methane emissions from stationary combustion
are primarily a function of the CH4 content of the fuel and combustion efficiency. The CH4 and N2O emission
estimation methodology was revised in 2010 to utilize the facility-specific technology and fuel use data reported to
EPA's Acid Rain Program (see Methodology section for CH4 and N2O from stationary combustion). Please refer to
Table 3-7 for the corresponding presentation of all direct emission sources of fuel combustion.
Table 3-9: COz Emissions from Stationary Fossil Fuel Combustion (Tg COz Eq.)
Sector/Fuel Type
Electricity Generation
Coal
Natural Gas
Fuel Oil
Geothermal
Industrial
Coal
Natural Gas
Fuel Oil
Commercial
Coal
Natural Gas
Fuel Oil
Residential
Coal
Natural Gas
Fuel Oil
U.S. Territories
Coal
Natural Gas
Fuel Oil
Total
+ Does not exceed 0.05 Tg
1990
1,820.8
1,547.6
175.3
97.5 1
0.4
845.1
155.3
408.9
280.9
219.0
12.0 1
142.1
64.9
338.3
3.0 1
238.0
97.4
27.9 1
0.6 1
NO 1
27.2
3,251.1
; C02 Eq.
2005
2,402.1 1
1,983.8
318.8
99.2 1
0.4
827.6
115.3
388.5
323.8
223.5
9.3 1
162.9
51.3
357.9
0.8 1
262.2
94.9
50.0 1
45.7
3,861.1
2008
2,360.9
1,959.4
361.9
39.2
0.4
804.1
102.4
401.3
300.4
224.7
7.6
171.1
46.0
346.2
+
265.5
80.7
41.0
3.4
1.6
36.0
3,777.0
2009
2,146.4
1,740.9
372.2
33.0
0.4
727.5
83.0
377.6
266.8
223.9
6.9
168.9
48.1
336.4
+
258.8
77.5
43.8
3.4
1.5
39.0
3,478.0
2010
2,259.2
1,827.6
399.0
32.2
0.4
775.6
90.1
407.2
278.3
220.7
6.6
167.7
46.4
334.8
+
258.6
76.3
49.6
3.4
1.5
44.7
3,639.9
2011
2,158.5
1,722.7
408.8
26.6
0.4
768.7
82.0
417.3
269.4
221.5
5.8
170.5
45.2
324.9
+
254.7
70.3
49.6
3.4
1.4
44.7
3,523.2
2012
2,022.7
1,511.2
492.2
18.8
0.4
774.2
74.3
434.7
265.2
197.4
4.1
156.9
36.4
288.9
+
224.8
64.1
49.6
3.4
1.4
44.7
3,332.7
NO: Not occurring
65 Since emission estimates for U.S. territories cannot be disaggregated by gas in Table 3-10 and Table 3-11, the values for CELi
and N2O exclude U.S. territory emissions.
3-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 3-10: CH4 Emissions from Stationary Combustion (Tg COz Eq.)
Sector/Fuel Type
Electricity Generation
Coal
Fuel Oil
Natural Gas
Wood
Industrial
Coal
Fuel Oil
Natural Gas
Wood
Commercial
Coal
Fuel Oil
Natural Gas
Wood
Residential
Coal
Fuel Oil
Natural Gas
Wood
U.S. Territories
Coal
Fuel Oil
Natural Gas
Wood
Total
1990 2005
0.3 0.5
0.3 1
+
+
1.6
0.3
0.2
0.2
0.9
0.9
0.2
0.3
0.4
4.6
0.2
0.3
0.4
3.7
+
+
+
+
0.3
0.1
1.5
0.3
0.2
0.1
0.9
0.9
0.2
0.3
0.5
3.6
0.1
0.3
0.5
2.8
0.1
+
0.1
+
7.5 6.6
2008
0.5
0.3
0.1
1.4
0.2
0.2
0.2
0.9
0.9
0.1
0.3
0.5
3.8
+
0.2
0.5
3.0
0.1
+
0.1
+
+
6.6
2009
0.4
0.3
0.1
1.2
0.2
0.1
0.1
0.8
0.9
0.1
0.3
0.5
4.0
+
0.2
0.5
3.3
0.1
+
0.1
+
+
6.6
2010
0
0
0
1
0
0
0
0
0
0
0
0
3
0
0
2
0
0
6
5
3
2
3
2
1
2
8
9
1
3
5
6
+
2
5
9
1
+
1
+
+
4
2011
0.4
0.3
0.2
1.3
0.2
0.1
0.2
0.9
0.9
0.1
0.3
0.4
3.6
+
0.2
0.5
2.9
0.1
+
0.1
+
+
6.3
2012
0.5
0.1
0.1
0.4
1.2
0.2
0.1
0.2
0.8
0.8
0.1
0.3
0.4
3.1
+
0.2
0.4
2.5
0.1
+
0.1
+
+
5.7
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 3-11: NzO Emissions from Stationary Combustion (Tg COz Eq.)
Sector/Fuel Type
Electricity Generation
Coal
Fuel Oil
Natural Gas
Wood
Industrial
Coal
Fuel Oil
Natural Gas
Wood
Commercial
Coal
Fuel Oil
Natural Gas
Wood
Residential
Coal
Fuel Oil
Natural Gas
Wood
U.S. Territories
Coal
Fuel Oil
Natural Gas
Wood
Total
1990 2005
7.4 16.0
6.3
0.1
1.0
+
3.3
0.8
0.5
0.2
1.8
0.4
0.1
0.2
0.1
0.1
1.1
+
0.3
0.1
0.7
0.1
0.1
11.6
0.1
4.3
+ 1
3.2
0.6
0.5
0.2
1.9
0.4
U.J
0.1
0.6
12.3 20.6 •
2008
16.8
11.6
+
5.2
+
2.9
0.5
0.5
0.2
1.7
0.3
+
0.1
0.1
0.1
1.0
+
0.2
0.1
0.6
0.1
0.1
21.1
2009
16.8
11.2
+
5.6
+
2.5
0.4
0.4
0.2
1.5
0.3
+
0.1
0.1
0.1
1.0
+
0.2
0.1
0.6
0.1
0.1
20.8
2010
18.5
12.5
+
5.9
+
2.7
0.4
0.4
0.2
1.6
0.3
+
0.1
0.1
0.1
0.9
+
0.2
0.1
0.6
0.1
0.1
22.5
2011
17.6
11.5
+
6.1
+
2.7
0.4
0.4
0.2
1.7
0.3
+
0.1
0.1
0.1
0.9
+
0.2
0.1
0.6
0.1
0.1
21.6
2012
18.3
9.1
0.3
8.7
0.1
2.5
0.4
0.3
0.2
1.6
0.3
+
0.1
0.1
0.1
0.8
+
0.2
0.1
0.5
0.1
0.1
22.0
Energy 3-13
-------
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Electricity Generation
The process of generating electricity is the single largest source of CO2 emissions in the United States, representing
38 percent of total CO2 emissions from all CO2 emissions sources across the United States. Methane and N2O
accounted for a small portion of emissions from electricity generation, representing less than 0.1 percent and 0.9
percent, respectively. Electricity generation also accounted for the largest share of CO2 emissions from fossil fuel
combustion, approximately 40 percent in 2012. Methane and N2O from electricity generation represented 7 and 48
percent of emissions from fossil fuel combustion in 2012, respectively. Electricity was consumed primarily in the
residential, commercial, and industrial end-use sectors for lighting, heating, electric motors, appliances, electronics,
and air conditioning (see Figure 3-9). Electricity generators, including those using low-CO2 emitting technologies,
relied on coal for approximately 37 percent their total energy requirements in 2012. Recently a decrease in the
carbon intensity of fuels consumed to generate electricity has occurred due to a decrease in coal consumption, and
increased natural gas consumption and other generation sources. Total U.S. electricity generators used natural gas
for approximately 30 percent of their total energy requirements in 2012 (ElA 2014b).
Figure 3-9: Electricity Generation Retail Sales by End-Use Sector
1,500
1,400
1,300 -
1,200 -
1,100 -
1,000 -
900 -
800
Residential
Commercial
Industrial
vl V*
-------
electricity generation decreased by 12.3 percent and 27.6 percent, respectively, in 2012 and the consumption of
natural gas for electricity generation, increased by 20.4 percent.
Industrial Sector
The industrial sector accounted for 15 percent of CO2 emissions from fossil fuel combustion, 17 percent of CH4
emissions from fossil fuel combustion, and 6 percent of N2O emissions from fossil fuel combustion. CO2, CH4, and
N2O emissions resulted from the direct consumption of fossil fuels for steam and process heat production.
The industrial sector, per the underlying energy consumption data from EIA, includes activities such as
manufacturing, construction, mining, and agriculture. The largest of these activities in terms of energy consumption
is manufacturing, of which six industries—Petroleum Refineries, Chemicals, Paper, Primary Metals, Food, and
Nonmetallic Mineral Products—represent the vast majority of the energy use (EIA 2014a and EIA 2009b).
In theory, emissions from the industrial sector should be highly correlated with economic growth and industrial
output, but heating of industrial buildings and agricultural energy consumption are also affected by weather
conditions.67 In addition, structural changes within the U.S. economy that lead to shifts in industrial output away
from energy-intensive manufacturing products to less energy-intensive products (e.g., from steel to computer
equipment) also have a significant effect on industrial emissions.
From 2011 to 2012, total industrial production and manufacturing output increased by 3.6 and 4.2 percent,
respectively (FRB 2013). Over this period, output increased across production indices for Food, Petroleum
Refineries, Chemicals, Primary Metals, and Nonmetallic Mineral Products, and decreased slightly for Paper (see
Figure 3-10).
67 Some commercial customers are large enough to obtain an industrial price for natural gas and/or electricity and are
consequently grouped with the industrial end-use sector in U.S. energy statistics. These misclassifications of large commercial
customers likely cause the industrial end-use sector to appear to be more sensitive to weather conditions.
Energy 3-15
-------
Figure 3-10: Industrial Production Indices (Index 2007=100)
110
100
90 -
80 -
70 -
60 -
50 -
120
110
100
90
80
70
110
100
90
80
70
60
110
100
90
80
70
60
Total excluding Computers,
Communications Equipment, and
Semiconductors
Paper
Foods
-~
Stone, Clay & Glass
Products
Primary
Metals
CTio-icncncncncnaicncnooooooooooooo
Despite the growth in industrial output (56 percent) and the overall U.S. economy (73 percent) from 1990 to 2012,
CO2 emissions from fossil fuel combustion in the industrial sector decreased by 8.4 percent over the same time
series. A number of factors are believed to have caused this disparity between growth in industrial output and
decrease in industrial emissions, including: (1) more rapid growth in output from less energy -intensive industries
relative to traditional manufacturing industries, and (2) energy -intensive industries such as steel are employing new
methods, such as electric arc furnaces, that are less carbon intensive than the older methods. In 2012, CO2, CH4, and
N2O emissions from fossil fuel combustion and electricity use within the industrial end-use sector totaled 1,376.3 Tg
CO2 Eq., or approximately 1.9 percent below 201 1 emissions.
Residential and Commercial Sectors
The residential and commercial sectors accounted for 6 and 4 percent of CO2 emissions from fossil fuel combustion,
43 and 1 1 percent of CH4 emissions from fossil fuel combustion, and 2 and 1 percent of N2O emissions from fossil
fuel combustion, respectively. Emissions from these sectors were largely due to the direct consumption of natural
gas and petroleum products, primarily for heating and cooking needs. Coal consumption was a minor component of
energy use in both of these end-use sectors. In 2012, CO2, CH4, and N2O emissions from fossil fuel combustion and
electricity use within the residential and commercial end-use sectors were 1,025.0 Tg CO2 Eq. and 905.4 Tg CO2
Eq., respectively. Total CO2, CH4, and N2O emissions from the residential and commercial sectors decreased by 9. 1
and 6.4 percent from 201 1 to 2012, respectively.
Emissions from the residential and commercial sectors have generally been increasing since 1990, and are often
correlated with short-term fluctuations in energy consumption caused by weather conditions, rather than prevailing
economic conditions. In the long-term, both sectors are also affected by population growth, regional migration
trends, and changes in housing and building attributes (e.g., size and insulation).
3-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Combustion emissions from natural gas consumption represent 78 percent and 79 percent of the direct fossil fuel
CO2 emissions from the residential and commercial sectors, respectively. In 2012, natural gas combustion CO2
emissions from the residential and commercial sectors decreased by 11.7 percent and 8.0 percent from 2011 levels,
respectively.
U.S. Territories
Emissions from U.S. territories are based on the fuel consumption in American Samoa, Guam, Puerto Rico, U.S.
Virgin Islands, Wake Island, and other U.S. Pacific Islands. As described in the Methodology section for CO2 from
fossil fuel combustion, this data is collected separately from the sectoral-level data available for the general
calculations. As sectoral information is not available for U.S. Territories, CO2, CH4, and N2O emissions are not
presented for U.S. Territories in the tables above, though the emissions will include some transportation and mobile
combustion sources.
Transportation Sector and Mobile Combustion
This discussion of transportation emissions follows the alternative method of presenting combustion emissions by
allocating emissions associated with electricity generation to the transportation end-use sector, as presented in Table
3-8. For direct emissions from transportation (i.e., not including emissions associated with the sector's electricity
consumption), please see Table 3-7.
Transportation End-Use Sector
The transportation end-use sector accounted for 1,761.7 Tg CO2 Eq. in 2012, which represented 35 percent of CO2
emissions, 23 percent of CH4 emissions, and 43 percent of N2O emissions from fossil fuel combustion, respectively.
Fuel purchased in the U.S. for international aircraft and marine travel accounted for an additional 105.8 Tg CO2 Eq.
in 2012; these emissions are recorded as international bunkers and are not included in U.S. totals according to
UNFCCC reporting protocols. Among domestic transportation sources, light duty vehicles (including passenger
cars and light-duty trucks) represented 61 percent of CO2 emissions, medium- and heavy-duty trucks 22 percent,
commercial aircraft 6 percent, and other sources 10 percent. See Table 3-12 for a detailed breakdown of CO2
emissions by mode and fuel type. Emissions of CO2 from the combustion of ethanol for transportation and emissions
associated with the agricultural and industrial processes involved in the production of ethanol are captured in other
sectors.68 Ethanol consumption from the transportation sector has increased from 0.7 billion gallons in 1990 to 12.3
billion gallons in 2012. For further information, see the section on wood biomass and ethanol consumption at the
end of this chapter, and Table A-91 in Annex 3.2.
From 1990 to 2012, transportation emissions rose by 18 percent due, in large part, to increased demand for travel
with limited gains in fuel efficiency over the same time period. The number of vehicle miles traveled by light-duty
motor vehicles (passenger cars and light-duty trucks) increased 35 percent from 1990 to 2012, as a result of a
confluence of factors including population growth, economic growth, urban sprawl, and low fuel prices during the
beginning of this period.
From 2011 to 2012, CO2 emissions from the transportation end-use sector decreased by 0.5 percent. The decrease in
emissions can largely be attributed to a small increase in fuel efficiency across different transportation modes and
limited new demand for passenger transportation. Commercial aircraft emissions continued to fall, having
decreased 19 percent since 2007. Decreases in jet fuel emissions (excluding bunkers) are due in part to improved
operational efficiency that results in more direct flight routing, improvements in aircraft and engine technologies to
reduce fuel burn and emissions, and the accelerated retirement of older, less fuel efficient aircraft.
Almost all of the energy consumed for transportation was supplied by petroleum-based products, with more than
half being related to gasoline consumption in automobiles and other highway vehicles. Other fuel uses, especially
diesel fuel for freight trucks and jet fuel for aircraft, accounted for the remainder. The primary driver of
transportation-related emissions was CO2 from fossil fuel combustion, which increased by 16 percent from 1990 to
2012. This rise in CO2 emissions, combined with an increase in HFCs from close to zero emissions in 1990 to 72.9
68 Net carbon fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for Land Use, Land-Use
Change and Forestry, in line with IPCC methodological guidance and UNFCCC reporting obligations.
Energy 3-17
-------
Tg COa Eq. in 2012, led to an increase in overall GHG emissions from transportation activities of 18 percent (see
Table 2-14).
Transportation Fossil Fuel Combustion CO2 Emissions
Domestic transportation CCh emissions increased by 16 percent (246.4 Tg CC>2 Eq.) between 1990 and 2012, an
annualized increase of 0.7 percent. However, between 2011 and 2012, CC>2 emissions from domestic transportation
decreased by 0.5 percent, which was similar to the previous year's trend of decreasing emissions. Almost all of the
energy consumed by the transportation sector is petroleum-based, including motor gasoline, diesel fuel, jet fuel, and
residual oil.69 Transportation sources also produce CH4 and N2O; these emissions are included in Table 3-13 and
Table 3-14 in the "Mobile Combustion" Section. Annex 3.2 presents total emissions from all transportation and
mobile sources, including CC>2, N2O, CH4, and HFCs.
Carbon dioxide emissions from passenger cars and light-duty trucks totaled 1,061.0 Tg CCh Eq. in 2012, an increase
of 12 percent (110.6 Tg CC>2 Eq.) from 1990. CC>2 emissions from passenger cars and light-duty trucks peaked at
1,184.3 Tg CO2 Eq. in 2004, and since then have declined about 10 percent. Over the 1990s through the early
2000s, growth in vehicle travel substantially outweighed improvements in vehicle fuel economy; however, the rate
of Vehicle Miles Traveled (VMT) growth slowed considerably starting in 2005 (and declined rapidly in 2008) while
average vehicle fuel economy increased. Among new vehicles sold annually, average fuel economy gradually
declined from 1990 to 2004 (Figure 3-11), reflecting substantial growth in sales of light-duty trucks—in particular,
growth in the market share of sport utility vehicles—relative to passenger cars (Figure 3-12). New vehicle fuel
economy improved beginning in 2005, largely due to higher light-duty truck fuel economy standards, which have
risen each year since 2005. The overall increase in fuel economy is also due to a slightly lower light-duty truck
market share, which peaked in 2004 at 48 percent and declined to 36 percent in 2012 (EPA 2013d).
Passenger car CCh emissions increased by 21 percent from 1990 to 2012, light-duty truck CCh emissions decreased
by 6 percent and medium- and heavy-duty trucks increased by 70 percent.70 Carbon dioxide from the domestic
operation of commercial aircraft increased by 3 percent (3.4 Tg CO2 Eq.) from 1990 to 2012. Across all categories
of aviation, CCh emissions decreased by 22.5 percent (42.2 Tg CCh Eq.) between 1990 and 2012.71 This includes a
65 percent (22.9 Tg CCh Eq.) decrease in emissions from domestic military operations. For further information on
all greenhouse gas emissions from transportation sources, please refer to Annex 3.2.
69 Biofuel estimates are presented in the Energy chapter for informational purposes only, in line with IPCC methodological
guidance and UNFCCC reporting obligations. Net carbon fluxes from changes in biogenic carbon reservoirs in croplands are
accounted for in the estimates for Land Use, Land-Use Change, and Forestry (see Chapter 7). More information and additional
analyses on biofuels are available at EPA's "Renewable Fuels: Regulations & Standards;" See
.
70 Includes "light-duty trucks" fueled by gasoline, diesel and LPG.
71 Includes consumption of jet fuel and aviation gasoline. Does not include aircraft bunkers, which are not included in national
emission totals, in line with IPCC methodological guidance and UNFCCC reporting obligations.
3-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure 3-11: Sales-Weighted Fuel Economy of New Passenger Cars and Light-Duty Trucks,
1990-2012
24.0 -,
23.5 -
23.0
22.5
22.0
21.5
21.0
20.5
2().o
19.5
19.0 ^
18.5
18.0
8
o
rN
oo
rMrN
o
fN
o
rM
o
rvl
o
fM
o
rM
oo
rMfN
o
r-J
o
fN
Model Year
Figure 3-12: Sales of New Passenger Cars and Light-Duty Trucks, 1990-2012
100'% n
75% ^
50%
25% -
0%
Passenger Cars
Light-Duty Trucks
° S
CTi Ol
CF*
Table 3-12: COz Emissions from Fossil Fuel Combustion in Transportation End-Use Sector
(Tg CQ2 Eg.)
Fuel/Vehicle Type
Gasoline
Passenger Cars
Light-Duty Trucks
1990
983.7
621.4
309.1
2005
1,187.8
658.0
478.7
2008a
1,130.3
765.6
298.9
2009
1,128.5
762.4
304.1
2010
1,124.9
760.0
303.7
2011
1,102.8
756.0
289.3
2012
1,099.9
755.6
286.6
Medium- and Heavy-Duty
Trucks'5 38.7
Buses 0.3
Motorcycles 1.7
Recreational Boats 12.4
Distillate Fuel Oil (Diesel) 262.9
Passenger Cars 7.9
Light-Duty Trucks 11.5
Medium- and Heavy-Duty
34.9
0.4
1.6
14.1
458.1
4.2
25.8
47.2
0.8
4.4
13.5
451.6
3.7
12.1
43.6
0.8
4.2
13.3
409.7
3.6
12.1
43.6
0.8
3.8
13.1
426.4
3.8
12.6
40.1
0.8
3.7
13.0
436.3
4.1
13.2
39.8
0.8
4.2
12.9
435.4
4.2
13.2
Trucksb
Buses
Rail
Recreational Boats
190.5
8.0
35.5 1
2.0
360.6
10.6
45.6
3.1
366.1
15.2
43.2
3.4
332.2
14.1
36.3
3.5
345.9
14.1
39.0
3.5
348.9
15.2
41.0
3.6
350.2
16.3
40.2
3.7
Energy 3-19
-------
Ships and Other Boats0
International Bunker Fueld
Jet Fuelc
Commercial Aircraft6
Military Aircraft
General Aviation Aircraft
International Bunker Fuels'1
International Bunker Fuels
From Commercial Aviation
Aviation Gasoline
General Aviation Aircraft
Residual Fuel Oil
Ships and Other Boats0
International Bunker Fuel1
Natural Gas
Passenger Cars
Light-Duty Trucks
Buses
Pipelinef
LPG
Light-Duty Trucks
Medium- and Heavy-Duty
Trucks'5
Buses
Electricity
Rail
Ethanolz
Total
Total (Including Bunkers)"1
7.5
11.7 1
184.2
109.9
35.0 1
39.4 1
38.0 1
30.0 1
3.1 1
3.1 1
22.6 1
22.6 1
53.7 1
36.0 1
+ 1
+ 1
+ 1
36.0 1
1.4 \
0.6 1
0.8 1
+ 1
3.0 1
3.0 •
4.1
1,497.0
1,600.5
8.1
9.4
189.3 1
132.7
19.4
37.3
60.1
55.6
2.4
2.4
19.3
19.3
43.6
33.1
1
+ 1
0.8
32.2
1.7
1.3
0.4
+ 1
4.7
1 4.7 •
1 22.4
1,896.5
2,009.6
7.9
9.0
173.0
127.3
17.6
28.2
56.1
52.4
2.0
2.0
20.4
20.4
49.2
36.7
+
+
1.1
35.6
2.5
1.8
0.7
+
4.7
4.7
53.8
1,821.2
1,935.5
7.9
8.2
154.1
119.5
15.4
19.2
52.8
49.2
1.8
1.8
13.9
13.9
45.4
37.9
+
+
1.2
36.7
1.7
1.2
0.5
+
4.5
4.5
6_L2_
1,752.2
1,858.6
7.5
9.5
151.5
113.3
13.6
24.6
61.0
57.4
1.9
1.9
20.4
20.4
46.5
38.1
+
+
1.1
37.1
1.8
1.3
0.6
+
4.5
4.5
7_L3
1,769.5
1,886.5
10.3
7.9
146.6
114.6
11.6
20.4
64.8
61.7
1.9
1.9
19.4
19.4
38.9
38.9
+
+
1.1
37.8
2.0
1.4
0.6
+
4.3
4.3
71.5
1,752.1
1,863.8
7.7
6.8
143.4
113.3
12.1
18.0
64.5
61.4
1.7
1.7
15.8
15.8
34.5
41.2
+
+
1.1
40.1
2.1
1.5
0.6
+
3.9
3.9
71.5
1,743.4
1,849.2
Note: This table does not include emissions from non-transportation mobile sources, such as agricultural equipment and
construction/mining equipment; it also does not include emissions associated with electricity consumption by pipelines or
lubricants used in transportation.
a In 2011, FHWA changed how vehicles are classified, moving from a system based on body-type to one that is based on
wheelbase. This change in methodology in FHWA's VM-1 table resulted in large changes in VMT by vehicle class, thus leading
to a shift in emissions among on-road vehicle classes in the 2007 to 2012 time period.
b Includes medium- and heavy-duty trucks over 8,500 Ibs.
0 Fluctuations in emission estimates reflect data collection problems.
d Official estimates exclude emissions from the combustion of both aviation and marine international bunker fuels; however,
estimates including international bunker fuel-related emissions are presented for informational purposes.
e Commercial aircraft, as modeled in FAA's AEDT, consists of passenger aircraft, cargo, and other chartered flights.
f Pipelines reflect CO2 emissions from natural gas powered pipelines transporting natural gas.
gEthanol estimates are presented for informational purposes only. See section 3.10 of this chapter and the estimates in Land Use,
Land-Use Change, and Forestry (see Chapter 7), in line with IPCC methodological guidance and UNFCCC reporting obligations,
for more information on ethanol.
Note: Totals may not sum due to independent rounding.
+ Less than 0.05 Tg CO2 Eq.
Mobile Fossil Fuel Combustion CH4and IM2O Emissions
Mobile combustion includes emissions of CH4 and N2O from all transportation sources identified in the U.S.
inventory with the exception of pipelines, which are stationary;72 mobile sources also include non-transportation
sources such as construction/mining equipment, agricultural equipment, vehicles used off-road, and other sources
(e.g., snowmobiles, lawnmowers, etc.). Annex 3.2 includes a summary of all emissions from both transportation
and mobile sources. Table 3-13 and Table 3-14 provide CH4 and N2O emission estimates in Tg CCh Eq.73
72 Fugitive emissions of CELi from natural gas systems are reported under the Industrial economic sector. More information on
the methodology used to calculate these emissions are included in Annex 3.4
73 See Annex 3.2 for a complete time series of emission estimates for 1990 through 2012.
3-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Mobile combustion was responsible for a small portion of national CH4 emissions (0.3 percent) but was the fourth
largest source of U.S. N2O emissions (4 percent). From 1990 to 2012, mobile source CH4 emissions declined by 63
percent, to 1.7 Tg CO2 Eq. (81 Gg), due largely to control technologies employed in on-road vehicles since the mid-
1990s to reduce CO, NOX, NMVOC, and CH4 emissions. Mobile source emissions of N2O decreased by 62 percent,
to 16.5 Tg CO2 Eq. (53 Gg). Earlier generation control technologies initially resulted in higher N2O emissions,
causing a 26 percent increase in N2O emissions from mobile sources between 1990 and 1997. Improvements in
later-generation emission control technologies have reduced N2O output, resulting in a 70 percent decrease in
mobile source N2O emissions from 1997 to 2012 (Figure 3-13). Overall, CH4 and N2O emissions were
predominantly from gasoline-fueled passenger cars and light-duty trucks.
Figure 3-13: Mobile Source CH4 and NzO Emissions
60
50
. 40
s
Q 30
20 -
10
0
N,0
CH4
in 10 r-~ co
i-
o o o o
o o o
Table 3-13: CH4 Emissions from Mobile Combustion (Tg COz Eq.)
Fuel Type/Vehicle Type3
Gasoline On-Road
Passenger Cars
Light-Duty Trucks
1990
4.2
2.6
1.4
2005
1.9
l.ll
().?•
2008e
1.4
1.0
0.3
2009
1.3
0.9
0.3
2010
1.2
0.9
0.3
2011
1.2
0.8
0.3
2012
1.1
0.8
0.3
Medium- and Heavy-Duty
Trucks and Buses
Motorcycles
Diesel On-Road
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty
Trucks and Buses
Alternative Fuel On-Road
Non-Road
Ships and Boats
Rail
Aircraft
Agricultural Equipment15
Construction/Mining
Equipment0
Otherd
0.5
'
0.1
0.1
0.1
0.1
0.1
0.1
0.4
+
0.1
+
0.1
0.1
0.1
0.1
0.4
+
0.1
+
0.1
0.1
0.1
0.1
0.4
+
0.1
+
0.1
0.1
0.1
Total
4.6
2.4
1.9
1.8
1.8
0.1
0.1
0.5
+
0.1
+
0.1
0.1
0.1
1.7
0.1
0.1
0.5
+
0.1
+
0.2
0.1
0.1
1.7
a See Annex 3.2 for definitions of on-road vehicle types.
b Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in
agriculture.
0 Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in
construction.
Energy 3-21
-------
d "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad
equipment, airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are
used off-road for commercial/industrial purposes.
e In 2011, FHWA changed how vehicles are classified, moving from a system based on body-type to one that is based on
wheelbase. This change in methodology in FHWA's VM-1 table resulted in large changes in VMT by vehicle class, thus leading
to a shift in emissions among on-road vehicle classes in the 2007 to 2012 time period.
Note: Totals may not sum due to independent rounding.
+ Less than 0.05 Tg CO2 Eq.
Table 3-14: NzO Emissions from Mobile Combustion (Tg COz Eg.)
Fuel Type/Vehicle Type3 1990 2005 2008e 2009 2010 2011 2012
Gasoline On-Road 40.1 32.2 20.7 18.3 16.1 13.9 12.0
Passenger Cars 25.4B 17.8B 14.6 12.4 10.8 9.4 8.0
Light-Duty Trucks 14.ll \3.6U 5.2 5.1 4.6 4.0 3.5
Medium- and Heavy-Duty
Trucks and Buses 0.6U O.sB 0.9 0.7 0.6 0.5 0.5
Motorcycles +H + + + + + +
Diesel On-Road 0.2M 0.3M 0.4 0.4 0.4 0.4 0.4
Passenger Cars +B +B + + + + +
Light-Duty Trucks +H +H + + + + +
Medium- and Heavy-Duty
Trucks and Buses 0.2 O.sH 0.4 0.4 0.4 0.4 0.4
Alternative Fuel On-Road 0.11 0.2! 0.2 0.2 0.2 0.2 0.2
Non-Road 3.7M 4.3M 4.1 3.8 4.0 4.0 3.9
Ships and Boats 0.6^ 0.6^ 0.6 0.5 0.6 0.7 0.6
Rail 0.3M QAM 0.3 0.3 0.3 0.3 0.3
Aircraft l.sl l.sl 1.7 1.5 1.5 1.4 1.4
Agricultural Equipment15 0.2B 0.4B 0.4 0.4 0.4 0.4 0.4
Construction/Mining
Equipment0 0.3M O.sB 0.5 0.5 0.6 0.6 0.6
Otherd 0.4 0.6 0.6 0.6 0.6 0.6 0.6
Total 44.0 36.9 25.5 22.7 20.7 18.5 16.5
a See Annex 3.2 for definitions of on-road vehicle types.
b Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in
agriculture.
0 Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in
construction.
d "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad
equipment, airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are
used off-road for commercial/industrial purposes.
e In 2011, FHWA changed how vehicles are classified, moving from a system based on body-type to one that is based on
wheelbase. This change in methodology in FHWA's VM-1 table resulted in large changes in VMT by vehicle class, thus leading
to a shift in emissions among on-road vehicle classes in the 2007 to 2012 time period.
Note: Totals may not sum due to independent rounding.
+ Less than 0.05 Tg CO2 Eq.
CO2 from Fossil Fuel Combustion
Methodology
The methodology used by the United States for estimating CC>2 emissions from fossil fuel combustion is
conceptually similar to the approach recommended by the IPCC for countries that intend to develop detailed,
sectoral-based emission estimates in line with a Tier 2 method in the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC 2006).74 The use of the most recently published calculation methodologies by
74 The IPCC Tier 3B methodology is used for estimating emissions from commercial aircraft.
3-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
the IPCC, as contained in the 2006IPCC Guidelines, is considered to improve the rigor and accuracy of this
inventory and is fully in line with IPCC Good Practice Guidance. A detailed description of the U.S. methodology is
presented in Annex 2.1, and is characterized by the following steps:
1. Determine total fuel consumption by fuel type and sector. Total fossil fuel consumption for each year is
estimated by aggregating consumption data by end-use sector (e.g., commercial, industrial, etc.), primary
fuel type (e.g., coal, petroleum, gas), and secondary fuel category (e.g., motor gasoline, distillate fuel oil,
etc.). Fuel consumption data for the United States were obtained directly from the EIA of the U.S.
Department of Energy (DOE), primarily from the Monthly Energy Review and published supplemental
tables on petroleum product detail (EIA 2014). The EIA does not include territories in its national energy
statistics, so fuel consumption data for territories were collected separately from EIA's International
Energy Statistics (EIA 2012) and Jacobs (2010).75
For consistency of reporting, the IPCC has recommended that countries report energy data using the
International Energy Agency (IEA) reporting convention and/or IEA data. Data in the IEA format are
presented "top down"—that is, energy consumption for fuel types and categories are estimated from energy
production data (accounting for imports, exports, stock changes, and losses). The resulting quantities are
referred to as "apparent consumption." The data collected in the United States by EIA on an annual basis
and used in this inventory are predominantly from mid-stream or conversion energy consumers such as
refiners and electric power generators. These annual surveys are supplemented with end-use energy
consumption surveys, such as the Manufacturing Energy Consumption Survey, that are conducted on a
periodic basis (every 4 years). These consumption data sets help inform the annual surveys to arrive at the
national total and sectoral breakdowns for that total.76
It is also important to note that U.S. fossil fuel energy statistics are generally presented using gross calorific
values (GCV) (i.e., higher heating values). Fuel consumption activity data presented here have not been
adjusted to correspond to international standards, which are to report energy statistics in terms of net
calorific values (NCV) (i.e., lower heating values).77
2. Subtract uses accounted for in the Industrial Processes chapter. Portions of the fuel consumption data for
seven fuel categories—coking coal, distillate fuel, industrial other coal, petroleum coke, natural gas,
residual fuel oil, and other oil—were reallocated to the industrial processes chapter, as they were consumed
during non-energy related industrial activity. To make these adjustments, additional data were collected
from AISI (2004 through 2013), Coffeyville (2013), U.S. Census Bureau (2011), EIA (2013c), USGS
(1991 through 2011), USGS (1994 through 2011), USGS (1995, 1998, 2000 through 2002), USGS (2007),
USGS (2009), USGS (2010), USGS (2011), USGS (1991 through 20lOa), USGS (1991 through 20lOb),
USGS (2012a) and USGS (2012b).78
3. Adjust for conversion of fuels and exports ofCC>2. Fossil fuel consumption estimates are adjusted
downward to exclude fuels created from other fossil fuels and exports of CCh.79 Synthetic natural gas is
created from industrial coal, and is currently included in EIA statistics for both coal and natural gas.
Therefore, synthetic natural gas is subtracted from energy consumption statistics.80 Since October 2000,
75 Fuel consumption by U.S. territories (i.e., American Samoa, Guam, Puerto Rico, U.S. Virgin Islands, Wake Island, and other
U.S. Pacific Islands) is included in this report and contributed emissions of 49.8 Tg CCh Eq. in 2012.
76 See IPCC Reference Approach for estimating CCh emissions from fossil fuel combustion in Annex 4 for a comparison of U.S.
estimates using top-down and bottom-up approaches.
77 A crude convention to convert between gross and net calorific values is to multiply the heat content of solid and liquid fossil
fuels by 0.95 and gaseous fuels by 0.9 to account for the water content of the fuels. Biomass-based fuels in U.S. energy statistics,
however, are generally presented using net calorific values.
78 See sections on Iron and Steel Production and Metallurgical Coke Production, Ammonia Production and Urea Consumption,
Petrochemical Production, Titanium Dioxide Production, Ferroalloy Production, Aluminum Production, and Silicon Carbide
Production and Consumption in the Industrial Processes chapter.
79 Energy statistics from EIA (2014) are already adjusted downward to account for ethanol added to motor gasoline, and biogas
in natural gas.
80 These adjustments are explained in greater detail in Annex 2.1.
Energy 3-23
-------
the Dakota Gasification Plant has been exporting CC>2 to Canada by pipeline. Since this COa is not emitted
to the atmosphere in the United States, energy used to produce this CC>2 is subtracted from energy
consumption statistics. To make these adjustments, additional data for ethanol were collected from EIA
(2014), data for synthetic natural gas were collected from EIA (2013d), and data for €62 exports were
collected from the Eastman Gasification Services Company (2011), Dakota Gasification Company (2006),
Fitzpatrick (2002), Erickson (2003), EIA (2008) and DOE (2012).
4. Adjust Sectoral Allocation of Distillate Fuel Oil and Motor Gasoline. EPA had conducted a separate
bottom-up analysis of transportation fuel consumption based on data from the Federal Highway
Administration that indicated that the amount of distillate and motor gasoline consumption allocated to the
transportation sector in the EIA statistics should be adjusted. Therefore, for these estimates, the
transportation sector's distillate fuel and motor gasoline consumption was adjusted upward to match the
value obtained from the bottom-up analysis. As the total distillate and motor gasoline consumption estimate
from EIA are considered to be accurate at the national level, the distillate and motor gasoline consumption
totals for the residential, commercial, and industrial sectors were adjusted downward proportionately. The
data sources used in the bottom-up analysis of transportation fuel consumption include AAR (2008 through
2013), Benson (2002 through 2004), DOE (1993 through 2013), EIA (2007a), EIA (1991 through 2013),
EPA (2013b), and FHWA (1996 through 2014).81
5. Adjust for fuels consumed for non-energy uses. U.S. aggregate energy statistics include consumption of
fossil fuels for non-energy purposes. These are fossil fuels that are manufactured into plastics, asphalt,
lubricants, or other products. Depending on the end-use, this can result in storage of some or all of the C
contained in the fuel for a period of time. As the emission pathways of C used for non-energy purposes are
vastly different than fuel combustion (since the C in these fuels ends up in products instead of being
combusted), these emissions are estimated separately in the Carbon Emitted and Stored in Products from
Non-Energy Uses of Fossil Fuels section in this chapter. Therefore, the amount of fuels used for non-
energy purposes was subtracted from total fuel consumption. Data on non-fuel consumption was provided
by EIA (2014).
6. Subtract consumption of international bunker fuels. According to the UNFCCC reporting guidelines
emissions from international transport activities, or bunker fuels, should not be included in national totals.
U.S. energy consumption statistics include these bunker fuels (e.g., distillate fuel oil, residual fuel oil, and
jet fuel) as part of consumption by the transportation end-use sector, however, so emissions from
international transport activities were calculated separately following the same procedures used for
emissions from consumption of all fossil fuels (i.e., estimation of consumption, and determination of C
content).82 The Office of the Under Secretary of Defense (Installations and Environment) and the Defense
Energy Support Center (Defense Logistics Agency) of the U.S. Department of Defense (DoD) (DLA
Energy 2013) supplied data on military jet fuel and marine fuel use. Commercial jet fuel use was obtained
from FAA (2014); residual and distillate fuel use for civilian marine bunkers was obtained from DOC
(1991 through 2013) for 1990 through 2001 and 2007 through 2012, and DHS (2008) for 2003 through
2006. Consumption of these fuels was subtracted from the corresponding fuels in the transportation end-
use sector. Estimates of international bunker fuel emissions for the United States are discussed in detail
later in the International Bunker Fuels section of this chapter.
81 The source of highway vehicle VMT and fuel consumption is FHWA's VM-1 table. In 2011, FHWA changed its methods for
estimating data in the VM-1 table. These methodological changes included how vehicles are classified, moving from a system
based on body-type to one that is based on wheelbase. These changes were first incorporated for the 2010 Inventory and apply to
the 2007-12 time period. This resulted in large changes in VMT and fuel consumption data by vehicle class, thus leading to a
shift in emissions among on-road vehicle classes. For example, the category "Passenger Cars" has been replaced by "Light-duty
Vehicles-Short Wheelbase" and "Other 2 axle-4 Tire Vehicles" has been replaced by "Light-duty Vehicles, Long Wheelbase."
This change in vehicle classification has moved some smaller trucks and sport utility vehicles from the light truck category to the
passenger vehicle category in this emission inventory. These changes are reflected in a large drop in light-truck emissions
between 2006 and 2007.
82 See International Bunker Fuels section in this chapter for a more detailed discussion.
3-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
7. Determine the total C content of fuels consumed. Total C was estimated by multiplying the amount of fuel
consumed by the amount of C in each fuel. This total C estimate defines the maximum amount of C that
could potentially be released to the atmosphere if all of the C in each fuel was converted to CCh. The C
content coefficients used by the United States were obtained from EIA's Emissions of Greenhouse Gases in
the United States 2008 (EIA 2009a), and an EPA analysis of C content coefficients used in the mandatory
reporting rule (EPA 2010a). A discussion of the methodology used to develop the C content coefficients
are presented in Annexes 2.1 and 2.2.
8. Estimate CO2 Emissions. Total CCh emissions are the product of the adjusted energy consumption (from
the previous methodology steps 1 through 6), the C content of the fuels consumed, and the fraction of C
that is oxidized. The fraction oxidized was assumed to be 100 percent for petroleum, coal, and natural gas
based on guidance in IPCC (2006) (see Annex 2.1).
9. Allocate transportation emissions by vehicle type. This report provides a more detailed accounting of
emissions from transportation because it is such a large consumer of fossil fuels in the United States. For
fuel types other than jet fuel, fuel consumption data by vehicle type and transportation mode were used to
allocate emissions by fuel type calculated for the transportation end-use sector.
• For on-road vehicles, annual estimates of combined motor gasoline and diesel fuel consumption by
vehicle category were obtained from FHWA (1996 through 2014); for each vehicle category, the
percent gasoline, diesel, and other (e.g., CNG, LPG) fuel consumption are estimated using data from
DOE (1993 through 2013).
• For non-road vehicles, activity data were obtained from AAR (2008 through 2013), APTA (2007
through 2013), APTA (2006), BEA (1991 through 2012), Benson (2002 through 2004), DOE (1993
through 2013), DLA Energy (2013), DOC (1991 through 2014), DOT (1991 through 2013), EIA
(2009a), EIA (2013c), EIA (2002), EIA (1991 through 2013), EPA (2013b), and Gaffney (2007).
• For jet fuel used by aircraft, CO2 emissions from commercial aircraft were developed by the U.S.
Federal Aviation Administration (FAA) using a Tier 3B methodology, consistent with the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories (see Annex 3.3). CO2 emissions from other
aircraft were calculated directly based on reported consumption of fuel as reported by EIA. Allocation
to domestic military uses was made using DoD data (see Annex 3.8). General aviation jet fuel
consumption is calculated as the remainder of total jet fuel use (as determined by EIA) net all other jet
fuel use as determined by FAA and DoD. For more information, see Annex 3.2.
83
Heat contents and densities were obtained from EIA (2013a) and USAF (1998).
Box 3-4: Uses of Greenhouse Gas Reporting Program Data and Improvements in Reporting Emissions from
Industrial Sector Fossil Fuel Combustion
As described in the calculation methodology, total fossil fuel consumption for each year is based on aggregated end-
use sector consumption published by the EIA. The availability of facility-level combustion emissions through
EPA's Greenhouse Gas Reporting Program (GHGRP) has provided an opportunity to better characterize the
industrial sector's energy consumption and emissions in the United States, through a disaggregation of EIA's
industrial sector fuel consumption data from select industries.
For EPA's GHGRP 2010, 2011, and 2012 reporting years, facility-level fossil fuel combustion emissions reported
through the GHGRP were categorized and distributed to specific industry types by utilizing facility-reported NAICS
codes (as published by the U.S. Census Bureau), and associated data available from EIA's 2010 Manufacturing
Energy Consumption Survey (MECS). As noted previously in this report, the definitions and provisions for
reporting fuel types in EPA's GHGRP include some differences from the inventory's use of EIA national fuel
83 For a more detailed description of the data sources used for the analysis of the transportation end use sector see the Mobile
Combustion (excluding CCh) and International Bunker Fuels sections of the Energy chapter, Annex 3.2, and Annex 3.8.
Energy 3-25
-------
statistics to meet the UNFCCC reporting guidelines. The IPCC has provided guidance on aligning facility-level
reported fuels and fuel types published in national energy statistics, which guided this exercise.84
This year's effort represents an attempt to align, reconcile, and coordinate the facility-level reporting of fossil fuel
combustion emissions under EPA's GHGRP with the national-level approach presented in this report. Consistent
with recommendations for reporting the inventory to the UNFCCC, progress was made on certain fuel types for
specific industries and has been included in the Common Reporting Format (CRF) tables that are submitted to the
UNFCCC along with this report.*5 However, a full mapping was not completed this year due to fuel category
differences between national statistics published by EIA and facility-level reported GHGRP data. Furthermore,
given that calendar year 2010 was the first year in which emissions data were reported to EPA's GHGRP, the
current inventory's examination only focused on 2010, 201 land, 2012. For the current exercise, the efforts in
reconciling fuels focused on standard, common fuel types (e.g., natural gas, distillate fuel oil, etc.) where the fuels in
EIA's national statistics aligned well with facility-level GHGRP data. For these reasons, the current information
presented in the CRF tables should be viewed as an initial attempt at this exercise. Additional efforts will be made
for future inventory reports to improve the mapping of fuel types, and examine ways to reconcile and coordinate any
differences between facility-level data and national statistics. Additionally, in order to expand this effort through the
full time series presented in this report, further analyses will be conducted linking GHGRP facility-level reporting
with the information published by EIA in its MECS data, other available MECS survey years , and any further
informative sources of data. It is believed that the current analysis has led to improvements in the presentation of
data in the Inventory, but further work will be conducted, and future improvements will be realized in subsequent
Inventory reports.
Additionally, to assist in the disaggregation of industrial fuel consumption, EIA will now synthesize energy
consumption data using the same procedure as is used for the last historical (benchmark) year of the Annual Energy
Outlook (AEO). This procedure reorganizes the most recent data from the Manufacturing Energy Consumption
Survey (MECS) (conducted every four years) into the nominal data submission year using the same energy-
economy integrated model used to produce the AEO projections, the National Energy Modeling System (NEMS).
EIA believes this "nowcasting" technique provides an appropriate estimate of energy consumption for the CRF.
To address gaps in the time series, EIA performs a NEMS model projection, using the MECS baseline sub-sector
energy consumption. The NEMS model accounts for changes in factors that influence industrial sector energy
consumption, and has access to data which may be more recent than MECS, such as industrial sub-sector macro
industrial output (i.e., shipments) and fuel prices. By evaluating the impact of these factors on industrial subsector
energy consumption, NEMS can anticipate changes to the energy shares occurring post-MECS and can provide a
way to appropriately disaggregate the energy-related emissions data into the CRF.
While the fuel consumption values for the various manufacturing sub-sectors are not directly surveyed for all years,
they represent EIA's best estimate of historical consumption values for non-MECS years. Moreover, as an integral
part of each AEO publication, this synthetic data series is likely to be maintained consistent with all available EIA
and non-EIA data sources even as the underlying data sources evolve for both manufacturing and non-
manufacturing industries alike.
Other sectors' fuel consumption (commercial, residential, transportation) will be benchmarked with the latest
aggregate values from the Monthly Energy Review.86 EIA will work with the U.S. Environmental Protection
Agency to back cast these values to 1990.
84 See Section 4 "Use of Facility-Level Data in Good Practice National Greenhouse Gas Inventories" of the IPCC
meeting report, and specifically the section on using facility-level data in conjunction with energy data, at
.
85 See < http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html>.
%6 See .
3-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Box 3-5: Carbon Intensity of U.S. Energy Consumption
Fossil fuels are the dominant source of energy in the United States, and CCh is the dominant greenhouse gas emitted
as a product from their combustion. Energy-related CCh emissions are impacted by not only lower levels of energy
consumption but also by lowering the C intensity of the energy sources employed (e.g., fuel switching from coal to
natural gas). The amount of C emitted from the combustion of fossil fuels is dependent upon the C content of the
fuel and the fraction of that C that is oxidized. Fossil fuels vary in their average C content, ranging from about 53
Tg CO2 Eq./QBtu for natural gas to upwards of 95 Tg CCh Eq./QBtu for coal and petroleum coke.87 In general, the
C content per unit of energy of fossil fuels is the highest for coal products, followed by petroleum, and then natural
gas. The overall C intensity of the U.S. economy is thus dependent upon the quantity and combination of fuels and
other energy sources employed to meet demand.
Table 3-15 provides a time series of the C intensity for each sector of the U.S. economy. The time series
incorporates only the energy consumed from the direct combustion of fossil fuels in each sector. For example, the C
intensity for the residential sector does not include the energy from or emissions related to the consumption of
electricity for lighting. Looking only at this direct consumption of fossil fuels, the residential sector exhibited the
lowest C intensity, which is related to the large percentage of its energy derived from natural gas for heating. The C
intensity of the commercial sector has predominantly declined since 1990 as commercial businesses shift away from
petroleum to natural gas. The industrial sector was more dependent on petroleum and coal than either the residential
or commercial sectors, and thus had higher C intensities over this period. The C intensity of the transportation
sector was closely related to the C content of petroleum products (e.g., motor gasoline and jet fuel, both around 70
Tg CO2 Eq./EJ), which were the primary sources of energy. Lastly, the electricity generation sector had the highest
C intensity due to its heavy reliance on coal for generating electricity.
Table 3-15: Carbon Intensity from Direct Fossil Fuel Combustion by Sector (Tg COz
Eq./QBtu)
Sector
Residential3
Commercial*
Industrial*
Transportation*
Electricity Generation15
U.S. Territories0
All Sectors0
1990
57.4
59.2 1
2005
56.6
57.5
64.3 1 64.3
71.1 1 71.4
87.3 1
73.0
73.0
85.8
73.4
73.5
2008
55.9
56.8
63.5
71.6
84.9
73.3
73.1
2009
55.9
56.9
63.0
71.5
83.7
73.1
72.4
2010
55.8
56.8
62.9
71.5
83.6
73.1
72.4
2011
55.7
56.6
62.4
71.5
82.9
73.1
72.0
2012
55.6
56.1
61.9
71.5
79.9
73.1
70.9
* Does not include electricity or renewable energy consumption.
b Does not include electricity produced using nuclear or renewable energy.
c Does not include nuclear or renewable energy consumption.
Note: Excludes non-energy fuel use emissions and consumption.
Over the twenty-three-year period of 1990 through 2012, however, the C intensity of U.S. energy consumption has
been fairly constant, as the proportion of fossil fuels used by the individual sectors has not changed significantly.
Per capita energy consumption fluctuated little from 1990 to 2007, but in 2012 was approximately 10.4 percent
below levels in 1990 (see Figure 3-14). Due to a general shift from a manufacturing-based economy to a service-
based economy, as well as overall increases in efficiency, energy consumption and energy-related CCh emissions
per dollar of gross domestic product (GDP) have both declined since 1990 (BEA 2013).
87 One exajoule (EJ) is equal to 1018 joules or 0.9478 QBtu.
Energy 3-27
-------
Figure 3-14: U.S. Energy Consumption and Energy-Related COz Emissions Per Capita and Per
Dollar GDP
C02/capita OyEnergy
Consumption
^ /
Energy Consumption/capita-"""^
C intensity estimates were developed using nuclear and renewable energy data from EIA (2012a), EPA (2010a), and
fossil fuel consumption data as discussed above and presented in Annex 2.1.
Uncertainty and Time Series Consistency
For estimates of CCh from fossil fuel combustion, the amount of CCh emitted is directly related to the amount of
fuel consumed, the fraction of the fuel that is oxidized, and the carbon content of the fuel. Therefore, a careful
accounting of fossil fuel consumption by fuel type, average carbon contents of fossil fuels consumed, and
production of fossil fuel-based products with long-term carbon storage should yield an accurate estimate of CCh
emissions.
Nevertheless, there are uncertainties in the consumption data, carbon content of fuels and products, and carbon
oxidation efficiencies. For example, given the same primary fuel type (e.g., coal, petroleum, or natural gas), the
amount of carbon contained in the fuel per unit of useful energy can vary. For the United States, however, the
impact of these uncertainties on overall CO2 emission estimates is believed to be relatively small. See, for example,
Marland and Pippin (1990).
Although statistics of total fossil fuel and other energy consumption are relatively accurate, the allocation of this
consumption to individual end-use sectors (i.e., residential, commercial, industrial, and transportation) is less
certain. For example, for some fuels the sectoral allocations are based on price rates (i.e., tariffs), but a commercial
establishment may be able to negotiate an industrial rate or a small industrial establishment may end up paying an
industrial rate, leading to a misallocation of emissions. Also, the deregulation of the natural gas industry and the
more recent deregulation of the electric power industry have likely led to some minor problems in collecting
accurate energy statistics as firms in these industries have undergone significant restructuring.
To calculate the total CCh emission estimate from energy-related fossil fuel combustion, the amount of fuel used in
these non-energy production processes were subtracted from the total fossil fuel consumption. The amount of CC>2
emissions resulting from non-energy related fossil fuel use has been calculated separately and reported in the Carbon
Emitted from Non-Energy Uses of Fossil Fuels section of this report. These factors all contribute to the uncertainty
in the CO2 estimates. Detailed discussions on the uncertainties associated with C emitted from Non-Energy Uses of
Fossil Fuels can be found within that section of this chapter.
Various sources of uncertainty surround the estimation of emissions from international bunker fuels, which are
subtracted from the U.S. totals (see the detailed discussions on these uncertainties provided in the International
Bunker Fuels section of this chapter). Another source of uncertainty is fuel consumption by U. S. territories. The
United States does not collect energy statistics for its territories at the same level of detail as for the fifty states and
the District of Columbia. Therefore, estimating both emissions and bunker fuel consumption by these territories is
difficult.
3-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Uncertainties in the emission estimates presented above also result from the data used to allocate €62 emissions
from the transportation end-use sector to individual vehicle types and transport modes. In many cases, bottom-up
estimates of fuel consumption by vehicle type do not match aggregate fuel-type estimates from EIA. Further
research is planned to improve the allocation into detailed transportation end-use sector emissions.
The uncertainty analysis was performed by primary fuel type for each end-use sector, using the IPCC-recommended
Tier 2 uncertainty estimation methodology, Monte Carlo Stochastic Simulation technique, with @RISK software.
For this uncertainty estimation, the inventory estimation model for CCh from fossil fuel combustion was integrated
with the relevant variables from the inventory estimation model for International Bunker Fuels, to realistically
characterize the interaction (or endogenous correlation) between the variables of these two models. About 120 input
variables were modeled for CCh from energy-related Fossil Fuel Combustion (including about 10 for non-energy
fuel consumption and about 20 for International Bunker Fuels).
In developing the uncertainty estimation model, uniform distributions were assumed for all activity-related input
variables and emission factors, based on the SAIC/EIA (2001) report.88 Triangular distributions were assigned for
the oxidization factors (or combustion efficiencies). The uncertainty ranges were assigned to the input variables
based on the data reported in SAIC/EIA (2001) and on conversations with various agency personnel.89
The uncertainty ranges for the activity-related input variables were typically asymmetric around their inventory
estimates; the uncertainty ranges for the emissions factors were symmetric. Bias (or systematic uncertainties)
associated with these variables accounted for much of the uncertainties associated with these variables (SAIC/EIA
2001).90 For purposes of this uncertainty analysis, each input variable was simulated 10,000 times through Monte
Carlo Sampling.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 3-16. Fossil fuel combustion
CO2 emissions in 2012 were estimated to be between 4,958.2 and 5,314.8 Tg CO2 Eq. at a 95 percent confidence
level. This indicates a range of 2 percent below to 5 percent above the 2012 emission estimate of 5,072.3 Tg CCh
Eq.
Table 3-16: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Energy-related
Fossil Fuel Combustion by Fuel Type and Sector (Tg COz Eq. and Percent)
Fuel/Sector
Coal"
Residential
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
Natural Gas b
Residential
2012 Emission
Estimate
(Tg COz Eq.)
1,593.0
NE
4.1
74.3
NE
1,511.2
3.4
1,351.2
224.8
Uncertainty Range Relative to Emission
(Tg COz
Lower
Bound
1,538.9
NE
3.9
70.8
NE
1,453.1
3.0
1,336.5
218.5
Eq.)
Upper
Bound
1,742.9
NE
4.7
85.9
NE
1,655.9
4.0
1,412.8
240.6
(°x
Lower
Bound
-3%
NE
-5%
-5%
NA
-4%
-12%
-1%
-3%
Estimate3
'»)
Upper
Bound
9%
NE
15%
16%
NA
10%
19%
5%
7%
88 SAIC/EIA (2001) characterizes the underlying probability density function for the input variables as a combination of uniform
and normal distributions (the former to represent the bias component and the latter to represent the random component).
However, for purposes of the current uncertainty analysis, it was determined that uniform distribution was more appropriate to
characterize the probability density function underlying each of these variables.
89 In the SAIC/EIA (2001) report, the quantitative uncertainty estimates were developed for each of the three major fossil fuels
used within each end-use sector; the variations within the sub-fuel types within each end-use sector were not modeled. However,
for purposes of assigning uncertainty estimates to the sub-fuel type categories within each end-use sector in the current
uncertainty analysis, SAIC/EIA (2001)-reported uncertainty estimates were extrapolated.
90 Although, in general, random uncertainties are the main focus of statistical uncertainty analysis, when the uncertainty
estimates are elicited from experts, their estimates include both random and systematic uncertainties. Hence, both these types of
uncertainties are represented in this uncertainty analysis.
Energy 3-29
-------
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
Petroleum b
Residential
Commercial
Industrial
Transportation
Electric Utilities
U.S. Territories
Total (excluding Geothermal) b
Geo thermal
Total (including Geothermal) b>c
156.9
434.7
41.2
492.2
1.4
2,127.6
64.1
36.4
265.2
1,698.3
18.8
44.7
5,071.9
0.4
5,072.3
152.5
421.5
40.0
478.1
1.3
1,996.2
60.5
34.6
212.7
1,585.3
17.9
41.3
4,957.8
NE
4,958.2
167.9
465.8
44.1
517.3
1.7
2,253.5
67.5
38.1
313.1
1,811.7
20.3
49.7
5,314.4
NE
5,314.8
-3%
-3%
-3%
-3%
-12%
-6%
-6%
-5%
-20%
-7%
-5%
-8%
-2%
NA
-2%
7%
7%
7%
5%
17%
6%
5%
5%
18%
7%
8%
11%
5%
NA
5%
NA (Not Applicable)
NE (Not Estimated)
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
b The low and high estimates for total emissions were calculated separately through simulations and, hence, the low
and high emission estimates for the sub-source categories do not sum to total emissions.
c Geothermal emissions added for reporting purposes, but an uncertainty analysis was not performed for CCh
emissions from geothermal production.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A source-specific QA/QC plan for CO2 from fossil fuel combustion was developed and implemented. This effort
included a Tier 1 analysis, as well as portions of a Tier 2 analysis. The Tier 2 procedures that were implemented
involved checks specifically focusing on the activity data and methodology used for estimating CCh emissions from
fossil fuel combustion in the United States. Emission totals for the different sectors and fuels were compared and
trends were investigated to determine whether any corrective actions were needed. Minor corrective actions were
taken.
Recalculations Discussion
The Energy Information Administration (EIA 2014) updated energy consumption statistics across the time series
relative to the previous Inventory. One such revision is the inclusion of past residential coal estimates into
commercial coal statistics for the years 2008 to 2011. These revisions primarily impacted the previous emission
estimates from 2008 to 2011; however, additional revisions to industrial and transportation petroleum consumption
as well as industrial natural gas and coal consumption impacted emission estimates across the time series. Overall,
these changes resulted in an average annual increase of 1.3 Tg CO2 Eq. (less than 0.1 percent) in CO2 emissions
from fossil fuel combustion for the period 1990 through 2011, relative to the previous report.
Planned Improvements
To reduce uncertainty of CO2 from fossil fuel combustion estimates, efforts will be taken to work with EIA and
other agencies to improve the quality of the U.S. territories data. This improvement is not all-inclusive, and is part
of an ongoing analysis and efforts to continually improve the CO2 from fossil fuel combustion estimates. In
addition, further expert elicitation may be conducted to better quantify the total uncertainty associated with
emissions from this source.
The availability of facility-level combustion emissions through EPA's (GHGRP) will continue to be examined to
help better characterize the industrial sector's energy consumption in the United States, and further classify business
3-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
establishments according to industrial economic activity type. Most methodologies used in EPA's GHGRP are
consistent with IPCC, though for EPA's GHGRP, facilities collect detailed information specific to their operations
according to detailed measurement standards, which may differ with the more aggregated data collected for the
Inventory to estimate total, national U.S. emissions. In addition, and unlike the reporting requirements for this
chapter under the UNFCCC reporting guidelines, some facility-level fuel combustion emissions reported under the
GHGRP may also include industrial process emissions.91 In line with UNFCCC reporting guidelines, fuel
combustion emissions are included in this chapter, while process emissions are included in the Industrial Processes
chapter of this report. In examining data from EPA's GHGRP that would be useful to improve the emission
estimates for the CO2 from fossil fuel combustion category, particular attention will also be made to ensure time
series consistency, as the facility-level reporting data from EPA's GHGRP are not available for all inventory years
as reported in this inventory. Additional, analyses will be conducted to align reported facility-level fuel types and
IPCC fuel types per the national energy statistics. Additional work will commence to ensure CCh emissions from
biomass are separated in the facility-level reported data, and maintaining consistency with national energy statistics
provided by EIA. In implementing improvements and integration of data from EPA's GHGRP, the latest guidance
from the IPCC on the use of facility-level data in national inventories will continue to be relied upon.92
Another planned improvement is to develop improved estimates of domestic waterborne fuel consumption. The
inventory estimates for residual and distillate fuel used by ships and boats is based in part on data on bunker fuel use
from the U.S. Department of Commerce. Domestic fuel consumption is estimated by subtracting fuel sold for
international use from the total sold in the United States. It may be possible to more accurately estimate domestic
fuel use and emissions by using detailed data on marine ship activity. The feasibility of using domestic marine
activity data to improve the estimates is currently being investigated.
CH4and N2O from Stationary Combustion
Methodology
Methane and N2O emissions from stationary combustion were estimated by multiplying fossil fuel and wood
consumption data by emission factors (by sector and fuel type for industrial, residential, commercial, and U.S.
Territories; and by fuel and technology type for the electric power sector). Beginning with the current Inventory
report, the electric power sector utilizes a Tier 2 methodology, whereas all other sectors utilize a Tier 1
methodology. The activity data and emission factors used are described in the following subsections.
Industrial, Residential, Commercial, and U.S. Territories
National coal, natural gas, fuel oil, and wood consumption data were grouped by sector: industrial, commercial,
residential, and U.S. territories. For the CH4 and N2O estimates, wood consumption data for the United States was
obtained from EIA's Monthly Energy Review (EIA 2014). Fuel consumption data for coal, natural gas, and fuel oil
for the United States were also obtained from EIA's Monthly Energy Review and unpublished supplemental tables
on petroleum product detail (EIA 2012). Because the United States does not include territories in its national energy
statistics, fuel consumption data for territories were provided separately by EIA's International Energy Statistics
(EIA 2012) and Jacobs (2010).93 Fuel consumption for the industrial sector was adjusted to subtract out
construction and agricultural use, which is reported under mobile sources.94 Construction and agricultural fuel use
was obtained from EPA (2010a). Estimates for wood biomass consumption for fuel combustion do not include
wood wastes, liquors, municipal solid waste, tires, etc., that are reported as biomass by EIA. Tier 1 default emission
91 See .
92 See .
93 U.S. territories data also include combustion from mobile activities because data to allocate territories' energy use were
unavailable. For this reason, CH4 and N2O emissions from combustion by U.S. territories are only included in the stationary
combustion totals.
94 Though emissions from construction and farm use occur due to both stationary and mobile sources, detailed data was not
available to determine the magnitude from each. Currently, these emissions are assumed to be predominantly from mobile
sources.
Energy 3-31
-------
factors for these three end-use sectors were provided by the 2006IPCC Guidelines for National Greenhouse Gas
Inventories (IPCC 2006) which, according to this guidance, "are based on the IPCC 1996 Guidelines" U.S.
territories' emission factors were estimated using the U.S. emission factors for the primary sector in which each fuel
was combusted.
Electric Power Sector
The electric power sector now uses a Tier 2 emission estimation methodology as fuel consumption for the electricity
generation sector by control-technology type was obtained from EPA's Acid Rain Program Dataset (EPA 2013).
This combustion technology- and fuel-use data was available by facility from 1996 to 2012. The Tier 2 emission
factors used were taken from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006),
which in turn are based on emission factors published by EPA.
Since there was a difference between the EPA (2013) and EIA (2014) total energy consumption estimates, the
remaining energy consumption from EIA (2014) was apportioned to each combustion technology type and fuel
combination using a ratio of energy consumption by technology type from 1996 to 2012.
Energy consumption estimates were not available from 1990 to 1995 in the EPA (2013) dataset, and as a result,
consumption was calculated using total electric power consumption from EIA (2014) and the ratio of combustion
technology and fuel types from EPA (2013). The consumption estimates from 1990 to 1995 were estimated by
applying the 1996 consumption ratio by combustion technology type to the total EIA consumption for each year
from 1990 to 1995. Emissions were estimated by multiplying fossil fuel and wood consumption by technology- and
fuel-specific Tier 2 IPCC emission factors.
Lastly, there were significant differences between wood biomass consumption in the electric power sector between
the EPA (2013) and EIA (2014) datasets. The higher wood biomass consumption from EIA (2014) in the electric
power sector was distributed to the residential, commercial, and industrial sectors according to their percent share of
wood biomass energy consumption calculated from EIA (2013a).
More detailed information on the methodology for calculating emissions from stationary combustion, including
emission factors and activity data, is provided in Annex 3.1.
Uncertainty and Time-Series Consistency
Methane emission estimates from stationary sources exhibit high uncertainty, primarily due to difficulties in
calculating emissions from wood combustion (i.e., fireplaces and wood stoves). The estimates of CH4 and N2O
emissions presented are based on broad indicators of emissions (i.e., fuel use multiplied by an aggregate emission
factor for different sectors), rather than specific emission processes (i.e., by combustion technology and type of
emission control).
An uncertainty analysis was performed by primary fuel type for each end-use sector, using the IPCC-recommended
Tier 2 uncertainty estimation methodology, Monte Carlo Stochastic Simulation technique, with @RISK software.
The uncertainty estimation model for this source category was developed by integrating the CH4 and N2O stationary
source inventory estimation models with the model for CO2 from fossil fuel combustion to realistically characterize
the interaction (or endogenous correlation) between the variables of these three models. About 55 input variables
were simulated for the uncertainty analysis of this source category (about 20 from the CO2 emissions from fossil
fuel combustion inventory estimation model and about 35 from the stationary source inventory models).
In developing the uncertainty estimation model, uniform distribution was assumed for all activity-related input
variables and N2O emission factors, based on the SAIC/EIA (2001) report.95 For these variables, the uncertainty
95 SAIC/EIA (2001) characterizes the underlying probability density function for the input variables as a combination of uniform
and normal distributions (the former distribution to represent the bias component and the latter to represent the random
component). However, for purposes of the current uncertainty analysis, it was determined that uniform distribution was more
appropriate to characterize the probability density function underlying each of these variables.
3-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
ranges were assigned to the input variables based on the data reported in SAIC/EIA (2001).96 However, the CH4
emission factors differ from those used by EIA. Since these factors were obtained from IPCC/UNEP/OECD/IEA
(1997), uncertainty ranges were assigned based on IPCC default uncertainty estimates (IPCC 2000).
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 3-17. Stationary combustion
CH4 emissions in 2012 (including biomass) were estimated to be between 3.6 and 13.2 Tg CO2 Eq. at a 95 percent
confidence level. This indicates a range of 36 percent below to 132 percent above the 2012 emission estimate of 5.7
Tg CO2 Eq.97 Stationary combustion N2O emissions in 2012 (including biomass) were estimated to be between 17.6
and 33.1 Tg CO2 Eq. at a 95 percent confidence level. This indicates a range of 20 percent below to 51 percent
above the 2012 emissions estimate of 22.0 Tg CO2 Eq.
Table 3-17: Tier 2 Quantitative Uncertainty Estimates for Cm and NzO Emissions from
Energy-Related Stationary Combustion, Including Biomass (Tg COz Eq. and Percent)
Source Gas 2012 Emission Uncertainty Range Relative to Emission Estimate3
Estimate
(Tg C02 Eq.) (Tg COz Eq.) (%)
Stationary Combustion
Stationary Combustion
CH4
N20
5.7
22.0
Lower
Bound
3.6
17.6
Upper
Bound
13.2
33.1
Lower
Bound
-36%
-20%
Upper
Bound
+132%
+51%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence
interval.
The uncertainties associated with the emission estimates of CH4 and N2O are greater than those associated with
estimates of CO2 from fossil fuel combustion, which mainly rely on the carbon content of the fuel combusted.
Uncertainties in both CH4 and N2O estimates are due to the fact that emissions are estimated based on emission
factors representing only a limited subset of combustion conditions. For the indirect greenhouse gases, uncertainties
are partly due to assumptions concerning combustion technology types, age of equipment, emission factors used,
and activity data projections.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A source-specific QA/QC plan for stationary combustion was developed and implemented. This effort included a
Tier 1 analysis, as well as portions of a Tier 2 analysis. The Tier 2 procedures that were implemented involved
checks specifically focusing on the activity data and emission factor sources and methodology used for estimating
CH4, N2O, and the indirect greenhouse gases from stationary combustion in the United States. Emission totals for
the different sectors and fuels were compared and trends were investigated.
Recalculations Discussion
CH4 and N2O emissions from stationary sources (excluding CO2) across the entire time series were revised due
revised data from EIA (2014) and EPA (2013) relative to the previous Inventory. The historical data changes
resulted in an average annual decrease of less than 0.1 Tg CO2 Eq. (0.5 percent) in CH4 emissions from stationary
96 In the SAIC/EIA (2001) report, the quantitative uncertainty estimates were developed for each of the three major fossil fuels
used within each end-use sector; the variations within the sub-fuel types within each end-use sector were not modeled. However,
for purposes of assigning uncertainty estimates to the sub-fuel type categories within each end-use sector in the current
uncertainty analysis, SAIC/EIA (2001)-reported uncertainty estimates were extrapolated.
97 The low emission estimates reported in this section have been rounded down to the nearest integer values and the high
emission estimates have been rounded up to the nearest integer values.
Energy 3-33
-------
combustion and an average annual increase of less than 0.1 Tg CO2 Eq. (less than 0.1 percent) in N2O emissions
from stationary combustion for the period 1990 through 2011.
Planned Improvements
Several items are being evaluated to improve the CH4 and N2O emission estimates from stationary combustion and
to reduce uncertainty. Efforts will be taken to work with EIA and other agencies to improve the quality of the U.S.
territories data. Because these data are not broken out by stationary and mobile uses, further research will be aimed
at trying to allocate consumption appropriately. In addition, the uncertainty of biomass emissions will be further
investigated since it was expected that the exclusion of biomass from the uncertainty estimates would reduce the
uncertainty; and in actuality the exclusion of biomass increases the uncertainty. These improvements are not all-
inclusive, but are part of an ongoing analysis and efforts to continually improve these stationary estimates.
Future improvements to the CH4 and N2O from Stationary Combustion category involve research into the
availability of CH4 and N2O from stationary combustion data, and analyzing data reported under EPA's GHGRP. In
examining data from EPA's GHGRP that would be useful to improve the emission estimates for CH4 and N2O from
Stationary Combustion category, particular attention will be made to ensure time series consistency, as the facility-
level reporting data from EPA's GHGRP are not available for all Inventory years as reported in this inventory. In
implementing improvements and integration of data from EPA's GHGRP, the latest guidance from the IPCC on the
use of facility-level data in national inventories will be relied upon.98
CH4 and N2O from Mobile Combustion
Methodology
Estimates of CH4 and N2O emissions from mobile combustion were calculated by multiplying emission factors by
measures of activity for each fuel and vehicle type (e.g., light-duty gasoline trucks). Activity data included vehicle
miles traveled (VMT) for on-road vehicles and fuel consumption for non-road mobile sources. The activity data and
emission factors used are described in the subsections that follow. A complete discussion of the methodology used
to estimate CH4 and N2O emissions from mobile combustion and the emission factors used in the calculations is
provided in Annex 3.2.
On-Road Vehicles
Estimates of CH4 and N2O emissions from gasoline and diesel on-road vehicles are based on VMT and emission
factors by vehicle type, fuel type, model year, and emission control technology. Emission estimates for alternative
fuel vehicles (AFVs) are based on VMT and emission factors by vehicle and fuel type."
Emission factors for gasoline and diesel on-road vehicles utilizing Tier 2 and Low Emission Vehicle (LEV)
technologies were developed by ICF (2006b); all other gasoline and diesel on-road vehicle emissions factors were
developed by ICF (2004). These factors were derived from EPA, California Air Resources Board (CARD) and
Environment Canada laboratory test results of different vehicle and control technology types. The EPA, CARD and
Environment Canada tests were designed following the Federal Test Procedure (FTP), which covers three separate
driving segments, since vehicles emit varying amounts of greenhouse gases depending on the driving segment.
These driving segments are: (1) a transient driving cycle that includes cold start and running emissions, (2) a cycle
that represents running emissions only, and (3) a transient driving cycle that includes hot start and running
emissions. For each test run, a bag was affixed to the tailpipe of the vehicle and the exhaust was collected; the
content of this bag was then analyzed to determine quantities of gases present. The emissions characteristics of
segment 2 were used to define running emissions, and subtracted from the total FTP emissions to determine start
emissions. These were then recombined based upon the ratio of start to running emissions for each vehicle class
98 See .
99 Alternative fuel and advanced technology vehicles are those that can operate using a motor fuel other than gasoline or diesel.
This includes electric or other bi-fuel or dual-fuel vehicles that may be partially powered by gasoline or diesel.
3-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
from MOBILE6.2, an EPA emission factor model that predicts gram per mile emissions of CO2, CO, HC, NOX, and
PM from vehicles under various conditions, to approximate average driving characteristics.100
Emission factors for AFVs were developed by ICF (2006a) after examining Argonne National Laboratory's GREET
1.7-Transportation Fuel Cycle Model (ANL 2006) and Lipman and Delucchi (2002). These sources describe AFV
emission factors in terms of ratios to conventional vehicle emission factors. Ratios of AFV to conventional vehicle
emissions factors were then applied to estimated Tier 1 emissions factors from light-duty gasoline vehicles to
estimate light-duty AFVs. Emissions factors for heavy-duty AFVs were developed in relation to gasoline heavy-
duty vehicles. A complete discussion of the data source and methodology used to determine emission factors from
AFVs is provided in Annex 3.2.
Annual VMT data for 1990 through 2012 were obtained from the Federal Highway Administration's (FHWA)
Highway Performance Monitoring System database as reported in Highway Statistics (FHWA 1996 through
2014).101 VMT estimates were then allocated from FHWA's vehicle categories to fuel-specific vehicle categories
using the calculated shares of vehicle fuel use for each vehicle category by fuel type reported in DOE (1993 through
2013) and information on total motor vehicle fuel consumption by fuel type from FHWA (1996 through 2014).
VMT for AFVs were taken from Browning (2003). The age distributions of the U.S. vehicle fleet were obtained
from EPA (2013c, 2000), and the average annual age-specific vehicle mileage accumulation of U.S. vehicles were
obtained from EPA (2000).
Control technology and standards data for on-road vehicles were obtained from EPA's Office of Transportation and
Air Quality (EPA 2007a, 2007b, 2000, 1998, and 1997) and Browning (2005). These technologies and standards are
defined in Annex 3.2, and were compiled from EPA (1993, 1994a, 1994b, 1998, 1999a) and
IPCC/UNEP/OECD/IEA (1997).
Non-Road Vehicles
To estimate emissions from non-road vehicles, fuel consumption data were employed as a measure of activity, and
multiplied by fuel-specific emission factors (in grams of N2O and CH4 per kilogram of fuel consumed).102 Activity
data were obtained from AAR (2008 through 2013), APTA (2007 through 2013), APTA (2006), BEA (1991 through
2012), Benson (2002 through 2004), DHS (2008), DESC (2013), DOC (1991 through 2013), DOE (1993 through
2013), DOT (1991 through 2013), EIA (2008a, 2007a, 2013a, 2002), EIA (2007 through 2011), EIA (1991 through
2013), EPA (2013b), Esser (2003 through 2004), FAA (2014), FHWA (1996 through 2014), Gaffney (2007), and
Whorton (2006 through 2012). Emission factors for non-road modes were taken from IPCC/UNEP/OECD/IEA
(1997) and Browning (2009).
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis was conducted for the mobile source sector using the IPCC-recommended Tier 2
uncertainty estimation methodology, Monte Carlo Stochastic Simulation technique, using @RISK software. The
uncertainty analysis was performed on 2012 estimates of CH4 and N2O emissions, incorporating probability
distribution functions associated with the major input variables. For the purposes of this analysis, the uncertainty
was modeled for the following four major sets of input variables: (1) vehicle miles traveled (VMT) data, by on-road
vehicle and fuel type and (2) emission factor data, by on-road vehicle, fuel, and control technology type, (3) fuel
100 Additional information regarding the model can be found online at .
101 The source of VMT is FHWA's VM-1 table. In 2011, FHWA changed its methods for estimating data in the VM-1 table.
These methodological changes included how vehicles are classified, moving from a system based on body-type to one that is
based on wheelbase. These changes were first incorporated for the 2010 Inventory and apply to the 2007-12 time period. This
resulted in large changes in VMT by vehicle class, thus leading to a shift in emissions among on-road vehicle classes. For
example, the category "Passenger Cars" has been replaced by "Light-duty Vehicles-Short Wheelbase" and "Other 2 axle-4 Tire
Vehicles" has been replaced by "Light-duty Vehicles, Long Wheelbase." This change in vehicle classification has moved some
smaller trucks and sport utility vehicles from the light truck category to the passenger vehicle category in this emission inventory.
These changes are reflected in a large drop in light-truck emissions between 2006 and 2007.
102 The consumption of international bunker fuels is not included in these activity data, but is estimated separately under the
International Bunker Fuels source category.
Energy 3-35
-------
consumption, data, by non-road vehicle and equipment type, and (4) emission factor data, by non-road vehicle and
equipment type.
Uncertainty analyses were not conducted for NOX, CO, or NMVOC emissions. Emission factors for these gases
have been extensively researched since emissions of these gases from motor vehicles are regulated in the United
States, and the uncertainty in these emission estimates is believed to be relatively low. For more information, see
Section 3.8. However, a much higher level of uncertainty is associated with CH4 and N2O emission factors due to
limited emission test data, and because, unlike CO2 emissions, the emission pathways of CH4 and N2O are highly
complex.
Mobile combustion CH4 emissions from all mobile sources in 2012 were estimated to be between 1.5 and 2.0 Tg
CO2 Eq. at a 95 percent confidence level. This indicates a range of 11 percent below to 16 percent above the
corresponding 2012 emission estimate of 1.7 Tg CO2 Eq. Also at a 95 percent confidence level, mobile combustion
N2O emissions from mobile sources in 2012 were estimated to be between 16.0 and 21.0 Tg CO2 Eq., indicating a
range of 3 percent below to 27 percent above the corresponding 2012 emission estimate of 16.5 Tg CO2 Eq.
Table 3-18: Tier 2 Quantitative Uncertainty Estimates for ChU and NzO Emissions from
Mobile Sources (Tg COz Eq. and Percent)
Source
Gas
2012 Emission Uncertainty Range Relative to Emission Estimate3
Estimate3
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Lower Bound Upper Bound Lower Bound Upper Bound
Mobile Sources
Mobile Sources
CH4
N2O
1.7 1.5 2.0 -11% +16%
16.5 16.0 21.0 -3% +27%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence
interval.
This uncertainty analysis is a continuation of a multi-year process for developing quantitative uncertainty estimates
for this source category using the IPCC Tier 2 approach to uncertainty analysis. As a result, as new information
becomes available, uncertainty characterization of input variables may be improved and revised. For additional
information regarding uncertainty in emission estimates for CH4 and N2O please refer to the Uncertainty Annex.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A source-specific QA/QC plan for mobile combustion was developed and implemented. This plan is based on the
IPCC-recommended QA/QC Plan. The specific plan used for mobile combustion was updated prior to collection and
analysis of this current year of data. This effort included a Tier 1 analysis, as well as portions of a Tier 2 analysis.
The Tier 2 procedures focused on the emission factor and activity data sources, as well as the methodology used for
estimating emissions. These procedures included a qualitative assessment of the emissions estimates to determine
whether they appear consistent with the most recent activity data and emission factors available. A comparison of
historical emissions between the current Inventory and the previous inventory was also conducted to ensure that the
changes in estimates were consistent with the changes in activity data and emission factors.
Planned Improvements
While the data used for this report represent the most accurate information available, two areas have been identified
that could potentially be improved in the near term given available resources.
• Develop improved estimates of domestic waterborne fuel consumption. The inventory estimates for
residual and distillate fuel used by ships and boats is based in part on data on bunker fuel use from the U. S.
Department of Commerce. Domestic fuel consumption is estimated by subtracting fuel sold for
international use from the total sold in the United States. It may be possible to more accurately estimate
domestic fuel use and emissions by using detailed data on marine ship activity. The feasibility of using
3-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
domestic marine activity data to improve the estimates is currently being investigated.
• Continue to examine the use of EPA's MOVES model in the development of the inventory estimates,
including use for uncertainty analysis. Although the inventory uses some of the underlying data from
MOVES, such as vehicle age distributions by model year, MOVES is not used directly in calculating
mobile source emissions. The use of MOVES will be further explored.
3.2 Carbon Emitted from Non-Energy Uses of
Fossil Fuels (IPCC Source Category 1A)
In addition to being combusted for energy, fossil fuels are also consumed for non-energy uses (NEU) in the United
States. The fuels used for these purposes are diverse, including natural gas, liquefied petroleum gases (LPG),
asphalt (a viscous liquid mixture of heavy crude oil distillates), petroleum coke (manufactured from heavy oil), and
coal (metallurgical) coke (manufactured from coking coal). The non-energy applications of these fuels are equally
diverse, including feedstocks for the manufacture of plastics, rubber, synthetic fibers and other materials; reducing
agents for the production of various metals and inorganic products; and non-energy products such as lubricants,
waxes, and asphalt (IPCC 2006).
CO2 emissions arise from non-energy uses via several pathways. Emissions may occur during the manufacture of a
product, as is the case in producing plastics or rubber from fuel-derived feedstocks. Additionally, emissions may
occur during the product's lifetime, such as during solvent use. Overall, throughout the time series and across all
uses, about 65 percent of the total C consumed for non-energy purposes was stored in products, and not released to
the atmosphere; the remaining 35 percent was emitted.
There are several areas in which non-energy uses of fossil fuels are closely related to other parts of this Inventory.
For example, some of the NEU products release CO2 at the end of their commercial life when they are combusted
after disposal; these emissions are reported separately within the Energy chapter in the Incineration of Waste source
category. In addition, there is some overlap between fossil fuels consumed for non-energy uses and the fossil-
derived CO2 emissions accounted for in the Industrial Processes chapter, especially for fuels used as reducing
agents. To avoid double-counting, the "raw" non-energy fuel consumption data reported by EIA are modified to
account for these overlaps. There are also net exports of petrochemicals that are not completely accounted for in the
EIA data, and the inventory calculations adjust for the effect of net exports on the mass of C in non-energy
applications.
As shown in Table 3-19, fossil fuel emissions in 2012 from the non-energy uses of fossil fuels were 110.3 Tg CO2
Eq., which constituted approximately 2 percent of overall fossil fuel emissions. In 2012, the consumption of fuels
for non-energy uses (after the adjustments described above) was 4,373.0 TBtu, an increase of 3.7 percent since 1990
(see Table 3-20). About 56.0 Tg (205.2 Tg CO2 Eq.) of the C in these fuels was stored, while the remaining 30.1 Tg
C (110.3 Tg CO2 Eq.) was emitted.
Table 3-19: COz Emissions from Non-Energy Use Fossil Fuel Consumption (Tg COz Eq.)
Year 1990 2005 2008 2009 2010 2011 2012
Potential Emissions 312.1 377.4 339.4 307.5 328.4 323.3 315.7
C Stored 191.3 236.4 211.4 199.4 207.6 206.0 205.4
Emissions as a % of Potential 39%B 37%B 38% 35% 37% 36% 35%
Emissions 120.8 141.0 128.0 108.1 120.8 117.3 110.3
Methodology
The first step in estimating C stored in products was to determine the aggregate quantity of fossil fuels consumed for
non-energy uses. The C content of these feedstock fuels is equivalent to potential emissions, or the product of
consumption and the fuel-specific C content values. Both the non-energy fuel consumption and C content data were
Energy 3-37
-------
supplied by the EIA (2013a, 2013b) (see Annex 2.1). Consumption of natural gas, LPG, pentanes plus, naphthas,
other oils, and special naphtha were adjusted to account for net exports of these products that are not reflected in the
raw data from EIA. Consumption values for industrial coking coal, petroleum coke, other oils, and natural gas in
Table 3-20 and Table 3-21 have been adjusted to subtract non-energy uses that are included in the source categories
of the Industrial Processes chapter.103 Consumption values were also adjusted to subtract net exports of
intermediary chemicals.
For the remaining non-energy uses, the quantity of C stored was estimated by multiplying the potential emissions by
a storage factor.
• For several fuel types—petrochemical feedstocks (including natural gas for non-fertilizer uses, LPG,
pentanes plus, naphthas, other oils, still gas, special naphtha, and industrial other coal), asphalt and road oil,
lubricants, and waxes—U.S. data on C stocks and flows were used to develop C storage factors, calculated
as the ratio of (a) the C stored by the fuel's non-energy products to (b) the total C content of the fuel
consumed. A lifecycle approach was used in the development of these factors in order to account for losses
in the production process and during use. Because losses associated with municipal solid waste
management are handled separately in this sector under the Incineration of Waste source category, the
storage factors do not account for losses at the disposal end of the life cycle.
• For industrial coking coal and distillate fuel oil, storage factors were taken from IPCC/UNEP/OECD/IEA
(1997), which in turn draws from Marland and Rotty (1984).
• For the remaining fuel types (petroleum coke, miscellaneous products, and other petroleum), IPCC does not
provide guidance on storage factors, and assumptions were made based on the potential fate of C in the
respective NEU products.
Table 3-20: Adjusted Consumption of Fossil Fuels for Non-Energy Uses (TBtu)
Year
Industry
Industrial Coking Coal
Industrial Other Coal
Natural Gas to Chemical Plants
Asphalt & Road Oil
LPG
Lubricants
Pentanes Plus
Naphtha (<401 ° F)
Other Oil (>401 ° F)
Still Gas
Petroleum Coke
Special Naphtha
Distillate Fuel Oil
Waxes
Miscellaneous Products
Transportation
Lubricants
U.S. Territories
Lubricants
Other Petroleum (Misc. Prod.)
Total
1990
4,215.8
+ 1
8 2
281.6
1,170.2
1,120.5
186. 3!
in.eB
326.3 1
662. 1 1
36.7B
27.2
100.91
7.0 1
33.3
137.sB
176.0 1
176.0 1
86.7
0,
86.0
4,478.5
2005
5,110.7
80. 5 1
11.9
260.9
1,323.2
1,610.0
160.2
95.51
679.5 1
499.4B
67.7B
105.2B
60.91
11.71
31.4
112. sl
151.3 B
isi.si
121.91
4.6
117.3
5,383.9
2008
4,579.5
29.2
11.9
227.2
1,012.0
1,559.9
149.6
75.0
467.1
598.9
47.3
139.5
83.2
17.5
19.1
142.0
141.3
141.3
132.1
2.7
129.4
4,852.9
2009
4,282.8
6.4
11.9
220.3
873.1
1,663.8
134.5
61.0
451.0
392.7
133.9
108.4
44.2
17.5
12.2
151.8
127.1
127.1
59.6
1.0
58.5
4,469.4
2010
4,549.5
64.7
10.3
297.8
877.8
1,829.4
149.5
75.1
473.4
405.2
147.8
0.0
25.2
17.5
17.1
158.7
141.2
141.2
123.6
1.0
122.6
4,814.3
2011
4,502.0
60.8
10.3
296.7
859.5
1,914.1
141.8
26.3
468.9
340.7
163.6
0.0
21.8
17.5
15.1
164.7
133.9
133.9
123.6
1.0
122.6
4,759.5
2012
4,373.0
122.4
10.3
293.2
826.7
1,903.0
130.5
43.8
432.9
240.7
161.1
0.0
14.1
17.5
15.3
161.6
123.2
123.2
123.6
1.0
122.6
4,619.9
103 Lhese source categories include Iron and Steel Production, Lead Production, Zinc Production, Ammonia Manufacture,
Carbon Black Manufacture (included in Petrochemical Production), Titanium Dioxide Production, Ferroalloy Production, Silicon
Carbide Production, and Aluminum Production.
3-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
+ Does not exceed 0.05 Tbtu
NA: Not applicable.
Table 3-21: 2012 Adjusted Non-Energy Use Fossil Fuel Consumption, Storage, and Emissions
Adjusted Carbon
Non-Energy Content Potential Storage
Use3 Coefficient Carbon Factor
Sector/Fuel Type (TBtu) (Tg C/QBtu) (Tg C)
Industry
Industrial Coking
Coal
Industrial Other Coal
Natural Gas to
Chemical Plants
Asphalt & Road Oil
LPG
Lubricants
Pentanes Plus
Naphtha (<401°F)
Other Oil (>401°F)
Still Gas
Petroleum Coke
Special Naphtha
Distillate Fuel Oil
Waxes
Miscellaneous
Products
Transportation
Lubricants
U.S. Territories
Lubricants
Other Petroleum
(Misc. Prod.)
Total
4,373.0
122.4
10.3
293.2
826.7
1,903.0
130.5
43.8
432.9
240.7
161.1
+
14.1
17.5
15.3
161.6
123.2
123.2
123.6
1.0
122.6
4,619.9
NA
31.00
25.82
14.47
20.55
17.06
20.20
19.10
18.55
20.17
17.51
27.85
19.74
20.17
19.80
20.31
NA
20.20
NA
20.20
20.00
81.1
3.8
0.3
4.2
17.0
32.5
2.6
0.8
8.0
4.9
2.8
+
0.3
0.4
0.3
3.3
2.5
2.5
2.5
0.0
2.5
86.1
NA
0.10
0.70
0.70
1.00
0.70
0.09
0.70
0.70
0.70
0.70
0.30
0.70
0.50
0.58
0.00
NA
0.09
NA
0.09
0.10
Carbon Carbon Carbon
Stored Emissions Emissions
(Tg C) (Tg C) (Tg C02 Eq.)
55.5
0.4
0.2
3.0
16.9
22.7
0.2
0.6
5.6
3.4
2.0
+
0.2
0.2
0.2
+
0.2
0.2
0.2
+
0.2
56.0
25.6
3.4
0.1
1.3
0.1
9.8
2.4
0.3
2.4
1.5
0.8
+
0.1
0.2
0.1
3.3
2.3
2.3
2.2
+
2.2
30.1
93.9
12.5
0.3
4.7
0.3
35.7
8.8
0.9
88
5.3
3.1
+
0.3
0.6
0.5
12.0
8.3
8.3
8.2
0.1
8.1
110.3
+ Does not exceed 0.05 Tbtu
NA: Not applicable.
a To avoid double counting, net exports have been deducted.
Note: Totals may not sum due to independent rounding.
Lastly, emissions were estimated by subtracting the C stored from the potential emissions (see Table 3-19). More
detail on the methodology for calculating storage and emissions from each of these sources is provided in Annex
2.3.
Where storage factors were calculated specifically for the United States, data were obtained on (1) products such as
asphalt, plastics, synthetic rubber, synthetic fibers, cleansers (soaps and detergents), pesticides, food additives,
antifreeze and deicers (glycols), and silicones; and (2) industrial releases including energy recovery, Toxics Release
Inventory (TRI) releases, hazardous waste incineration, and volatile organic compound, solvent, and non-
combustion CO emissions. Data were taken from a variety of industry sources, government reports, and expert
communications. Sources include EPA reports and databases such as compilations of air emission factors (EPA
2001), National Emissions Inventory (NEI) Air Pollutant Emissions Trends Data (EPA 2013a), Toxics Release
Inventory, 1998 (2000b), Biennial Reporting System (EPA 2004, 2009), Resource Conservation and Recovery Act
Information System (EPA 2013c), and pesticide sales and use estimates (EPA 1998, 1999, 2002, 2004, 2011); the
EIA Manufacturer's Energy Consumption Survey (MECS) (EIA 1994, 1997, 2001, 2005, 2010, 2013b); the
National Petrochemical & Refiners Association (NPRA 2002); the U.S. Bureau of the Census (1999, 2004, 2009);
Bank of Canada (2012, 2013); Financial Planning Association (2006); INEGI (2006); the United States International
Trade Commission (1990-2013); Gosselin, Smith, and Hodge (1984); EPA's Municipal Solid Waste (MSW) Facts
Energy 3-39
-------
and Figures (EPA 2013b; 2014); the Rubber Manufacturers' Association (RMA 2009, 2011); the International
Institute of Synthetic Rubber Products (IISRP 2000, 2003); the Fiber Economics Bureau (FEE 2001-2013); and the
American Chemistry Council (ACC 2003-2011, 2012, 2013). Specific data sources are listed in full detail in Annex
2.3.
Uncertainty and Time-Series Consistency
An uncertainty analysis was conducted to quantify the uncertainty surrounding the estimates of emissions and
storage factors from non-energy uses. This analysis, performed using @RISK software and the IPCC-recommended
Tier 2 methodology (Monte Carlo Stochastic Simulation technique), provides for the specification of probability
density functions for key variables within a computational structure that mirrors the calculation of the inventory
estimate. The results presented below provide the 95 percent confidence interval, the range of values within which
emissions are likely to fall, for this source category.
As noted above, the non-energy use analysis is based on U.S.-specific storage factors for (1) feedstock materials
(natural gas, LPG, pentanes plus, naphthas, other oils, still gas, special naphthas, and other industrial coal), (2)
asphalt, (3) lubricants, and (4) waxes. For the remaining fuel types (the "other" category in Table 3-20 and Table
3-21), the storage factors were taken directly from the IPCC Guidelines for National Greenhouse Gas Inventories,
where available, and otherwise assumptions were made based on the potential fate of carbon in the respective NEU
products. To characterize uncertainty, five separate analyses were conducted, corresponding to each of the five
categories. In all cases, statistical analyses or expert judgments of uncertainty were not available directly from the
information sources for all the activity variables; thus, uncertainty estimates were determined using assumptions
based on source category knowledge.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 3-22 (emissions) and Table 3-23
(storage factors). Carbon emitted from non-energy uses of fossil fuels in 2012 was estimated to be between 87.6 and
149.1 Tg COa Eq. at a 95 percent confidence level. This indicates a range of 21 percent below to 35 percent above
the 2012 emission estimate of 110.3 Tg COa Eq. The uncertainty in the emission estimates is a function of
uncertainty in both the quantity of fuel used for non-energy purposes and the storage factor.
Table 3-22: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Non-Energy
Uses of Fossil Fuels (Tg COz Eq. and Percent)
Source
Feedstocks
Asphalt
Lubricants
Waxes
Other
Total
Gas
CO2
CO2
CO2
C02
C02
C02
2012 Emission
Estimate
(Tg COz Eq.)
59.2
0.3
17.1
0.5
33.3
110.3
Uncertainty Range Relative to Emission Estimate3
(Tg C02
Lower Bound
45.8
0.1
14.2
0.3
18.8
87.6
Eq.)
Upper Bound
104.0
0.6
19.8
0.8
35.0
149.1
(°/
Lower Bound
23%
-57%
-17%
-28%
-44%
-21%
Upper Bound
76%
123%
16%
62%
5%
35%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
3-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 3-23: Tier 2 Quantitative Uncertainty Estimates for Storage Factors of Non-Energy
Uses of Fossil Fuels (Percent)
Source
Feedstocks
Asphalt
Lubricants
Waxes
Other
Gas
CO2
CO2
C02
C02
CO2
2012 Storage
Factor
(%)
70%
100%
9%
58%
8%
Uncertainty
(%)
Lower Bound
56%
99%
4%
49%
8%
Range Relative
Upper Bound
72%
100%
17%
71%
47%
to Emission Estimate3
(%, Relative)
Lower Bound
-19%
-1%
-57%
-15%
0%
Upper Bound
2%
0%
91%
22%
481%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval, as a
percentage of the inventory value (also expressed in percent terms).
In Table 3-23, feedstocks and asphalt contribute least to overall storage factor uncertainty on a percentage basis.
Although the feedstocks category—the largest use category in terms of total carbon flows—appears to have tight
confidence limits, this is to some extent an artifact of the way the uncertainty analysis was structured. As discussed
in Annex 2.3, the storage factor for feedstocks is based on an analysis of six fates that result in long-term storage
(e.g., plastics production), and eleven that result in emissions (e.g., volatile organic compound emissions). Rather
than modeling the total uncertainty around all of these fate processes, the current analysis addresses only the storage
fates, and assumes that all C that is not stored is emitted. As the production statistics that drive the storage values
are relatively well-characterized, this approach yields a result that is probably biased toward understating
uncertainty.
As is the case with the other uncertainty analyses discussed throughout this document, the uncertainty results above
address only those factors that can be readily quantified. More details on the uncertainty analysis are provided in
Annex 2.3.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
and
A source-specific QA/QC plan for non-energy uses of fossil fuels was developed and implemented. This effort
included a Tier 1 analysis, as well as portions of a Tier 2 analysis for non-energy uses involving petrochemical
feedstocks and for imports and exports. The Tier 2 procedures that were implemented involved checks specifically
focusing on the activity data and methodology for estimating the fate of C (in terms of storage and emissions) across
the various end-uses of fossil C. Emission and storage totals for the different subcategories were compared, and
trends across the time series were analyzed to determine whether any corrective actions were needed. Corrective
actions were taken to rectify minor errors and to improve the transparency of the calculations, facilitating future
QA/QC.
For petrochemical import and export data, special attention was paid to NAICS numbers and titles to verify that
none had changed or been removed. Import and export totals were compared for 2011 as well as their trends across
the time series.
Petrochemical input data reported by EIA will continue to be investigated in an attempt to address an input/output
discrepancy in the NEU model. Since 2001, the C accounted for in the feedstocks C balance outputs (i.e., storage
plus emissions) exceeds C inputs. Prior to 2001, the C balance inputs exceed outputs. Starting in 2001 through
2009, outputs exceeded inputs. In 2010 and 2011, inputs exceeded outputs, and in 2012, outputs slightly exceeded
inputs. A portion of this discrepancy has been reduced (see Recalculations Discussion, below) and two strategies
have been developed to address the remaining portion (see Planned Improvements, below).
Energy 3-41
-------
Recalculations Discussion
Relative to the previous Inventory, emissions from non-energy uses of fossil fuels decreased by an average of 3.2 Tg
CO2 Eq. (2.3 percent) across the entire time series. Changes ranged from an increase of about 3 Tg CC>2 Eq. in 1990
to a decrease of about 13 Tg CCh Eq. in 2009. The main catalyst for these recalculations was changes to historic
fossil fuel consumption input data acquired from the Energy Information Agency (EIA). The EIA annually revises
its fossil fuel consumption estimates, which may affect historic Inventory emissions from non-energy uses of fossil
fuels. Since the methodology for calculating emissions from non-energy uses of fossil fuels remained the same
relative to the previous inventory, changes to consumption input data is the primary cause of the recalculations.
Overall, the net effect of these changes was a slight decrease in emission estimates across the entire time series. In
addition, NEI released updated data in December 2013, which included new data through 2011 and updated data for
previous years, and MSW Facts and Figures data for 2012 was released in February 2014. Some of the previous
years' data was updated in this version.
Planned Improvements
There are several improvements planned for the future:
• More accurate accounting of C in petrochemical feedstocks. EPA has worked with EIA to determine the
cause of input/output discrepancies in the C mass balance contained within the NEU model. In the future,
two strategies to reduce or eliminate this discrepancy will continue to be pursued. First, accounting of C in
imports and exports will be improved. The import/export adjustment methodology will be examined to
ensure that net exports of intermediaries such as ethylene and propylene are fully accounted for. Second,
reconsider the use of top-down C input calculation in estimating emissions will be reconsidered.
Alternative approaches that rely more substantially on the bottom-up C output calculation will be
considered instead.
• Response to potential changes in NEU input data. In 2013 EIA initiated implementation of new data
reporting definitions for Natural Gas Liquids (NGL) and Liquefied Petroleum Gases (LPG); the new
definitions may affect the characterization of the input data that EIA provides for the NEU model and may
therefore result in the need for changes to the NEU methodology. EIA also obtains and applies proprietary
data for LPG inputs that are not directly applied as NEU input data because the data are proprietary. The
potential use of the proprietary data (in an aggregated, non-proprietary form) as inputs to the NEU model
will be investigated with EIA.
• Improving the uncertainty analysis. Most of the input parameter distributions are based on professional
judgment rather than rigorous statistical characterizations of uncertainty.
• Better characterizing flows of fossil C. Additional fates may be researched, including the fossil C load in
organic chemical wastewaters, plasticizers, adhesives, films, paints, and coatings. There is also a need to
further clarify the treatment of fuel additives and backflows (especially methyl tert-butyl ether, MTBE).
• Reviewing the trends in fossil fuel consumption for non-energy uses. Annual consumption for several fuel
types is highly variable across the time series, including industrial coking coal and other petroleum
(miscellaneous products). A better understanding of these trends will be pursued to identify any
mischaracterized or misreported fuel consumption for non-energy uses. For example, "miscellaneous
products" category includes miscellaneous products that are not reported elsewhere in the EIA data set.
The EIA does not have firm data concerning the amounts of various products that are being reported in the
"miscellaneous products" category, however, EIA has indicated that recovered sulfur from petroleum and
natural gas processing, and potentially also carbon black feedstock could be reported in this category.
Recovered sulfur would not be reported in the NEU calculation or elsewhere in the inventory.
• Updating the average C content of solvents was researched, since the entire time series depends on one
year's worth of solvent composition data. Unfortunately, the data on C emissions from solvents that were
readily available do not provide composition data for all categories of solvent emissions and also have
conflicting definitions for volatile organic compounds, the source of emissive C in solvents. Additional
sources of solvents data will be identified in order to update the C content assumptions.
3-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
• Updating the average C content of cleansers (soaps and detergents) was researched; although production
and consumption data for cleansers are published every 5 years by the Census Bureau, the composition (C
content) of cleansers has not been recently updated. Recently available composition data sources may
facilitate updating the average C content for this category.
• Revising the methodology for consumption, production, and C content of plastics was researched; because
of recent changes to the type of data publicly available for plastics, the NEU model for plastics applies data
obtained from personal communications. Potential revisions to the plastics methodology to account for the
recent changes in published data will be investigated.
• Although U. S.-specific storage factors have been developed for feedstocks, asphalt, lubricants, and waxes,
default values from IPCC are still used for two of the non-energy fuel types (industrial coking coal,
distillate oil), and broad assumptions are being used for miscellaneous products and other petroleum. Over
the long term, there are plans to improve these storage factors by analyzing C fate similar to those
described in Annex 2.3 or deferring to more updated default storage factors from IPCC where available.
3.3 Incineration of Waste (IPCC Source
Category lAla)
Incineration is used to manage about 7 to 19 percent of the solid wastes generated in the United States, depending on
the source of the estimate and the scope of materials included in the definition of solid waste (EPA 2000, Goldstein
and Matdes 2001, Kaufman etal. 2004, Simmons etal. 2006, van Haaren et al. 2010). In the context of this section,
waste includes all municipal solid waste (MSW) as well as tires. In the United States, almost all incineration of
MSW occurs at waste-to-energy facilities or industrial facilities where useful energy is recovered, and thus
emissions from waste incineration are accounted for in the Energy chapter. Similarly, tires are combusted for energy
recovery in industrial and utility boilers. Incineration of waste results in conversion of the organic inputs to CO2.
According to IPCC guidelines, when the CO2 emitted is of fossil origin, it is counted as a net anthropogenic
emission of CO2 to the atmosphere. Thus, the emissions from waste incineration are calculated by estimating the
quantity of waste combusted and the fraction of the waste that is C derived from fossil sources.
Most of the organic materials in municipal solid wastes are of biogenic origin (e.g., paper, yard trimmings), and
have their net C flows accounted for under the Land Use, Land-Use Change, and Forestry chapter. However, some
components—plastics, synthetic rubber, synthetic fibers, and carbon black—are of fossil origin. Plastics in the U.S.
waste stream are primarily in the form of containers, packaging, and durable goods. Rubber is found in durable
goods, such as carpets, and in non-durable goods, such as clothing and footwear. Fibers in municipal solid wastes
are predominantly from clothing and home furnishings. As noted above, tires (which contain rubber and carbon
black) are also considered a "non-hazardous" waste and are included in the waste incineration estimate, though
waste disposal practices for tires differ from municipal solid waste. Estimates on emissions from hazardous waste
incineration can be found in Annex 2.3 and are accounted for as part of the C mass balance for non-energy uses of
fossil fuels.
Approximately 26.5 million metric tons of MSW was incinerated in the United States in 2012 (EPA 2014). CO2
emissions from incineration of waste rose 53 percent since 1990, to an estimated 12.2 Tg CO2 Eq. (12,195 Gg) in
2012, as the volume of tires and other fossil C-containing materials in waste increased (see Table 3-24 and Table
3-25). Waste incineration is also a source of N2O and CH4 emissions (De Soete 1993, IPCC 2006). N2O emissions
from the incineration of waste were estimated to be 0.4 Tg CO2 Eq. (1 Gg N2O) in 2012, and have not changed
significantly since 1990. CH4 emissions from the incineration of waste were estimated to be less than 0.05 Tg CO2
Eq. (less than 0.5 Gg CH4) in 2012, and have not changed significantly since 1990.
Table 3-24: COz and NzO Emissions from the Incineration of Waste (Tg COz Eq.)
Gas/Waste Product 1990 2005 2008 2009 2010 2011 2012
CO2
Plastics
8.0
5.6
12.5
6.9
11.9
6.1
11.7
6.2
12.0
6.6
12.1
6.7
12.2
6.6
Energy 3-43
-------
Synthetic Rubber in Tires
Carbon Black in Tires
Synthetic Rubber in MSW
Synthetic Fibers
CH4
N20
Total
0.3
0.4 1
0.9 1
0.8 1
+
0.5
8.4
0.4
12.9
11.7
2.1
0.8
1.2
0.4
12.2
1.6
1.9
0.8
1.2
+
0.4
12.0
1.6
1.9
0.8
1.2
+
0.4
12.4
1.6
1.9
0.8
1.2
+
0.4
12.5
1.6
1.9
0.8
1.3
+
0.4
12.6
+ Does not exceed 0.05 Tg
Table 3-25: COz and NzO Emissions from the Incineration of Waste (Gg)
Gas/Waste Product
C02
Plastics
Synthetic Rubber in Tires
Carbon Black in Tires
Synthetic Rubber in MSW
Synthetic Fibers
N2O
CH4
1990
7,972
5,588
308 1
385
854 1
838 1
2 1
+
2005
12,454
6,919
1,599
1,958
765 1
1,212
1 1
+
2008
11,867
6,148
1,693
2,085
755
1,186
1
+
2009
11,672
6,233
1,560
1,903
767
1,211
1
+
2010
12,033
6,573
1,560
1,903
112
1,225
1
+
2011
12,142
6,678
1,560
1,903
111
1,225
1
+
2012
12,195
6,623
1,560
1,903
111
1,333
1
+
+ Does not exceed 0.5 Gg.
Methodology
Emissions of CCh from the incineration of waste include CC>2 generated by the incineration of plastics, synthetic
fibers, and synthetic rubber, as well as the incineration of synthetic rubber and carbon black in tires. These emissions
were estimated by multiplying the amount of each material incinerated by the C content of the material and the
fraction oxidized (98 percent). Plastics incinerated in municipal solid wastes were categorized into seven plastic
resin types, each material having a discrete C content. Similarly, synthetic rubber is categorized into three product
types, and synthetic fibers were categorized into four product types, each having a discrete C content. Scrap tires
contain several types of synthetic rubber, as well as carbon black. Each type of synthetic rubber has a discrete C
content, and carbon black is 100 percent C. Emissions of CO2 were calculated based on the amount of scrap tires
used for fuel and the synthetic rubber and carbon black content of tires.
More detail on the methodology for calculating emissions from each of these waste incineration sources is provided
in Annex 3.7.
For each of the methods used to calculate CCh emissions from the incineration of waste, data on the quantity of
product combusted and the C content of the product are needed. For plastics, synthetic rubber, and synthetic fibers,
the amount of specific materials discarded as municipal solid waste (i.e., the quantity generated minus the quantity
recycled) was taken from Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts
and Figures (EPA 2000 through 2003, 2005 through 2014) and detailed unpublished backup data for some years not
shown in the reports (Schneider 2007). The proportion of total waste discarded that is incinerated was derived from
data in BioCycle's "State of Garbage in America" (van Haaren et al. 2010). The most recent data provides the
proportion of waste incinerated for 2008, so the corresponding proportion in 2009 through 2012 is assumed to be
equal to the proportion in 2008. For synthetic rubber and carbon black in scrap tires, information was obtained from
U.S. Scrap Tire Management Summary for 2005 through 2009 data (RMA 2011). For 2010 through 2012, synthetic
rubber mass in tires is assumed to be equal to that in 2009 due to a lack of more recently available data.
Average C contents for the "Other" plastics category and synthetic rubber in municipal solid wastes were calculated
from 1998 and 2002 production statistics: C content for 1990 through 1998 is based on the 1998 value; C content for
1999 through 2001 is the average of 1998 and 2002 values; and C content for 2002 to date is based on the 2002
value. Carbon content for synthetic fibers was calculated from 1999 production statistics. Information about scrap
tire composition was taken from the Rubber Manufacturers' Association internet site (RMA 2012a).
3-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The assumption that 98 percent of organic C is oxidized (which applies to all waste incineration categories for CO2
emissions) was reported in EPA's life cycle analysis of greenhouse gas emissions and sinks from management of
solid waste (EPA 2006).
Incineration of waste, including MSW, also results in emissions of N2O and CH4. These emissions were calculated
as a function of the total estimated mass of waste incinerated and an emission factor. As noted above, N2O and CH4
emissions are a function of total waste incinerated in each year; for 1990 through 2008, these data were derived from
the information published in BioCycle (van Haaren et al. 2010). Data on total waste incinerated was not available
for 2009 through 2012, so this value was assumed to equal the most recent value available (2008).
Table 3-26 provides data on municipal solid waste discarded and percentage combusted for the total waste stream.
According to Covanta Energy (Bahor 2009) and confirmed by additional research based on ISWA (ERC 2009), all
municipal solid waste combustors in the United States are continuously fed stoker units. The emission factors of
N2O and CH4 emissions per quantity of municipal solid waste combusted are default emission factors for this
technology type and were taken from the 2006IPCC Guidelines for National Greenhouse Gas Inventories (IPCC
2006).
Table 3-26: Municipal Solid Waste Generation (Metric Tons) and Percent Combusted
Year
Waste Discarded Waste Incinerated
Incinerated (% of
Discards)
1990
235,733,657
30,632,057
13.0
2008
2009
2010
2011
2012
268,541,088
268,541,088*
268,541,088*
268,541,088*
268,541,088*
23,674,017
23,674,017*
23,674,017*
23,674,017*
23,674,017*
8.8
8.8*
8.8*
8.8*
8.8*
* Assumed equal to 2008 value.
Source: van Haaren et al. (2010).
Uncertainty and Time-Series Consistency
A Tier 2 Monte Carlo analysis was performed to determine the level of uncertainty surrounding the estimates of CO2
emissions and N2O emissions from the incineration of waste (given the very low emissions for CH4, no uncertainty
estimate was derived). IPCC Tier 2 analysis allows the specification of probability density functions for key
variables within a computational structure that mirrors the calculation of the inventory estimate. Uncertainty
estimates and distributions for waste generation variables (i.e., plastics, synthetic rubber, and textiles generation)
were obtained through a conversation with one of the authors of the Municipal Solid Waste in the United States
reports. Statistical analyses or expert judgments of uncertainty were not available directly from the information
sources for the other variables; thus, uncertainty estimates for these variables were determined using assumptions
based on source category knowledge and the known uncertainty estimates for the waste generation variables.
The uncertainties in the waste incineration emission estimates arise from both the assumptions applied to the data
and from the quality of the data. Key factors include MSW incineration rate; fraction oxidized; missing data on
waste composition; average C content of waste components; assumptions on the syntheti^iogenic C ratio; and
combustion conditions affecting N2O emissions. The highest levels of uncertainty surround the variables that are
based on assumptions (e.g., percent of clothing and footwear composed of synthetic rubber); the lowest levels of
uncertainty surround variables that were determined by quantitative measurements (e.g., combustion efficiency, C
content of C black).
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 3-27. Waste incineration CO2
emissions in 2012 were estimated to be between 10.9 and 13.8 Tg CO2 Eq. at a 95 percent confidence level. This
indicates a range of 10 percent below to 14 percent above the 2012 emission estimate of 12.2 Tg CO2 Eq. Also at a
95 percent confidence level, waste incineration N2O emissions in 2012 were estimated to be between 0.2 and 1.5 Tg
Energy 3-45
-------
CChEq. This indicates a range of 50 percent below to 313 percent above the 2012 emission estimate of 0.4 Tg CCh
Eq.
Table 3-27: Tier 2 Quantitative Uncertainty Estimates for COz and NzO from the Incineration
of Waste (Tg COz Eq. and Percent)
Source
Incineration of Waste
Incineration of Waste
Gas
CO2
N20
2012
Emission
Estimate
(Tg C02 Eq.)
12.2
0.4
Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (%)
Lower Upper Lower
Bound Bound Bound
10.9 13.8 -10%
0.2 1.5 -50%
Upper
Bound
+14%
+313%
a Range of emission estimates predicted by Monte Carlo Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A source-specific QA/QC plan was implemented for incineration of waste. This effort included a Tier 1 analysis, as
well as portions of a Tier 2 analysis. The Tier 2 procedures that were implemented involved checks specifically
focusing on the activity data and specifically focused on the emission factor and activity data sources and
methodology used for estimating emissions from incineration of waste. Trends across the time series were analyzed
to determine whether any corrective actions were needed. Actions were taken to streamline the activity data
throughout the calculations on incineration of waste.
Recalculations Discussion
The emissions from plastics, synthetic rubber in MSW, and synthetic fibers were updated for 2011 based on data
obtained from EPA's MSW Facts and Figures report, which was updated in February 2014. This update resulted in a
1 percent increase in emissions for 2011, relative to the previous report.
Planned Improvements
The availability of facility-level waste incineration through EPA's GHGRP will be examined to help better
characterize waste incineration operations in the United States. This characterization could include future
improvements as to the operations involved in waste incineration for energy, whether in the power generation sector
or the industrial sector. Additional examinations will be necessary as, unlike the reporting requirements for this
chapter under the UNFCCC reporting guidelines,104 some facility-level waste incineration emissions reported under
the GHGRP may also include industrial process emissions. In line with UNFCCC reporting guidelines, emissions
for waste incineration with energy recovery are included in this chapter, while process emissions are included in the
industrial processes chapter of this report. In examining data from EPA's GHGRP that would be useful to improve
the emission estimates for the waste incineration category, particular attention will also be made to ensure time
series consistency, as the facility-level reporting data from EPA's GHGRP are not available for all inventory years
as reported in this inventory. Additionally, analyses will focus on ensuring CCh emissions from the biomass
component of waste are separated in the facility-level reported data, and on maintaining consistency with national
waste generation and fate statistics currently used to estimate total, national U.S. greenhouse gas emissions. In
implementing improvements and integration of data from EPA's GHGRP, the latest guidance from the IPCC on the
104 See .
3-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
use of facility-level data in national inventories will be relied upon.105 GHGRP data is available for MSW
combustors, which contains information on the CCh, CH4, and N2O emissions from MSW combustion, plus the
fraction of the emissions that are biogenic. To calculate biogenic versus total CCh emissions, a default biogenic
fraction of 0.6 is used. The biogenic fraction will be calculated using the current input data and assumptions to
verify the current MSW emission estimates.
Additional improvements will be to improve the transparency in the current reporting of waste incineration.
Currently, hazardous industrial waste incineration is included within the overall calculations for the carbon emitted
from the non-energy uses of fossil fuels category. Additional examinations will be made in to any waste
incineration activities covered that do not include energy recovery.
3.4 Coal Mining (IPCC Source Category IBla)
Three types of coal mining-related activities release CH4 to the atmosphere: underground mining, surface mining,
and post-mining (i.e., coal-handling) activities. While surface mines account for the majority of U.S. coal
production (see Table 3-28 and Table 3-29), underground coal mines contribute the largest share of CH4 emissions
due to the higher CH4 content of coal in the deeper underground coal seams. In 2012, 488 underground coal mines
and 719 surface mines were operating in the U.S. Also in 2012, the U.S. was the second largest coal producer in the
world (921million metric tons), after China (3,549 MMT) and followed by India (595 MMT) (IEA 2013).
Underground mines liberate CH4 from ventilation systems and from degasification systems. Ventilation systems
pump air through the mine workings to dilute noxious gases and ensure worker safety; these systems can exhaust
significant amounts of CH4 to the atmosphere in low concentrations. Degasification systems are wells drilled from
the surface or boreholes drilled inside the mine that remove large, often highly-concentrated, volumes of CH4
before, during, or after mining. Some mines recover and use CH4 generated from ventilation and degasification
systems, thereby reducing emissions to the atmosphere.
Surface coal mines liberate CH4 as the overburden is removed and the coal is exposed to the atmosphere. Methane
emissions are normally a function of coal rank and depth. Surface coal mines typically produce lower rank coals and
remove less than 250 feet of overburden, thus the level of emissions is much lower than from underground mines.
In addition, CH4 is released during post-mining activities, as the coal is processed, transported and stored for use.
Total CH4 emissions in 2012 were estimated to be 55.8 Tg CO2 Eq. (2,658 Gg CH4), a decline of 31 percent since
1990 (see Table 3-28 and Table 3-29). Of this amount, underground mines accounted for approximately 71 percent,
surface mines accounted for 15 percent, and post-mining emissions accounted for 13 percent.
Table 3-28: CH4 Emissions from Coal Mining (Tg COz Eq.)
Activity
UG Mining
Liberated
Recovered & Used
Surface Mining
Post-Mining (UG)
Post-Mining (Surface)
Total
1990
62.3
67.9
(5.6)
9.0
7.7
2.0
81.1
2005
35.0
50.2
(15.2)
10.0
6.4 1
2.2
53.6
2008
44.4
60
(16.
10
6
2
63
5
1)
.7
.1
.3
.5
2009
49.7
66.1
(16.4)
9.7
5.6
2.1
67.1
2010
51.7
71.5
(19.8)
9.7
5.7
2.1
69.2
2011
42.2
59.1
(17.0)
9.8
5.8
2.1
59.8
2012
39.7
55.3
(15.5)
8.6
5.6
1.9
55.8
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table 3-29: CH4 Emissions from Coal Mining (Gg)
Activity
UG Mining
Liberated
1990
2,968
3,234
2005
1,668
2,390 |
2008
2,113
2,881
2009
2,367
3,149
2010
2,463
3,406
2011
2,008
2,839
2012
1,891
2,631
105
See.
Energy 3-47
-------
Recovered & Used
Surface Mining
Post-Mining (UG)
Post-Mining (Surface)
Total
(266)
430
368
93
3,860
(722)
475
306
103
2,552
(768)
510
292
111
3,026
(782)
461
267
100
3,194
(943)
461
270
100
3,293
(831)
465
276
101
2,849
(740)
410
268
89
2,658
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Methodology
The methodology for estimating CH4 emissions from coal mining consists of two steps. The first step is to estimate
emissions from underground mines. There are two sources of underground mine emissions: ventilation systems and
degasification systems. These emissions are estimated on a mine-by-mine basis and then are summed to determine
total emissions. The second step of the analysis involves estimating CH4 emissions from surface mines and post-
mining activities. In contrast to the methodology for underground mines, which uses mine-specific data, the
methodology for estimating emissions from surface mines and post-mining activities consists of multiplying basin-
specific coal production by basin-specific gas content and an emission factor.
Step 1: Estimate ChU Liberated and ChU Emitted from Underground Mines
Underground mines generate CH4 from ventilation systems and from degasification systems. Some mines recover
and use the generated CH4, thereby reducing emissions to the atmosphere. Total CH4 emitted from underground
mines equals the CH4 liberated from ventilation systems, plus the CH4 liberated from degasification systems, minus
the CH4 recovered and used.
Step 1.1: Estimate CM Liberated from Ventilation Systems
Because the U.S. Mine Safety and Health Administration (MSHA) samples CH4 emissions from ventilation systems
for all mines with detectable CH4 concentrations106 to ensure miner safety, these mine-by-mine measurements are
used to estimate CH4 emissions from ventilation systems. While since 2011 the EPA has also collected information
on ventilation emissions from underground coal mines liberating greater than 36,500,000 actual cubic feet of CH4
per year (about 14,700 metric tons CC>2 Eq.) through its Greenhouse Gas Reporting Program (GHGRP), as of the
publication of this inventory the reported GHGRP data on ventilation emissions had not been fully reconciled with
the MSHA data used to estimate emissions in previous years. As a result, MSHA data was used to estimate
ventilation emissions for 2012.
Step 1.2: Estimate CH4 Liberated from Degasification Systems
Some gassier underground mines also use degasification systems (e.g., wells or boreholes) to remove CH4 before,
during, or after mining. This CH4 can then be collected for use or vented to the atmosphere. Several data sets were
used to estimate the quantity of CH4 collected by each of the twenty-four mines using degasification systems in
2012. First, for mines that sold recovered CH4 to a pipeline, pipeline sales data published by state petroleum and
natural gas agencies were used to estimate degasification emissions. For those mines that vented CH4 from
degasification systems rather than selling it to a pipeline, data on degasification emissions reported to the EPA's
GHGRP (EPA 2013) were used.
Step 1.3: Estimate CM Recovered from Degasification Systems and Utilized (Emissions Avoided)
Finally, the amount of CH4 recovered by degasification and ventilation systems and then used (i.e., not vented) was
estimated. In 2012, sixteen active coal mines had CH4 recovery and use projects, of which fourteen mines sold the
recovered CH4 to a pipeline. One of the mines that sold gas to a pipeline also used CH4 to fuel a thermal coal dryer.
One mine used recovered CH4 for electrical power generation, and another mine used recovered CH4 to heat mine
ventilation air. Emissions avoided as a result of pipeline sales projects were estimated using gas sales data reported
106 MSHA records coal mine CH4 readings with concentrations of greater than 50 ppm (parts per million) CH4. Readings below
this threshold are considered non-detectable.
3-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
by various state agencies. For those mines that used methane for electric power or heating, coal mine operators or
project developers supplied information regarding methane recovery.
Step 2: Estimate ChU Emitted from Surface Mines and Post-Mining Activities
Mine-specific data were not available for estimating CH4 emissions from surface coal mines or for post-mining
activities. For surface mines, basin-specific coal production obtained from the Energy Information Administration's
Annual Coal Report (see Table 3-30) (EIA 2013) was multiplied by basin-specific gas contents and a 150 percent
emission factor (to account for CH4from over- and under-burden) to estimate CH4 emissions. The emission factor
was revised downward in 2012 from 200 percent, based on more recent studies in Canada and Australia (King 1994,
Saghafi 2013). The 150 percent emission factor was applied to all inventory years since 1990, retroactively. For
post-mining activities, basin-specific coal production was multiplied by basin-specific gas contents and a 32.5
percent emission factor for CH4 desorption during coal transportation and storage (Greedy 1993). Basin-specific in
situ gas content data was compiled from AAPG (1984) and USBM (1986). Revised data on in situ CH4 content and
emissions factors are taken from EPA (1996) and EPA (2005).
Table 3-30: Coal Production (Thousand Metric Tons)
Year Underground Surface Total
1990 384,244 546,808 931,052
2005 334,398 691,448 1,025,846
2008
2009
2010
2011
2012
323,932
301,241
305,862
313,529
310,608
737,832
671,475
676,177
684,807
610,307
1,061,764
972,716
982,039
998,337
920,915
A quantitative uncertainty analysis was conducted for the coal mining source category using the IPCC-
recommended Tier 2 uncertainty estimation methodology. Because emission estimates from underground
ventilation systems were based on actual measurement data, uncertainty is relatively low. A degree of imprecision
was introduced because the measurements used were not continuous but rather an average of quarterly instantaneous
readings. Additionally, the measurement equipment used can be expected to have resulted in an average of 10
percent overestimation of annual CH4 emissions (Mutmansky & Wang 2000).
Estimates of CH4 recovered by degasification systems are relatively certain for utilized CH4 because of the
availability of gas sales information. In addition, many coal mine operators provided information on mined-through
dates for pre-drainage wells. Many of the recovery estimates use data on wells within 100 feet of a mined area.
However, uncertainty exists concerning the radius of influence of each well. The number of wells counted, and thus
the avoided emissions, may vary if the drainage area is found to be larger or smaller than estimated. The 2012
GHGRP data (EPA 2013) used for determining CH4 emissions from vented degasification wells are based on
weekly measurements, an improvement over the previous year's estimates, thus lowering the uncertainty of that
subsource.
Compared to underground mines, there is considerably more uncertainty associated with surface mining and post-
mining emissions because of the difficulty in developing accurate emission factors from field measurements.
However, since underground emissions comprise the majority of total coal mining emissions, the uncertainty
associated with underground emissions is the primary factor that determines overall uncertainty. The results of the
Tier 2 quantitative uncertainty analysis are summarized in Table 3-31. Coal mining CH4 emissions in 2012 were
estimated to be between 49.1 and 64.5 Tg CCh Eq. at a 95 percent confidence level. This indicates a range of 12.1
percent below to 15.6 percent above the 2012 emission estimate of 55.8 Tg CCh Eq.
Energy 3-49
-------
Table 3-31: Tier 2 Quantitative Uncertainty Estimates for ChU Emissions from Coal Mining
(Tg COz Eq. and Percent)
Source
Gas
2012 Emission
Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (%)
Lower Bound Upper Bound Lower Bound Upper Bound
Coal Mining
CH4
55.8
49.1 64.5 -12.1% +15.6%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section.
Recalculations Discussion
For the current inventory, updated mine maps were received for the Jim Walter Resources Blue Creek #4 and #7
mines (JWR 2010) that showed changes in the planned locations of areas to be mined through. The updated mine
plans provided a more accurate depiction of the dates and locations at which the pre-drainage wells were mined
through. As a result, the mined-through dates were adjusted for some wells relative to the previous inventory, and
underground emissions avoided values changed slightly for 2011.
Prior to the current inventory, vented degasification emissions from underground coal mines were typically
estimated based on drainage efficiencies reported by either the mining company or MSHA. However, beginning in
2011, underground coal mines began reporting CH4 emissions from degasification systems to EPA under its
GHGRP, which requires degasification quantities to be measured weekly, thus offering a more accurate account than
previous methods. As a result, data reported to EPA's GHGRP in 2012 were used to estimate vented degasification
volumes for those mines. In 2012, GHGRP-reported vented degasification emissions totals were approximately 30
percent lower when compared to the previous estimation method; however, the difference only represents
approximately 1.5 percent of the overall coal mining emission inventory.
In 2012, the surface mining emission factor was revised downward from 200 percent to 150 percent of the average
in situ CH4 content of the mined coal seam. In previous years, EPA used a 200 percent factor as a conservative
measure due to a lack of U.S. data. Based on surface mine emissions studies conducted used in Canada and
Australia (King 1994, Saghatfi 2013), this emission factor was adjusted to be more closely aligned with those
studies where actual measurements have been taken of similar coals. While the gas content of the coal accounts for
CH4 liberated from the mined coal, this emission factor accounts for additional CH4 released from the over- and
under-lying strata surrounding the mined coal seam. The change was made for all inventory years 1990 through
2012.
Planned Improvements
Future improvements to the Coal Mining category will include continued analysis and possible integration into the
national inventory of the ventilation systems data reported by underground coal mines to EPA's GHGRP. Many of
the underground coal mines reporting to the GHGRP use the same quarterly MSHA samples currently used to
develop the estimates for the inventory. However, some mines use their own measurements and samples, which are
taken monthly (rather than quarterly). It is possible that more frequent measurements could lower the uncertainty of
the annual ventilation systems estimate. EPA anticipates that reconciliation of its GHGRP and inventory data sets
will be complete in preparation for the 2013 inventory. In implementing improvements and integrating data from
the GHGRP, the latest guidance from the IPCC on the use of facility-level data in national inventories will be relied
upon (IPCC 2013).
3-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
3.5 Abandoned Underground Coal Mines (IPCC
Source Category IBla)
Underground coal mines contribute the largest share of CH4 emissions, with active underground mines the leading
source of underground emissions. However, mines also continue to release CH4 after closure. As mines mature and
coal seams are mined through, mines are closed and abandoned. Many are sealed and some flood through intrusion
of groundwater or surface water into the void. Shafts or portals are generally filled with gravel and capped with a
concrete seal, while vent pipes and boreholes are plugged in a manner similar to oil and gas wells. Some abandoned
mines are vented to the atmosphere to prevent the buildup of CH4 that may find its way to surface structures through
overburden fractures. As work stops within the mines, CH4 liberation decreases but it does not stop completely.
Following an initial decline, abandoned mines can liberate CH4 at a near-steady rate over an extended period of
time, or, if flooded, produce gas for only a few years. The gas can migrate to the surface through the conduits
described above, particularly if they have not been sealed adequately. In addition, diffuse emissions can occur when
CH4 migrates to the surface through cracks and fissures in the strata overlying the coal mine. The following factors
influence abandoned mine emissions:
• Time since abandonment;
• Gas content and adsorption characteristics of coal;
• CH4 flow capacity of the mine;
• Mine flooding;
• Presence of vent holes; and
• Mine seals.
Gross abandoned mine CH4 emissions ranged from 6.0 to 9.1 Tg CO2 Eq. from 1990 through 2012, varying, in
general, by less than 1 percent to approximately 19 percent from year to year. Fluctuations were due mainly to the
number of mines closed during a given year as well as the magnitude of the emissions from those mines when
active. Gross abandoned mine emissions peaked in 1996 (9.1 Tg CCh Eq.) due to the large number of mine closures
from 1994 to 1996 (70 gassy mines closed during the three-year period). In spite of this rapid rise, abandoned mine
emissions have been generally on the decline since 1996. Since 2005, there have been fewer than twelve gassy mine
closures each year. There were seven gassy mine closures in 2012. By 2012, gross abandoned mine emissions
decreased slightly to 7.0 Tg CCh Eq. (see Table 3-32 and Table 3-33). Gross emissions are reduced by CH4
recovered and used at 38 mines, resulting in net emissions in 2012 of 4.7 Tg CCh Eq.
Table 3-32: CH4 Emissions from Abandoned Coal Mines (Tg COz Eq.)
Activity
Abandoned Underground Mines
Recovered & Used
Total
1990
6.0
+
6.0
2005
7.0
1 15 1
5.5
2008
9.0
3.7
5.3
2009
8.1
3.0
5.1
2010
7.6
2.7
5.0
2011
7.3
2.4
4.8
2012
7.0
2.3
4.7
+ Does not exceed 0.05 Tg
Note: Totals may not sum due to independent rounding.
Table 3-33: ChU Emissions from Abandoned Coal Mines (Gg)
Activity
Abandoned Underground Mines
Recovered & Used
Total
1990
288
+
288
2005
334
1 70
264
2008
429
177
253
2009
388
143
244
2010
364
126
237
2011
347
116
231
2012
335
109
226
+ Does not exceed 0.05 Tg
Note: Totals may not sum due to independent rounding.
Energy 3-51
-------
Estimating CH4 emissions from an abandoned coal mine requires predicting the emissions of a mine from the time
of abandonment through the inventory year of interest. The flow of CH4 from the coal to the mine void is primarily
dependent on the mine's emissions when active and the extent to which the mine is flooded or sealed. The CH4
emission rate before abandonment reflects the gas content of the coal, rate of coal mining, and the flow capacity of
the mine in much the same way as the initial rate of a water-free conventional gas well reflects the gas content of the
producing formation and the flow capacity of the well. A well or a mine which produces gas from a coal seam and
the surrounding strata will produce less gas through time as the reservoir of gas is depleted. Depletion of a reservoir
will follow a predictable pattern depending on the interplay of a variety of natural physical conditions imposed on
the reservoir. The depletion of a reservoir is commonly modeled by mathematical equations and mapped as a type
curve. Type curves which are referred to as decline curves have been developed for abandoned coal mines. Existing
data on abandoned mine emissions through time, although sparse, appear to fit the hyperbolic type of decline curve
used in forecasting production from natural gas wells.
In order to estimate CH4 emissions over time for a given abandoned mine, it is necessary to apply a decline function,
initiated upon abandonment, to that mine. In the analysis, mines were grouped by coal basin with the assumption
that they will generally have the same initial pressures, permeability and isotherm. As CH4 leaves the system, the
reservoir pressure, Pr, declines as described by the isotherm. The emission rate declines because the mine pressure
(Pw) is essentially constant at atmospheric pressure for a vented mine, and the productivity index or PI term, which
is expressed as the flow rate per unit of pressure change, is essentially constant at the pressures of interest
(atmospheric to 30 psia). A rate-time equation can be generated that can be used to predict future emissions. This
decline through time is hyperbolic in nature and can be empirically expressed as:
q = qt (1 + fcDjt) (-V*0
where,
q = Gas flow rate at time t in million cubic feet per day (mmcfd)
q; = Initial gas flow rate at time zero (to), mmcfd
b = The hyperbolic exponent, dimensionless
D; = Initial decline rate, 1/yr
t = Elapsed time from to (years)
This equation is applied to mines of various initial emission rates that have similar initial pressures, permeability and
adsorption isotherms (EPA 2003).
The decline curves created to model the gas emission rate of coal mines must account for factors that decrease the
rate of emission after mining activities cease, such as sealing and flooding. Based on field measurement data, it was
assumed that most U.S. mines prone to flooding will become completely flooded within eight years and therefore no
longer have any measurable CH4 emissions. Based on this assumption, an average decline rate for flooded mines
was established by fitting a decline curve to emissions from field measurements. An exponential equation was
developed from emissions data measured at eight abandoned mines known to be filling with water located in two of
the five basins. Using a least squares, curve-fitting algorithm, emissions data were matched to the exponential
equation shown below. There was not enough data to establish basin-specific equations as was done with the
vented, non-flooding mines (EPA 2003).
q = <7i*(-Dt)
where,
q = Gas flow rate at time t in mmcfd
q; = Initial gas flow rate at time zero (to), mmcfd
D = Decline rate, 1/yr
t = Elapsed time from to (years)
Seals have an inhibiting effect on the rate of flow of CH4 into the atmosphere compared to the flow rate that would
exist if the mine had an open vent. The total volume emitted will be the same, but emissions will occur over a
longer period of time. The methodology, therefore, treats the emissions prediction from a sealed mine similarly to
the emissions prediction from a vented mine, but uses a lower initial rate depending on the degree of sealing. A
3-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
computational fluid dynamics simulator was used with the conceptual abandoned mine model to predict the decline
curve for inhibited flow. The percent sealed is defined as 100 x (1 - (initial emissions from sealed mine / emission
rate at abandonment prior to sealing)). Significant differences are seen between 50 percent, 80 percent and 95
percent closure. These decline curves were therefore used as the high, middle, and low values for emissions from
sealed mines (EPA 2003).
For active coal mines, those mines producing over 100 thousand cubic feet per day (mcfd) account for 98 percent of
all CH4 emissions. This same relationship is assumed for abandoned mines. It was determined that 469 abandoned
mines closing after 1972 produced emissions greater than 100 mcfd when active. Further, the status of 274 of the
483 mines (or 57 percent) is known to be either: 1) vented to the atmosphere; 2) sealed to some degree (either
earthen or concrete seals); or, 3) flooded (enough to inhibit CH4 flow to the atmosphere). The remaining 43 percent
of the mines whose status is unknown were placed in one of these three categories by applying a probability
distribution analysis based on the known status of other mines located in the same coal basin (EPA 2003).
Table 3-34: Number of gassy abandoned mines present in U.S. basins, grouped by class
according to post-abandonment state
Basin
Central Appl.
Illinois
Northern Appl.
Warrior Basin
Western Basins
Total
Sealed Vented Flooded Total Known Unknown Total Mines
26
30
42
0
27
125
25
3
22
0
3
53
48
14
16
16
2
96
99
47
80
16
32
274
136
27
36
0
10
209
235
74
116
16
42
483
Inputs to the decline equation require the average emission rate and the date of abandonment. Generally this data is
available for mines abandoned after 1971; however, such data are largely unknown for mines closed before 1972.
Information that is readily available, such as coal production by state and county, are helpful but do not provide
enough data to directly employ the methodology used to calculate emissions from mines abandoned after 1971. It is
assumed that pre-1972 mines are governed by the same physical, geologic, and hydrologic constraints that apply to
post-1971 mines; thus, their emissions may be characterized by the same decline curves.
During the 1970s, 78 percent of CH4 emissions from coal mining came from seventeen counties in seven states. In
addition, mine closure dates were obtained for two states, Colorado and Illinois, for the hundred year period
extending from 1900 through 1999. The data were used to establish a frequency of mine closure histogram (by
decade) and applied to the other five states with gassy mine closures. As a result, basin-specific decline curve
equations were applied to the 145 gassy coal mines estimated to have closed between 1920 and 1971 in the United
States, representing 78 percent of the emissions. State-specific, initial emission rates were used based on average
coal mine CH4 emissions rates during the 1970s (EPA 2003).
Abandoned mine emission estimates are based on all closed mines known to have active mine CH4 ventilation
emission rates greater than 100 mcfd at the time of abandonment. For example, for 1990 the analysis included 145
mines closed before 1972 and 258 mines closed between 1972 and 1990. Initial emission rates based on MSHA
reports, time of abandonment, and basin-specific decline curves influenced by a number of factors were used to
calculate annual emissions for each mine in the database. Coal mine degasification data are not available for years
prior to 1990, thus the initial emission rates used reflect ventilation emissions only for pre-1990 closures. CH4
degasification amounts were added to the quantity of CH4 vented to determine the total CH4 liberation rate for all
mines that closed between 1992 and 2012. Since the sample of gassy mines (with active mine emissions greater
than 100 mcfd) is assumed to account for 78 percent of the pre-1972 and 98 percent of the post-1971 abandoned
mine emissions, the modeled results were multiplied by 1.22 and 1.02 to account for all U.S. abandoned mine
emissions.
From 1993 through 2012, emission totals were downwardly adjusted to reflect abandoned mine CH4 emissions
avoided from those mines. The inventory totals were not adjusted for abandoned mine reductions from 1990
through 1992 because no data was reported for abandoned coal mining CH4 recovery projects during that time.
Energy 3-53
-------
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis was conducted to estimate the uncertainty surrounding the estimates of emissions
from abandoned underground coal mines. The uncertainty analysis described below provides for the specification of
probability density functions for key variables within a computational structure that mirrors the calculation of the
inventory estimate. The results provide the range within which, with 95 percent certainty, emissions from this
source category are likely to fall.
As discussed above, the parameters for which values must be estimated for each mine in order to predict its decline
curve are: 1) the coal's adsorption isotherm; 2) CH4 flow capacity as expressed by permeability; and 3) pressure at
abandonment. Because these parameters are not available for each mine, a methodological approach to estimating
emissions was used that generates a probability distribution of potential outcomes based on the most likely value and
the probable range of values for each parameter. The range of values is not meant to capture the extreme values, but
rather values that represent the highest and lowest quartile of the cumulative probability density function of each
parameter. Once the low, mid, and high values are selected, they are applied to a probability density function.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 3-35. Abandoned coal mine CH4
emissions in 2012 were estimated to be between 3.83 and 5.97 Tg CCh Eq. at a 95 percent confidence level. This
indicates a range of 19 percent below to 26 percent above the 2012 emission estimate of 4.74 Tg COa Eq. One of
the reasons for the relatively narrow range is that mine-specific data is used in the methodology. The largest degree
of uncertainty is associated with the unknown status mines (which account for 43 percent of the mines), with a
-467+62 percent uncertainty.
Table 3-35: Tier 2 Quantitative Uncertainty Estimates for Cm Emissions from Abandoned
Underground Coal Mines (Tg COz Eq. and Percent)
2012 Emission Uncertainty Range Relative to Emission Estimate3
Estimate
Source Gas (Tg CCh Eq.) (Tg CCh Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Abandoned Underground ^ ^ 6Q _19% +26%
Coal Mines
a Range of emission estimates predicted by Monte Carlo Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
3.6 Petroleum Systems (IPCC Source Category
lB2a)
Methane emissions from petroleum systems are primarily associated with crude oil production, transportation, and
refining operations. During each of these activities, CH4 emissions are released to the atmosphere as fugitive
emissions, vented emissions, emissions from operational upsets, and emissions from fuel combustion. Fugitive and
vented CO2 emissions from petroleum systems are primarily associated with crude oil production and refining
operations but are negligible in transportation operations. Combustion CO2 emissions from fuels are already
accounted for in the Fossil Fuels Combustion source category, and hence have not been taken into account in the
Petroleum Systems source category. Total CH4 and CO2 emissions from petroleum systems in 2012 were 31.7 Tg
CO2 Eq. (1,511 Gg CH4) and 0.4 Tg CO2 Eq. (406 Gg), respectively. Since 1990, CH4 emissions have declined by
11.3 percent. The largest decreases are due to decreases in the numbers of offshore shallow water platforms
(decrease of 18.2 percent since 1990), and decreases in the numbers of pneumatic devices and gas engines which
both relate to total oil production, which has decreased by 11.7 percent since 1990. However, in recent years,
domestic oil production has begun to increase again, resulting in greater CH4 emissions from petroleum systems.
3-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Since 2008, when production began to increase, CH4 emissions from petroleum systems have increased by 10.2
percent (see Table 3-36 and Table 3-37) primarily due to increases in vented emissions from oil tanks in the
production segment. COa emissions have increased by 3.3 percent since 1990, and have similarly experienced an
increase in recent years due to increased domestic production, with the largest increases occurring in tank venting
CO2 emissions. Since 2008, CCh emissions have increased by 35.4 percent (see Table 3-38 and Table 3-39).
Production Field Operations. Production field operations account for 98.4 percent of total CH4 emissions from
petroleum systems. Vented CH4 from field operations account for approximately 89.9 percent of the emissions from
the production sector, uncombusted CH4 emissions (i.e. unburned fuel) account for 6.5 percent, fugitive emissions
are 3.4 percent, and process upset emissions are slightly under two-tenths of a percent. The most dominant sources
of emissions, in order of magnitude, are shallow water offshore oil platforms, natural-gas-powered high bleed
pneumatic devices, oil tanks, natural-gas powered low bleed pneumatic devices, gas engines, deep water offshore oil
platforms, and chemical injection pumps. These seven sources alone emit about over 90 percent of the production
field operations emissions. Offshore platform emissions are a combination of fugitive, vented, and uncombusted
fuel emissions from all equipment housed on oil platforms producing oil and associated gas. Emissions from high
and low-bleed pneumatics occur when pressurized gas that is used for control devices is bled to the atmosphere as
they cycle open and closed to modulate the system. Emissions from oil tanks occur when the CH4 entrained in crude
oil under pressure volatilizes once the crude oil is put into storage tanks at atmospheric pressure. Emissions from
gas engines are due to unburned CH4 that vents with the exhaust. Emissions from chemical injection pumps are due
to the estimated 25 percent of such pumps that use associated gas to drive pneumatic pumps. The remaining 6
percent of the emissions are distributed among 26 additional activities within the four categories: vented, fugitive,
combustion, and process upset emissions. For more detailed, source-level data on CH4 emissions in production field
operations, refer to Annex 3.5.
Since 1990, CH4 emissions from production of crude oil have decreased by 11.5 percent. This reduction was a result
of a significant decrease in annual domestic production. From 1990 until 2008, CH4 emissions from domestic
production of crude oil decreased by 19.7 percent. However, since 2008, domestic production of oil has begun to
increase again, resulting in greater emissions of CH4. Since 2008, CH4 emissions from crude oil production have
increased by 10.2 percent. This is mainly from production activities such as pneumatic device venting, tank venting,
process upsets, and combustion.
Vented CC>2 associated with field operations account for 99.2 percent of the total CCh emissions from production
field operations, while fugitive and process upsets together account for less than 1 percent of the emissions. The
most dominant sources of vented emissions are oil tanks, high bleed pneumatic devices, shallow water offshore oil
platforms, low bleed pneumatic devices, and chemical injection pumps. These five sources together account for 98.7
percent of the non-combustion CCh emissions from production field operations, while the remaining 1.3 percent of
the emissions is distributed among 24 additional activities within the three categories: vented, fugitive, and process
upsets. Note that CO2from associated gas flaring is accounted in natural gas systems production emissions. CO2
emissions from flaring for both natural gas and oil were 13 MMT CC>2 Eq. in 2012.
Crude Oil Transportation. Crude oil transportation activities account for less than 0.4 percent of total CH4
emissions from the oil industry. Venting from tanks, truck loading, and marine vessel loading operations account for
73.8 percent of CH4 emissions from crude oil transportation. Fugitive emissions, almost entirely from floating roof
tanks, account for 16.3 percent of CH4 emissions from crude oil transportation. The remaining 9.9 percent is
distributed among three additional sources within the vented emissions category. Emissions from pump engine
drivers and heaters were not estimated due to lack of data.
Since 1990, CH4 emissions from transportation have decreased by almost 10.0 percent. However, because emissions
from crude oil transportation account for such a small percentage of the total emissions from the petroleum industry,
this has had little impact on the overall emissions.
Crude Oil Refining. Crude oil refining processes and systems account for less than 1.3 percent of total CH4
emissions from the oil industry because most of the CH4 in crude oil is removed or escapes before the crude oil is
delivered to the refineries. There is an insignificant amount of CH4 in all refined products. Within refineries, vented
emissions account for about 81.4 percent of the emissions, while fugitive and combustion emissions account for
approximately 8.9 and 9.8 percent, respectively. Refinery system blowdowns for maintenance and the process of
asphalt blowing—with air, to harden the asphalt—are the primary venting contributors. Most of the fugitive CH4
emissions from refineries are from leaks in the fuel gas system. Refinery combustion emissions include small
amounts of unburned CH4 in process heater stack emissions and unburned CH4 in engine exhausts and flares.
Energy 3-55
-------
CH4 emissions from refining of crude oil have increased 7.0 percent since 1990; however, similar to the
transportation subcategory, this increase has had little effect on the overall emissions of CH4. Since 1990, CH4
emissions have fluctuated between 17 and 20 Gg.
Asphalt blowing from crude oil refining accounts for 3.3 percent of the total non-combustion CCh emissions in
petroleum systems. Since 2000, the year in which CCh emissions from refining peaked, CCh emissions from crude
oil refining have dropped by approximately 33.6 percent.
Table 3-36: CH4 Emissions from Petroleum Systems (Tg COz Eq.)
Activity
Production Field Operations
(Potential)
Pneumatic device venting
Tank venting
Combustion & process upsets
Misc. venting & fugitives
Wellhead fugitives
Production Voluntary Reductions
Production Field Operations (Net)
Crude Oil Transportation
Refining
Total
Note: Totals may not sum due to independent
1990 2005
35.3 29.1
10.3 8.3
5.3 1 3.9
2.4 1 1.9
16.8 1 14.5
0.5 1 0.4
(0.0) (0.8)
35.3 1 28.3
0.1 1 0.1
0.4 0.4
35.8 28.8
rounding.
2008
29.9
8.7
3.9
2.0
14.8
0.5
(1.6)
28.3
0.1
0.4
28.8
2009
30.1
8.8
4.2
2.0
14.6
0.5
(1.4)
28.7
0.1
0.4
29.1
2010
30.3
8.7
4.4
2.0
14.7
0.5
(1.3)
29.0
0.1
0.4
29.5
2011
31.0
9.0
4.7
2.1
14.7
0.5
(0.9)
30.0
0.1
0.4
30.5
2012
32.2
9.1
5.6
2.2
14.8
0.5
(1.0)
31.2
0.1
0.4
31.7
Table 3-37: CH4 Emissions from Petroleum Systems (Gg)
Activity
Production Field Operations
(Potential)
Pneumatic device venting
Tank venting
Combustion & process upsets
Misc. venting & fugitives
Wellhead fugitives
Production Voluntary Reductions
Production Field Operations (Net)
Crude Oil Transportation
Refining
Total
Note: Totals may not sum due to independent
1990 2005
1,680 1,385
489 398
250 1 188
115 1 90
799 1 690
26 1 19
(0) (36)
1,679 1,349
7 1 5 1
18 19
1,704 1,374
rounding.
2008
1,425
416
185
94
706
24
(77)
1,348
5
19
1,372
2009
1,432
419
202
94
694
23
(67)
1,365
5
18
1,388
2010
1,443
416
211
95
700
22
(60)
1,383
5
19
1,407
2011
1,474
428
222
98
702
24
(45)
1,429
5
19
1,453
2012
1,531
435
267
103
703
24
(45)
1,486
6
19
1,511
Table 3-38: COz Emissions from Petroleum Systems (Tg COz Eq.)
Activity 1990 2005 2008 2009
Production Field
Operations 0.4
Pneumatic device venting + 1
Tank venting 0.3
Misc. venting & fugitives + 1
Wellhead fugitives + 1
Crude Refining +
Total 0.4
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent
0.3 1 0.3 0.3
+ 1 + +
0.2 1 0.2 0.3
+ 1 +
+ I + +
+ + +
0.3 0.3 0.3
rounding.
2010
0.3
+
0.3
+
+
+
0.3
2011
0.3
+
0.3
+
+
+
0.3
2012
0.4
+
0.4
+
+
+
0.4
3-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 3-39: COz Emissions from Petroleum Systems (Gg)
Activity
Production Field
Operations
Pneumatic device venting
Tank venting
Misc. venting & fugitives
Wellhead fugitives
Crude Refining
Total
1990
376
27
328
18
1
18
394
2005
285
22
246
16
1
20
306
2008
284
23
243
16
1
16
300
2009
306
23
265
16
1
14
320
2010
317
23
276
16
1
15
332
2011
332
24
291
16
1
15
347
2012
392
24
350
16
1
14
406
Note: Totals may not sum due to independent rounding.
Methodology
The methodology for estimating CH4 emissions from petroleum systems is based on comprehensive studies of CH4
emissions from U.S. petroleum systems (EPA 1996, EPA 1999). These studies calculated emission estimates for 64
activities occurring in petroleum systems from the oil wellhead through crude oil refining, including 33 activities for
crude oil production field operations, 11 for crude oil transportation activities, and 20 for refining operations.
Annex 3.5 provides greater detail on the emission estimates for these 64 activities. The estimates of CH4 emissions
from petroleum systems do not include emissions downstream of oil refineries because these emissions are
negligible.
Key references for activity data and emission factors are the Energy Information Administration annual and monthly
reports (EIA 1990 through 2013), (EIA 1995 through 2013a, 2013b, 2013c), "Methane Emissions from the Natural
Gas Industry by the Gas Research Institute and EPA" (EPA/GRI 1996a-d), "Estimates of Methane Emissions from
the U.S. Oil Industry" (EPA 1999), consensus of industry peer review panels, BOEMRE and BOEM reports
(BOEMRE 2005, BOEM 2011), analysis of BOEMRE data (EPA 2005, BOEMRE 2004), the Oil & Gas Journal
(OGJ 2013a, 2013b), the Interstate Oil and Gas Compact Commission (IOGCC 2011), and the United States Army
Corps of Engineers, (1995-2010).
The methodology for estimating CH4 emissions from the 64 oil industry activities employs emission factors initially
developed by EPA (1999). Activity data for the years 1990 through 2012 were collected from a wide variety of
statistical resources. Emissions are estimated for each activity by multiplying emission factors (e.g., emission rate
per equipment item or per activity) by the corresponding activity data (e.g., equipment count or frequency of
activity). EPA (1999) provides emission factors for all activities except those related to offshore oil production and
field storage tanks. For offshore oil production, two emission factors were calculated using data collected over a
one-year period for all federal offshore platforms (EPA 2005, BOEMRE 2004). One emission factor is for oil
platforms in shallow water, and one emission factor is for oil platforms in deep water. Emission factors are held
constant for the period 1990 through 2012. The number of platforms in shallow water and the number of platforms
in deep water are used as activity data and are taken from Bureau of Ocean Energy Management (BOEM) (formerly
Bureau of Ocean Energy Management, Regulation, and Enforcement [BOEMRE]) datasets (BOEM 2011). For oil
storage tanks, the emissions factor was calculated as the total emissions per barrel of crude charge from E&P Tank
data weighted by the distribution of produced crude oil gravities from the HPDI production database (EPA 1999,
HPDI2011).
For some years, complete activity data were not available. In such cases, one of three approaches was employed.
Where appropriate, the activity data was calculated from related statistics using ratios developed for EPA (1996).
For example, EPA (1996) found that the number of heater treaters (a source of CH4 emissions) is related to both
number of producing wells and annual production. To estimate the activity data for heater treaters, reported
statistics for wells and production were used, along with the ratios developed for EPA (1996). In other cases, the
activity data was held constant from 1990 through 2012 based on EPA (1999). Lastly, the previous year's data were
used when data for the current year were unavailable. The CH4 and CO2 sources in the production sector share
common activity data. See Annex 3.5 for additional detail.
Energy 3-57
-------
This year's inventory estimate for Petroleum Systems takes into account Natural Gas STAR reductions that were
previously deducted from the Natural Gas System emissions estimates. See Recalculations Discussion for more
information.
The methodology for estimating CC>2 emissions from petroleum systems combines vented, fugitive, and process
upset emissions sources from 29 activities for crude oil production field operations and one activity from petroleum
refining. Emissions are estimated for each activity by multiplying emission factors by their corresponding activity
data. The emission factors for CC>2 are estimated by multiplying the CH4 emission factors by a conversion factor,
which is the ratio of CC>2 content and methane content in produced associated gas. The only exceptions to this
methodology are the emission factors for crude oil storage tanks, which are obtained from E&P Tank simulation
runs, and the emission factor for asphalt blowing, which was derived using the methodology and sample data from
API (2009).
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis was conducted for previous Inventories to determine the level of uncertainty
surrounding estimates of emissions from petroleum systems using the recommended methodology from IPCC. EPA
produced the results presented below in Table 3-40, which provide with 95 percent certainty the range within which
emissions from this source category are likely to fall for the year 2012. Performed using @RISK software and the
IPCC-recommended Tier 2 methodology (Monte Carlo Simulation technique), this analysis provides for the
specification of probability density functions for key variables within a computational structure that mirrors the
calculation of the inventory estimate. The IPCC guidance notes that in using this method, "some uncertainties that
are not addressed by statistical means may exist, including those arising from omissions or double counting, or other
conceptual errors, or from incomplete understanding of the processes that may lead to inaccuracies in estimates
developed from models." As a result, the understanding of the uncertainty of emissions estimates for this category
will evolve and will improve as the underlying methodologies and datasets improve.
Performed using @RISK software and the IPCC-recommended Tier 2 methodology (Monte Carlo Stochastic
Simulation technique), the method employed provides for the specification of probability density functions for key
variables within a computational structure that mirrors the calculation of the inventory estimate. The results provide
the range within which, with 95 percent certainty, emissions from this source category are likely to fall.
The detailed, bottom-up inventory analysis used to evaluate U.S. petroleum systems reduces the uncertainty related
to the CH4 emission estimates in comparison to a top-down approach. However, some uncertainty still remains.
Emission factors and activity factors are based on a combination of measurements, equipment design data,
engineering calculations and studies, surveys of selected facilities and statistical reporting. Statistical uncertainties
arise from natural variation in measurements, equipment types, operational variability and survey and statistical
methodologies. Published activity factors are not available every year for all 64 activities analyzed for petroleum
systems; therefore, some are estimated. Because of the dominance of seven major sources, which account for 92
percent of the total methane emissions, the uncertainty surrounding these sources has been estimated most
rigorously, and serves as the basis for determining the overall uncertainty of petroleum systems emission estimates.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 3-40. Petroleum systems CH4
emissions in 2012 were estimated to be between 24.1 and 78.9 Tg COa Eq., while CCh emissions were estimated to
be between 0.3 and 1.0 Tg CCh Eq. at a 95 percent confidence level. This indicates a range of 24 percent below to
149 percent above the 2012 emission estimates of 31.7 and 0.4 Tg COa Eq. for CH4 and CCh, respectively.
Table 3-40: Tier 2 Quantitative Uncertainty Estimates for ChU Emissions from Petroleum
Systems (Tg COz Eq. and Percent)
2012 Emission Uncertainty Range Relative to Emission Estimate3
Estimate
Source Gas (TgCChEq.)" (TgCChEq.) (%)
Petroleum Systems CH4
Petroleum Systems CCh
Lower
Bound"
31.7 24.1
0.4 0.3
Upper
Bound"
78.9
1.0
Lower
Bound"
-24%
-24%
Upper
Bound"
149%
149%
3-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
a Range of 2012 relative uncertainty predicted by Monte Carlo Stochastic Simulation, based on 1995 base
year activity factors, for a 95 percent confidence interval.
b All reported values are rounded after calculation. As a result, lower and upper bounds may not be
duplicable from other rounded values as shown in table.
Note: Totals may not sum due to independent rounding
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification Discussion
The petroleum inventory is continually being reviewed and assessed to determine whether emission factors and
activity factors accurately reflect current industry practice. A QA/QC analysis was performed for data gathering and
input, documentation, and calculation. The primary focus of the QA/QC checks is determining if the assumptions in
the Inventory are consistent with current industry practices through review of regulations, public webcasts, and the
Natural Gas STAR Program. Finally, QA/QC checks are consistently conducted to minimize human error in the
model calculations.
In some areas, EPA identified that certain assumptions in the inventory are not consistent with current industry
practice. EPA received several comments suggesting updates to emissions calculations for Petroleum Systems.
Commenters noted that the emission factor for oil wells has not been updated to reflect emissions from hydraulically
fractured well completions, and suggested data sources for developing updated factors for this source. Commenters
also suggested updated data sources for petroleum refineries and pneumatic devices.
See Planned Improvements for more information on these sources.
Recalculations Discussion
Most revisions for the current Inventory relative to the previous report were due to updating the previous report's
data with revised data from existing data sources. In addition, when activity data updates are made for a particular
emissions source, the entire time series is revised or corrected, which may result in slight changes in estimated
emissions from past years.
Gas STAR Reduction Data
EPA has reviewed Gas STAR reduction data and determined that some of the reductions previously deducted from
the Natural Gas System emissions estimates should instead be deducted from the Petroleum Systems emissions
estimates. In the 2014 inventory, EPA has moved the following reduction activities from the Natural Gas Systems
estimates to the Petroleum Systems estimates - Artificial lift: gas lift, Artificial lift: use compression, Artificial lift:
use pumping unit, Consolidate crude oil prod and water storage tanks, Lower heater-treater temperature, Re-inject
gas for enhanced oil recovery, Re-inject gas into crude, and Route casinghead gas to VRU or compressor).
Implementing this change has resulted in a decrease in emissions of 1.0 Tg CCh Eq. from petroleum systems.
Planned Improvements
Offshore Platforms
In order to improve the offshore platform emission calculations, more current (post-2000) inventories of the Gulf of
Mexico platforms will be reviewed. For example, the GOADS data set to be updated in late 2014, may provide
improved information on the number of platforms, platform activity, deep water assignments, and oil and gas
production.
GHGRP Data
Energy 3-59
-------
EPA's GHGRP has published 2011 and 2012 emissions data from the oil and gas sector. GHGRP data is being
reviewed for potential incorporation in the Inventory. Expert review and public review draft commenters supported
use of GHGRP data from petroleum systems.
Oil Well Completions and Workovers
The Inventory does not currently distinguish between oil wells with hydraulic fracturing and oil wells without
hydraulic fracturing for this source. In addition, current Inventory emission factors were developed using an
assumption that all oil well workovers and completions are flared. EPA is seeking available information on
emissions, activity data, and control technologies for oil well completions and workovers.
Commenters suggested that updated emission factors could be developed for these completion types using GHGRP
data on gas well completions and workovers in oil formations, UT Austin-EDF data on co-producing wells, or from
initial production data from Bakken, Eagle Ford, and/or Wallenberg fields. Commenters noted lhal using these dala
sources resulls in average factors of 6.2 Mg CH4 (GHGRP, wells wilh and wilhoul controls), 3.1 Mg CH4 (UT
Auslin-EDF, wells wilh controls), 9.7 Mg CH4 and 24.7 Mg CH4 (analyses of Wallenberg and Eagle Ford, wells
wilhoul controls) per complelion/workover, and lhal lolal national emissions could be belween 96 and 247 Gg CH4,
similar in magnitude to or higher than emissions from gas well completions and workovers. Commenters suggested
lhal Ihese emissions estimates provide a reasonable estimate for oil well completions. Other commenters suggested
lhal existing dala from recenl field studies or from extrapolation from gas wells in oil formations do nol provide a
reliable estimate of potential emissions from oil well completions and workovers. EPA will continue to review dala
available to update emission factors for this source.
Commenters on the expert review draft of Ihe inventory suggested lhal significanl numbers of new oil wells are
completed wilh hydraulic fracturing (75-90 percent of all new oil wells). Commenters on the public review draft
noted lhal FracFocus includes records from 12,056 oil wells fractured in 2012. EPA will assess melhods for
determining the number of hydraulically fractured oil well completions.
The GHG Inventory currently applies a 7.5 percenl workover (refraclure) rale for all oil wells. Expert review and
public review commenters suggested lhal Ihis is an incorrecl assumption, bul lhal dala is nol currently available to
update Ihe assumption. EPA will continue to seek dala on a refraclure rale for oil wells.
Petroleum Refineries
EPA received commenls on the expert review draft suggesting thai EPA replace Ihe Inventory estimate wilh dala
from Ihe GHGRP. GHGRP reporters reported emissions of 39 Gg CH4 from pelroleum refineries (0.8 Tg CCh Eq.),
while Ihe national lolal in Ihe GHG inventory is 19 Gg CH4 (0.4 Tg CO2 Eq.). EPA reviewed Ihe GHGRP dala and
plans to make Ihis update in future Inventories.
Pneumatic Devices
Commenters on the expert review and public review drafts noted a number of currenl and upcoming dala sources
relevanl to bolh natural gas and oil emissions lhal could be used to update CH4 emission factors from pneumatic
devices, including UT Austin-EDF, and a 2013 British Columbia pneumatic device study (Prasino 2013).
Commenters suggested lhal EPA develop nel factors for differenl categories of pneumatic devices, such as high-
bleed, inlermiltenl-bleed, low-bleed, and no bleed, noting thai GHGRP could be a source of activity dala for this
approach in 2015 when activity dala is reported, or lhal EPA could estimate activity counts using GHGRP dala and
an estimate of coverage of Ihe reporting rule. One commenler suggested lhal EPA update pneumatic device
emissions estimates in Ihe Inventory using GHGRP dala (noting thai lolal emissions for pneumatic devices in bolh
Ihe oil and gas sectors in GHGRP are 861 Gg CH4 (18.0 Tg CCh Eq.) and scaling up emissions to Ihe national level.
Further, the commenter recalculated emissions from this source using emission factors developed wilh UT Austin-
EDF dala, and calculated natural gas and oil emissions from pneumatic devices to be 1,139 Gg CH4 (23.9 Tg CCh
Eq.). EPA will continue to review dala available to update emission factors and activity dala for Ihis source.
Natural Gas STAR Reductions
The Pelroleum Systems Inventory deducls an aggregated value for the Gas STAR reductions for the production
induslry segment For future Inventories, EPA will determine whether Ihe reductions can be disaggregated and
displayed al Ihe activity-level in the Inventory.
Associated Gas
3-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Commenters on the public review draft of the Inventory suggested that the Associated Gas Venting and Flaring
emissions reported to Subpart W should be included in the Inventory, specifically to update the "stripper wells"
emissions source category. EPA notes that the Natural Gas Systems Inventory includes a source for flaring of
natural gas in the upstream production and processing segments. EPA is analyzing the overlap between the
information reported in Subpart W for "Associated Gas Venting and Flaring" and "Flaring" sources with the
information available from EIA that is used in the Inventory. EPA will evaluate potential updates to the Inventory
for these sources in the future.
Uncertainty Analysis
EPA plans to review and update its uncertainty analysis.
Box 3-6: Carbon Dioxide Transport, Injection, and Geological Storage
Carbon dioxide is produced, captured, transported, and used for Enhanced Oil Recovery (EOR) as well as
commercial and non-EOR industrial applications. This CO2 is produced from both naturally-occurring CO2
reservoirs and from industrial sources such as natural gas processing plants and ammonia plants. In the Inventory,
emissions from naturally-produced CO2 are estimated based on the application.
In the inventory, CO2 that is used in non-EOR industrial and commercial applications (e.g., food processing,
chemical production) is assumed to be emitted to the atmosphere during its industrial use. These emissions are
discussed in the Carbon Dioxide Consumption section. The naturally-occurring CO2 used in EOR operations is
assumed to be fully sequestered. Additionally, all anthropogenic CO2 emitted from natural gas processing and
ammonia plants is assumed to be emitted to the atmosphere, regardless of whether the CO2 is captured or not. These
emissions are currently included in the Natural Gas Systems and the Ammonia Production sections of the inventory
report, respectively.
IPCC includes methodological guidance to estimate emissions from the capture, transport, injection, and geological
storage of CO2. The methodology is based on the principle that the carbon capture and storage system should be
handled in a complete and consistent manner across the entire Energy sector. The approach accounts for CO2
captured at natural and industrial sites as well as emissions from capture, transport, and use. For storage
specifically, a Tier 3 methodology is outlined for estimating and reporting emissions based on site-specific
evaluations. However, IPCC (IPCC 2006) notes that if a national regulatory process exists, emissions information
available through that process may support development of CO2 emissions estimates for geologic storage.
In the United States, facilities that conduct geologic sequestration of CO2 and all other facilities that inject CO2,
including facilities conducting enhanced oil and gas recovery, are required to report greenhouse gas data annually to
EPA through its GHGRP. Facilities conducting geologic sequestration of CO2 are required to develop and
implement an EPA-approved site-specific monitoring, reporting and verification plan, and to report the amount of
CO2 sequestered using a mass balance approach. Data from this program will be evaluated closely and opportunities
for improving the emission estimates will be considered.
Preliminary estimates indicate that the amount of CO2 captured from industrial and natural sites is 46.2 Tg CO2 Eq.
(46,198 Gg) (see Table 3-41and Table 3-42). Site-specific monitoring and reporting data for CO2 injection sites
(i.e., EOR operations) were not readily available, therefore, these estimates assume all CO2 is emitted. Values for
2012 were proxied from 2011 data.
Table 3-41: Potential Emissions from COz Capture and Transport (Tg COz Eq.)
Stage
Acid Gas Removal Plants
Naturally Occurring CO2
Ammonia Production Plants
Pipelines Transporting CO2
1990
4
20
.8
.8
+
+
2005
5
28,
0,
.8
.3
.7
+
2008
6,
36,
0,
.6
.1
.6
+
2009
7.0
39.7
0.6
+
2010
11.6
34.0
0.7
+
2011
11.6
34.0
0.7
+
2012
11.6
34.0
0.7
+
Total 25.6 34.7 43.3 47.3 46.2 46.2 46.2
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Energy 3-61
-------
Table 3-42: Potential Emissions from COz Capture and Transport (Gg)
Stage
Acid Gas Removal Plants
Naturally Occurring CCh
Ammonia Production Plants
Pipelines Transporting CCh
Total
1990
4,832
20,811
+
8
25,643
2005
1
5
28
34
,798 1
,267 1
676 1
7
,742
• 2008
6,630
36,102
580
• 8
43,311
2009
7,035
39,725
580
8
47,340
2010
11,554
33,967
677
8
46,198
2011
11
33
46
,554
,967
677
8
,198
2012
11,554
33,967
677
8
46,198
+ Does not exceed 0.5 Gg.
Note: Totals do not include emissions from pipelines transporting CCh. Totals may not sum due to independent rounding.
3.7 Natural Gas Systems (IPCC Source Category
lB2b)
The U.S. natural gas system encompasses hundreds of thousands of wells, hundreds of processing facilities, and
over a million miles of transmission and distribution pipelines. Overall, natural gas systems emitted 129.9 Tg CCh
Eq. (6,186 Gg) of CH4 in 2012, a 17 percent decrease compared to 1990 emissions (see Table 3-43, Table 3-44, and
Table 3-45) and 35.2 Tg CC>2 Eq. (35,232 Gg) of non-combustion CC>2 in 2012, a 7 percent decrease compared to
1990 emissions (see Table 3-46 and Table 3-47). The decrease in CH4 emissions is because of the large decrease in
emissions from production and distribution. The decrease in production emissions is due to increased voluntary
reductions, from activities such as replacing high bleed pneumatic devices, regulatory reductions, and the increased
use of plunger lifts for liquids unloading. The decrease in distribution emissions is due to a decrease in cast iron and
unprotected steel pipelines.
CH4 and non-combustion CC>2 emissions from natural gas systems are generally process related, with normal
operations, routine maintenance, and system upsets being the primary contributors. Emissions from normal
operations include: natural gas engine and turbine uncombusted exhaust, bleed and discharge emissions from
pneumatic devices, and fugitive emissions from system components. Routine maintenance emissions originate from
pipelines, equipment, and wells during repair and maintenance activities. Pressure surge relief systems and
accidents can lead to system upset emissions. Below is a characterization of the four major stages of the natural gas
system. Each of the stages is described and the different factors affecting CH4 and non-combustion CC>2 emissions
are discussed.
Field Production. In this initial stage, wells are used to withdraw raw gas from underground formations. Emissions
arise from the wells themselves, gathering pipelines, and well-site gas treatment facilities such as dehydrators and
separators. Emissions from pneumatic devices, gas wells with liquids unloading, and gas well completions and
refracturing (workovers) with and without hydraulic fracturing account for the majority of CH4 emissions. Flaring
emissions account for the majority of the non-combustion CC>2 emissions. Emissions from field production account
for approximately 32.2 percent of CH4 emissions and about 38.8 percent of non-combustion CC>2 emissions from
natural gas systems in 2012. CH4 emissions from field production decreased by 25.2 percent from 1990-2012;
however, the trend was not stable over the time series - emissions from field production increased 23.5 percent from
1990-2006 due primarily to increases in hydraulically fractured well completions and workovers, and then declined
by 39.4 percent from 2006 to 2012. Reasons for the 2006-2012 trend include an increase in plunger lift use for
liquids unloading, increased voluntary reductions over that time period (including those associated with pneumatic
devices), and increased RECs use for well completions and workovers with hydraulic fracturing. CC>2 emissions
from field production increased 38.9 percent from 1990 to 2012 due to increases in onshore and offshore flaring.
Processing. In this stage, natural gas liquids and various other constituents from the raw gas are removed, resulting
in "pipeline quality" gas, which is injected into the transmission system. Fugitive CH4 emissions from compressors,
including compressor seals, are the primary emission source from this stage. The majority of non-combustion CC>2
emissions come from acid gas removal units, which are designed to remove CC>2 from natural gas. Processing plants
account for about 14.4 percent of CH4 emissions and approximately 60.9 percent of non-combustion CC>2 emissions
3-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
from natural gas systems. CH4 emissions from processing increased by 4.7 percent from 1990 to 2012 as emissions
from compressors increased as gas produced increased. CCh emissions from processing decreased by 22.7 percent
from 1990 to 2012, as a decrease in the quantity of gas processed resulted in a decrease in acid gas removal
emissions.
Transmission and Storage. Natural gas transmission involves high pressure, large diameter pipelines that transport
gas long distances from field production and processing areas to distribution systems or large volume customers
such as power plants or chemical plants. Compressor station facilities, which contain large reciprocating and turbine
compressors, are used to move the gas throughout the United States transmission system. Fugitive CH4 emissions
from these compressor stations, pneumatic devices, and from metering and regulating stations account for the
majority of the emissions from this stage. Uncombusted engine exhaust is also a source of CH4 emissions from
transmission facilities. Natural gas is also injected and stored in underground formations, or liquefied and stored in
above ground tanks, during periods of low demand (e.g., summer), and withdrawn, processed, and distributed during
periods of high demand (e.g., winter). Compressors and dehydrators are the primary contributors to emissions from
these storage facilities. CH4 emissions from the transmission and storage sector account for approximately 33.5
percent of emissions from natural gas systems, while CC>2 emissions from transmission and storage account for less
than 1 percent of the non-combustion CCh emissions from natural gas systems. CH4 emissions from this source
decreased by nearly 11.6 percent from 1990 to 2012 due to increased voluntary reductions (e.g., replacement of high
bleed pneumatics with low bleed pneumatics). CCh emissions from transmission and storage have increased by 2.7
percent from 1990 to 2012 as the number of compressors has increased.
Distribution. Distribution pipelines take the high-pressure gas from the transmission system at "city gate" stations,
reduce the pressure and distribute the gas through primarily underground mains and service lines to individual end
users. There were 1,244,470 miles of distribution mains in 2012, an increase of approximately 300,000 miles since
1990 (PHMSA 2013). Distribution system emissions, which account for approximately 19.9 percent of CH4
emissions from natural gas systems and less than 1 percent of non-combustion CO2 emissions, result mainly from
fugitive emissions from gate stations and pipelines. An increased use of plastic piping, which has lower emissions
than other pipe materials, has reduced both CH4 and CCh emissions from this stage. Distribution system CH4
emissions in 2012 were 22.6 percent lower than 1990 levels (changed from 33.4 Tg CO2 Eq. to 25.9 Tg CO2 Eq.),
while distribution CO2 emissions in 2012 were 19.8 percent lower than 1990 levels (CO2 emission from this segment
are less than 0.1 TgCChe Eq. across the time series).
Total CH4 emissions for the four major stages of natural gas systems are shown in Tg CO2 Eq. (Table 3-43) and Gg
(Table 3-44). Table 3-45 provides additional information on how the estimates in Table 3-43 were calculated.
Table 3-45 shows the calculated CH4 release (i.e. potential emissions before any controls are applied) from each
stage, and the amount of CH4 that is estimated to have been flared, captured, or otherwise controlled, and therefore
not emitted to the atmosphere. Subtracting the value for CH4 that is controlled, from the value for calculated
potential release of CH4, results in the total emissions values. More disaggregated information on potential
emissions and emissions is available in the Annex 3.5. See Methodology for Estimating CH4 and CO2 Emissions
from Natural Gas Systems.
Table 3-43: CH4 Emissions from Natural Gas Systems (Tg COz Eq.)a
Stage
Field Production
Processing
Transmission and Storage
Distribution
Total
1990
56.0
17.9 1
49.2 1
33.4
156.4
2005
67.3
13.7
41.2
29.7 1
152.0
2008
64.0
14.9
43.1
29.6
151.6
2009
53.9
16.1
44.3
28.7
142.9
2010
48.2
15.1
43.4
28.1
134.7
2011
42.6
17.9
45.2
27.5
133.2
2012
41.8
18.7
43.5
25.9
129.9
a These values represent CH4 emitted to the atmosphere. CH4 that is captured, flared, or otherwise
controlled (and not emitted to the atmosphere) has been calculated and removed from emission totals.
Note: Totals may not sum due to independent rounding.
Table 3-44: Cm Emissions from Natural Gas Systems (Gg)a
Stage 1990 2005 2008 2009 2010 2011 2012
Field Production 2,664 3,206 3,049 2,566 2,295 2,028 1,992
Energy 3-63
-------
Processing
Transmission and Storage
Distribution
Total
852 1
2,343
1,591
7,450
655
1,963
1,417
7,240
708
2,050
1,411
7,218
768
2,107
1,365
6,806
717
2,065
1,336
6,413
851
2,153
1,311
6,343
892
2,071
1,231
6,186
a These values represent CELi emitted to the atmosphere. CELi that is captured, flared, or otherwise controlled (and not
emitted to the atmosphere) has been calculated and removed from emission totals.
Note: Totals may not sum due to independent rounding.
Table 3-45: Calculated Potential CH4 and Captured/Combusted CH4 from Natural Gas
Systems (Tg COz Eq.)
1990 •
Calculated Potential3
Field Production
Processing
Transmission and Storage
Distribution
Captured/Combusted
Field Production
Processing
Transmission and Storage
Distribution
Net Emissions
Field Production
Processing
Transmission and Storage
Distribution
156
56
17
49
33
0
0
156
56
17
49
33
.7
.1
.9
.2
.4
.2
?
j
.4
0
.9
.2
.4
2005
180.7
80,
17,
51,
30,
28.
13
3,
10,
1
152.
67
13,
41
29,
.8
.3
.9
.8
,7
5
.5
.6
.0
0
3
.7
.2
.7
2008
188.7
86.5
19.0
52.5
30.7
37.1
22.5
4.1
9.5
1.1
151.6
64.0
14.9
43.1
29.6
2009
182.1
80.3
19.3
52.5
30.0
39.2
26.4
3.2
8.2
1.3
142.9
53.9
16.1
44.3
28.7
2010
182.2
80.4
19.9
52.7
29.2
47.5
32.2
4.8
9.4
1.1
134.7
48.2
15.1
43.4
28.1
2011
180.9
78.3
21.1
52.7
28.8
47.7
35.7
3.3
7.5
1.2
133.2
42.6
17.9
45.2
27.5
2012
178.8
78.3
22.0
51.7
26.8
48.9
36.4
3.3
8.2
0.9
129.9
41.8
18.7
43.5
25.9
Note: Totals may not sum due to independent rounding.
+ Emissions are less than 0.1 Tg CCh Eq.
a In this context, "potential" means the total emissions calculated before voluntary reductions and regulatory controls are applied.
Table 3-46: Non-combustion COz Emissions from Natural Gas Systems (Tg COz Eq.)
Stage
Field Production
Processing
Transmission and Storage
Distribution
Total
1990
9.8
27.8
0.1
37.7
2005
8.1
21.7
0.1
30.0
2008
11
21
0
32,
.2
.4
.1
.7
2009
10.9
21.2
0.1
32.2
2010
10.9
21.3
0.1
32.4
2011
13,
21,
0,
35.
.5
.5
.1
1
2012
13
21
0
35.
.7
.5
.1
,2
Note: Totals may not sum due to independent rounding.
+ Emissions are less than 0.1 Tg CCh Eq.
Table 3-47: Non-combustion COz Emissions from Natural Gas Systems (Gg)
Stage
Field Production
Processing
Transmission and Storage
Distribution
Total
1990
9,835
27,763
62
46
37,705
2005
8
21
29
2008
,136 1 11,215
,746 1 21,385
64 1 65
42 42
,988
32,707
2009
10,939
21,188
65
41
32,234
2010
10,911
21,346
65
40
32,362
2011
13,511
21,466
65
40
35,082
2012
13,663
21,469
63
37
35,232
Note: Totals may not sum due to independent rounding.
3-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The methodology for natural gas emissions estimates presented in this Inventory involves the calculation of CH4 and
CO2 emissions for over 100 emissions sources, and then the summation of emissions for each natural gas sector
stage.
The calculation of emissions for each source of emissions in natural gas systems generally occurs in three steps:
Step 1. Calculate Potential Methane - Collect activity data on production and equipment in use and
apply emission factors (i.e., scf gas per unit or activity)
Step 2. Compile Reductions Data -Calculate the amount of the methane that is not emitted, using data on
voluntary action and regulations
Step 3. Calculate Net Emissions - Deduct methane that is not emitted from the total methane potential
estimates to develop net CH4 emissions, and calculate CCh emissions
This approach of calculating potential CH4 and then applying reductions data to calculate net emissions was used to
ensure an accurate time series that reflects real emission trends. As noted below, key data on emissions from many
sources are from a 1996 report containing data collected in 1992. Since the time of this study, practices and
technologies have changed. While this study still represents best available data for some emission sources, using
these emission factors alone to represent actual emissions without adjusting for emissions controls would in many
cases overestimate emissions. As updated emission factors reflecting changing practices are not available for most
sources, the 1992 emission factors continue to be used for many sources for all years of the Inventory, but they are
considered to be potential emissions factors, representing what emissions would be if practices and technologies had
not changed over time.
For the Inventory, the calculated potential emissions are adjusted using data on reductions reported to GasSTAR,
and data on regulations that result in CH4 reductions. As more data become available, alternate approaches may be
considered. For example, new data on liquids unloading and on hydraulically fractured gas well completions and
workovers enabled EPA to disaggregate or stratify these sources into distinct sub-categories based upon different
technology types, each with unique emission factors and/or activity data.
Step 1. Calculate Potential Methane—Collect activity data on production and equipment in use and apply
emission factors
In the first step, potential CH4 is calculated by multiplying activity data (such as miles of pipeline or number of
wells) by factors that relate that activity data to potential CH4. Potential CH4 is the amount of CH4 that would be
emitted in the absence of any control technology or mitigation activity. It is important to note that potential CH4
factors in most cases do not represent emitted CH4, and must be adjusted for any emissions-reducing technologies,
or practices, as appropriate. For more information, please see the Annex.
Potential Methane Factors
The primary basis for estimates of CH4 and non-combustion-related CCh emissions from the U.S. natural gas
industry is a detailed study by the Gas Research Institute and EPA (EPA/GRI1996). The EPA/GRI study developed
over 80 CH4 emission factors to characterize emissions from the various components within the operating stages of
the U.S. natural gas system. The EPA/GRI study was based on a combination of process engineering studies,
collection of activity data and measurements at representative gas facilities conducted in the early 1990s. Methane
compositions from GTI2001 are adjusted year to year using gross production for oil and gas supply National
Energy Modeling System (NEMS) regions from the EIA. Therefore, emission factors may vary from year to year
due to slight changes in the CH4 composition for each NEMS oil and gas supply module region. The majority of
emission factors used in the Inventory were derived from the EPA/GRI study. The emission factors used to estimate
CH4were also used to calculate non-combustion CO2 emissions. The Gas Technology Institute's (GTI, formerly
GRI) Unconventional Natural Gas and Gas Composition Databases (GTI 2001) were used to adapt the CH4 emission
factors into non-combustion related CCh emission factors. Additional information about CCh content in
transmission quality natural gas was obtained from numerous U.S. transmission companies to help further develop
the non-combustion CCh emission factors.
Energy 3-65
-------
Although the Inventory primarily uses EPA/GRI emission factors, significant updates were made to the emissions
estimates for two sources in recent Inventories: liquids unloading; and gas well completions with hydraulic
fracturing and workovers with hydraulic fracturing (refracturing). In the case of liquids unloading, in the 2013
Inventory, the methodology was revised to calculate national emissions through the use region-specific emission
factors developed from well data collected in a survey conducted by API/ANGA (API/ANGA 2012). This approach
may result in slight differences in the national results provided by API/ANGA. It is important to note that in this
new methodology, the emission factors used for liquids unloading are not potential factors, but are factors for actual
emissions. In the case of gas well completions with hydraulic fracturing and workovers with hydraulic fracturing
(refracturing), in this Inventory, EPA used the 2011 and 2012 GHGRP Subpart W data to stratify the emission
sources into 4 different categories and developed methane emission factors for each category. See the Recalculation
Discussion below, and EPA's memo "Updating GHG Inventory Estimate for Hydraulically Fractured" for more
information on the methodology for this emission source (EPA 2013d). In addition, recent Inventories introduced
updates to emission factors for production condensate tank vents (both with and without control devices) and
transmission and storage centrifugal compressors (both with wet seals and with dry seals). See the Annex 3.5 for
more detailed information on the methodology and data used to calculate CH4 and non-combustion CCh emissions
from natural gas systems.
Updates to emission factors using GHGRP data for natural gas systems (40 CFR 98, subpart W) and other data
continue to be evaluated.
Activity Data
Activity data were taken from the following sources: Drillinglnfo, Inc (Drillinglnfo 2014), American Gas
Association (AGA 1991-1998); Bureau of Ocean Energy Management, Regulation and Enforcement (previous
Minerals and Management Service) (BOEMRE 201 la, 20lib, 20lie, 201 Id); Natural Gas Liquids Reserves Report
(EIA 2005); Natural Gas Monthly (EIA 2013a, 2013b, 2013c); the Natural GasSTAR Program annual emissions
savings (EPA 2013c); Oil and Gas Journal (OGJ 1997-2013); Pipeline and Hazardous Materials Safety
Administration (PHMSA 2013); Federal Energy Regulatory Commission (FERC 2011); Greenhouse Gas Reporting
Program (EPA 2012 & 2013e); other Energy Information Administration data and publications (EIA 2001, 2004,
2012, 2013, 2014). Data for estimating emissions from hydrocarbon production tanks were incorporated (EPA
1999). Coalbed CH4 well activity factors were taken from the Wyoming Oil and Gas Conservation Commission
(Wyoming 2013) and the Alabama State Oil and Gas Board (Alabama 2013).
For many sources, recent direct activity data are not available. For these sources, either 2011 data was used as proxy
for 2012 data, or a set of industry activity data drivers was developed and used to update activity data. Drivers
include statistics on gas production, number of wells, system throughput, miles of various kinds of pipe, and other
statistics that characterize the changes in the U.S. natural gas system infrastructure and operations. For example,
recent data on various types of field separation equipment in the production stage (i.e., heaters, separators, and
dehydrators) are unavailable. Each of these types of field separation equipment was determined to relate to the
number of non-associated gas wells. Using the number of each type of field separation equipment estimated by
GRI/EPA in 1992, and the number of non-associated gas wells in 1992, a factor was developed that is used to
estimate the number of each type of field separation equipment throughout the time series. More information on
activity data and drivers is available in Annex 3.5.
Step 2. Compile Reductions Data—Calculate the amount of the methane that is not emitted, using data on
voluntary action and regulations
The emissions calculated in Step 1 above represent potential emissions from an activity, and do not take into account
any use of technologies and practices that reduce emissions. To take into account use of such technologies, data,
where available, are collected on both regulatory and voluntary reductions. Regulatory actions reducing emissions
include National Emission Standards for Hazardous Air Pollutants (NESHAP) regulations for dehydrator vents and
condensate tanks. Voluntary reductions included in the Inventory are those reported to Gas STAR for activities such
as replacing a high bleed pneumatic device with a low bleed device, and replacing wet seals with dry seals at
reciprocating compressors. For more information on these reductions, please see the Annex. The emission
estimates presented in Table 3-43 and Table 3-44 are the CH4 that is emitted to the atmosphere (i.e., net emissions),
not potential emissions without capture or flaring.
Current and future Inventories will include impacts of the New Source Performance Standards (NSPS), which came
into effect in October 2012, for oil and gas (EPA 2013b). By separating gas well completions and workovers with
3-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
hydraulic fracturing into four categories and developing control technology-specific methane emission factors for
each category, EPA is implicitly accounting for NSPS reductions from hydraulically fractured gas wells. The NSPS
also has VOC reduction requirements for compressors, storage vessels, pneumatic controllers, and equipment leaks
at processing plants, which will also impact CH4 emissions in future Inventories.
Step 3. Calculate Net Emissions—Deduct methane that is not emitted from the total methane potential estimates to
develop net CH4 emissions, and calculate CCh emissions
In the final step, emission reductions from voluntary and regulatory actions are deducted from the total calculated
potential emissions to estimate the net emissions that are presented in Table 3-43, and included in the Inventory
totals. Note that for liquids unloading, condensate tanks, gas well completions and workovers with hydraulic
fracturing, and centrifugal compressors, emissions are calculated directly using emission factors that vary by
technology and account for any control measures in place that reduce methane emissions. See Annex table A-17 for
more information on net emissions for specific sources.
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis was conducted for previous Inventories to determine the level of uncertainty
surrounding estimates of emissions from natural gas systems using the recommended methodology from IPCC.
EPA produced the results presented below in Table 3-48, which provide with 95 percent certainty the range within
which emissions from this source category are likely to fall for the year 2012. Performed using @RISK software
and the IPCC-recommended Tier 2 methodology (Monte Carlo Simulation technique), this analysis provides for the
specification of probability density functions for key variables within a computational structure that mirrors the
calculation of the Inventory estimate. The IPCC guidance notes that in using this method, "some uncertainties that
are not addressed by statistical means may exist, including those arising from omissions or double counting, or other
conceptual errors, or from incomplete understanding of the processes that may lead to inaccuracies in estimates
developed from models." As a result, the understanding of the uncertainty of emissions estimates for this category
will evolve and will improve as the underlying methodologies and datasets improve.
The @RISK model was used to quantify the uncertainty associated with the emissions estimates using the top
twelve emission sources for the year 2009. The uncertainty analysis has not yet been updated for the 1990 through
2012 Inventory; instead, the uncertainty ranges calculated previously were applied to 2012 emissions estimates. The
majority of sources in the current inventory were calculated using the same emission factors and activity data for
which PDFs were developed in the 1990 through 2009 uncertainty analysis. As noted above, several emissions
sources have been updated with the current Inventory, and the 2009 uncertainty ranges will not reflect the
uncertainty associated with the recently updated emission factors and activity data sources. Please see the
Recalculations discussion.
The results presented below provide with 95 percent certainty the range within which emissions from this source
category are likely to fall for the year 2012, based on the previously conducted uncertainty assessment using the
recommended IPCC methodology. The heterogeneous nature of the natural gas industry makes it difficult to sample
facilities that are completely representative of the entire industry. Additionally, highly variable emission rates were
measured among many system components, making the calculated average emission rates uncertain. The results of
the Tier 2 quantitative uncertainty analysis are summarized in Table 3-48. Natural gas systems CH4 emissions in
2012 were estimated to be between 105.2 and 168.9 Tg COa Eq. at a 95 percent confidence level. Natural gas
systems non-energy CCh emissions in 2012 were estimated to be between 28.5 and 45.8 Tg COa Eq. at 95 percent
confidence level.
Table 3-48: Tier 2 Quantitative Uncertainty Estimates for Cm and Non-energy COz Emissions
from Natural Gas Systems (Tg COz Eq. and Percent)
Source
2012 Emission
Estimate
Gas (Tg CO2 Eq.)b
Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (%)
Lower Upper
Boundb Boundb
Lower Upper
Boundb Boundb
Natural Gas Systems CH4 129.9 105.2 168.9 -19% +30%
Energy 3-67
-------
Natural Gas Systems0 CO2 35.2 28.5 45.8 -19% +30%
a Range of emission estimates predicted by Monte Carlo Simulation for a 95 percent confidence interval.
b All reported values are rounded after calculation. As a result, lower and upper bounds may not be duplicable from
other rounded values as shown in Table 3-44 and Table 3-46.
c An uncertainty analysis for the non-energy CCh emissions was not performed. The relative uncertainty estimated
(expressed as a percent) from the CH4 uncertainty analysis was applied to the point estimate of non-energy CCh
emissions.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification Discussion
The natural gas emission estimates in the Inventory are continually being reviewed and assessed to determine
whether emission factors and activity factors accurately reflect current industry practices. A QA/QC analysis was
performed for data gathering and input, documentation, and calculation. QA/QC checks are consistently conducted
to minimize human error in the model calculations. In addition, a thorough review of information associated with
regulations, public webcasts, and the Natural GasSTAR Program, is performed to determine that the assumptions in
the Inventory are consistent with current industry practices.
In some areas, EPA identified that certain assumptions in the inventory may not be consistent with current industry
practice or that improved data sources may be available to update the inventory. EPA received several comments
suggesting updates to emissions calculations for Natural Gas Systems, such as improvements to pneumatic devices.
Commenters also highlighted information from recent measurements studies. Several recent studies have measured
emissions at the source level (e.g., Allen et al. 2013) and at the national or regional level (e.g., Petron 2012, Miller et
al. 2013, Karion 2013) with results that differ from EPA's estimate of emissions. A recent study (Brandt etal. 2014)
reviewed technical literature on methane emissions and estimated methane emissions from all anthropogenic sources
including oil and gas to be greater than EPA's estimate.
See the Planned Improvements Discussion for more information.
QA/QC of Update to Completions and Workovers with Hydraulic Fracturing
(Refracturing)
In advance of the expert review period, EPA developed a memo describing the update to hydraulically fractured
completions and workovers, and made it available to stakeholders by posting it to EPA's website (EPA 2013d).
This memo allowed additional time to review and incorporate feedback on this update into the current Inventory.
EPA received comments from several stakeholders. All comments received considered the update to be an
improvement over the previous methodology, particularly with respect to improved transparency. Many
commenters had suggestions for improvements to the methodology. Commenters noted that using GHGRP activity
data to calculate national emissions underestimates emissions as not all completions and workovers are reported to
GHGRP. Other commenters suggested that the completion and workover counts developed using GHGRP may be
overestimated due to different interpretations of the data. EPA will continue to assess completion and workover
counts in the Inventory.
EPA continues to evaluate these comments and consider how they may be used to update the Inventory.
Information on suggestions received can be found in the Recalculations Discussion and Planned Improvements
sections of the Inventory.
Recalculations Discussion
EPA received information and data related to the emission estimates through the Inventory preparation process and
previous Inventories' formal public notice periods. EPA carefully evaluated all relevant information provided, and
made updates to estimates for completions with hydraulic fracturing and workovers with hydraulic fracturing
3-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
(refracturing), Natural GasSTAR reductions, and well counts and completion and workover counts. Emission
estimates will continue to be refined to reflect the most robust data and information available.
The recalculations in the current Inventory relative to the previous report primarily impacted CH4 emission estimates
in the production sector, which in 2011 decreased from 53.4 Tg CC>2 Eq. in the previous Inventory to 42.6 Tg CCh
Eq. in the current Inventory.
Completions with Hydraulic Fracturing and Workovers with Hydraulic Fracturing
(Refracturing)
Changes made to the methodology for completions with hydraulic fracturing and workovers with hydraulic
fracturing (refracturing) resulted in a decrease in the estimate of CH4 emissions.
A number of stakeholder comments to the previous Inventory supported moving away from the use of a potential
methane factor, and moving toward use of control technology-specific, net emission factors for HF gas well
completions and workovers. Commenters suggested that EPA continue to review data reported to the Petroleum and
Natural Gas Systems source category (Subpart W) of EPA's GHGRP, and seek other data on emissions from HF gas
well completions and workovers to evaluate emission factors and the coverage of the data on reductions from RECs
and flaring.
During the development of the current Inventory, EPA evaluated the 2011 and 2012 GHGRP Subpart W data on gas
well completions and workovers with hydraulic fracturing. Completions and workovers from the data set were
stratified into four different categories: hydraulic fracturing completions and workovers that vent, flared hydraulic
fracturing completions and workovers, hydraulic fracturing completions and workovers with RECs, and hydraulic
fracturing completions and workovers with RECs that flare. For each category, 2011 and 2012 GHGRP Subpart W
data were used to develop control technology-specific methane emission factors and estimate corresponding activity
data for the entire time series. Further description of the methodology is available in EPA's published memo
"Updating GHG Inventory Estimate for Hydraulically Fractured Gas Well Completions and Workovers" (EPA
2013).
EPA developed a time series of activity data for each category for 1990 through 2012. For RECs, EPA assumed 0
percent RECs use from 1990-2000, used GHGRP RECs percentage for 2011 and 2012, and then uses linear
interpolation between the 2000 and 2011 percentages. For flaring, EPA used an assumption of 10 percent (the
average of the percentage of completions and workovers that were flared in 2011 and 2012 GHGRP data) flaring
from 1990-2010 to recognize that some flaring has occurred over that time period. For 2011 and 2012, EPA uses
the GHGRP data on flaring. The remainder of completions and workovers are assigned to the venting category.
EPA plans to use GHGRP data annually to develop activity data for the four completion/workover categories. This
will allow the inventory to reflect changes in RECs counts and flaring, including those resulting from NSPS OOOO.
Emissions of CH4 from gas well completions and workovers with hydraulic fracturing were calculated at the NEMS
regional level, to be consistent with other production sector subcategories. EPA calculated emissions at the NEMS
region level using regional counts for workovers and completions, but used national emission factors and national
assumptions for the split between completions and workovers in the four categories. Emissions rates and practices
will vary by region, and future inventories may reflect this variability.
Because the revised emission factors for this source vary by technology and account for any control measures in
place that reduce methane emissions, reductions reported to the Natural Gas STAR Program from the use of reduced
emissions completions are implicitly included in the net emissions estimate, and are no longer deducted from the
Inventory. Likewise, regulatory reductions are implicitly included using the new methodology and are therefore not
separately deducted. Stakeholders interested in information on the reductions reported to the Natural Gas STAR
Program can find this information at http://www.epa.gov/gasstar/accomplislments/index.htmMthree.
EPA received several comments on the expert review and public drafts generally supporting this approach, and
several comments recommending improvements to the data used in the approach.
Several commenters recommended use of data from a study of production sector emissions led by University of
Texas Austin, sponsored by the Environmental Defense Fund (UT Austin-EDF study) (Allen et al. 2013) to develop
the emission factors, and several commenters suggested using data from UT Austin-EDF for comparison and
verification of the emissions factors. Commenters provided comparison to UT Austin-EDF study emission factors.
Energy 3-69
-------
The average emissions from the GHGRP data for wells that vent without flaring or RECs (41 Mg CH4) are
significantly higher than the UT Austin-EDF study average value (0.83 Mg CH4); however, according to
commenters, it is within the range of expected values based on the study (1-124 Mg CH4). The UT Austin-EDF
study had an average emissions value for wells with RECs that do not flare that was similar to the GHGRP data (4
Mg CH4 for UT Austin-EDF, versus 3 Mg CH4 for GHGRP). UT Austin-EDF observed lower emissions from wells
with RECs that flare (1.5-1.8 Mg CH4) than the GHGRP data (6 Mg CH4).
Other commenters suggested that EPA use only measured data in its calculation, and suggested that EPA remove
outliers from the GHGRP data set. Commenters provided factors calculated using only GHGRP measured data for
2011 and 2012. Factors from measured data for wells that vent and wells with RECs that vent were less than 50
percent the value of the factors calculated using all data. Factors calculated using measured data for RECs with
flaring were higher than values calculated using all data, and factors using measured data only for wells that flare
without RECs were lower than using all data in 2011 and higher in 2012. Commenters noted that the accuracy of
the proposed approach depends on the accuracy of the GHGRP data. Commenters noted their concerns with the use
of calculation equations in GHGRP, noting that they may over- or under estimate emissions, compared with
measured data. Commenters suggesting removal of outliers noted that a small number of companies reporting to
GHGRP represent a majority of total emissions in 2012.
EPA used all of the data on hydraulically fractured gas well completions and workovers, as reported to the GHGRP.
EPA received comments suggesting moving to a two category approach—a category for wells without RECs or
flaring, and a category for wells with RECs and/or flaring. The commenter suggested that the emission factors for
wells with RECs with and without flaring and wells that flare without RECs are very similar. The commenter also
notes some potential ambiguity in how wells with purposefully designed separation equipment are categorized in the
RECs and flaring categories. EPA has not changed the approach in the final Inventory, but will consider these
changes for future inventories.
Natural GasSTAR Reductions
In general, the Inventory continues to use aggregated GasSTAR reductions by natural gas system segment (i.e.,
production, processing, transmission and storage, and distribution). For some sources, specific emissions reductions
activities reported to GasSTAR are matched to potential emissions calculated in the Inventory to calculate net
emissions for those sources. The QA/QC of the Inventory identified areas for improvement and resulted in several
updates to the Gas STAR reductions methodology for the current Inventory.
Two updates impacted the total reductions included in the Inventory.
(1) EPA revised the categorization of some reduction activities from one-year to ongoing. One-year reduction
activities refer to those activities that accrue reductions for only the year in which they were conducted and have to
be repeated every year to accrue reductions every year. For example, "directed inspection and maintenance" has to
be conducted every year and in each year the reduction from that year is accounted in the inventory. On the other
hand, ongoing reductions refer to those activities that once implemented accrue reductions every year that point
onwards, such as a vapor recovery system on a crude oil storage tank. In QA/QC of the Gas STAR reduction data
for this year's Inventory, EPA identified certain ongoing reduction activities such as "reduce emissions when taking
compressors offline" that were miscategorized as one year reductions in previous Inventories, and recategorized
them to ongoing reductions, as appropriate.
(2) EPA moved the following eight reduction activities (Artificial lift: gas lift, Artificial lift: use compression,
Artificial lift: use pumping unit, Consolidate crude oil prod and water storage tanks, Lower heater-treater
temperature, Re-inject gas for enhanced oil recovery, Re-inject gas into crude, and Route casinghead gas to VRU or
compressor) to the Petroleum Systems estimate from the Natural Gas Systems estimate because the corresponding
emission sources reside in Petroleum Systems.
The net impact of these two changes is an average increase in Gas STAR reductions of around 1% across the time
series.
Other changes did not impact the quantity of Gas STAR reductions removed from the Inventory, but instead
impacted the allocation of reductions between activities and segments of the Inventory.
3-70 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Several categories have been added to the net emissions table (Annex Table A-125) (chemical injection pumps in
production, fugitives reciprocating compressors and AGR vents in processing, gas engines in transmission and
storage, mishaps/dig-ins in distribution), and several categories have been removed from the net emissions table
(kimray pumps from production and blowdowns and venting from processing) and included in the "other
reductions" category. EPA determined that several reduction activities that were in prior years included in the "other
reductions" category can be mapped to relevant emission sources. For example, the reduction activity "convert
natural gas driven chemical pumps" can be mapped to the "chemical injection pumps" emission source in the
production segment. On the other hand, some reduction activities were incorrectly mapped to the emission sources
in the previous Inventory. For example, the reduction activity "use composite wrap repair" was earlier associated
with "blowdown venting" in processing. However, this is incorrect as the reduction activity is not related to the
blowdown activity. Such discrepancies were addressed in this year's Inventory.
In addition, some reduction activities have been re-allocated to different segments of the industry. For example, the
reduction activity "identify and rehabilitate leaky distribution pipe" was moved from the "other" category in
transmission to the "other" category in distribution.
"CH4 Reductions Derived from the Natural GasSTAR Program (Gg)" in Annex 3.5 presents sources for which
GasSTAR reductions can be matched to Inventory emissions sources, and net emissions values for these sources are
presented in Table "Net emissions for select sources (Gg)" of Annex 3.5.
Well Counts and Completion and Workover Counts
EPA reassessed its processing of Drillinglnfo data for well counts and completion counts, and updated its
methodology for the Inventory. As a result, total gas well counts across the time series increased by around 6
percent compared to the 2013 Inventory counts, leading to an increase in calculated emissions. This is primarily due
to two factors that differ from last year's Inventory methodology:
(1) The methodology which processes the raw Drillinglnfo data into a table containing individual well information
by year was recently updated to distribute reported lease-level production among all wells on the lease. Previously,
lease-level production was attributed to a single well on a lease, meaning the other wells on the lease were not
eligible to be counted as actively producing in a given year. In the previous Inventory, state-specific processing was
conducted to account for this as a known issue for Michigan wells. Now that the distribution methodology is applied
across the board, well counts in other states also increased.
(2) The crosswalk used to assign individual wells to a NEMS region is on a state level except for Texas and New
Mexico; these states span multiple NEMS regions, and the crosswalk is on a county level. During QC, slight errors
in the reported county name (e.g., misspellings) were identified in several Drillinglnfo records. The NEMS
crosswalk was updated to include all reported variations of county names in TX and MM, increasing well counts in
these states across the time series in each category.
Flaring Emissions
In addition to the methodological updates described above, an update to the data source for COa from flaring
resulted in an increase in those emissions of approximately 3 Tg CC>2 Eq. This change in the emissions estimate
does not reflect a change in the Inventory methodology, but instead an update to the underlying data source from
EIA. EIA activity data on the amount of natural gas vented and flared for 2011 were not available for last year's
Inventory. EPA used 2010 activity data from EIA as a proxy for 2011. Updated EIA activity data for 2011 showed a
larger quantity of gas vented and flared than the previous EIA estimate. The use of the updated activity data
increased the emissions estimate from this source. The vented and flared gas volume published by EIA includes
both onshore production and processing segment estimates, but the label in previous Inventory tables incorrectly
indicated the flaring emissions to be from the production segment only. EPA has updated the annex tables to
indicate that this source of emissions includes both production and processing.
EPA will continue to refine the emission estimates to reflect the most robust information available. Substantial
amounts of new information will be made available in the coming years through a number of channels, including
Energy 3-71
-------
EPA's GHGRP, research studies by various organizations, government and academic researchers, and industry.
There are relevant ongoing studies that are collecting new information related to natural gas system emissions (e.g.
GTI data on pipelines, University of Texas at Austin (UT Austin) and Environmental Defense Fund (EDF) data on
natural gas systems). EPA looks forward to reviewing information and data from these studies as they become
available for potential incorporation in the Inventory. For example, EPA anticipates reviewing upcoming data on
transmission and storage, and distribution system emissions for potential updates to the 1990-2013 Inventory report.
Well Counts and Completion and Workover Counts
Commenters on the public review draft suggested that EPA review its estimate of completions and workover counts
from the GHGRP data, noting that there are different interpretations of the data that would result in different counts.
For example, for the Inventory, EPA calculated total workovers in GHGRP as the sum of the number of reported
vented and flared workovers plus the number of reported workovers with purposely designed separation equipment.
Other groups have interpreted the workovers with purposely designed separation equipment category to be a subset
of total vented and flared workovers. Other commenters noted that using GHGRP activity data to calculate national
emissions underestimates emissions as not all completions and workovers are reported to GHGRP. EPA will
continue to assess well counts and completions and workover counts in the Inventory to determine where
improvements can be made.
Uncertainty Analysis
Since EPA last calculated uncertainty in the Inventory, several updates have been made which may impact the
uncertainty estimate (e.g. updates to liquids unloading and hydraulically fractured well completions and workovers).
EPA plans to update uncertainty in future inventories.
Methane Measurement Studies
Several recent studies have measured emissions at the source level (e.g., Allen et al. 2013) and at the national or
regional level (e.g., Petron et al. 2012, Miller et al. 2013, Karion et al. 2013) and calculated emissions estimates that
differ from EPA's estimate of emissions. A recent study (Brandt et al. 2014) reviewed technical literature on
methane emissions, and estimated methane emissions from all anthropogenic sources including oil and gas to be
greater than EPA's estimate. EPA is considering how such measurement studies can be used to update Inventory
estimates. Some factors for consideration include whether measurements taken are representative of all natural gas
producing areas in the United States, and what activities were taking place at the time of measurement (general
operating conditions or high-emission venting events), and how such measurements can inform emission factors and
activity data used to calculate national emissions.
Commenters on the public review draft specifically highlighted articles and studies on leaks from distribution
systems in cities (e.g., McGeehan et al. 2014, Jackson et al. 2014, Payne and Ackley 2012, Payne and Ackley 2013a,
2013b; Phillips et al. 2012; Peischl et al. 2013), and recommended that EPA update its estimates of emissions from
distribution systems.
Some commenters suggested that top down studies are complementary to bottom up calculations and noted that as
studies improve, they will illuminate specific sources for re-examination in bottom up studies. Commenters
suggested that these studies can provide independent data on overall emissions from Industry. Some commenters
encouraged EPA to find ways to utilize measurement data to update the Inventory. Commenters suggested that
based on review of atmospheric and other studies, sources that may be underestimated include wells, pneumatic
devices, and liquids unloading.
Other commenters suggested that top down studies can be used for gross verification of estimates, but that data from
bottom up studies are more suitable for updates to the Inventory. Commenters suggested that the existing studies
have been either regional and not representative of the U.S., or do not represent current operations. Commenters
noted that studies attempting to reconcile differences between top down and bottom up estimates are underway.
EPA will continue to review new data from measurement studies to assess and potentially update Inventory
estimates.
3-72 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Pneumatic Devices
Commenters suggested that emissions from pneumatic devices may be underestimated and noted a number of
current and upcoming data sources that could be used to update CH4 emission factors from pneumatic devices,
including UT Austin-EDF, GHGRP, and a 2013 British Columbia pneumatic device study (Prasino 2013).
Commenters on the public and expert review draft Inventories suggested that EPA develop net factors for different
categories of pneumatic devices, such as high-bleed, intermittent-bleed, low-bleed, and no-bleed, noting that in the
future, GHGRP could be a source of activity data for this approach in 2015 when activity data is reported, or that
EPA could estimate activity counts using GHGRP data and an estimate of coverage of GHGRP. One commenter
suggested that EPA update pneumatic device emissions estimates in the Inventory using GHGRP data (noting that
total emissions for pneumatic devices in both the oil and gas sectors in GHGRP are 861 Gg CH4 (18.0 Tg COa Eq.)
compared with 787 Gg CH4 (16.5 Tg CC>2 Eq.) in the expert review draft Inventory) and scaling up emissions to the
national level. Further, commenters recalculated emissions from this source using emission factors developed with
UT Austin-EDF data, and calculated natural gas and oil emissions from pneumatic devices to be 1,139 Gg CH4 (23.9
Tg COa Eq.). EPA is evaluating potential improvements to its pneumatic devices estimates for future Inventories.
Other Methodological and Data Updates
EPA is evaluating potential improvements and clarifications to equipment leaks and gathering and boosting
calculations.
EPA will review its methodology for hydraulically fractured gas well completions and workovers and consider
moving to a two-factor approach (controlled and uncontrolled) instead of the four-factor approach (uncontrolled,
flared, RECs without flaring and RECs with flaring) used in the current Inventory. Commenters on the public
review draft suggested that EPA use national factors for liquids unloading instead of regional factors. EPA will
consider these approaches for future inventories.
Energy 3-73
-------
3.8 Energy Sources of Indirect Greenhouse Gas
Emissions
In addition to the main greenhouse gases addressed above, many energy-related activities generate emissions of
indirect greenhouse gases. Total emissions of nitrogen oxides (NOX), carbon monoxide (CO), and non-CH4 volatile
organic compounds (NMVOCs) from energy-related activities from 1990 to 2012 are reported in Table 3-49.
Table 3-49: NQX, CO, and NMVOC Emissions from Energy-Related Activities (Gg)
Gas/Source
1990
2005
2008
2009
2010
2011
" These values are presented for informational purposes only and are not included in totals.
Note: Totals may not sum due to independent rounding.
2012
NOx
Mobile Combustion
Stationary Combustion
Oil and Gas Activities
Waste Combustion
International Bunker
Fuels"
CO
Mobile Combustion
Stationary Combustion
Waste Combustion
Oil and Gas Activities
International Bunker
Fuels*
NMVOCs
Mobile Combustion
Oil and Gas Activities
Stationary Combustion
Waste Combustion
International Bunker
Fuels"
21
10
10
1
125
119
5
12
10
,106
,862
,023
139
82
,956
,640
,360
,000
978
302
103
,620
,932
554
912
222
57
16,542
10,250
5,847
317 1
128 1
1,704
64,427
58,062
4,644
1,402
318
133
7,133
5,667
510
715 1
241 1
54
13,651
8,481
4,698
386
85
1,832
51,444
46,003
3,959
1,244
238
129
7,283
5,059
1,580
530
114
57
12,720
7,809
4,365
464
81
1,692
44,785
39,219
4,036
1,164
366
121
7,114
4,652
1,806
553
103
53
11,959
7,307
4,031
543
77
1,790
45,158
39,468
4,112
1,085
493
136
7,295
4,596
2,032
576
92
56
11,696
7,214
3,787
621
73
1,553
43,300
37,486
4,188
1,005
621
137
7,058
4,118
2,257
602
81
51
10,964
6,732
3,538
621
73
1,398
43,300
37,486
4,188
1,005
621
133
6,865
3,925
2,257
602
81
46
Methodology
Emission estimates for 1990 through 2012 were obtained from data published on the National Emission Inventory
(NET) Air Pollutant Emission Trends web site (EPA 2013), and disaggregated based on EPA (2003). Emission
estimates for 2012 for non-EGU and non-mobile sources are held constant from 2011 in EPA (2013). Emissions
were calculated either for individual categories or for many categories combined, using basic activity data (e.g., the
amount of raw material processed) as an indicator of emissions. National activity data were collected for individual
applications from various agencies.
Activity data were used in conjunction with emission factors, which together relate the quantity of emissions to the
activity. Emission factors are generally available from the EPA's Compilation of Air Pollutant Emission Factors,
AP-42 (EPA 1997). The EPA currently derives the overall emission control efficiency of a source category from a
variety of information sources, including published reports, the 1985 National Acid Precipitation and Assessment
Program emissions inventory, and other EPA databases.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
3-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Uncertainty and Time-Series Consistency
Uncertainties in these estimates are partly due to the accuracy of the emission factors used and accurate estimates of
activity data. A quantitative uncertainty analysis was not performed.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
3.9 International Bunker Fuels (IPCC Source
Category 1: Memo Items)
Emissions resulting from the combustion of fuels used for international transport activities, termed international
bunker fuels under the UNFCCC, are not included in national emission totals, but are reported separately based upon
location of fuel sales. The decision to report emissions from international bunker fuels separately, instead of
allocating them to a particular country, was made by the Intergovernmental Negotiating Committee in establishing
the Framework Convention on Climate Change.107 These decisions are reflected in the IPCC methodological
guidance, including the 2006 IPCC Guidelines, in which countries are requested to report emissions from ships or
aircraft that depart from their ports with fuel purchased within national boundaries and are engaged in international
transport separately from national totals (IPCC 2006).108
Two transport modes are addressed under the IPCC definition of international bunker fuels: aviation and marine.109
Greenhouse gases emitted from the combustion of international bunker fuels, like other fossil fuels, include CO2,
CH4 and N2O for marine transport modes, and CO2 and N2O for aviation transport modes. Emissions from ground
transport activities—by road vehicles and trains—even when crossing international borders are allocated to the
country where the fuel was loaded into the vehicle and, therefore, are not counted as bunker fuel emissions.
The IPCC Guidelines distinguish between different modes of air traffic. Civil aviation comprises aircraft used for
the commercial transport of passengers and freight, military aviation comprises aircraft under the control of national
armed forces, and general aviation applies to recreational and small corporate aircraft. The IPCC Guidelines further
define international bunker fuel use from civil aviation as the fuel combusted for civil (e.g., commercial) aviation
purposes by aircraft arriving or departing on international flight segments. However, as mentioned above, and in
keeping with the IPCC Guidelines, only the fuel purchased in the United States and used by aircraft taking-off (i.e.,
departing) from the United States are reported here. The standard fuel used for civil aviation is kerosene-type jet
fuel, while the typical fuel used for general aviation is aviation gasoline.110
Emissions of CO2 from aircraft are essentially a function of fuel use. N2O emissions also depend upon engine
characteristics, flight conditions, and flight phase (i.e., take-off, climb, cruise, decent, and landing). Recent data
suggest that little or no CH4 is emitted by modern engines (Anderson et al., 2011), and as a result, CH4 emissions
from this category are considered zero. In jet engines, N2O is primarily produced by the oxidation of atmospheric
nitrogen, and the majority of emissions occur during the cruise phase. International marine bunkers comprise
emissions from fuels burned by ocean-going ships of all flags that are engaged in international transport. Ocean-
going ships are generally classified as cargo and passenger carrying, military (i.e., U.S. Navy), fishing, and
107 See report of the Intergovernmental Negotiating Committee for a Framework Convention on Climate Change on the work of
its ninth session, held at Geneva from 7 to 18 February 1994 (A/AC.237/55, annex I, para. Ic).
108 Note that the definition of international bunker fuels used by the UNFCCC differs from that used by the International Civil
Aviation Organization.
109 Most emission related international aviation and marine regulations are under the rubric of the International Civil Aviation
Organization (ICAO) or the International Maritime Organization (IMO), which develop international codes, recommendations,
and conventions, such as the International Convention of the Prevention of Pollution from Ships (MARPOL).
110 Naphtha-type jet fuel was used in the past by the military in turbojet and turboprop aircraft engines.
Energy 3-75
-------
miscellaneous support ships (e.g., tugboats). For the purpose of estimating greenhouse gas emissions, international
bunker fuels are solely related to cargo and passenger carrying vessels, which is the largest of the four categories,
and military vessels. Two main types of fuels are used on sea-going vessels: distillate diesel fuel and residual fuel
oil. CO2 is the primary greenhouse gas emitted from marine shipping.
Overall, aggregate greenhouse gas emissions in 2012 from the combustion of international bunker fuels from both
aviation and marine activities were 106.9 Tg CCh Eq., or 2.3 percent above emissions in 1990 (see Table 3-50 and
Table 3-51). Emissions from international flights and international shipping voyages departing from the United
States have increased by 69.7 percent and decreased by 36.9 percent, respectively, since 1990. The majority of these
emissions were in the form of CCh; however, small amounts of CH4 (from marine transport modes) and N2O were
also emitted.
Table 3-50: COz, CH4, and NzO Emissions from International Bunker Fuels (Tg COz Eq.)
Gas/Mode
CO2
Aviation
Commercial
Military
Marine
CH4
Aviation
Marine
N2O
Aviation
Marine
Total
1990
103.5
38.0
30.0
8.1
65.4
0.1
0
0.1
0.9
0.4
0.5
104.5
2005
113.1
60.1
55. 6
4.5
53.0
0.1
0
0.1
1.0
0.6
0.4
114.3
2008
114.3
56.1
52.4
3.8
58.2
0.1
0
0.1
1.0
0.6
0.5
115.5
2009
106.4
52.8
49.2
3.6
53.6
0.1
0
0.1
0.9
0.5
0.4
107.5
2010
117.0
61.0
57.4
3.6
56.0
0.1
0
0.1
1.0
0.6
0.4
118.2
2011
111.7
64.8
61.7
3.1
46.9
0.1
0
0.1
1.0
0.6
0.4
112.8
2012
105.8
64.5
61.4
3.1
41.3
0.1
0
0.1
1.0
0.6
0.3
106.9
Note: Totals may not sum due to independent rounding. Includes aircraft cruise altitude
emissions.
Table 3-51: COz, ChU and NzO Emissions from International Bunker Fuels (Gg)
Gas/Mode
1990
2005
2008
2009
2010
2011
2012
CO2
Aviation
Marine
CH4
Aviation
Marine
N2O
Aviation
Marine
Note: Totals may not sum due to independent rounding. Includes aircraft cruise altitude emissions.
Table 3-52: Aviation COz and NzO Emissions for International Transport (Tg COz Eq.)
Aviation Mode
Commercial Aircraft
Military Aircraft
Total
1990
30.0
8.1
38.0
2005
55.6
4.5
60.1
2008
52.4
1 3.8
56.1
2009
49.2
3.6
52.8
2010
57.4
3.6
61.0
2011
61.7
3.1
64.8
2012
61.4
3.1
64.5
Note: Totals may not sum due to independent rounding. Includes aircraft cruise altitude emissions.
3-76 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Emissions of CO2 were estimated by applying C content and fraction oxidized factors to fuel consumption activity
data. This approach is analogous to that described under CO2 from Fossil Fuel Combustion. Carbon content and
fraction oxidized factors for jet fuel, distillate fuel oil, and residual fuel oil were taken directly from EIA and are
presented in Annex 2.1, Annex 2.2, and Annex 3.8 of this Inventory. Density conversions were taken from Chevron
(2000), ASTM (1989), and USAF (1998). Heat content for distillate fuel oil and residual fuel oil were taken from
EIA (2014) and USAF (1998), and heat content for jet fuel was taken from EIA (2013). A complete description of
the methodology and a listing of the various factors employed can be found in Annex 2.1. See Annex 3.8 for a
specific discussion on the methodology used for estimating emissions from international bunker fuel use by the U.S.
military.
Emission estimates for CH4 and N2O were calculated by multiplying emission factors by measures of fuel
consumption by fuel type and mode. Emission factors used in the calculations of CH4 and N2O emissions were
obtained from the Revised 1996IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997) and the 2006IPCC Guidelines
(IPCC 2006). For aircraft emissions, the following values, in units of grams of pollutant per kilogram of fuel
consumed (g/kg), were employed: 0.1 for N2O (IPCC 2006). For marine vessels consuming either distillate diesel or
residual fuel oil the following values (g/MJ), were employed: 0.32 for CH4 and 0.08 for N2O. Activity data for
aviation included solely jet fuel consumption statistics, while the marine mode included both distillate diesel and
residual fuel oil.
Activity data on domestic and international aircraft fuel consumption were developed by the U.S. Federal Aviation
Administration (FAA) using radar-informed data from the FAA Enhanced Traffic Management System (ETMS) for
1990, 2000 through 2013 as modeled with the Aviation Environmental Design Tool (AEDT). This bottom-up
approach is built from modeling dynamic aircraft performance for each flight occurring within an individual
calendar year. The analysis incorporates data on the aircraft type, date, flight identifier, departure time, arrival time,
departure airport, arrival airport, ground delay at each airport, and real-world flight trajectories. To generate results
for a given flight within AEDT, the radar-informed aircraft data is correlated with engine and aircraft performance
data to calculate fuel burn and exhaust emissions. Information on exhaust emissions for in-production aircraft
engines comes from the International Civil Aviation Organization (ICAO) Aircraft Engine Emissions Databank
(EDB). This bottom-up approach is in accordance with the Tier 3B method from the 2006 IPCC Guidelines for
National Greenhouse Gas Inventories.
International aviation CO2 estimates for 1990 and 2000 through 2012 are obtained from FAA's AEDT model (FAA
2013). The radar-informed method that was used to estimate CO2 emissions for commercial aircraft for 1990, and
2000 through 2012 is not possible for 1991 through 1999 because the radar data set is not available for years prior to
2000. FAA developed OAG schedule-informed inventories modeled with AEDT and great circle trajectories for
1990, 2000 and 2010. Because fuel consumption and CO2 emission estimates foryears 1991-1999 are unavailable,
consumption estimates for these years were calculated using fuel consumption estimates from the Bureau of
Transportation Statistics (DOT 1991 through 2012), adjusted based on 2000 through 2005 data.
Data on U.S. Department of Defense (DoD) aviation bunker fuels and total jet fuel consumed by the U.S. military
was supplied by the Office of the Under Secretary of Defense (Installations and Environment), DoD. Estimates of
the percentage of each Service's total operations that were international operations were developed by DoD.
Military aviation bunkers included international operations, operations conducted from naval vessels at sea, and
operations conducted from U.S. installations principally over international water in direct support of military
operations at sea. Military aviation bunker fuel emissions were estimated using military fuel and operations data
synthesized from unpublished data by the Defense Energy Support Center, under DoD's Defense Logistics Agency
(DLA Energy 2013). Together, the data allow the quantity of fuel used in military international operations to be
estimated. Densities for each jet fuel type were obtained from a report from the U.S. Air Force (USAF 1998). Final
jet fuel consumption estimates are presented in Table 3-53. See Annex 3.8 for additional discussion of military data.
Activity data on distillate diesel and residual fuel oil consumption by cargo or passenger carrying marine vessels
departing from U.S. ports were taken from unpublished data collected by the Foreign Trade Division of the U.S.
Department of Commerce's Bureau of the Census (DOC 2011) for 1990 through 2001, 2007, through 2011, and the
Department of Homeland Security's Bunker Report for 2003 through 2006 (DHS 2008). Fuel consumption data for
2002 was interpolated due to inconsistencies in reported fuel consumption data. Activity data on distillate diesel
consumption by military vessels departing from U.S. ports were provided by DLA Energy (2013). The total amount
Energy 3-77
-------
of fuel provided to naval vessels was reduced by 13 percent to account for fuel used while the vessels were not-
underway (i.e., in port). Data on the percentage of steaming hours underway versus not-underway were provided by
the U.S. Navy. These fuel consumption estimates are presented in. Table 3-54.
Table 3-53: Aviation Jet Fuel Consumption for International Transport (Million Gallons)
Nationality
U.S. and Foreign Carriers
U.S. Military
Total
1990
3,222
862
4,084
2005
5,983
462 1
6,445
2008
5,634
1 386
6,021
2009
5,293
367
5,660
2010
6,173
367
6,540
2011
6,634
319
6,953
2012
6,604
321
6,925
Note: Totals may not sum due to independent rounding.
Table 3-54: Marine Fuel Consumption for International Transport (Million Gallons)
Fuel Type
Residual Fuel Oil
Distillate Diesel Fuel & Other
U.S. Military Naval Fuels
Total
1990
4,781
617
522
5,920
2005
3,881
444
471
4,796
2008
4,373
445
1 437
5,254
2009
4,040
426
374
4,841
2010
4,141
476
448
5,065
2011
3,463
393
382
4,237
2012
3,069
280
381
3,730
Note: Totals may not sum due to independent rounding.
Uncertainty and Time-Series Consistency
Emission estimates related to the consumption of international bunker fuels are subject to the same uncertainties as
those from domestic aviation and marine mobile combustion emissions; however, additional uncertainties result
from the difficulty in collecting accurate fuel consumption activity data for international transport activities separate
from domestic transport activities.111 For example, smaller aircraft on shorter routes often carry sufficient fuel to
complete several flight segments without refueling in order to minimize time spent at the airport gate or take
advantage of lower fuel prices at particular airports. This practice, called tankering, when done on international
flights, complicates the use of fuel sales data for estimating bunker fuel emissions. Tankering is less common with
the type of large, long-range aircraft that make many international flights from the United States, however. Similar
practices occur in the marine shipping industry where fuel costs represent a significant portion of overall operating
costs and fuel prices vary from port to port, leading to some tankering from ports with low fuel costs.
Uncertainties exist with regard to the total fuel used by military aircraft and ships, and in the activity data on military
operations and training that were used to estimate percentages of total fuel use reported as bunker fuel emissions.
Total aircraft and ship fuel use estimates were developed from DoD records, which document fuel sold to the Navy
and Air Force from the Defense Logistics Agency. These data may slightly over or under estimate actual total fuel
use in aircraft and ships because each Service may have procured fuel from, and/or may have sold to, traded with,
and/or given fuel to other ships, aircraft, governments, or other entities. There are uncertainties in aircraft operations
and training activity data. Estimates for the quantity of fuel actually used in Navy and Air Force flying activities
reported as bunker fuel emissions had to be estimated based on a combination of available data and expert judgment.
Estimates of marine bunker fuel emissions were based on Navy vessel steaming hour data, which reports fuel used
while underway and fuel used while not underway. This approach does not capture some voyages that would be
classified as domestic for a commercial vessel. Conversely, emissions from fuel used while not underway preceding
an international voyage are reported as domestic rather than international as would be done for a commercial vessel.
There is uncertainty associated with ground fuel estimates for 1997 through 2001. Small fuel quantities may have
been used in vehicles or equipment other than that which was assumed for each fuel type.
There are also uncertainties in fuel end-uses by fuel-type, emissions factors, fuel densities, diesel fuel sulfur content,
aircraft and vessel engine characteristics and fuel efficiencies, and the methodology used to back-calculate the data
111 See uncertainty discussions under Carbon Dioxide Emissions from Fossil Fuel Combustion.
3-78 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
set to 1990 using the original set from 1995. The data were adjusted for trends in fuel use based on a closely
correlating, but not matching, data set. All assumptions used to develop the estimate were based on process
knowledge, Department and military Service data, and expert judgments. The magnitude of the potential errors
related to the various uncertainties has not been calculated, but is believed to be small. The uncertainties associated
with future military bunker fuel emission estimates could be reduced through additional data collection.
Although aggregate fuel consumption data have been used to estimate emissions from aviation, the recommended
method for estimating emissions of gases other than CCh in the 2006IPCC Guidelines is to use data by specific
aircraft type, number of individual flights and, ideally, movement data to better differentiate between domestic and
international aviation and to facilitate estimating the effects of changes in technologies. The IPCC also recommends
that cruise altitude emissions be estimated separately using fuel consumption data, while landing and take-off (LTO)
cycle data be used to estimate near-ground level emissions of gases other than CO2.112
There is also concern regarding the reliability of the existing DOC (2011) data on marine vessel fuel consumption
reported at U.S. customs stations due to the significant degree of inter-annual variation.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above
QA/QC and Verification
A source-specific QA/QC plan for international bunker fuels was developed and implemented. This effort included
a Tier 1 analysis, as well as portions of a Tier 2 analysis. The Tier 2 procedures that were implemented involved
checks specifically focusing on the activity data and emission factor sources and methodology used for estimating
CO2, CH4, and N2O from international bunker fuels in the United States. Emission totals for the different sectors and
fuels were compared and trends were investigated. No corrective actions were necessary.
Recalculations Discussion
Changes to emission estimates are due to revisions made to historical activity data for military aircraft consumption
from DLA Energy 2013. These historical data changes resulted in changes to the emission estimates for the most
recent inventory year compared to the previous Inventory. This equaled an increase in emissions from international
bunker fuels of 0.3 Tg CCh Eq. (0.3 percent) in total emissions in 2011.
112 U.S. aviation emission estimates for CO, NOX, and NMVOCs are reported by EPA's National Emission Inventory (NEI) Air
Pollutant Emission Trends web site, and reported under the Mobile Combustion section. It should be noted that these estimates
are based solely upon LTO cycles and consequently only capture near ground-level emissions, which are more relevant for air
quality evaluations. These estimates also include both domestic and international flights. Therefore, estimates reported under the
Mobile Combustion section overestimate IPCC-defined domestic CO, NOX, and NMVOC emissions by including landing and
take-off (LTO) cycles by aircraft on international flights, but underestimate because they do not include emissions from aircraft
on domestic flight segments at cruising altitudes. The estimates in Mobile Combustion are also likely to include emissions from
ocean-going vessels departing from U.S. ports on international voyages.
Energy 3-79
-------
3.10 Wood Biomass and Ethanol
Consumption (IPCC Source Category 1A)
The combustion of biomass fuels such as wood, charcoal, and wood waste and biomass-based fuels such as ethanol
generates CC>2 in addition to CH4 and N2O already covered in this chapter. In line with the reporting requirements
for inventories submitted under the UNFCCC, CC>2 emissions from biomass combustion have been estimated
separately from fossil fuel CC>2 emissions and are not directly included in the energy sector contributions to U.S.
totals. In accordance with IPCC methodological guidelines, any such emissions are calculated by accounting for net
carbon (C) fluxes from changes in biogenic C reservoirs in wooded or crop lands. For a more complete description
of this methodological approach, see the Land Use, Land-Use Change, and Forestry chapter (Chapter 7), which
accounts for the contribution of any resulting CCh emissions to U. S. totals within the Land Use, Land-Use Change
and Forestry sector's approach.
In 2012, total CCh emissions from the burning of woody biomass in the industrial, residential, commercial, and
electricity generation sectors were approximately 194.0 Tg CCh Eq. (194,003 Gg) (see Table 3-55 and Table 3-56).
As the largest consumer of woody biomass, the industrial sector was responsible for 64.3 percent of the CCh
emissions from this source. The residential sector was the second largest emitter, constituting 22.3 percent of the
total, while the commercial and electricity generation sectors accounted for the remainder.
Table 3-55: COz Emissions from Wood Consumption by End-Use Sector (Tg COz Eq.)
End-Use Sector
Industrial
Residential
Commercial
Electricity Generation
Total
1990
135.3
59.8
6.8 1
13.3
215.2
1 2005
136.3
44.3
1 19.1
206.9
2008
125.7
48.5
7.5
18.3
199.9
2009
110.6
51.6
7.5
18.6
188.2
2010
119.5
45.4
7.4
20.2
192.5
2011
122.9
46.4
7.1
18.8
195.2
2012
124.7
43.3
6.4
19.6
194.0
Note: Totals may not sum due to independent rounding.
Table 3-56: COz Emissions from Wood Consumption by End-Use Sector (Gg)
End-Use Sector
Industrial
Residential
Commercial
Electricity Generation
Total
1990
135,348
59,808
6,779
13,252
215,186
2005
136,269
44,340
7,218
19,074
206,901
I 2008
125,663
48,465
7,518
18,288
199,932
2009
110,610
51,558
7,486
18,566
188,220
2010
119,537
45,371
7,385
20,169
192,462
2011
122,865
46,402
7,131
18,784
195,182
2012
124,663
43,309
6,420
19,612
194,003
Note: Totals may not sum due to independent rounding.
The transportation sector is responsible for most of the ethanol consumption in the United States. Ethanol is
currently produced primarily from corn grown in the Midwest, but it can be produced from a variety of biomass
feedstocks. Most ethanol for transportation use is blended with gasoline to create a 90 percent gasoline, 10 percent
by volume ethanol blend known as E-10 or gasohol.
In 2012, the United States consumed an estimated 1,063.8 trillion Btu of ethanol, and as a result, produced
approximately 72.8 Tg CCh Eq. (72,827 Gg) (see Table 3-57 and Table 3-58) of CCh emissions. Ethanol production
and consumption has grown significantly since 1990 due to the favorable economics of blending ethanol into
gasoline and federal policies that have encouraged use of renewable fuels.
3-80 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 3-57: COz Emissions from Ethanol Consumption (Tg COz Eq.)
End-Use Sector
Transportation
Industrial
Commercial
Total
1990
4.1
0.1 1
+
4.2
2005
22.4
0.5 1
0.1
22.9
2008
53.8
0.8
0.1
54.7
2009
61.2
0.9
0.2
62.3
2010
71.3
1.1
0.2
72.6
2011
71.5
1.1
0.2
72.9
2012
71.5
1.1
0.2
72.8
+ Does not exceed 0.05 Tg CO2 Eq.
Table 3-58: COz Emissions from Ethanol Consumption (Gg)
End-Use Sector
Transportation*
Industrial
Commercial
Total
1990
4,136
56
34
4,227
2005
22,414
468
1 60 •
22,943
2008
53,796
797
1 146
54,739
2009
61,193
885
193
62,272
2010
71,287
1,134
226
72,647
2011
71,537
1,146
198
72,881
2012
71,548
1,083
196
72,827
a See Annex 3.2, Table A-92 for additional information on transportation consumption of these fuels.
Note: Totals may not sum due to independent rounding.
Methodology
Woody biomass emissions were estimated by applying two EIA gross heat contents (Lindstrom 2006) to U.S.
consumption data (EIA 2014) (see Table 3-59), provided in energy units for the industrial, residential, commercial,
and electric generation sectors. One heat content (16.95 MMBtu/MT wood and wood waste) was applied to the
industrial sector's consumption, while the other heat content (15.43 MMBtu/MT wood and wood waste) was applied
to the consumption data for the other sectors. An EIA emission factor of 0.434 MT C/MT wood (Lindstrom 2006)
was then applied to the resulting quantities of woody biomass to obtain CCh emission estimates. It was assumed
that the woody biomass contains black liquor and other wood wastes, has a moisture content of 12 percent, and is
converted into CCh with 100 percent efficiency. The emissions from ethanol consumption were calculated by
applying an emission factor of 18.67 Tg C/QBtu (EPA 2010) to U.S. ethanol consumption estimates that were
provided in energy units (EIA 2014) (see Table 3-60).
Table 3-59: Woody Biomass Consumption by Sector (Trillion Btu)
End-Use Sector
Industrial
Residential
Commercial
Electricity Generation
Total
1990
1,441.9
580.0
65.7
128.5
2,216.2
2005
1,451
430,
70,
185,
2,136.
7 1
.0
.0
.0 •
,7
2008
1,338.7
470.0
72.9
177.3
2,059.0
2009
1,178.4
500.0
72.6
180.0
1,931.0
2010
1,273.
440.
71.
195.
1,980.
,5
,0
,6
,6
,7
2011
1,308.
450.
69.
182.
2,010.
,9
,0
,2
,2
,2
2012
1,328.1
420.0
62.3
190.2
2,000.5
Note: Totals may not sum due to independent rounding.
Table 3-60: Ethanol Consumption by Sector (Trillion Btu)
End-Use Sector
Transportation
Industrial
Commercial
Total
1990
60.4
0.8
0.5
61.7
2005
327.4
6.8
0.9
335.1
2008
785.8
11.6
2.1
799.6
2009
893.9
12.9
2.8
909.7
2010
1,041.4
16.6
3.3
1,061.2
2011
1,045.0
16.7
2.9
1,064.6
2012
1,045.2
15.8
2.9
1,063.8
Note: Totals may not sum due to independent rounding.
Energy 3-81
-------
Uncertainty and Time-Series Consistency
It is assumed that the combustion efficiency for woody biomass is 100 percent, which is believed to be an
overestimate of the efficiency of wood combustion processes in the United States. Decreasing the combustion
efficiency would decrease emission estimates. Additionally, the heat content applied to the consumption of woody
biomass in the residential, commercial, and electric power sectors is unlikely to be a completely accurate
representation of the heat content for all the different types of woody biomass consumed within these sectors.
Emission estimates from ethanol production are more certain than estimates from woody biomass consumption due
to better activity data collection methods and uniform combustion techniques.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Wood and ethanol consumption values were revised relative to the previous Inventory for 2011 based on updated
information from ~EIA? s Monthly Energy Review (EIA2014). These revisions of historical data for wood biomass
consumption resulted in an average annual increase in emissions from wood biomass consumption of about 1.4 Tg
CO2 Eq. (0.7 percent) from 1990 through 2011. Slight adjustments were made to ethanol consumption based on
updated information from EIA (2014), which slightly increased estimates for ethanol consumed. As a result of
adjustments to historical EIA data, average annual emissions from ethanol consumption increased by less than 0.1
Tg CO2 Eq. (less than 0.1 percent) relative to the previous Inventory estimates.
This year woody biomass consumption data for the industrial, residential, commercial and electricity generation
sectors were obtained from EIA's Monthly Energy Review (EIA 2013). In previous years, woody biomass
consumption data for the electricity generation sector was estimated from EPA's Clean Air Market Acid Rain
Program dataset (EPA 2013), however, EPA is currently investigating the discrepancy in the 2012 wood biomass
estimates derived from EPA 2013. In the meantime for the final submission, the EPA reverted back to the EIA's
wood consumption dataset where the discrepancy under investigation does not exist.
Planned Improvements
The availability of facility-level combustion emissions through EPA's GHGRP will be examined to help better
characterize the industrial sector's energy consumption in the United States, and further classify business
establishments according to industrial economic activity type. Most methodologies used in EPA's GHGRP are
consistent with IPCC, though for EPA's GHGRP, facilities collect detailed information specific to their operations
according to detailed measurement standards, which may differ with the more aggregated data collected for the
Inventory to estimate total, national U.S. emissions. In addition, and unlike the reporting requirements for this
chapter under the UNFCCC reporting guidelines, some facility-level fuel combustion emissions reported under the
GHGRP may also include industrial process emissions.113 In line with UNFCCC reporting guidelines, fuel
combustion emissions are included in this chapter, while process emissions are included in the Industrial Processes
chapter of this report. In examining data from EPA's GHGRP that would be useful to improve the emission
estimates for the CCh from biomass combustion category, particular attention will also be made to ensure time series
consistency, as the facility-level reporting data from EPA's GHGRP are not available for all inventory years as
reported in this inventory. Additionally, analyses will focus on aligning reported facility-level fuel types and IPCC
fuel types per the national energy statistics, ensuring CCh emissions from biomass are separated in the facility-level
reported data, and maintaining consistency with national energy statistics provided by EIA. In implementing
improvements and integration of data from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-
level data in national inventories will be relied upon.114
113 See .
114 See.
3-82 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
4. Industrial Processes
Greenhouse gas emissions are produced as the by-products of various non-energy-related industrial activities. That
is, these emissions are produced from an industrial process itself and are not directly a result of energy consumed
during the process. For example, raw materials can be chemically transformed from one state to another. This
transformation can result in the release of greenhouse gases such as carbon dioxide (CO2), methane (CH4), and
nitrous oxide (N2O). The processes addressed in this chapter include iron and steel production and metallurgical
coke production, cement production, lime production, other process uses of carbonates (e.g., flux stone, flue gas
desulfurization, and glass manufacturing), ammonia production and urea consumption, petrochemical production,
aluminum production, soda ash production and use, titanium dioxide production, CO2 consumption, ferroalloy
production, glass production, zinc production, phosphoric acid production, lead production, silicon carbide
production and consumption, nitric acid production, and adipic acid production (see Figure 4-1).
In addition to the three greenhouse gases listed above, there are also industrial sources of man-made fluorinated
compounds called hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6). The present
contribution of these gases to the radiative forcing effect of all anthropogenic greenhouse gases is small; however,
because of their extremely long lifetimes, many of them will continue to accumulate in the atmosphere as long as
emissions continue. In addition, many of these gases have high global warming potentials; SF6 is the most potent
greenhouse gas the Intergovernmental Panel on Climate Change (IPCC) has evaluated. Usage of HFCs is growing
rapidly since they are the primary substitutes for ozone depleting substances (ODSs), which are being phased-out
under the Montreal Protocol on Substances that Deplete the Ozone Layer. In addition to their use as ODS
substitutes, HFCs, PFCs, and SF6 are employed and emitted by a number of other industrial sources in the United
States. These industries include aluminum production, HCFC-22 production, semiconductor manufacture, electric
power transmission and distribution, and magnesium metal production and processing.
In 2012, industrial processes generated emissions of 334.4 teragrams of CO2 equivalent (Tg CO2 Eq.), or 5.1 percent
of total U.S. greenhouse gas emissions. Carbon dioxide emissions from all industrial processes were 144.6 Tg CO2
Eq. (144,585 Gg) in 2012, or 2.7 percent of total U.S. CO2 emissions. Methane emissions from industrial processes
resulted in emissions of approximately 3.7 Tg CO2 Eq. (177 Gg) in 2012, which was less than 1 percent of U.S. CH4
emissions. N2O emissions from adipic acid and nitric acid production were 21.0 Tg CO2 Eq. (68 Gg) in 2012, or 5.1
percent of total U.S. N2O emissions. In 2012 combined emissions of HFCs, PFCs, and SF6 totaled 165.0 Tg CO2
Eq. Total emissions from Industrial Processes in 2012 were 5.8 percent more than 1990 emissions.
Industrial Processes 4-1
-------
Industrial Processes
as a Portion of all Emissions
5.1%
Figure 4-1: 2012 Industrial Processes Chapter Greenhouse Gas Sources
Substitution of Ozone Depleting Substances
Iron and Steel Prod. & Metallurgical Coke Prod.
Cement Production
Nitric Acid Production
Lime Production
Ammonia Production
Other Process Uses of Carbonates
Electrical Transmission and Distribution
Aluminum Production
Adipic Acid Production
Urea Consumption for Non-Agricultural Purposes
HCFC-22 Production
Semiconductor Manufacture
Soda Ash Production and Consumption
Caibon Dioxide Consumption
Titanium Dioxide Production
Magnesium Production and Processing
Ferroalloy Production
Zinc Production
Glass Production
Phosphoric Acid Production
Lead Production
Silicon Carbide Production and Consumption
147
<0.5
10 20 30 40 50 60
Tg COZ Eq.
70
The increase in overall Industrial Processes emissions since 1990 reflects a range of emission trends among the
industrial process emission sources. Emissions resulting from most types of metal production have declined
significantly since 1990, largely due to production shifting to other countries, but also due to transitions to less-
emissive methods of production (in the case of iron and steel) and to improved practices (in the case of PFC
emissions from aluminum production). Emissions from mineral sources have either increased or not changed
significantly since 1990 but largely track economic cycles, while CO2 and CH4 emissions from chemical sources
have either decreased or not changed significantly. HFC emissions from the substitution of ozone depleting
substances have increased drastically since 1990, while the emission trends of HFCs, PFCs, and SF6 from other
sources are mixed. Trends are explained further within each emission source category throughout the chapter.
Table 4-1 summarizes emissions for the Industrial Processes chapter in Tg CCh Eq., while unweighted native gas
emissions in Gg are provided in Table 4-2. The source descriptions that follow in the chapter are presented in the
order as reported to the UNFCCC in the common reporting format tables, corresponding generally to: mineral
products, chemical production, metal production, and emissions from the uses of HFCs, PFCs, and SF6.
4-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 4-1: Emissions from Industrial Processes (Tg COz Eq.)
Gas/Source
CO2
Iron and Steel Production &
Metallurgical Coke Production
Iron and Steel Production
Metallurgical Coke Production
Cement Production
Lime Production
Ammonia Production
Other Process Uses of Carbonates
Urea Consumption for Non-
Agricultural Purposes
Petrochemical Production
Aluminum Production
Soda Ash Production and
Consumption
Carbon Dioxide Consumption
Titanium Dioxide Production
Ferroalloy Production
Zinc Production
Glass Production
Phosphoric Acid Production
Lead Production
Silicon Carbide Production and
Consumption
CH4
Petrochemical Production
Iron and Steel Production &
Metallurgical Coke Production
Iron and Steel Production
Metallurgical Coke Production
Ferroalloy Production
Silicon Carbide Production and
Consumption
N20
Nitric Acid Production
Adipic Acid Production
HFCs
Substitution of Ozone Depleting
Substances*
HCFC-22 Production
Semiconductor Manufacture
PFCs
Semiconductor Manufacture
Aluminum Production
SF6
Electrical Transmission and
Distribution
Magnesium Production and
Processing
Semiconductor Manufacture
Total
+ Does not exceed 0.05 Tg CO2 Eq.
1990
188.6
99.8 1
97.3 1
33.3 1
11.4 1
13.0 1
6.8 1
27 1
1.4 1
1.2 1
2.2 1
0.6 1
1.5 1
1.6 1
0.5
0.4 1
3.3 1
2.3 1
10!
34.0 1
18.2 1
15.8 1
36.9
0.3 1
36.4 1
0.2 1
20.6 1
2.2 1
18.4 1
32.6 1
26.7 1
5.4 1
0.5
316.1
2005
166.7
66.7 1
64.6 1
45.9 1
14.0 1
9.2 1
6.3 1
4.3 1
4.1 1
2.9 1
1.3 1
1.8 1
1.4 1
1.0 1
1.9 1
1.4 1
0.6 1
0.2 1
1
0.7 1
0.7 1
1
24.4 1
16.9 1
7.4
119.8
103.8
15.8
0.2 1
5.6 1
2.6 1
3.0 1
14.7 1
11.0 1
2.9 1
0.7
334.9
2008
161.0
66.8
64.5
2.3
41.2
14.0
8.4
5.9
4.1
3.6
4.5
2.9
1.8
1.8
1.6
1.2
1.5
1.2
0.5
0.2
3.6
2.9
0.6
0.6
19.4
16.9
2.6
136.0
122.2
13.6
0.2
5.1
2.4
2.7
10.7
8.4
1.9
0.5
335.9
2009
119.7
43.0
42.1
1.0
29.4
10.9
8.5
7.6
3.4
2.8
3.0
2.5
1.8
1.6
1.5
0.9
1.0
1.0
0.5
0.1
3.3
2.9
0.4
0.4
16.8
14.0
2.8
135.1
129.6
5.4
0.1
3.3
1.7
1.6
9.6
7.5
1.7
0.3
287.8
2010
142.3
55.7
53.7
2.1
31.3
12.8
9.2
9.6
4.7
3.5
2.7
2.6
2.3
1.8
1.7
1.2
1.5
1.1
0.5
0.2
3.6
3.1
0.5
0.5
21.1
16.7
4.4
144.0
137.5
6.4
0.2
3.8
2.2
1.6
9.8
7.2
2.2
0.4
324.6
2011
147.4
60.0
58.6
1.4
32.0
13.5
9.4
9.3
4.0
3.5
3.3
2.6
1.8
1.7
1.7
1.3
1.3
1.2
0.5
0.2
3.7
3.1
0.6
0.6
26.5
15.8
10.6
148.6
141.5
6.9
0.2
6.0
3.0
2.9
10.8
7.2
2.9
0.7
342.9
2012
144.6
54.3
53.8
0.5
35.1
13.3
9.4
8.0
5.2
3.5
3.4
2.7
1.8
1.7
1.7
1.4
1.2
1.1
0.5
0.2
3.7
3.1
0.6
0.6
21.0
15.3
5.8
151.2
146.8
4.3
0.2
5.4
2.9
2.5
8.4
6.0
1.7
0.7
334.4
Note: Totals may not sum due to independent rounding.
a Small amounts of PFC emissions also
result from this
source.
Industrial Processes 4-3
-------
Table 4-2: Emissions from Industrial Processes (Gg)
Gas/Source
1990
2005
CO2 188,626 166,689
Iron and Steel Production &
Metallurgical Coke Production 99,781 66,666
Iron and Steel Production 97,311 I 64,623
Metallurgical Coke Production 2,470 I 2,043
Cement Production 33,278 I 45,910
Lime Production 11,420 I 13,990
Ammonia Production 13,047 I 9,196
Other Process Uses of Carbonates 4,907 | 6,339
Urea Consumption for Non-
Agricultural Purposes 3,784 I 3,653
Petrochemical Production 3,429 I 4,330
Aluminum Production 6,831 4,142
Soda Ash Production and
Consumption 2,741
Carbon Dioxide Consumption 1,416 1,321
Titanium Dioxide Production 1,195 1,755
Ferroalloy Production 2,152 I 1,392
Zinc Production 632 1,030
Glass Production 1,535 1,928
Phosphoric Acid Production 1,586 1,396
Lead Production 516 553
Silicon Carbide Production and
Consumption 375 219
CH4 156 184
Petrochemical Production 108 150
Iron and Steel Production &
Metallurgical Coke Production 46 34
Iron and Steel Production 46 34
Metallurgical Coke Production
Ferroalloy Production
Silicon Carbide Production and
Consumption
N20 110 79
Nitric Acid Production 59 55
Adipic Acid Production 51 24
HFCs M M
Substitution of Ozone Depleting
Substances* M M
HCFC-22 Production
Semiconductor Manufacture
PFCs M M
Semiconductor Manufacture M M
Aluminum Production M M
SF6
Electrical Transmission and
Distribution
Magnesium Production and
Processing
Semiconductor Manufacture
+ Does not exceed 0.5 Gg
M (Mixture of gases)
Note: Totals may not sum due to independent rounding.
a Small amounts of PFC emissions also result from this source.
2008
161,022
66,822
64,488
2,334
41,161
13,992
8,414
5,885
4,065
3,572
4,477
2,865
1,780
1,809
1,599
1,159
1,523
1,177
547
175
169
137
31
31
2009
119,745
43,029
42,073
956
29,432
10,914
8,454
7,583
3,427
2,833
3,009
2,488
1,784
1,648
1,469
943
1,045
1,016
525
145
156
138
17
17
2010
142,301
55,746
53,662
2,084
31,256
12,834
9,188
9,560
4,728
3,455
2,722
2,612
2,253
1,769
1,663
1,182
1,481
1,130
542
181
172
146
25
25
2011
147,399
60,008
58,583
1,425
32,010
13,471
9,428
9,335
3,999
3,505
3,292
2,624
1,843
1,729
1,663
1,286
1,299
1,199
538
170
177
148
28
28
2012
144,585
54,319
53,778
541
35,051
13,318
9,366
7,997
5,243
3,505
3,439
2,672
1,815
1,742
1,663
1,422
1,247
1,101
527
158
177
147
29
29
63
54
8
M
M
1
+
M
M
M
54
45
9
M
M
M
M
M
68
54
14
M
M
1
+
M
M
M
85
51
34
M
M
1
+
M
M
M
68
49
19
M
M
M
M
M
4-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The general methods employed to estimate emissions for industrial processes, as recommended by the IPCC,
involve multiplying production data (or activity data) for each process by an emission factor per unit of production.
It is noted that in this chapter the methodological guidance was primarily taken from the 2006 IPCC Guidelines for
National Greenhouse Gas Inventories. The use of the most recently published calculation methodologies by the
IPCC, as contained in the 2006 IPCC Guidelines, is fully in line with the IPCC Good Practice Guidance for
methodological choice to improve rigor and accuracy. In addition, the improvements in using the latest
methodological guidance from the IPCC have been recognized by the UNFCCC's Subsidiary Body for Scientific
and Technological Advice in the conclusions of its 30th Session.115 Furthermore, the United States hosted the July
2004 experts meeting for the development of the Industrial Processes & Product Use (IPPU) volume of the 2006
IPCC Guidelines, and numerous U.S. experts participated in developing the methodological guidance that was
published in that volume.116 In this regard, not only is it the most recent guidance from the IPCC, but the 2006
IPCC Guidelines reflects the input of U.S. experts, which makes it that much more applicable to the inventory as
explained in this chapter.
QA/QC and Verification Procedures
For industrial process sources of CCh and CH4 emissions, a detailed plan was developed and implemented. This plan
was based on the overall U.S. strategy, but was tailored to include specific procedures recommended for these
sources. Two types of checks were performed using this plan: (1) general, or Tier 1, procedures that focus on annual
procedures and checks to be used when gathering, maintaining, handling, documenting, checking, and archiving the
data, supporting documents, and files, and (2) source-category specific, or Tier 2, procedures that focus on
procedures and checks of the emission factors, activity data, and methodologies used for estimating emissions from
the relevant industrial process sources. Examples of these procedures include checks to ensure that activity data and
emission estimates are consistent with historical trends; that, where possible, consistent and reputable data sources
are used across sources; that interpolation or extrapolation techniques are consistent across sources; and that
common datasets and factors are used where applicable. Tier 1 quality assurance and quality control procedures
have been performed for all industrial process sources. Tier 2 procedures were performed for more significant
emission categories, consistent with IPCC good practice.
For most industrial process categories, activity data is obtained through a survey of manufacturers conducted by
various organizations (specified within each source); the uncertainty of the activity data is a function of the
reliability of plant-level production data and is influenced by the completeness of the survey response. The emission
factors used are defaults from IPCC derived using calculations that assume precise and efficient chemical reactions,
or were based upon empirical data in published references. As a result, uncertainties in the emission coefficients can
be attributed to, among other things, inefficiencies in the chemical reactions associated with each production process
or to the use of empirically-derived emission factors that are biased; therefore, they may not represent U.S. national
averages. Additional assumptions are described within each source.
The uncertainty analysis performed to quantify uncertainties associated with the 2012 inventory estimates from
industrial processes continues a multi-year process for developing credible quantitative uncertainty estimates for
these source categories using the IPCC Tier 2 approach. As the process continues, the type and the characteristics of
the actual probability density functions underlying the input variables are identified and better characterized
(resulting in development of more reliable inputs for the model, including accurate characterization of correlation
between variables), based primarily on expert judgment. Accordingly, the quantitative uncertainty estimates reported
in this section should be considered illustrative and as iterations of ongoing efforts to produce accurate uncertainty
estimates. The correlation among data used for estimating emissions for different sources can influence the
uncertainty analysis of each individual source. While the uncertainty analysis recognizes very significant
115 These Subsidiary Body for Scientific and Technological Advice (SBSTA) conclusions state, "The SBSTA acknowledged that
the 2006 IPCC Guidelines contain the most recent scientific methodologies available to estimate emissions by sources and
removals by sinks of greenhouse gases (GHGs) not controlled by the Montreal Protocol, and recognized that Parties have gained
experience with the 2006 IPCC Guidelines. The SBSTA also acknowledged that the information contained in the 2006 IPCC
Guidelines enables Parties to further improve the quality of their GHG inventories." See
.
116 See .
Industrial Processes 4-5
-------
connections among sources, a more comprehensive approach that accounts for all linkages will be identified as the
uncertainty analysis moves forward.
Box 4-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks
On October 30, 2009, the U.S. EPA published a rule for the mandatory reporting of greenhouse gases from large
GHG emissions sources in the United States. Implementation of 40 CFR Part 98 is referred to as EPA's Greenhouse
Gas Reporting Program (GHGRP). 40 CFR part 98 applies to direct greenhouse gas emitters, fossil fuel suppliers,
industrial gas suppliers, and facilities that inject CO2 underground for sequestration or other reasons and requires
reporting by 41 industrial categories. Reporting is at the facility level, except for certain suppliers of fossil fuels and
industrial greenhouse gases. In general, the threshold for reporting is 25,000 metric tons or more of CO2 Eq. per
year. Calendar year 2010 was the first year in which data were reported for many facilities subject to 40 CFR part
98.
EPA's GHGRP dataset and the data presented in this inventory report are complementary and, as indicated in the
respective planned improvements sections for source categories in this chapter, EPA is analyzing how to use
facility-level GHGRP data to improve the national estimates presented in this inventory, giving particular
consideration to ensuring time series consistency. Most methodologies used in EPA's GHGRP are consistent with
IPCC, though for EPA's GHGRP, facilities collect detailed information specific to their operations according to
detailed measurement standards. This may differ with the more aggregated data collected for the inventory to
estimate total, national U.S. emissions. It should be noted that the definitions for source categories in the GHGRP
may differ from those used in this inventory in meeting the UNFCCC reporting guidelines. In line with the
UNFCCC reporting guidelines, the inventory report is a comprehensive accounting of all emissions from source
categories identified in the IPCC guidelines. Further information on the reporting categorizations in EPA's GHGRP
and specific data caveats associated with monitoring methods in EPA's GHGRP has been provided on the EPA's
GHGRP website.
EPA presents the data collected by EPA's GHGRP through a data publication tool that allows data to be viewed in
several formats including maps, tables, charts and graphs for individual facilities or groups of facilities.
4.1 Cement Production (IPCC Source Category
2A1)
Cement production is an energy- and raw material-intensive process that results in the generation of CO2 from both
the energy consumed in making the cement and the chemical process itself. Emissions from fuels consumed for
energy purposes during the production of cement are accounted for in the Energy chapter.
During the cement production process, calcium carbonate (CaCOs) is heated in a cement kiln at a temperature of
about 1,450°C (2,400°F) to form lime (i.e., calcium oxide or CaO) and COa in a process known as calcination or
calcining. The quantity of COa emitted during cement production is directly proportional to the lime content of the
clinker. During calcination, each mole of limestone (CaCOs) heated in the clinker kiln forms one mole of lime
(CaO) and one mole of CO2:
+ heat -> CaO + C07
4-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Next, the lime is combined with silica-containing materials to produce clinker (an intermediate product), with the
earlier byproduct CO2 being released to the atmosphere. The clinker is then allowed to cool, mixed with a small
amount of gypsum and potentially other materials (e.g., slag), and used to make Portland cement.117
CO2 emitted from the chemical process of cement production is the second largest source of industrial CO2
emissions in the United States. Cement is produced in 35 states and Puerto Rico. Texas, Missouri, California,
Michigan, and Florida were the five leading cement-producing States in 2012 and accounted for approximately half
of U.S. production (USGS 2013). Clinker production in 2012 increased approximately 10 percent from 2011 levels.
This increase can be attributed to an increase in spending in new residential construction and nonresidential
buildings. In 2012, all U.S. cement plants operated at levels well below capacity output. Nationwide, two cement
plants were formally closed in 2012. Of these plants, one was idle in 2011 whereas the other one was operational in
2011 (USGS 2013). In 2012, U.S. clinker production totaled 67,784 thousand metric tons (Van Oss 2013b). The
resulting CO2 emissions were estimated to be 35.1 Tg CO2 Eq. (35,051 Gg) (see Table 4-3).
Table 4-3: COz Emissions from Cement Production (Tg COz Eq. and Gg)
Year Tg CCh Eq. Gg
1990 33.3 33,278
2005 45.9 45,910
2008
2009
2010
2011
2012
41.2
29.4
31.3
32.0
35.1
41,161
29,432
31,256
32,010
35,051
Greenhouse gas emissions from cement production increased every year from 1991 through 2006 (with the
exception of a slight decrease in 1997), but decreased in the following years until 2009. Emissions from cement
production were at their lowest levels in 2009 (2009 emissions are approximately 29 percent lower than 2008
emissions and 12 percent lower than 1990). Since 2010, emissions have increased slightly.
Emissions since 1990 have increased by five percent. Emissions decreased significantly between 2008 and 2009,
due to the economic recession and associated decrease in demand for construction materials. Emissions increased
slightly from 2009 levels in 2010, and increased slightly again in 2011 and in 2012 due to increasing consumption.
Cement continues to be a critical component of the construction industry; therefore, the availability of public and
private construction funding, as well as overall economic conditions, have considerable influence on cement
production.
CO2 emissions were estimated using the Tier 2 methodology from the 2006IPCC Guidelines. The Tier 2
methodology was used because detailed and complete data (including weights and composition) for carbonate(s)
consumed in clinker production are not available, and thus a rigorous Tier 3 approach is impractical. Tier 2 specifies
the use of aggregated plant or national clinker production data and an emission factor, which is the product of the
average lime fraction for clinker of 65 percent and a constant reflecting the mass of CO2 released per unit of lime
(van Oss 2013 a). This calculation yields an emission factor of 0.51 tons of CO2 per ton of clinker produced, which
was determined as follows:
117 Approximately three percent of total clinker production is used to produce masonry cement, which is produced using
plasticizers (e.g., ground limestone, lime) and Portland cement (USGS 2011). Carbon dioxide emissions that result from the
production of lime used to create masonry cement are included in the Lime Manufacture source category.
Industrial Processes 4-7
-------
Clinker
=0.6460 CaOx
44.01 g/moleCO
56.08 g/moleCaO
= 0.5070 tons CO /ton clinker
During clinker production, some of the clinker precursor materials remain in the kiln as non-calcinated, partially
calcinated, or fully calcinated cement kiln dust (CKD). The emissions attributable to the calcinated portion of the
CKD are not accounted for by the clinker emission factor. The IPCC recommends that these additional CKD CO2
emissions should be estimated as two percent of the CO2 emissions calculated from clinker production (when data
on CKD generation are not available).118 Total cement production emissions were calculated by adding the
emissions from clinker production to the emissions assigned to CKD (IPCC 2006).
Furthermore, small amounts of impurities (i.e., not calcium carbonate) may exist in the raw limestone used to
produce clinker. The proportion of these impurities is generally minimal, although a small amount (one to two
percent) of magnesium oxide (MgO) may be desirable as a flux. Per the IPCC Tier 2 methodology, a correction for
magnesium oxide is not used, since the amount of magnesium oxide from carbonate is likely very small and the
assumption of a 100 percent carbonate source of CaO already yields an overestimation of emissions (IPCC 2006).
The 1990 through 2012 activity data for clinker production (see Table 4-4) were obtained from USGS (Van Oss
2013b). The data were compiled by USGS (to the nearest ton) through questionnaires sent to domestic clinker and
cement manufacturing plants, including the facilities in Puerto Rico.
Table 4-4: Clinker Production (Gg)
Year Clinker
1990 64,355
2005 88,783
2008 79,599
2009 56,918
2010 60,444
2011 61,903
2012 67,784
Note: Clinker production from 1990-2012 includes Puerto Rico. Data were obtained from
USGS (Van Oss 2013b), whose original data source was USGS and US Bureau of Mines
Minerals Yearbooks (2012 data obtained from mineral industry surveys for cement in July
2013).
The uncertainties contained in these estimates are primarily due to uncertainties in the lime content of clinker and in
the percentage of CKD recycled inside the cement kiln. Uncertainty is also associated with the assumption that all
calcium-containing raw materials are CaCOs, when a small percentage likely consists of other carbonate and non-
carbonate raw materials. The lime content of clinker varies from 60 to 67 percent; 65 percent is used as a
representative value (van Oss 2013a). CKD loss can range from 1.5 to 8 percent depending upon plant
specifications. Additionally, some amount of CO2 is reabsorbed when the cement is used for construction. As
cement reacts with water, alkaline substances such as calcium hydroxide are formed. During this curing process,
these compounds may react with CO2 in the atmosphere to create calcium carbonate. This reaction only occurs in
roughly the outer 0.2 inches of surface area. Because the amount of CO2 reabsorbed is thought to be minimal, it was
not estimated.
118 Default IPCC clinker and CKD emission factors were verified through expert consultation with USGS (Van Oss 2013a).
4-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-5. Based on the uncertainties
associated with total U.S. clinker production, the CC>2 emission factor for clinker production, and the emission factor
for additional CCh emissions from CKD, 2012 CCh emissions from cement production were estimated to be between
33.0 and 37.2 Tg CCh Eq. at the 95 percent confidence level. This confidence level indicates a range of
approximately 5.8 percent below and 6.1 percent above the emission estimate of 35.1 Tg COa Eq.
Table 4-5: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Cement
Production (Tg COz Eq. and Percent)
2012 Emission Estimate Uncertainty Range Relative to Emission Estimate3
Source Gas (Tg CCh Eq.) (Tg CCh Eq.) (%)
Upper Lower
Lower Bound Bound Bound Upper Bound
Cement Production CCh 35.1 33.0 37.2 -5.8% +6.1%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Clinker production data for 1990 through 2012 were obtained from USGS (Van Oss 2013b) including Puerto Rico
for all years. These data contained updated clinker production data from USGS for more recent years. The emissions
estimates for the time series, 1990 through 2012 reflect use of the updated USGS data. In a given Inventory year,
advance clinker data is typically used. These data are typically finalized several years later by USGS. The
published time series was reviewed to ensure time series consistency. Published data generally differed from
advance data by approximately 1,000 metric tons, or 1 percent of the total. Details on the emission trends through
time are described in more detail in the Methodology section, above.
Planned Improvements
Future improvements involve evaluating and analyzing data reported under EPA's GHGRP that would be useful to
improve the emission estimates for the Cement Production source category. Particular attention will be made to
ensure time series consistency of the emissions estimates presented in future inventory reports, consistent with IPCC
and UNFCCC guidelines. This is required as facility-level reporting data from EPA's GHGRP, with the program's
initial requirements for reporting of emissions in calendar year 2010, are not available for all inventory years (i.e.,
1990 through 2009) as required for this inventory. In implementing improvements and integration of data from
EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in national inventories will be
relied upon.119
4.2 Lime Production (IPCC Source Category
2A2)
Lime is an important manufactured product with many industrial, chemical, and environmental applications. Lime
production involves three main processes: stone preparation, calcination, and hydration. Carbon dioxide is
generated during the calcination stage, when limestone—mostly calcium carbonate (CaCOs)—is roasted at high
119
See
Industrial Processes 4-9
-------
temperatures in a kiln to produce CaO and CO2. The CO2 is given off as a gas and is normally emitted to the
atmosphere.
CaC03 -> CaO + C02
Some of the CO2 generated during the production process, however, is recovered at some facilities for use in sugar
refining and precipitated calcium carbonate (PCC) production.120 Emissions from fuels consumed for energy
purposes during the production of lime are accounted for in the Energy chapter.
For U.S. operations, the term "lime" actually refers to a variety of chemical compounds. These include calcium
oxide (CaO), or high-calcium quicklime; calcium hydroxide (Ca(OH)2), or hydrated lime; dolomitic quicklime
([CaOMgO]); and dolomitic hydrate ([Ca(OH)2«MgO] or [Ca(OH)2«Mg(OH)2]).
The contemporary lime market is approximately distributed across five end-use categories as follows: metallurgical
uses, 38 percent; environmental uses, 31 percent; chemical and industrial uses, 22 percent; construction uses, 8
percent; and refractory dolomite, 1 percent. The major uses are in steel making, flue gas desulfurization systems at
coal-fired electric power plants, construction, and water purification. Lime is also used as a CO2 scrubber, and there
has been experimentation on the use of lime to capture CO2 from electric power plants.
Lime production in the United States—including Puerto Rico— was reported to be 18,767 thousand metric tons in
2012 (USGS 2013). Principal lime producing states are Alabama, Kentucky, Missouri, Nevada, Ohio, Pennsylvania,
and Texas.
U.S. lime production resulted in estimated net CO2 emissions of 13.3 Tg CO2 Eq. (13,318 Gg) (see Table 4-6 and
Table 4-7). The trends in CO2 emissions from lime production are directly proportional to trends in production,
which are described below.
Table 4-6: COz Emissions from Lime Production (Tg COz Eq. and Gg)
Year Tg CCh Eq.
1990
11.4
11,420
2008
2009
2010
2011
2012
14.0
10.9
12.8
13.5
13.3
13,992
10,914
12,834
13,471
13,318
Table 4-7: Potential, Recovered, and Net COz Emissions from Lime Production (Gg)
Year
2008
2009
2010
Potential
14,981
11,872
13,776
Recovered3 Net Emissions
13,992
10,914
12,834
120 PQQ js obtained from the reaction of CCh with calcium hydroxide. It is used as a filler and/or coating in the paper, food, and
plastic industries.
4-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
2011 14,389 917 13,471
2012 14,188 870 13,318
a For sugar refining and PCC production.
Note: Totals may not sum due to independent rounding.
In 2012, lime production was nearly the same as 2011 levels (decrease of 1 percent) at 18,767 thousand metric tons.
Lime production in 2010 rebounded from a 21 percent decline in 2009 to 18,219 thousand metric tons, which is still
8 percent below 2008 levels. Lime production declined in 2009 mostly due to the economic recession and the
associated significant downturn in major markets such as construction and steel. The surprising rebound in 2010 is
primarily due to increased consumption in steelmaking, chemical and industrial uses, and in flue gas desulfurization.
Also, there was a decrease in 2012 lime consumption for Precipitated Calcium Carbonate (PCC) production, due to
decreased consumption from paper mills caused by closure of paper mills from economic recession and shifting of
production overseas (Miller 2013).
To calculate emissions, the amounts of high-calcium and dolomitic lime produced were multiplied by their
respective emission factors using the Tier 2 approach from the 2006IPCC Guidelines (IPCC 2006). The emission
factor is the product of the stoichiometric ratio between CO2 and CaO, and the average CaO and MgO content for
lime. The CaO and MgO content for lime is assumed to be 95 percent for both high-calcium and dolomitic lime)
(IPCC 2006). The emission factors were calculated as follows:
For high-calcium lime:
[(44.01 g/mole CO2) - (56.08 g/mole CaO)] x (0.9500 CaO/lime) = 0.7455 g CO2/g lime
For dolomitic lime:
[(88.02 g/mole CO2) - (96.39 g/mole CaO)] x (0.9500 CaO/lime) = 0.8675 g CO2/g lime
Production was adjusted to remove the mass of chemically combined water found in hydrated lime, determined
according to the molecular weight ratios of H2O to (Ca(OH)2 and [Ca(OH)2«Mg(OH)2]) (IPCC 2000). These factors
set the chemically combined water content to 24.3 percent for high-calcium hydrated lime, and 27.2 percent for
dolomitic hydrated lime.
Lime kiln dust (LKD) is a byproduct of the lime manufacturing process. LKD is a very fine-grained material and is
especially useful for applications requiring very small particle size. Most common LKD applications include soil
reclamation and agriculture. Currently, data on annual LKD production is not readily available. Lime emission
estimates were multiplied by a factor of 1.02 to account for lime kiln dust (LKD) (IPCC 2006).
Lime emission estimates were further adjusted to account for PCC producers and sugar refineries that recover CO2
emitted by lime production facilities for use as an input into production or refining processes. For CO2 recovery by
sugar refineries, lime consumption estimates (USGS 2013) were multiplied by a CO2 recovery factor to determine
the total amount of CO2 recovered from lime production facilities. According to industry outreach by state agencies
and USGS, sugar refineries use captured CO2 for 100 percent of their CO2 input (Lutter 2009, Miller 2013). Carbon
dioxide recovery by PCC producers was determined by multiplying lime consumption for PCC production (USGS
1992 through 2013) with the percentage CO2 of production weight for PCC production at lime plants (i.e.,
CO2/CaCO3 = 44/100) and a CO2 recovery factor based on the amount of purchased CO2by PCC manufacturers
(Prillaman 2008 through 2012, Miller 2013). As data were only available starting in 2007, CO2 recovery for the
period 1990 through 2006 was extrapolated by determining a ratio of PCC production at lime facilities to lime
consumption for PCC (USGS 1992 through 2008).
Lime production data (high-calcium- and dolomitic-quicklime, high-calcium- and dolomitic-hydrated, and dead-
burned dolomite) for 1990 through 2012 (see Table 4-8) were obtained from USGS (1992 through 2013) and are
compiled by USGS to the nearest ton. Natural hydraulic lime, which is produced from CaO and hydraulic calcium
silicates, is not produced in the United States (USGS 2011). Total lime production was adjusted to account for the
water content of hydrated lime by converting hydrate to oxide equivalent based on recommendations from the IPCC,
and is presented in Table 4-9 (IPCC 2000). The CaO and CaO'MgO contents of lime were obtained from the IPCC
(IPCC 2006). Since data for the individual lime types (high calcium and dolomitic) was not provided prior to 1997,
Industrial Processes 4-11
-------
total lime production for 1990 through 1996 was calculated according to the three year distribution from 1997 to
1999.
Table 4-8: High-Calcium- and Dolomitic-Quicklime, High-Calcium- and Dolomitic-Hydrated,
and Dead-Burned-Dolomite Lime Production (Gg)
Year
High-Calcium
Quicklime
1990
11,166
Dolomitic
Quicklime
High-Calcium
Hydrated
2,234
1,781
Table 4-9: Adjusted Lime Production (Gg)
Dolomitic
Hydrated
Dead-Burned
Dolomite
319
2008
2009
2010
2011
2012
14,600
11,800
13,300
13,900
13,600
2,630
1,830
2,570
2,690
2,710
2,070
1,690
1,910
2,010
2,020
358
261
239
230
237
200
200
200
200
200
Year High-Calcium
Dolomitic
1990
2008
2009
2010
2011
2012
12,466
16,111
13,034
14,694
15,367
15,075
2,800
3,081
2,213
2,937
3,051
3,076
Note: Minus water content of hydrated lime
Uncertainty and Time-Series Consistency
The uncertainties contained in these estimates can be attributed to slight differences in the chemical composition of
lime products and recovery rates for sugar refineries and PCC manufacturers located at lime plants. Although the
methodology accounts for various formulations of lime, it does not account for the trace impurities found in lime,
such as iron oxide, alumina, and silica. Due to differences in the limestone used as a raw material, a rigid
specification of lime material is impossible. As a result, few plants produce lime with exactly the same properties.
In addition, a portion of the CCh emitted during lime production will actually be reabsorbed when the lime is
consumed, especially at captive lime production facilities. As noted above, lime has many different chemical,
industrial, environmental, and construction applications. In many processes, CO2 reacts with the lime to create
calcium carbonate (e.g., water softening). Carbon dioxide reabsorption rates vary, however, depending on the
application. For example, 100 percent of the lime used to produce precipitated calcium carbonate reacts with CO2;
whereas most of the lime used in steel making reacts with impurities such as silica, sulfur, and aluminum
compounds. Quantifying the amount of CCh that is reabsorbed would require a detailed accounting of lime use in
the United States and additional information about the associated processes where both the lime and byproduct CCh
4-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
are "reused" are required to quantify the amount of CCh that is reabsorbed. Research conducted thus far has not
yielded the necessary information to quantify CCh reabsorption rates.121
In some cases, lime is generated from calcium carbonate byproducts at pulp mills and water treatment plants.122
The lime generated by these processes is included in the USGS data for commercial lime consumption. In the
pulping industry, mostly using the Kraft (sulfate) pulping process, lime is consumed in order to causticize a process
liquor (green liquor) composed of sodium carbonate and sodium sulfide. The green liquor results from the dilution
of the smelt created by combustion of the black liquor where biogenic C is present from the wood. Kraft mills
recover the calcium carbonate "mud" after the causticizing operation and calcine it back into lime—thereby
generating CCh—for reuse in the pulping process. Although this re-generation of lime could be considered a lime
manufacturing process, the CCh emitted during this process is mostly biogenic in origin, and therefore is not
included in the industrial processes totals (Miner and Upton 2002). In accordance with IPCC methodological
guidelines, any such emissions are calculated by accounting for net carbon (C) fluxes from changes in biogenic C
reservoirs in wooded or crop lands (see the Land Use, Land-Use Change, and Forestry chapter).
In the case of water treatment plants, lime is used in the softening process. Some large water treatment plants may
recover their waste calcium carbonate and calcine it into quicklime for reuse in the softening process. Further
research is necessary to determine the degree to which lime recycling is practiced by water treatment plants in the
United States.
Uncertainties also remain surrounding recovery rates used for sugar refining and PCC production. The recovery rate
for sugar refineries is based on consultation with USGS commodity expert (Miller 2013) and two sugar beet
processing and refining facilities located in California that use 100 percent recovered CCh from lime plants (Lutter
2009). This analysis assumes that all sugar refineries located on-site at lime plants also use 100 percent recovered
CO2. The recovery rate for PCC producers located on-site at lime plants is based on the 2012 value for PCC
manufactured at commercial lime plants, given by USGS (Miller 2012). However, most PCC production occurs at
non-commercial lime facilities, such as paper mills. Satellite PCC plants at paper mills tend to use CCh produced
from the paper mill (potentially biomass based). This could introduce additional uncertainty in the CC>2 estimates,
because CC>2 recovered from pulp and paper facilities is mostly biogenic in origin.
Another uncertainty is the assumption that calcination emissions for LKD are around 2 percent. The National Lime
association has commented that the estimates of emissions from LKD in the US could be closer to 6 percent. In
addition, they note emissions may also be generated through production of other byproducts/wastes at lime plants
(Seeger 2013). There is limited data publicly available on LKD generation rates and also quantities, types of other
byproducts/wastes produced at lime facilities. Further research is needed to improve understanding of additional
calcination emissions to consider revising the current assumptions based on the IPCC Guidelines
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-10. Lime CCh emissions for
2012 were estimated to be between 13.0 and 13.7 Tg CCh Eq. at the 95 percent confidence level. This confidence
level indicates a range of approximately 2.7 percent below and 2.7 percent above the emission estimate of 13.3 Tg
CO2 Eq.
121 Representatives of the National Lime Association estimate that CCh reabsorption that occurs from the use of lime may offset
as much as a quarter of the CCh emissions from calcination (Males 2003).
122 Some carbide producers may also regenerate lime from their calcium hydroxide byproducts, which does not result in
emissions of CCh. In making calcium carbide, quicklime is mixed with coke and heated in electric furnaces. The regeneration of
lime in this process is done using a waste calcium hydroxide (hydrated lime) [CaC2 + 2H2O —> C2H2 + Ca(OH) 2], not calcium
carbonate [CaCOs]. Thus, the calcium hydroxide is heated in the kiln to simply expel the water [Ca(OH)2 + heat —»CaO + EhO]
and no CO2 is released.
Industrial Processes 4-13
-------
Table 4-10: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Lime
Production (Tg COz Eq. and Percent) in 2012.
2012 Emission Estimate Uncertainty Range Relative to Emission Estimate3
Source Gas (Tg CCh Eq.) (Tg CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Lime Production CO2 13.3 13.0 13.7 -2.7% +2.7%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Production data for dead-burned dolomite for 2007 through 2011 were updated based on the 2012 Lime Minerals
Yearbook, which caused the CCh production from lime to change for all years from 2007 through 2011 relative to
the previous Inventory.
CO2 recovery emissions from PCC production were revised for the entire time series (1990 through 2012). In prior
versions of the Inventory, PCC production at commercial lime plants was used only to calculate CC>2 recovery
emissions. According to USGS and NLA (Miller 2013 and Seeger 2013), a majority of PCC production
(approximately 70 percent or more) occurs at facilities other than commercial lime facilities. A methodology change
was incorporated to calculate emissions from all PCC production rather than PCC production at commercial lime
facilities, only. This change caused an increase in CCh recovery emissions from PCC production (by approximately
250 percent).
Planned Improvements
Future improvements involve conducting research to improve current assumptions associated with emissions from
production of LKD and other byproducts/wastes as discussed in the Uncertainty and Time Series Consistency
section.
In addition, EPA is evaluating and analyzing data reported under EPA's GHGRP that would be useful to improve
the emission estimates for the Lime Production source category. Pending resources, a potential improvement to the
inventory estimates for this source category would include the derivation of an average CO2 recovery rated based on
the average of aggregated data reported by facilities under EPA's GHGRP regarding onsite use of CO2. Particular
attention will be made to ensure time series consistency of the emissions estimates presented in future inventory
reports, consistent with IPCC and UNFCCC guidelines. This is required as the facility-level reporting data from
EPA's GHGRP, with the program's initial requirements for reporting of emissions in calendar year 2010, are not
available for all inventory years (i.e., 1990 through 2009) as required for this Inventory. In implementing
improvements and integration of data from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-
level data in national Inventories will be relied upon.123
123
See.
4-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
4.3 Other Process Uses of Carbonates (IPCC
Source Category 2A3)
Limestone (CaCOs), dolomite (CaCOsMgCOs)124, and other carbonates such as magnesium carbonate and iron
carbonate are basic materials used by a wide variety of industries, including construction, agriculture, chemical,
metallurgy, glass production, and environmental pollution control. This section addresses only limestone and
dolomite use. For industrial applications, carbonates such as limestone and dolomite are heated sufficiently enough to
calcine the material and generate CO2 as a byproduct.
CaC03 -> CaO + C02
MgC03 -> MgO + C02
Examples of such applications include limestone used as a flux or purifier in metallurgical furnaces, as a sorbent in
flue gas desulfurization (FGD) systems for utility and industrial plants, and as a raw material for the production of
glass, lime, and cement. Emissions from limestone and dolomite used in other process sectors such as cement, lime,
glass production, and iron and steel, are excluded from this section and reported under their respective source
categories (e.g., glass manufacturing IPCC Source Category 2A7.) Emissions from fuels consumed for energy
purposes during these processes are accounted for in the Energy chapter.
Limestone is widely distributed throughout the world in deposits of varying sizes and degrees of purity. Large
deposits of limestone occur in nearly every state in the United States, and significant quantities are extracted for
industrial applications. The leading limestone producing States are Texas, Missouri, Pennsylvania, Kentucky, and
Ohio (USGS 2013c). Similarly, dolomite deposits are also widespread throughout the world. Dolomite deposits are
found in the United States, Canada, Mexico, Europe, Africa, and Brazil. In the United States, the leading dolomite
producing states are Illinois, Pennsylvania, New York, Michigan, and Indiana (USGS 2013c).
In 2012, 18,844 thousand metric tons of limestone and 559 thousand metric tons of dolomite were consumed for
these emissive applications, excluding glass manufacturing (Willett 2013). Usage of limestone and dolomite
resulted in aggregate CO2 emissions of 8.0 Tg CO2 Eq. (7,997Gg) (see Table 4-11 and Table 4-12). Overall,
emissions have increased 63 percent from 1990 through 2012.
Table 4-11: COz Emissions from Other Process Uses of Carbonates (Tg COz Eq.)
Year Flux Stone
FGD
Magnesium
Production
Other Miscellaneous
Uses
Total
1990
2.6
2008
2009
2010
2011
2012
1.0
1.8
1.6
1.5
1.1
3.8 +
5.4 +
7.1 +
5.4 +
5.8 +
1.1
0.4
0.9
2.4
1.1
5.9
7.6
9.6
9.3
8.0
Notes: Totals may not sum due to independent rounding. "Other miscellaneous uses" include chemical
stone, mine dusting or acid water treatment, acid neutralization, and sugar refining.
+ Emissions are less than 0.1 Tg CCh Eq.
Table 4-12: COz Emissions from Other Process Uses of Carbonates (Gg)
124 Limestone and dolomite are collectively referred to as limestone by the industry, and intermediate varieties are seldom
distinguished.
Industrial Processes 4-15
-------
Year Flux Stone
FGD
Magnesium
Production
Other Miscellaneous Uses
Total
1990
2,592
1,432
819
4,907
2008
2009
2010
2011
2012
974
1,784
1,560
1,467
1,072
3,799 +
5,403 +
7,064 +
5,420 +
5,779 +
1,113
396
937
2,449
1,145
5,885
7,583
9,560
9,335
7,997
+ Emissions are less than 0.1 Gg CCh Eq.
Methodology
CO2 emissions were calculated based on the IPCC 2006 Guidelines Tier 2 method by multiplying the quantity of
limestone or dolomite consumed by the emission factor for limestone or dolomite calcination, respectively, Table
2.1 - limestone: 0.43971 tonne CCVtonne carbonate, and dolomite: 0.47732 tonne CCh/tonne carbonate.125 This
methodology was used for flux stone, flue gas desulfurization systems, chemical stone, mine dusting or acid water
treatment, acid neutralization, and sugar refining. Flux stone used during the production of iron and steel was
deducted from the Other Process Uses of Carbonates estimate and attributed to the Iron and Steel Production
estimate. Similarly limestone and dolomite consumption for glass manufacturing, cement, and lime manufacturing
are excluded from this category and attributed to their respective categories.
Historically, the production of magnesium metal was the only other significant use of limestone and dolomite that
produced CC>2 emissions. At the end of 2001, the sole magnesium production plant operating in the United States
that produced magnesium metal using a dolomitic process that resulted in the release of CCh emissions ceased its
operations (USGS 1995 through 2012b; USGS 2013).
Consumption data for 1990 through 2012 of limestone and dolomite used for flux stone, flue gas desulfurization
systems, chemical stone, mine dusting or acid water treatment, acid neutralization, and sugar refining (see Table
4-13) were obtained from the USGS Minerals Yearbook: Crushed Stone Annual Report (1995 through 2012a),
preliminary data from USGS Crushed Stone Commodity Expert (Willett, 2013), and the U.S. Bureau of Mines
(1991 and 1993a), which are reported to the nearest ton. The production capacity data for 1990 through 2012 of
dolomitic magnesium metal also came from the USGS (1995 through 2012b, USGS 2013) and the U.S. Bureau of
Mines (1990 through 1993b). During 1990 and 1992, the USGS did not conduct a detailed survey of limestone and
dolomite consumption by end-use. Consumption for 1990 was estimated by applying the 1991 percentages of total
limestone and dolomite use constituted by the individual limestone and dolomite uses to 1990 total use. Similarly,
the 1992 consumption figures were approximated by applying an average of the 1991 and 1993 percentages of total
limestone and dolomite use constituted by the individual limestone and dolomite uses to the 1992 total.
Additionally, each year the USGS withholds data on certain limestone and dolomite end-uses due to confidentiality
agreements regarding company proprietary data. For the purposes of this analysis, emissive end-uses that contained
withheld data were estimated using one of the following techniques: (1) the value for all the withheld data points for
limestone or dolomite use was distributed evenly to all withheld end-uses; (2) the average percent of total limestone
or dolomite for the withheld end-use in the preceding and succeeding years; or (3) the average fraction of total
limestone or dolomite for the end-use over the entire time period.
There is a large quantity of crushed stone reported to the USGS under the category "unspecified uses." A portion of
this consumption is believed to be limestone or dolomite used for emissive end uses. The quantity listed for
"unspecified uses" was, therefore, allocated to each reported end-use according to each end-use's fraction of total
consumption in that year.126
125 IPCC 2006, Volume 3: Chapter 2
126 This approach was recommended by USGS, the data collection agency.
4-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 4-13: Limestone and Dolomite Consumption (Thousand Metric Tons)
Activity
Flux Stone
Limestone
Dolomite
FGD
Other Miscellaneous Uses
Total
1990
6,737
5,804 •
933M
3,258(
1,835
11,830
2005
7,022
3,165
3,857
6,761
1,632
15,415
2008
3,253
1,970
1,282
8,639
2,531
14,423
2009
4,623
1,631
2,992
12,288
898
17,809
2010
4,440
1,921
2,520
16,064
2,121
22,626
2011
4,396
2,531
1,865
12,326
5,548
22,270
2012
3,656
3,097
559
13,143
2,604
19,404
Uncertainty and Time-Series Consistency
The uncertainty levels presented in this section account for uncertainty associated with activity data. Data on
limestone and dolomite consumption are collected by USGS through voluntary national surveys. USGS contacts the
mines (i.e., producers of various types of crushed stone) for annual sales data. Data on other carbonate consumption
are not readily available. The producers report the annual quantity sold to various end-users/industry types. USGS
estimates the historical response rate for the crushed stone survey to be approximately 70 percent, the rest is
estimated by USGS. Large fluctuations in reported consumption exist, reflecting year-to-year changes in the number
of survey responders. The uncertainty resulting from a shifting survey population is exacerbated by the gaps in the
time series of reports. The accuracy of distribution by end use is also uncertain because this value is reported by the
producer/mines and not the end user. Additionally, there is significant inherent uncertainty associated with
estimating withheld data points for specific end uses of limestone and dolomite. Lastly, much of the limestone
consumed in the United States is reported as "other unspecified uses;" therefore, it is difficult to accurately allocate
this unspecified quantity to the correct end-uses.
Uncertainty in the estimates also arises in part due to variations in the chemical composition of limestone. In
addition to calcium carbonate, limestone may contain smaller amounts of magnesia, silica, and sulfur, among other
minerals. The exact specifications for limestone or dolomite used as flux stone vary with the pyrometallurgical
process and the kind of ore processed.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-14. Other Process Uses of
Carbonates CO2 emissions in 2012 were estimated to be between 6.8 and 9.7 Tg CO2 Eq. at the 95 percent
confidence level. This indicates a range of approximately 15 percent below and 20 percent above the emission
estimate of 8.0 Tg CO2 Eq.
Table 4-14: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Other Process
Uses of Carbonates (Tg COz Eq. and Percent)
Source
Gas
2012 Emission Estimate
(Tg CCh Eq.)
Uncertainty Range Relative to Emission Estimate3
(Tg CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Other Process
Uses of
Carbonates CO2 8.0
6.8 9.7 -15% +20%
1 Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Industrial Processes 4-17
-------
Recalculations Discussion
Other Process Uses of Carbonates consumption data for 2011 were revised to reflect updated USGS data. This
change resulted in a 2 percent increase of CC>2 emissions. In a given inventory year, USGS publishes advance
consumption data and data is typically finalized the following year with final quality assurance, or any late survey
responses. The data typically do not change significantly from the advance release. The published time series was
reviewed to ensure time series consistency. Details on the emission trends through time are described in more detail
in the Methodology section, above.
Planned Improvements
Future improvements involve evaluating and analyzing data reported under EPA's GHGRP that would be useful to
improve the emission estimates for the Other Process Uses of Carbonates source category. Particular attention will
be made to ensure time series consistency of the emissions estimates presented in future inventory reports, consistent
with IPCC and UNFCCC guidelines. This is required as the facility-level reporting data from EPA's GHGRP, with
the program's initial requirements for reporting of emissions in calendar year 2010, are not available for all
inventory years (i.e., 1990 through 2009) as required for this inventory. In implementing improvements and
integration of data from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in
national inventories will be relied upon.127
4.4 Soda Ash Production and Consumption
(IPCC Source Category 2A4)
Carbon dioxide is generated as a byproduct of calcining trona ore to produce soda ash, and is eventually emitted into
the atmosphere. In addition, CO2 may also be released when soda ash is consumed. Emissions from fuels
consumed for energy purposes during the production and consumption of soda ash are accounted for in the Energy
sector.
Calcining involves placing crushed trona ore into a kiln to convert sodium bicarbonate into crude sodium carbonate
that will later be filtered into pure soda ash. The emission of CO2 during trona-based production is based on the
following reaction:
2Na2C03 • NaHC03 • 2H20(Trona} -> 3Na2C03(SodaAsfi) + 5H20 + C02
Soda ash (sodium carbonate, Na2COs) is a white crystalline solid that is readily soluble in water and strongly
alkaline. Commercial soda ash is used as a raw material in a variety of industrial processes and in many familiar
consumer products such as glass, soap and detergents, paper, textiles, and food. (Emissions from soda ash used in
glass production are reported under IPCC Source Category 2A7. Glass production is its own sub-category and
historical soda ash consumption figures have been adjusted to reflect this change.) After glass manufacturing, soda
ash is used primarily to manufacture many sodium-base inorganic chemicals, including sodium bicarbonate, sodium
chromates, sodium phosphates, and sodium silicates (USGS 2012). Internationally, two types of soda ash are
produced, natural and synthetic. The United States produces only natural soda ash and is second only to China in
total soda ash production. Trona is the principal ore from which natural soda ash is made.
The United States represents about one-fourth of total world soda ash output. Only two states produce natural soda
ash: Wyoming and California. Of these two states, only net emissions of CO2 from Wyoming were calculated due
to specifics regarding the production processes employed in the state.128 Based on final 2012 reported data, the
127 See.
128 In California, soda ash is manufactured using sodium carbonate-bearing brines instead of trona ore. To extract the sodium
carbonate, the complex brines are first treated with CCh in carbonation towers to convert the sodium carbonate into sodium
4-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
estimated distribution of soda ash by end-use in 2012 (excluding glass production) was chemical production, 56
percent; soap and detergent manufacturing, 15 percent; distributors, 11 percent; flue gas desulfurization, 6 percent;
other uses, 7 percent; pulp and paper production, 3 percent; and water treatment, 2 percent (USGS 2013).
U.S. natural soda ash is competitive in world markets because the majority of the world output of soda ash is made
synthetically. Although the United States continues to be a major supplier of world soda ash, China, which
surpassed the United States in soda ash production in 2003, is the world's leading producer. Despite this
competition, U.S. soda ash exports are expected to increase, causing domestic production to increase slightly (USGS
2012).
In 2012, CO2 emissions from the production of soda ash from trona were approximately 1.6 Tg CCh Eq. (1,582 Gg).
Soda ash consumption in the United States generated 1.1 Tg CCh Eq. (1,090 Gg) in 2012. Total emissions from
soda ash production and consumption in 2012 were 2.7 Tg CCh Eq. (2,672 Gg) (see Table 4-15 and Table 4-16).
Total emissions in 2012 increased by approximately 1.8 percent from emissions in 2011, and have decreased overall
by approximately 2.5 percent since 1990.
Emissions have remained relatively constant over the time series with some fluctuations since 1990. In general,
these fluctuations were related to the behavior of the export market and the U.S. economy. The U.S. soda ash
industry continued a trend of increased production and value in 2012 since experiencing a decline in domestic and
export sales caused by adverse global economic conditions in 2009. The annual average unit value of soda ash set a
record high in 2012, and soda ash exports increased as well, accounting for 55 percent of total production (USGS
2013).
Table 4-15: COz Emissions from Soda Ash Production and Consumption Not Associated with
Glass Manufacturing (Tg COz Eq.)
Year
Production Consumption
Total
1990
1.4
1.4
2008
2009
2010
2011
2012
1.7
1.5
1.5
1.6
1.6
1.2
1.1
1.1
1.1
1.1
3.0
2.6
2.7
2.7
2.7
Note: Totals may not sum due to independent rounding.
Table 4-16: COz Emissions from Soda Ash Production and Consumption Not Associated with
Glass Manufacturing (Gg)
Year
Production Consumption
Total
bicarbonate, which then precipitates from the brine solution. The precipitated sodium bicarbonate is then calcined back into
sodium carbonate. Although CCh is generated as a byproduct, the CCh is recovered and recycled for use in the carbonation stage
and is not emitted. A third state, Colorado, produced soda ash until the plant was idled in 2004. The lone producer of sodium
bicarbonate no longer mines trona in the state. For a brief time, sodium bicarbonate was produced using soda ash feedstocks
mined in Wyoming and shipped to Colorado. Prior to 2004, because the trona was mined in Wyoming, the production numbers
given by the USGS included the feedstocks mined in Wyoming and shipped to Colorado. In this way, the sodium bicarbonate
production that took place in Colorado was accounted for in the Wyoming numbers.
Industrial Processes 4-19
-------
2009
2010
2011
2012
1,397
1,471
1,526
1,582
1,091
1,141
1,098
1,090
2,488
2,612
2,624
2,672
Note: Totals may not sum due to independent rounding.
Methodology
During the production process, trona ore is calcined in a rotary kiln and chemically transformed into a crude soda
ash that requires further processing. Carbon dioxide and water are generated as byproducts of the calcination
process. Carbon dioxide emissions from the calcination of trona can be estimated based on the chemical reaction
shown above. Based on this formula, which is consistent with an IPCC Tier 1 approach, approximately 10.27 metric
tons of trona are required to generate one metric ton of CO2, or an emission factor of 0.097 metric tons CO2 per
metric ton trona (IPCC 2006). Thus, the 17.1 million metric tons of trona mined in 2012 for soda ash production
(USGS 2013) resulted in CO2 emissions of approximately 1.6 Tg CO2 Eq. (1,582 Gg).
Once produced, most soda ash is consumed in chemical and soap production, with minor amounts in pulp and paper,
flue gas desulfurization, and water treatment. As soda ash is consumed for these purposes, additional CO2 is usually
emitted. In these applications, it is assumed that one mole of carbon is released for every mole of soda ash used.
Thus, approximately 0.113 metric tons of carbon (or 0.415 metric tons of CO2) are released for every metric ton of
soda ash consumed.
The activity data for trona production and soda ash consumption (see Table 4-17) between 1990 and 2012 were
taken from USGS Minerals Yearbook for Soda Ash (1994 through 2013). Soda ash production and consumption
data were collected by the USGS from voluntary surveys of the U. S. soda ash industry.
Table 4-17: Soda Ash Production and Consumption Not Associated with Glass Manufacturing
(Gg)
Year Production" Consumption"
1990 14,700 3,351
2008
2009
2010
2011
2012
17,800
15,100
15,900
16,500
17,100
2,957
2,647
2,768
2,663
2,645
Soda ash produced from trona ore only.
" Soda ash consumption is sales reported by
producers which exclude imports. Historically,
imported soda ash is less than 1 percent of the
total U.S. consumption (Kostick, 2012).
Uncertainty and Time-Series Consistency
Emission estimates from soda ash production have relatively low associated uncertainty levels in that reliable and
accurate data sources are available for the emission factor and activity data. Soda ash production data was collected
by the USGS from voluntary surveys. A survey request was sent to each of the five soda ash producers, all of which
responded, representing 100 percent of the total production data (USGS 2013). One source of uncertainty is the
purity of the trona ore used for manufacturing soda ash. The emission factor used for this estimate assumes the ore
is 100 percent pure, and likely overestimates the emissions from soda ash manufacture. The average water-soluble
sodium carbonate-bicarbonate content for ore mined in Wyoming ranges from 85.5 to 93.8 percent (USGS
4-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
1995a).The primary source of uncertainty, however, results from the fact that emissions from soda ash consumption
are dependent upon the type of processing employed by each end-use. Specific emission factors for each end-use
are not available, so a Tier 1 default emission factor is used for all end uses. Therefore, there is uncertainty
surrounding the emission factors from the consumption of soda ash.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-18. Soda Ash Production and
Consumption CCh emissions were estimated to be between 2.5 and 2.8 Tg CCh Eq. at the 95 percent confidence
level. This indicates a range of approximately 6 percent below and 5 percent above the emission estimate of 2.7 Tg
CO2 Eq.
Table 4-18: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Soda Ash
Production and Consumption (Tg COz Eq. and Percent)
Source Gas 2012 Emission Estimate Uncertainty Range Relative to Emission Estimate3
(TgCChEq.) (Tg CQ2 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Soda Ash Production
and Consumption CCh 2/7 2.5 2.8 -6% +5%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
In inventories prior to 2011, emissions from soda ash included CCh from glass production. Emissions from glass
production are now included in the Glass Production source category, and historical production figures in Table 4-17
have been adjusted to remove the amount of soda ash associated with non-glass uses. This resulted in an average
emission decrease of 1.3 Tg of CC>2 across the time-series. All emissions shown in Table 4-15 and Table 4-16 were
revised accordingly.
Planned Improvements
Future inventory reports are anticipated to estimate emissions from other uses of soda ash. To add specificity, future
inventories will extract soda ash consumed for other uses of carbonates from the current soda ash consumption
emission estimates and include them under those sources; in 2011 and 2012 glass production is its own sub-
category.
In examining data from EPA's GHGRP that would be useful to improve the emission estimates for Soda Ash and
Consumption category, particular attention will be made to ensure time series consistency of the emissions estimates
presented in future inventory reports, consistent with IPCC and UNFCCC guidelines. This is required as the facility-
level reporting data from EPA's GHGRP, with the program's initial requirements for reporting of emissions in
calendar year 2010, are not available for all inventory years (i.e., 1990 through 2009) as required for this inventory.
In implementing improvements and integration of data from EPA's GHGRP, the latest guidance from the IPCC on
the use of facility-level data in national inventories will be relied upon.129
129
See.
Industrial Processes 4-21
-------
4.5 Glass Production (IPCC Source Category
2A7)
Glass production is an energy and raw-material intensive process that results in the generation of CO2 from both the
energy consumed in making glass and the glass process itself. Emissions from fuels consumed for energy purposes
during the production of glass are accounted for in the Energy sector.
Glass production employs a variety of raw materials in a glass-batch. These include formers, fluxes, stabilizers, and
sometimes colorants. The major raw materials (i.e., fluxes and stabilizers) which emit process-related CO2 emissions
during the glass melting process are limestone, dolomite, and soda ash. The main former in all types of glass is silica
(SiO2). Other major formers in glass include feldspar and boric acid (i.e., borax). Fluxes are added to lower the
temperature at which the batch melts. Most commonly used flux materials are soda ash (sodium carbonate, Na2COs)
and potash (potassium carbonate, K2O). Stabilizers are used to make glass more chemically stable and to keep the
finished glass from dissolving and/or falling apart. Commonly used stabilizing agents in glass production are
limestone (CaCOs), dolomite (CaCOsMgCOs), alumina (Al2Os), magnesia (MgO), barium carbonate (BaCOs),
strontium carbonate (SrCOs), lithium carbonate (Li2CO3), and zirconia (ZrO2) (OIT 2002). Glass makers also use a
certain amount of recycled scrap glass (cullet), which comes from in-house return of glassware broken in the process
or other glass spillage or retention such as recycling or cullet broker services.
The raw materials (primarily limestone, dolomite and soda ash) release CO2 emissions in a complex high-
temperature chemical reaction during the glass melting process. This process is not directly comparable to the
calcination process used in lime manufacturing, cement manufacturing, and Process Carbonates Use (i.e.,
limestone/dolomite use), but has the same net effect in terms of CO2 emissions (IPCC 2006). The U.S. glass industry
can be divided into four main categories: containers, flat (window) glass, fiber glass, and specialty glass. The
majority of commercial glass produced is container and flat glass (EPA 2009). The United States is one of the major
global exporters of glass. Domestically, demand comes mainly from the construction, auto, bottling, and container
industries. There are over 1,500 companies that manufacture glass in the United States, with the largest being
Corning, Guardian Industries, Owens-Illinois, and PPG Industries.130
In 2012, 553 thousand metric tons of limestone and 2,420 thousand metric tons of soda ash were consumed for glass
production; no dolomite was used in 2012 for glass production (USGS 2013b, Willett 2013). Use of limestone,
dolomite, and soda ash in glass production resulted in aggregate CO2 emissions of 1.25 Tg CO2 Eq. (1,247.5 Gg)
(see Table 4-19). Overall, emissions have decreased 19 percent from 1990 through 2012.
Emissions from glass production have remained relatively constant over the time series with some fluctuations since
1990. In general, these fluctuations were related to the behavior of the export market and the U.S. economy.
Specifically, the extended downturn in residential and commercial construction and automotive industries between
2008 and 2010 resulted in reduced consumption of glass products, causing a drop in global demand for
limestone/dolomite and soda ash, and a corresponding decrease in emissions. Furthermore, the glass container sector
is one of the leading soda ash consuming sectors in the United States. Some commercial food and beverage package
manufacturers are shifting from glass containers towards lighter and more cost effective polyethylene terephthalate
(PET) based containers, putting downward pressure on domestic consumption of soda ash (USGS 1995 through
2013b).
Table 4-19: COz Emissions from Glass Production (Tg COz Eq. and Gg)
Year Tg CCh Eq. Gg
1990 1.5 1,535
13° Excerpt from Glass & Glass Product Manufacturing Industry Profile, First Research. Available online at
.
4-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
2008
2009
2010
2011
2012
1.5
1.0
1.5
1.3
1.2
1,523
1,045
1,481
1,299
1,247
Methodology
CO2 emissions were calculated based on the IPCC 2006 Guidelines Tier 3 method by multiplying the quantity of
input carbonates (limestone, dolomite, and soda ash) by the carbonate-based emission factor (in metric tons
CO2/metric ton carbonate): limestone -0.43971; dolomite -0.47732; and soda ash -0.41492.
Consumption data for 1990 through 2012 of limestone, dolomite, and soda ash used for glass manufacturing were
obtained from the USGS Minerals Yearbook: Crushed Stone Annual Report (1995 through 2012a), 2012 preliminary
data from the USGS Crushed Stone Commodity Expert (Willett 2013), the USGS Minerals Yearbook: Soda Ash
Annual Report (1995 through 2013b), and the U.S. Bureau of Mines (1991 and 1993a), which are reported to the
nearest ton. During 1990 and 1992, the USGS did not conduct a detailed survey of limestone and dolomite
consumption by end-use. Consumption for 1990 was estimated by applying the 1991 percentages of total limestone
and dolomite use constituted by the individual limestone and dolomite uses to 1990 total use. Similarly, the 1992
consumption figures were approximated by applying an average of the 1991 and 1993 percentages of total limestone
and dolomite use constituted by the individual limestone and dolomite uses to the 1992 total.
Additionally, each year the USGS withholds data on certain limestone and dolomite end-uses due to confidentiality
agreements regarding company proprietary data. For the purposes of this analysis, emissive end-uses that contained
withheld data were estimated using one of the following techniques: (1) the value for all the withheld data points for
limestone or dolomite use was distributed evenly to all withheld end-uses; or (2) the average percent of total
limestone or dolomite for the withheld end-use in the preceding and succeeding years.
There is a large quantity of limestone and dolomite reported to the USGS under the categories "unspecified -
reported" and "unspecified - estimated." A portion of this consumption is believed to be limestone or dolomite used
for glass manufacturing. The quantities listed under the "unspecified" categories were, therefore, allocated to glass
manufacturing according to the percent limestone or dolomite consumption for glass manufacturing end use for that
year.131
Based on the 2012 reported data, the estimated distribution of soda ash consumption for glass production compared
to total domestic soda ash consumption is 48 percent (USGS 2013b).
Table 4-20: Limestone, Dolomite, and Soda Ash Consumption Used in Glass Production
(Thousand Metric Tons)
Activity
Limestone
Dolomite
Soda Ash
Total
1990
3
3
430
59
,177
,666
2005
3
4
920
541
,050
,511
2008
879
0
2,740
3,619
2009
139
0
2,370
2,509
2010
999
0
2,510
3,509
2011
614
0
2,480
3,094
2012
553
0
2,420
2,973
Uncertainty and Time-Series Consistency
The uncertainty levels presented in this section arise in part due to variations in the chemical composition of
limestone used in glass production. In addition to calcium carbonate, limestone may contain smaller amounts of
magnesia, silica, and sulfur, among other minerals (potassium carbonate, strontium carbonate and barium carbonate,
131
This approach was recommended by USGS.
Industrial Processes 4-23
-------
and dead burned dolomite). Similarly, the quality of the limestone (and mix of carbonates) used for glass
manufacturing will depend on the type of glass being manufactured.
The estimates below also account for uncertainty associated with activity data. Large fluctuations in reported
consumption exist, reflecting year-to-year changes in the number of survey responders. The uncertainty resulting
from a shifting survey population is exacerbated by the gaps in the time series of reports. The accuracy of
distribution by end use is also uncertain because this value is reported by the manufacturer of the input carbonates
(limestone, dolomite & soda ash) and not the end user. For 2012, there has been no reported consumption of
dolomite for glass manufacturing. This data has been reported to USGS by dolomite manufacturers and not end-
users (i.e., glass manufacturers). There is a high uncertainty associated with this estimate, as dolomite is a major raw
material consumed in glass production. Additionally, there is significant inherent uncertainty associated with
estimating withheld data points for specific end uses of limestone and dolomite. The uncertainty of the estimates for
limestone and dolomite used in glass making is especially high; however, since glass making accounts for a small
percent of consumption, its contribution to the overall emissions estimate is low. Lastly, much of the limestone
consumed in the United States is reported as "other unspecified uses;" therefore, it is difficult to accurately allocate
this unspecified quantity to the correct end-uses. Further research is needed into alternate and more complete
sources of data on carbonate-based raw material consumption by the glass industry.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-21. In 2012, Glass production
CO2 emissions were estimated to be between 1.2 and 1.3 Tg CCh Eq. at the 95 percent confidence level. This
indicates a range of approximately 5 percent below and 5 percent above the emission estimate of 1.2 Tg CCh Eq.
Table 4-21: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Glass
Production (Tg COz Eq. and Percent)
2012 Emission Estimate Uncertainty Range Relative to Emission Estimate3
Source Gas (Tg CCh Eq.) (Tg CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Glass Production CCh l_2 l_2 l_3 -5% +5%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Pending resources, future improvements involve evaluating and analyzing data reported under EPA's GHGRP that
would be useful to improve the emission estimates for the Glass Production source category. Particular attention will
be made to ensure time series consistency of the emissions estimates presented in future inventory reports, consistent
with IPCC and UNFCCC guidelines. This is required as the facility-level reporting data from EPA's GHGRP, with
the program's initial requirements for reporting of emissions in calendar year 2010, are not available for all
inventory years (i.e., 1990 through 2009) as required for this inventory. Further, EPA's GHGRP has an emission
threshold for reporting, so the data do not account for all glass production in the United States. In implementing
improvements and integration of data from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-
level data in national inventories will be relied upon.132
132
See.
4-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
4.6 Ammonia Production (IPCC Source
Category 2B1) _
Emissions of CO2 occur during the production of synthetic ammonia, primarily through the use of natural gas,
petroleum coke, or naphtha as a feedstock. The natural gas-, naphtha-, and petroleum coke-based processes produce
CO2 and hydrogen (H2), the latter of which is used in the production of ammonia. Emissions from fuels consumed
for energy purposes during the production of ammonia are accounted for in the Energy chapter.
In the United States, the majority of ammonia is produced using a natural gas feedstock; however one synthetic
ammonia production plant located in Kansas is producing ammonia from petroleum coke feedstock. In some U.S.
plants, some of the CO2 produced by the process is captured and used to produce urea rather than being emitted to
the atmosphere. There are approximately 13 companies operating 25 ammonia producing facilities in 16 states.
More than half of domestic ammonia production is concentrated in the States of Louisiana, Oklahoma, and Texas
(USGS 2012). The brine electrolysis process for production of ammonia does not lead to process-based CO2
emissions.
There are five principal process steps in synthetic ammonia production from natural gas feedstock. The primary
reforming step converts CH4 to CO2, carbon monoxide (CO), and H2 in the presence of a catalyst. Only 30 to 40
percent of the CH4 feedstock to the primary reformer is converted to CO and CO2 in this step of the process. The
secondary reforming step converts the remaining CH4 feedstock to CO and CO2. The CO in the process gas from
the secondary reforming step (representing approximately 15 percent of the process gas) is converted to CO2 in the
presence of a catalyst, water, and air in the shift conversion step. Carbon dioxide is removed from the process gas
by the shift conversion process, and the hydrogen gas is combined with the nitrogen (N2) gas in the process gas
during the ammonia synthesis step to produce ammonia. The CO2 is included in a waste gas stream with other
process impurities and is absorbed by a scrubber solution. In regenerating the scrubber solution, CO2 is released
from the solution.
The conversion process for conventional steam reforming of CH4, including the primary and secondary reforming
and the shift conversion processes, is approximately as follows:
0.88C7/4 +1.26Air + 1.24//20 -> 0.88C02 + N2 + 3H2
N2 + 3H2 -
To produce synthetic ammonia from petroleum coke, the petroleum coke is gasified and converted to CO2 and H2.
These gases are separated, and the H2 is used as a feedstock to the ammonia production process, where it is reacted
with N2 to form ammonia.
Not all of the CO2 produced during the production of ammonia is emitted directly to the atmosphere. Some of the
ammonia and some of the CO2 produced by the synthetic ammonia process are used as raw materials in the
production of urea [CO(NH2)2] , which has a variety of agricultural and industrial applications.
The chemical reaction that produces urea is:
2NH3+ C02 -^NH2COONH4 -> CO(NH2}2 + H20
Only the CO2 emitted directly to the atmosphere from the synthetic ammonia production process are accounted for
in determining emissions from ammonia production. The CO2 that is captured during the ammonia production
process and used to produce urea does not contribute to the CO2 emission estimates for ammonia production
presented in this section. Instead, CO2 emissions resulting from the consumption of urea are attributed to the urea
consumption or urea application source category (under the assumption that the carbon stored in the urea during its
manufacture is released into the environment during its consumption or application). Emissions of CO2 resulting
from agricultural applications of urea are accounted for in the Cropland Remaining Cropland section of the Land-
use, Land-use Change, and Forestry chapter. Emissions of CO2 resulting from non-agricultural applications of urea
(e.g., use as a feedstock in chemical production processes) are accounted for in the Urea Consumption for Non-
Agricultural Purposes section of the Industrial Process chapter.
Industrial Processes 4-25
-------
Total emissions of CO2 from ammonia production in 2012 were 9.4 Tg CO2 Eq. (9,366 Gg), and are summarized in
Table 4-22 and Table 4-23. Ammonia production relies on natural gas as both a feedstock and a fuel, and as such,
market fluctuations and volatility in natural gas prices affect the production of ammonia. .
Table 4-22: COz Emissions from Ammonia Production (Tg COz Eq.)
Source
Ammonia Production
Total
1990
13.0
13.0
2005
9.2
9.2
2008
8.4
8.4
2009
8.5
8.5
2010
9.2
9.2
2011
9.4
9.4
2012
9.4
9.4
Table 4-23: COz Emissions from Ammonia Production (Gg)
Source 1990 2005 2008 2009 2010 2011 2012
Ammonia Production 13,047 9,196 8,414 8,454 9,188 9,428 9,366
Total 13,047 9,196 8,414 8,454 9,188 9,428 9,366
CO2 emissions from production of synthetic ammonia from natural gas feedstock is based on the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories (IPCC 2006) Tier 1 and 2 method. A country-specific emission
factor is developed and applied to national ammonia production to estimate emissions. The method uses a CO2
emission factor published by the European Fertilizer Manufacturers Association (EFMA) that is based on natural
gas-based ammonia production technologies that are similar to those employed in the United States. The CO2
emission factor of 1.2 metric tons CO^metric ton NH3 (EFMA 2000a) is applied to the percent of total annual
domestic ammonia production from natural gas feedstock.
Emissions of CO2 from ammonia production are then adjusted to account for the use of some of the CO2 produced
from ammonia production as a raw material in the production of urea. The CO2 emissions reported for ammonia
production are reduced by a factor of 0.733 multiplied by total annual domestic urea production. This corresponds
to a stochiometric CO2/urea factor of 44/60, assuming complete conversion of NH3 and CO2 to urea (IPCC 2006,
EFMA 2000b).
All synthetic ammonia production and subsequent urea production are assumed to be from the same process—
conventional catalytic reforming of natural gas feedstock, with the exception of ammonia production from
petroleum coke feedstock at one plant located in Kansas. Annual ammonia and urea production are shown in Table
4-24. The CO2 emission factor for production of ammonia from petroleum coke is based on plant specific data,
wherein all carbon contained in the petroleum coke feedstock that is not used for urea production is assumed to be
emitted to the atmosphere as CO2 (Bark 2004). Ammonia and urea are assumed to be manufactured in the same
manufacturing complex, as both the raw materials needed for urea production are produced by the ammonia
production process. The CO2 emission factor of 3.57 metric tons CO2/metric ton NH3 for the petroleum coke
feedstock process (Bark 2004) is applied to the percent of total annual domestic ammonia production from
petroleum coke feedstock.
The emission factor of 1.2 metric ton CO^metric ton NH3 for production of ammonia from natural gas feedstock
was taken from the EFMA Best Available Techniques publication, Production of Ammonia (EFMA 2000a). The
EFMA reported an emission factor range of 1.15 to 1.30 metric ton CO^metric ton NH3, with 1.2 metric ton
CO2/metric ton NH3 as a typical value (EFMA 2000a). Technologies (e.g., catalytic reforming process, etc.)
associated with this factor are found to closely resemble those employed in the United States for use of natural gas
as a feedstock. The EFMA reference also indicates that more than 99 percent of the CH4 feedstock to the catalytic
reforming process is ultimately converted to CO2. The emission factor of 3.57 metric ton CO^metric ton NH3 for
production of ammonia from petroleum coke feedstock was developed from plant-specific ammonia production data
and petroleum coke feedstock utilization data for the ammonia plant located in Kansas (Bark 2004). As noted
earlier, emissions from fuels consumed for energy purposes during the production of ammonia are accounted for in
the Energy chapter. The total ammonia production data for 201 land 2012 was obtained from American Chemistry
Council (2012, 2013). Foryears before 2011, ammonia production data (See Table 4-24) was obtained from
4-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Coffeyville Resources (Coffeyville 2005, 2006, 2007a, 2007b, 2009, 2010, 2011, and 2012) and the Census Bureau
of the U.S. Department of Commerce (U.S. Census Bureau 1991 through 1994, 1998 through 2010) as reported in
Current Industrial Reports Fertilizer Materials and Related Products annual and quarterly reports. Urea-ammonia
nitrate production from petroleum coke for years through 2011 was obtained from Coffeyville Resources
(Coffeyville 2005, 2006, 2007a, 2007b, 2009, 2010, 2011, and 2012), and from CVR Energy, Inc. Annual Report
(CVR 2012) for 2012. Urea production data for 1990 through 2008 were obtained from the Minerals Yearbook:
Nitrogen (USGS 1994 through 2009). Urea production data for 2009 through 2010 were obtained from the U.S.
Bureau of the Census (U.S. Bureau of the Census 2010 and 2011). The U.S. Bureau of the Census ceased collection
of urea production statistics, and urea production data for 201 land 2012 were estimated using the ammonia
production information in 2011 and assuming that the ratio of urea production to ammonia production is the same as
the production ratio in 2010.
Table 4-24: Ammonia Production and Urea Production (Gg)
Year Ammonia Production Urea Production
1990
15,425
7,450
2008
2009
2010
2011
2012
9,570
9,372
10,084
10,325
10,305
5,240
5,084
5,122
5,245
5,235
Uncertainty and Time-Series Consistency
The uncertainties presented in this section are primarily due to how accurately the emission factor used represents an
average across all ammonia plants using natural gas feedstock. Uncertainties are also associated with ammonia
production estimates and the assumption that all ammonia production and subsequent urea production was from the
same process—conventional catalytic reforming of natural gas feedstock, with the exception of one ammonia
production plant located in Kansas that is manufacturing ammonia from petroleum coke feedstock. Uncertainty is
also associated with the representativeness of the emission factor used for the petroleum coke-based ammonia
process. It is also assumed that ammonia and urea are produced at collocated plants from the same natural gas raw
material.
Recovery of CCh from ammonia production plants for purposes other than urea production (e.g., commercial sale,
etc.) has not been considered in estimating the CCh emissions from ammonia production, as data concerning the
disposition of recovered CCh are not available. Such recovery may or may not affect the overall estimate of CCh
emissions depending upon the end use to which the recovered CO2 is applied. Further research is required to
determine whether byproduct CO2 is being recovered from other ammonia production plants for application to end
uses that are not accounted for elsewhere.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-25. Ammonia Production CCh
emissions were estimated to be between 8.8 and 10.0 Tg CCh Eq. at the 95 percent confidence level. This indicates
a range of approximately 6.3 percent below and 6.7 percent above the emission estimate of 9.4 Tg CCh Eq.
Table 4-25: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Ammonia
Production (Tg COz Eq. and Percent)
Source
2012 Emission
Estimate
Gas (Tg CCh Eq.)
Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Ammonia Production
CO2
9.4
10.0
-6.3%
+6.7%
Industrial Processes 4-27
-------
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Production estimates for ammonia production from petroleum coke were updated for the years 2008 through 2012
using information obtained in the CVR Energy Annual reports (CVR 2008, 2012). The values for ammonia
production from petroleum coke for the years 2008 through 2011 included in the previous Inventory did not account
for ammonia that was used for urea ammonium nitrate (UAN) production. Emission estimates were revised to
include these data.
Planned Improvements
Future improvements involve continuing to evaluate and analyze data reported under EPA's GHGRP that would be
useful to improve the emission estimates for the Ammonia Production source category. Particular attention will be
made to ensure time series consistency of the emissions estimates presented in future inventory reports, consistent
with IPCC and UNFCCC guidelines. This is required as the facility-level reporting data from EPA's GHGRP, with
the program's initial requirements for reporting of emissions in calendar year 2010, are not available for all
inventory years (i.e., 1990 through 2009) as required for this Inventory. In implementing improvements and
integration of data from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in
national inventories will be relied upon.133 Specifically, the planned improvements include assessing data to update
the emission factors to include both fuel and feedstock CCh emissions and incorporate CO2 capture and storage.
Methodologies will also be updated if additional ammonia-production plants are found to use hydrocarbons other
than natural gas for ammonia production.
4.7 Urea Consumption for Non-Agricultural
Purposes
Urea is produced using ammonia and CCh as raw materials. All urea produced in the United States is assumed to be
produced at ammonia production facilities where both ammonia and CCh are generated. There are approximately 20
of these facilities operating in the U.S.
The chemical reaction that produces urea is:
2NH3+ C02 -^NH2COONH4 -> CO(NH2)2 + H20
This section accounts for CCh emissions associated with urea consumed exclusively for non-agricultural purposes.
CO2 emissions associated with urea consumed for fertilizer are accounted for in the Cropland Remaining Cropland
section of the Land Use, Land-Use Change, and Forestry chapter.
Urea is used as a nitrogenous fertilizer for agricultural applications and also in a variety of industrial applications.
Urea's industrial applications include its use in adhesives, binders, sealants, resins, fillers, analytical reagents,
catalysts, intermediates, solvents, dyestuffs, fragrances, deodorizers, flavoring agents, humectants and dehydrating
agents, formulation components, monomers, paint and coating additives, photosensitive agents, and surface
treatments agents. In addition, urea is used for abating nitrous oxide emissions from coal-fired power plants and
diesel transportation motors.
133
See.
4-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Emissions of CCh from urea consumed for non-agricultural purposes in 2012 were estimated to be 5.2 Tg CCh Eq.
(5,243 Gg), and are summarized in Table 4-26 and Table 4-27.
Table 4-26: COz Emissions from Urea Consumption for Non-Agricultural Purposes (Tg COz
Eq.)
Source
Urea Consumption
Total
1990
3.8
3.8
2005
3.7
3.7
2008
4.1
4.1
2009
3.4
3.4
2010
4.7
4.7
2011
4.0
4.0
2012
5.2
5.2
Table 4-27: COz Emissions from Urea Consumption for Non-Agricultural Purposes (Gg)
Source 1990 2005 2008 2009 2010 2011 2012
Urea Consumption 3,784 3,653 4,065 3,427 4,728 3,999 5,243
Total 3,784 3,653 4,065 3,427 4,728 3,999 5,243
Emissions of COa resulting from urea consumption for non-agricultural purposes are estimated by multiplying the
amount of urea consumed in the United States for non-agricultural purposes by a factor representing the amount of
CO2 used as a raw material to produce the urea. This method is based on the assumption that all of the carbon in
urea is released into the environment as CC>2 during use, and consistent with the 2006IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC 2006).
The amount of urea consumed for non-agricultural purposes in the United States is estimated by deducting the
quantity of urea fertilizer applied to agricultural lands, which is obtained directly from the Land Use, Land-Use
Change, and Forestry chapter (see Table 7-28) and is reported in Table 4-28, from the total domestic supply of urea.
The domestic supply of urea is estimated based on the amount of urea produced plus the sum of net urea imports and
exports. A factor of 0.73 tons of CCh per ton of urea consumed is then applied to the resulting supply of urea for
non-agricultural purposes to estimate CCh emissions from the amount of urea consumed for non-agricultural
purposes. The 0.733 tons of CC>2 per ton of urea emission factor is based on the stoichiometry of producing urea
from ammonia and CO2. This corresponds to a stochiometric CCVurea factor of 44/60, assuming complete
conversion of NH3 and CO2to urea (IPCC 2006, EFMA 2000).
Urea production data for 1990 through 2008 were obtained from the Minerals Yearbook: Nitrogen (USGS 1994
through 2009). Urea production data for 2009 through 2010 were obtained from the U.S. Bureau of the Census
(2011). The U.S. Bureau of the Census ceased collection of urea production statistics in 2011, therefore urea
production data for 201 land 2012 were estimated using the ammonia production information in 2011 and assuming
that the ratio of urea production to ammonia production is the same as the production ratio in 2010. Urea import data
for 2011 and 2012 were taken from U.S. Fertilizer Import/Exports from USDA Economic Research Service Data
Sets (U.S. Department of Agriculture 2012). Urea import data for the previous years were obtained from the U.S.
Census Bureau Current Industrial Reports Fertilizer Materials and Related Products annual and quarterly reports for
1997 through 2010 (U.S. Census Bureau 1998 through 2011), The Fertilizer Institute (TFI2002) for 1993 through
1996, and the United States International Trade Commission Interactive Tariff and Trade DataWeb (U.S. ITC 2002)
for 1990 through 1992 (see Table 4-28). Urea export data for 1990 through 2012 were taken from U.S. Fertilizer
Import/Exports from USDA Economic Research Service Data Sets (U.S. Department of Agriculture 2012).
Industrial Processes 4-29
-------
Table 4-28: Urea Production, Urea Applied as Fertilizer, Urea Imports, and Urea Exports (Gg)
Year
Urea
Production
Urea Applied
as Fertilizer
Urea Imports Urea Exports
2008
2009
2010
2011
2012
5,240
5,084
5,122
5,245
5,235
4,927
4,848
5,154
5,444
4,693
5,459
4,727
6,631
5,860
6,944
230
289
152
207
336
Uncertainty and Time-Series Consistency
There is limited publicly available data on the quantities of urea produced and consumed for non-agricultural
purposes. Therefore, the amount of urea used for non-agricultural purposes is estimated based on a balance that
relies on estimates of urea production, urea imports, urea exports, and the amount of urea used as fertilizer. The
primary uncertainties associated with this source category are associated with the accuracy of these estimates as well
as the fact that each estimate is obtained from a different data source. Because urea production estimates are no
longer available from the USGS, there is additional uncertainty associated with urea produced beginning in 2011.
There is also uncertainty associated with the assumption that all of the carbon in urea is released into the
environment as CCh during use.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-29. CC>2 emissions associated
with urea consumption for non-agricultural purposes were estimated to be between 4.7 and 5.8 Tg CCh Eq. at the 95
percent confidence level. This indicates a range of approximately 9.5 percent below and 9.8 percent above the
emission estimate of 5.2 Tg CCh Eq.
Table 4-29: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Urea
Consumption for Non-Agricultural Purposes (Tg COz Eq. and Percent)
Source
Gas
2012 Emission Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Urea Consumption for
Non-Agricultural
Purposes
C02
5.2
4.7
-9.5%
+9.8%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future improvements to the urea consumption for non-agricultural purposes source category involve continuing to
research obtaining data on how much urea is consumed for specific application (especially non-agricultural) in the
United States and whether carbon is released to the environment fully during each application. Future improvements
also include identifying a new data source for the production of urea in the United States.
4-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
4.8 Nitric Acid Production (IPCC Source
Category 2B2) _
Nitrous oxide (N2O) is emitted during the production of nitric acid (HNOs), an inorganic compound used primarily
to make synthetic commercial fertilizers. It is also a major component in the production of adipic acid — a feedstock
for nylon — and explosives. Virtually all of the nitric acid produced in the United States is manufactured by the
catalytic oxidation of ammonia (EPA 1997). During this reaction, N2O is formed as a byproduct and is released
from reactor vents into the atmosphere. Emissions from fuels consumed for energy purposes during the production
of nitric acid are accounted for in the Energy chapter.
Nitric acid is made from the reaction of ammonia (NH3) with oxygen (O2) in two stages. The overall reaction is:
+802 -
Currently, the nitric acid industry controls for emissions of NO and NO2 (i.e., NOX). As such, the industry in the
United States uses a combination of non-selective catalytic reduction (NSCR) and selective catalytic reduction
(SCR) technologies. In the process of destroying NOX, NSCR systems are also very effective at destroying N2O.
However, NSCR units are generally not preferred in modern plants because of high energy costs and associated high
gas temperatures. NSCR systems were widely installed in nitric plants built between 1971 and 1977. As of 2012,
approximately 44 percent of nitric acid plants use NSCR or other catalyst-based N2O abatement technology,
representing 28 percent of estimated national nitric acid production (EPA 2010, IFDC 2012, CAR 2013, EPA 2013a,
EPA 2013b, EPA 2013c,). The remaining 72 percent of nitric acid production occurs using SCR or extended
absorption, neither of which is known to reduce N2O emissions.134
N2O emissions from this source were estimated to be 15.3 Tg CO2 Eq. (49 Gg) in 2012 (see Table 4-30). Emissions
from nitric acid production have decreased by 16 percent since 1990, with the trend in the time series closely
tracking the changes in production. Emissions have decreased by 30 percent since 1997, the highest year of
production in the time series.
Table 4-30: NzO Emissions from Nitric Acid Production (Tg COz Eq. and Gg)
Year Tg CCh Eq. Gg
1990 18.2
2008
2009
2010
2011
2012
16.9
14.0
16.7
15.8
15.3
54
45
54
51
49
Methodology
To estimate N2O emissions from nitric acid production, weighted emission factors were developed both for
processes that employ NSCR and for processes that do not employ NSCR following an approach based on the 2006
134 Number of plants and production lines using N2O abatement technology is based on publicly available N2O abatement project
and permit information (EPA 2010, CAR 2013, EPA 2013c), supplemented with information available from trade associations
(IFDC 2012) and non-confidential business information data elements from EPA's GHGRP (EPA 2013a). Using boilerplate
production capacity information available for each plant and a national estimate of nitric acid production capacity utilization, we
estimate that approximately 28.1 percent of estimated national nitric acid was produced on lines using NSCR or other catalyst-
basedN2O abatement technology as of 2012 (EPA 2010, IFDC 2012, CAR 2013, EPA 2013a , EPA 2013c).
Industrial Processes 4-31
-------
IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) Tier 2 method. Using data on use of
abatement technologies reported for 2012 in EPA's GHGRP, and publicly available capacity data (EPA 2012), the
portion of national production where abatement technologies are applied and portion produced without use of N2O
abatement technology were determined. In 2012, an estimated 28 percent of national production was produced with
NSCR or catalyst-based N2O abatement technology. Facility level production capacity was used as proxy for
production since EPA does not have access to publicly available time series of production data at the facility level.
From this information, and using the assumption that the utilization rate is approximately the same for all facilities, a
ratio of uncontrolled to controlled production was calculated. The emission factor was determined as a weighted
average of the two IPCC default emission factors: 2.0 kg N2O/metric ton HNOs produced at plants using NSCR
systems and 9.0 kg N2O/metric ton HNOs produced at plants not equipped with NSCR. The weighted emission
factor for 2012 is 7.0 kg N2O/metric ton HNO3.
The percent production with and without NSCR will likely change year over year due to changes in facility-level
abatement technologies used and also due to plant closures and start-ups. Weighted emission factors were developed
for 1990-2008, 2009, 2010, 2011, and 2012. The methodology used for calculating the weighted emission factors for
the different time-periods is essentially the same for all years.
Publicly available GHGRP information on plant-level abatement technology type was used to estimate percent
production with and without abatement for the years 2010, 2011, and 2012. In 2011 approximately 24 percent of
national production was produced with NSCR or catalyst-based N2O abatement technologies. The resulting
weighted emission factor for 2011 is 7.3 kg N2O/metric ton HNOs. In 2010, one facility that did not have abatement
systems installed was not operational. As a result, in 2010 approximately 17 percent of national production was
produced with NSCR or catalyst-based N2O abatement technologies. The resulting weighted emission factor for
2010 is 7.8 kg N2O/metric ton HNO3.
For years prior to 2010, publicly available information on plant-level abatement technologies was used to estimate
percent production with and without abatement (EPA 2010, EPA 2013c). This information was obtained through
publicly available sources such as operating permits and state agencies. In 2009, several nitric acid production
facilities that did not have NSCR abatement systems installed were closed (Desai 2012, EPA 2012) and one facility
installed catalyst-based N2O abatement technology (CAR 2013). As a result, in 2009 approximately 26 percent of
HNOs plants in the United States were equipped with NSCR or catalyst-based N2O abatement technology
representing 19.7 percent of estimated national production. Therefore, the resulting emission factor is 7.6 kg
N2O/metric ton HNO3 for 2009.
For 1990 through 2008, N2O emissions were calculated by multiplying nitric acid production by the weighted
emission factor developed using publicly available data on use of abatement technologies, e.g. obtained from
operating permits and state agencies (EPA 2012). During 1990 through 2008, it was estimated that approximately
88 percent of nitric acid was produced without using NSCR systems (EPA 2010, EPA 2013c), resulting in an
emission factor of 8.1 kg N2O/metric ton HNOs.
Nitric acid production data for the United States for 1990 through 2002 were obtained from the U.S. Census Bureau
(2010b); 2003 production data were obtained from the U.S. Census Bureau (2008); 2004 through 2007 production
data were obtained from the U.S. Census Bureau (2009); and 2008 and 2009 production data were obtained from the
U.S. Census Bureau (2010a) (see Table 4-31). The U.S. Census Bureau (2012) ceased collecting production data
after the second quarter of 2011. The nitric acid production data for years 2010 and 2011 were obtained from USGS
(USGS 2012). Since 2012 data are not yet available, 2011 production data were used as proxy for 2012.
Table 4-31: Nitric Acid Production (Gg)
Year Gg
1990 7,195
2008 6,686
2009 5,924
2010 6,930
2011 7,000
4-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
2012 7,000
Uncertainty and Time-Series Consistency
Uncertainty associated with the parameters used to estimate N2O emissions includes that of production data, the
share of U.S. nitric acid production attributable to each emission abatement technology over the time series, and the
emission factors applied to each abatement technology type. While some information has been obtained through
outreach with industry associations, limited information is available over the time series for a variety of facility level
variables, including plant specific production levels, plant production technology (e.g., low, high pressure, etc.) and
abatement technology type, installation date of abatement technology, and accurate destruction and removal
efficiency rates.
The results of this Tier 2 quantitative uncertainty analysis are summarized in Table 4-32. N2O emissions from nitric
acid production were estimated to be between 9.5 and 21.0 Tg CCh Eq. at the 95 percent confidence level. This
indicates a range of approximately 37 percent below to 38 percent above the 2012 emissions estimate of 15.3 Tg
CO2 Eq.
Table 4-32: Tier 2 Quantitative Uncertainty Estimates for NzO Emissions from Nitric Acid
Production (Tg COz Eq. and Percent)
2012 Emission Uncertainty Range Relative to Emission Estimate3
Source Gas Estimate (Tg CCh Eq.) (%)
(Tg COi Eq.) Lower Bound Upper Bound Lower Bound Upper Bound
Nitric Acid Production N2O 15.3 9.5 21.0 -37% +38%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Methodological recalculations were applied to 2010 and 2011 to ensure time-series consistency and to reflect
improved information on use of abatement technologies publicly available through EPA's GHGRP. The information
on use of abatement technologies at the facility-level and information on plant closures and start-ups were updated
for 2010 and 2011. This resulted in a revised weighted emission factor for 2010 and 2011. Details on the emission
trends and abatement technology trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
This inventory incorporates research into the availability of facility level nitric acid production data, abatement
technology type and installation dates, the share of nitric acid production attributable to various abatement
technologies in recent years, as well as efforts to analyze data reported under EPA's GHGRP. These research efforts
are especially important given the suspension of the U.S. Census Bureau's Current Industrial Reports data series,
from which national Nitric Acid production data have historically been derived. In examining data from EPA's
GHGRP that would be useful to improve the emission estimates for nitric acid production category, particular
attention was made to ensure time series consistency of the emissions estimates presented in future inventory
reports, consistent with IPCC and UNFCCC guidelines. This is required as the facility-level reporting data from
EPA's GHGRP, with the program's initial requirements for reporting of emissions in calendar year 2010, are not
available for all inventory years (i.e., 1990 through 2009) as reported in this inventory. Similar research is planned
for upcoming years as additional GHGRP data become available. In implementing future improvements and
Industrial Processes 4-33
-------
integration of data from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in
national inventories will be relied upon.135
A potential improvement to the inventory estimates for this source category would be to derive country-specific
emission factors using data from EPA's GHGPJ3 for the reported emissions and the aggregated facility production.
The emission factors developed for the current Inventory, and applied for years prior to 2010, were based upon 2011
production estimates produced by the U.S. Census Bureau (2011). Emission factors developed using aggregated
actual facility production could potentially reduce the uncertainty of the calculated emission factor, for more recent
years.
4.9 Adipic Acid Production (IPCC Source
Category 2B3)
Adipic acid is produced through a two-stage process during which N2O is generated in the second stage. Emissions
from fuels consumed for energy purposes during the production of adipic acid are accounted for in the Energy
chapter. The first stage of manufacturing usually involves the oxidation of cyclohexane to form a cyclohexanone/
cyclohexanol mixture. The second stage involves oxidizing this mixture with nitric acid to produce adipic acid.
N2O is generated as a byproduct of the nitric acid oxidation stage and is emitted in the waste gas stream (Thiemens
and Trogler 1991). The second stage is represented by the following chemical reaction:
(CH^CO (cyclohexanone) + (CH2)zCHOH(cyclohexanoV) + wHN03
-> HOOC(CH2)4COOH(adipic acid) + xN20 + yH20
Process emissions from the production of adipic acid vary with the types of technologies and level of emission
controls employed by a facility. In 1990, two of the three major adipic acid-producing plants had N2O abatement
technologies in place and, as of 1998, the three major adipic acid production facilities had control systems in place
(Reimer et al. 1999). One small plant, which last operated in April 2006 and represented approximately two percent
of production, did not control for N2O (VA DEQ 2009; ICIS 2007; VA DEQ 2006).
Worldwide, only a few adipic acid plants exist. The United States, Europe, and China are the major producers. In
2012, the United States had two companies with a total of three adipic acid production facilities, all of which were
operational (EPA 2013). The United States accounts for the largest share of global adipic acid production capacity
(30 percent), followed by the European Union (29 percent) and China (22 percent) (SEI2010). Adipic acid is a
white crystalline solid used in the manufacture of synthetic fibers, plastics, coatings, urethane foams, elastomers,
and synthetic lubricants. Commercially, it is the most important of the aliphatic dicarboxylic acids, which are used
to manufacture polyesters. Eighty-four percent of all adipic acid produced in the United States is used in the
production of nylon 6,6; 9 percent is used in the production of polyester polyols; 4 percent is used in the production
of plasticizers; and the remaining 4 percent is accounted for by other uses, including unsaturated polyester resins and
food applications (ICIS 2007). Food grade adipic acid is used to provide some foods with a "tangy" flavor
(Thiemens and Trogler 1991).
N2O emissions from adipic acid production were estimated to be 5.8 Tg CO2 Eq. (19 Gg) in 2012 (see Table 4-33).
National adipic acid production has increased by approximately 1 percent over the period of 1990 through 2012, to
roughly 760,000 metric tons. Over the period 1990 to 2012, emissions have been reduced by 64 percent due to both
the widespread installation of pollution control measures in the late 1990s and plant idling in the late 2000s. In
April 2006, the smallest of the four facilities ceased production of adipic acid (VA DEQ 2009); furthermore, one of
the major adipic acid production facilities was not operational in 2009 or 2010 (Desai 2010). All three remaining
facilities were in operation in 2012.
Table 4-33: NzO Emissions from Adipic Acid Production (Tg COz Eq. and Gg)
135
See.
4-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Year Tg CCh Eq. Gg
1990 15.8
2008
2009
2010
2011
2012
2.6
2.8
4.4
10.6
5.8
8
9
14
34
19
Methodology
Very little information on annual trends in the activity data exist for adipic acid. Primary production data is derived
from the American Chemistry Council (ACC) Guide to the Business of Chemistry, which does not provide source
specific trend information, however information for adipic acid was not available from this source for 2012. The
USGS does not currently publish a Minerals Yearbook for adipic acid, and it is not included in the general USGS
Minerals Commodity Summary.
Due to confidential business information, plant names are not provided in this section. Therefore, the four adipic
acid-producing plants will be referred to as Plants 1 through 4.
All emission estimates for 2012 were obtained through analysis of the GGHGRP data (EPA, 2013), which is
consistent with the 2006IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) Tier 3 method.
For Plants 1 and 2, 1990 to 2011 emission estimates were obtained directly from the plant engineer and account for
reductions due to control systems in place at these plants during the time series (Desai 2010, EPA 2012). These
estimates were based on continuous process monitoring equipment installed at the two facilities. In 2009 and 2010,
no adipic acid production occurred at Plant 1 (EPA 2012). For Plant 4, N2O emissions were estimated until
shutdown of the plant in 2006 using the following equation:
Eaa = Qaa X EFaa X (1 - [DF X UF])
where,
Eaa = N2O emissions from adipic acid production, metric tons
Qaa = Quantity of adipic acid produced, metric tons
EFaa = Emission factor, metric ton N2O/metric ton adipic acid produced
DF = N2O destruction factor
UF = Abatement system utility factor
The adipic acid production is multiplied by an emission factor (i.e., N2O emitted per unit of adipic acid produced),
which has been estimated, based on experiments that the reaction stoichiometry for N2O production in the
preparation of adipic acid at approximately 0.3 metric tons of N2O per metric ton of product (IPCC 2006). The
"N2O destruction factor" in the equation represents the percentage of N2O emissions that are destroyed by the
installed abatement technology. The "abatement system utility factor" represents the percentage of time that the
abatement equipment operates during the annual production period. Overall, in the United States, two of the plants
employ catalytic destruction (Plants 1 and 2), one plant employs thermal destruction (Plant 3), and the smallest plant
that closed in 2006 used no N2O abatement equipment (Plant 4).
For Plant 3, 2005 through 2011 emissions were obtained directly from the plant engineer and analysis of GHGRP
data (EPA 2012, Desai 2012). For 1990 through 2004, emissions were estimated using plant-specific production
data and IPCC factors as described above for Plant 4. Production data for 1990 through 2003 was estimated by
allocating national adipic acid production data to the plant level using the ratio of known plant capacity to total
national capacity for all U.S. plants. For 2004, actual plant production data were obtained and used for emission
calculations (CW 2005).
Plant capacities for 1990 through 1994 were obtained from Chemical and Engineering News, "Facts and Figures"
and "Production of Top 50 Chemicals" (C&EN 1992 through 1995). Plant capacities for 1995 and 1996 were kept
Industrial Processes 4-35
-------
the same as 1994 data. The 1997 plant capacities were taken from Chemical Market Reporter "Chemical Profile:
Adipic Acid" (CMR 1998). The 1998 plant capacities for all four plants and 1999 plant capacities for three of the
plants were obtained from Chemical Week, Product Focus: Adipic Acid/Adiponitrile (CW 1999). Plant capacities
for 2000 for three of the plants were updated using Chemical Market Reporter, "Chemical Profile: Adipic Acid"
(CMR 2001). For 2001 through 2003, the plant capacities for three plants were kept the same as the year 2000
capacities. Plant capacity for 1999 to 2003 for the one remaining plant was kept the same as 1998. For Plant 4,
which last operated in April 2006 (VA DEQ 2009), plant-specific production data were obtained across the time
series from 1990 through 2008 (VA DEQ 2010). Since the plant has not operated since 2006, production through
2012 was assumed to be zero. The plant-specific production data were then used for calculating emissions as
described above.
National adipic acid production data (see Table 4-34) from 1990 through 2011 were obtained from the American
Chemistry Council (ACC 2012), although this data was not used in estimating the emissions from adipic acid plants.
Table 4-34: Adipic Acid Production (Gg)
Year Gg
1990755~
2008 805
2009 760
2010 710
2011 760
2012 N/A
Uncertainty and Time-Series Consistency
Uncertainty associated with N2O emission estimates included that of the methods used by companies to monitor and
estimate emissions.
The results of this Tier 2 quantitative uncertainty analysis are summarized in Table 4-35. N2O emissions from
adipic acid production for 2012 were estimated to be between 5.5 and 6.0 Tg CCh Eq. at the 95 percent confidence
level. These values indicate a range of approximately 4 percent below to 4 percent above the 2012 emission
estimate of 5.8 Tg CO2 Eq.
Table 4-35: Tier 2 Quantitative Uncertainty Estimates for NzO Emissions from Adipic Acid
Production (Tg COz Eq. and Percent)
2012 Emission Estimate Uncertainty Range Relative to Emission Estimate3
Source Gas (Tg CCh Eq.) (Tg CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Adipic Acid Production N2O 5.8 5.5 6.0 -4% +4%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
• wi • i • ^ wi i • m* • ^^ w ^ ••• ^ • • v^
Future improvements involve continuing to evaluate, analyze, and use data reported under EPA's GHGRP that
would provide more accurate emission estimates for future years, and could also be useful to improve the emission
factors used for the Adipic Acid Production source category for years prior to 2010. Particular attention would be
4-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
made to ensure time series consistency of the emissions estimates presented in future Inventory reports, consistent
with IPCC and UNFCCC guidelines. This is required because the facility-level reporting data from EPA's GHGRP,
with the program's initial requirements for reporting of emissions in calendar year 2010, are not available for all
inventory years (i.e., 1990 through 2009) as required for this inventory. In implementing improvements and
integration of data from EPA' s GHGRP, the latest guidance from the IPCC on the use of facility -level data in
national inventories has been, and will continue to be, relied upon.136 Specifically, the planned improvements
include continuing to assess data to reflect abatement utility and destruction factors based on actual performance of
the latest catalytic and thermal abatement equipment at plants with continuous process and emission monitoring
equipment.
4.10 Silicon Carbide Production (IPCC Source
Category 2B4) and Consumption
Carbon dioxide (CCh) and methane CH4 are emitted from the production of silicon carbide (SiC), a material used as
an industrial abrasive. Silicon carbide is produced for abrasive, metallurgical, and other non-abrasive applications in
the United States. Production for metallurgical and other non-abrasive applications is not available and therefore
both CO2 and CH4 estimates are based solely upon production estimates of silicon carbide for abrasive applications.
Emissions from fuels consumed for energy purposes during the production of silicon carbide are accounted for in the
Energy chapter.
To produce SiC, silica sand or quartz (SiO2) is reacted with carbon in the form of petroleum coke. A portion (about
35 percent) of the carbon contained in the petroleum coke is retained in the SiC. The remaining carbon is emitted as
CO2, CH4, or CO. The overall reaction is shown below (but in practice it does not proceed according to
stoichiometry):
Si02 + 3C^ SiC + 2CO(+02 -> 2C02)
Carbon dioxide is also emitted from the consumption of SiC for metallurgical and other non-abrasive applications.
Markets for manufactured abrasives, including SiC, are heavily influenced by activity in the U.S. manufacturing
sector, especially in the aerospace, automotive, furniture, housing, and steel manufacturing sectors. The USGS
reports that a portion (approximately 50 percent) of SiC is used in metallurgical and other non-abrasive applications,
primarily in iron and steel production (USGS 2006a). As a result of the economic downturn in 2008 and 2009,
demand for SiC decreased in those years. Low cost imports, particularly from China, combined with high relative
operating costs for domestic producers, continue to put downward pressure on the production of SiC in the United
States. However, demand for SiC consumption in the United States has recovered somewhat from its lows in 2009
(USGS 2012a). Silicon carbide is manufactured at a single facility located in Illinois (USGS 2013b).
Carbon dioxide emissions from SiC production and consumption in 2012 were 0.16 Tg COa Eq. (158 Gg).
Approximately 58 percent of these emissions resulted from SiC production while the remainder resulted from SiC
consumption. Methane emissions from SiC production in 2012 were 0.01 Tg CCh Eq. (0.4 Gg CH4) (see Table
4-36: and Table 4-37). Emissions have fluctuated in recent years, but 2012 emissions are only about 42 percent of
emissions in 1990.
136
See.
Industrial Processes 4-37
-------
Table 4-36: COz and CH4 Emissions from Silicon Carbide Production and Consumption (Tg
COz Eq.)
Year
C02
CH4
Total
1990
0.4
+
0.4
2005
0.2
+
0.2
2008
0.2
+
0.2
2009
0.1
+
0.1
2010
0.2
+
0.2
2011
0.2
+
0.2
2012
0.2
+
0.2
+ Does not exceed 0.05 Tg CO2 Eq.
Table 4-37: COz and CH4 Emissions from Silicon Carbide Production and Consumption (Gg)
Year 1990 2005 2008 2009 2010 2011 2012
C02 375 219 175 145 181 170 158
CH4 1 + + + + + j_
+ Does not exceed 0.5 Gg.
Methodology
Emissions of CO2 and CH4 from the production of SiC were calculated using the Tier 1 method provided by the
2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006). Annual estimates of SiC production
were multiplied by the appropriate emission factor, as shown below:
Esc,C02 = EFsc,C02 X Qsc
metric ton\
= EFscfH4 x Qsc x
1000 kg )
where,
Esc,co2 = CO2 emissions from production of SiC, metric tons
Esc,co2 = Emission factor for production of SiC, metric ton CCh/metric ton SiC
Qsc = Quantity of SiC produced, metric tons
Esc,cH4 = CH4 emissions from production of SiC, metric tons
Esc,cH4 = Emission factor for production of SiC, kilogram CH4/metric ton SiC
Emission factors were taken from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC
2006):
• 2.62 metric tons CO2/metric ton SiC
• 11.6 kg CHVmetric ton SiC
Emissions of CCh from silicon carbide consumption for metallurgical uses were calculated by multiplying the
annual utilization of SiC for metallurgical uses (reported annually in the USGS Minerals Yearbook for Silicon) by
the carbon content of SiC (31.5 percent), which was determined according to the molecular weight ratio of SiC.
Emissions of CChfrom silicon carbide consumption for other non-abrasive uses were calculated by multiplying the
annual SiC consumption for non-abrasive uses by the carbon content of SiC (31.5 percent). The annual SiC
consumption for non-abrasive uses was calculated by multiplying the annual SiC consumption (production plus net
imports) by the percent used in metallurgical and other non-abrasive uses (50 percent) (USGS 2006a) and then
subtracting the SiC consumption for metallurgical use.
Production data for 1990 through 2010 were obtained from the Minerals Yearbook: Manufactured Abrasives (USGS
1991a through 2013b). Production data for 2011 and 2012 were taken from the Minerals Commodity Summary:
Abrasives (Manufactured) (USGS 2012a, 2013a). Silicon carbide consumption by major end use was obtained from
the Minerals Yearbook: Silicon (USGS 199Ib through 201 Ib, 2012c, and 2013c) (see Table 4-38). Net imports for
the entire time series were obtained from the U.S. Census Bureau (2005 through 2013).
4-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 4-38: Production and Consumption of Silicon Carbide (Metric Tons)
Year Production Consumption
1990 105,000 172,465
2008
2009
2010
2011
2012
35,000
35,000
35,000
35,000
35,000
144,928
92,280
154,540
136,222
114,265
Uncertainty and Time-Series Consistency
There is uncertainty associated with the emission factors used because they are based on stoichiometry as opposed to
monitoring of actual SiC production plants. An alternative would be to calculate emissions based on the quantity of
petroleum coke used during the production process rather than on the amount of silicon carbide produced. However,
these data were not available. For CH4, there is also uncertainty associated with the hydrogen-containing volatile
compounds in the petroleum coke (IPCC 2006). There is also uncertainty associated with the use or destruction of
methane generated from the process in addition to uncertainty associated with levels of production, net imports,
consumption levels, and the percent of total consumption that is attributed to metallurgical and other non-abrasive
uses.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-39. Silicon carbide production
and consumption CCh emissions were estimated to be between 9 percent below and 9 percent above the emission
estimate of 0.2 Tg CO2 Eq. at the 95 percent confidence level. Silicon carbide production CH4 emissions were
estimated to be between 9 percent below and 9 percent above the emission estimate of 0.01 Tg CO2 Eq. at the 95
percent confidence level.
Table 4-39: Tier 2 Quantitative Uncertainty Estimates for CH4 and COz Emissions from
Silicon Carbide Production and Consumption (Tg COz Eq. and Percent)
2012 Emission Estimate Uncertainty Range Relative to Emission Estimate3
Source Gas (Tg CCh Eq.) (Tg CCh Eq.) (%)
Silicon Carbide Production
and Consumption
Silicon Carbide Production
C02 0.2
CH4 +
Lower Upper Lower
Bound Bound Bound
0.1 0.2 -9%
+ + -9%
Upper
Bound
+9%
+9%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
+ Does not exceed 0.05 Tg CO2 Eq. or 0.5 Gg.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future improvements involve continuing to evaluate and analyze data reported under EPA's GHGRP that would be
useful to improve the emission estimates for the Silicon Carbide Production source category. Particular attention
will be made to ensure time series consistency of the emissions estimates presented in future inventory reports,
consistent with IPCC and UNFCCC guidelines. This is required as the facility-level reporting data from EPA's
GHGPJ3, with the program's initial requirements for reporting of emissions in calendar year 2010, are not available
Industrial Processes 4-39
-------
for all inventory years (i.e., 1990 through 2009) as required for this inventory. In implementing improvements and
integration of data from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in
national inventories will be relied upon.137 In addition, improvements will involve continued research to determine
if calcium carbide production and consumption data are available for the United States. If these data are available,
calcium carbide emission estimates will be included in this source category.
4.11 Petrochemical Production (IPCC Source
Category 2B5)
The production of some petrochemicals results in the release of small amounts of CH4 and CCh emissions.
Petrochemicals are chemicals isolated or derived from petroleum or natural gas. Methane (CH4) emissions from the
production of carbon black, ethylene, ethylene dichloride, and methanol and CCh emissions from the production of
carbon black are presented here and reported under IPCC Source Category 2B5. Acrylonitrile and ethylene oxide
are additional chemical processes that are included in the IPCC petrochemical production source category, but have
not been included in the U. S. estimates of emissions from this category due to the unavailability of data. The CCh
emissions from petrochemical processes other than carbon black are currently reported under Carbon Emitted from
Non-Energy Uses of Fossil Fuels in the Energy chapter. The CCh from carbon black production is included here to
allow for the direct reporting of CO2 emissions from the process and direct accounting of the feedstocks used in the
process.
Carbon black is a black powder generated by the incomplete combustion of an aromatic petroleum- or coal-based
feedstock at a high temperature. Most carbon black produced in the United States is added to rubber to impart
strength and abrasion resistance, and the tire industry is by far the largest consumer. The other major use of carbon
black is as a pigment. The predominant process used in the United States is the furnace black (or oil furnace)
process. In the furnace black process, carbon black oil (a heavy aromatic liquid) is continuously injected into the
combustion zone of a natural gas-fired furnace. Furnace heat is provided by the natural gas and a portion of the
carbon black feedstock; the remaining portion of the carbon black feedstock is pyrolyzed to carbon black. The
resultant CCh and uncombusted CH4 emissions are released from thermal incinerators used as control devices,
process dryers, and equipment leaks. Carbon black is also produced in the United States by the thermal cracking of
acetylene-containing feedstocks (i.e., acetylene black process), by the thermal cracking of other hydrocarbons (i.e.,
thermal black process), and by the open burning of carbon black feedstock (i.e., lamp black process); each of these
process are used at only one U.S. plant each (The Innovation Group 2004, EPA 2000). A total of 21 U.S. facilities
manufacture carbon black with the largest number located in Texas (8) and Louisiana (5) with additional facilities in
Alabama, Arkansas, California, Kansas, Ohio, Oklahoma, and West Virginia (2) (EPA 2008).
Ethylene (C2H4) is consumed in the production processes of the plastics industry including polymers such as high,
low, and linear low density polyethylene (HDPE, LDPE, LLDPE), polyvinyl chloride (PVC), ethylene dichloride,
ethylene oxide, and ethylbenzene. Virtually all ethylene is produced from steam cracking of ethane, propane, butane,
naphtha, gas oil, and other feedstocks. The representative chemical equation for steam cracking of ethane to ethylene
is shown below:
C2H6 -» C2H4 + H2
Small amounts of CH4 are also generated from the steam cracking process. In addition, CO2 and CH4 emissions are
also generated from combustion units. A total of 39 U.S. facilities produce ethylene with most facilities located in
Texas (24) and Louisiana (11); the additional facilities are located in Illinois, Iowa, Kentucky, and Pennsylvania
(EPA 2008).
Ethylene dichloride (C2H4Ci2) is used to produce vinyl chloride monomer, which is the precursor to polyvinyl
chloride (PVC). Ethylene dichloride was used as a fuel additive until 1996 when leaded gasoline was phased out.
Ethylene dichloride is produced from ethylene by either direct chlorination, oxychlorination, or a combination of the
137
See.
4-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
two processes (i.e., the "balanced process"); most U.S. facilities use the balanced process. The direct chlorination
and oxychlorination reactions are shown below:
C2H4 + C12 -> C2H4C12 (direct chlorination)
C2H4 + iQ2 + 2HCI -> C2H4C12 + 2H20 (oxychlorination)
C2H4+ 302 -> 2C02 + 2H20 (direct oxidation of ethylene during oxychlorination)
In addition to the by-product CCh produced from the direction oxidation of the ethylene feedstock, CCh and CH4
emissions are also generated from combustion units. A total of 16 U.S. facilities produce ethylene dichloride and are
located in only three states: Louisiana (8), Texas (7), and Kentucky (1) (EPA 2008).
Methanol (CH3OH) is a chemical feedstock most often converted into formaldehyde, acetic acid and olefins. It is
also an alternative transportation fuel, as well as an additive used by municipal wastewater treatment facilities in the
denitrification of wastewater. Methanol is most commonly synthesized from a synthesis gas (i.e., "syngas" - a
mixture containing H2, CO, and CCh) using a heterogeneous catalyst. There are a number of process techniques that
can be used to produce syngas. Worldwide, steam reforming of natural gas is the most common method; however, in
the U.S. only two facilities use steam reforming of natural gas. Other syngas production processes in the U.S.
include partial oxidation of natural gas and coal gasification. Only five U.S. facilities produce methanol; these
facilities are located in Louisiana, North Dakota, Oklahoma, Tennessee, and Texas.
Emissions of CCfeand CH4 from petrochemical production in 2012 were 3.5 TgCO2Eq. (3,505 GgCO2) and 3.1 Tg
CO2eq (147 Gg CH4), respectively (see Table 4-40 and Table 4-41), totaling 6.6 Tg CO2 Eq. There has been an
overall decrease in CO2 emissions from carbon black production of 1.3 percent since 1990. Methane emissions from
petrochemical production have increased by approximately 36 percent since 1990.
Table 4-40: COz and CH4 Emissions from Petrochemical Production (Tg COz Eq.)
Year
CO2
CH4
Total
1990
3.4
2.3 •
5.7
2005
4.3
3.1
7.5
2008
3.6
2.9
6.5
2009
2.8
2.9
5.7
2010
3.5
3.1
6.5
2011
3.5
3.1
6.6
2012
3.5
3.1
6.6
Table 4-41: COz and CH4 Emissions from Petrochemical Production (Gg)
Year 1990 2005 2008 2009 2010 2011 2012
CO2 3,429 4,330 3,572 2,833 3,455 3,505 3,505
CH4 108 150 137 138 146 148 147
Methodology
Emissions of CH4 were calculated using the Tier 1 method provided by the 2 006 IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC 2006). Annual estimates of chemical production were multiplied by the
appropriate emission factor, as shown below:
I metric ton
Ep=EFpxQpx
where,
Ep = CH4 emissions from production of petrochemical p, metric tons
EFP = Emission factor for petrochemical p, kilogram CH4/metric ton petrochemical p
Qp = Quantity of petrochemical /> produced
Emission factors were taken from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC
2006):
Industrial Processes 4-41
-------
• 0.06 kg CHVmetric ton carbon black
• 6 kg CHVmetric ton ethylene
• 0.0226 kg CH4/metric ton ethylene dichloride
• 2.3 kg CHVmetric ton methanol
Annual production data (see Table 4-42) were obtained from the American Chemistry Council's Guide to the
Business of Chemistry (ACC 2002, 2003, 2005 through 2012), the American Chemistry Council's U.S. Chemical
Industry Statistical Handbook (ACC 2013) and the International Carbon Black Association (Johnson 2003 and 2005
through 2013). Production data for ethylene dichloride were not available for 2012 from the American Chemistry
Council; the 2012 production was assumed to be equivalent to 2011 production. Methanol production data for 1990
through 2007 were obtained from the ACC Guide to the Business of Chemistry (ACC 2002, 2003, 2005 through
2011). The ACC discontinued its data series for Methanol after 2007, so methanol production data for 2008 through
2013 were obtained through the Methanol Institute (Jordan 2013).
Table 4-42: Production of Selected Petrochemicals (Thousand Metric Tons)
Chemical
Carbon Black
Ethylene
Ethylene Dichloride
Methanol
1990
1,307
16,542
6,283
3,785
2005
1,651
23,975 1
11,260 1
2,336
2008
1,362
22,555
8,975
| 810
2009
1,080
22,610
8,120
810
2010
1,317
23,975
8,810
903
2011
1,337
24,410
8,460
760
2012
1,337
23,975
8,460
1,100
Almost all carbon black in the United States is produced from petroleum- or coal-based feedstocks using the
"furnace black" process (European IPPC Bureau 2004). The furnace black process is a partial combustion process
in which a portion of the carbon black feedstock is combusted to provide energy to the process.
The calculation of the carbon lost during the production process is the basis for determining the amount
released during the process. The carbon content of national carbon black production is subtracted from the total
amount of carbon contained in primary and secondary carbon black feedstock to find the amount of carbon lost
during the production process. It is assumed that the carbon lost in this process is emitted to the atmosphere as
either CH4 or CO2. The carbon content of the CH4 emissions, estimated as described above, is subtracted from the
total carbon lost in the process to calculate the amount of carbon emitted as CO2. The total amount of primary and
secondary carbon black feedstock consumed in the process (see Table 4-43) is estimated using a primary feedstock
consumption factor and a secondary feedstock consumption factor estimated from U. S. Census Bureau (1999, 2004,
and 2007) data. The average carbon black feedstock consumption factor for U.S. carbon black production is 1.69
metric tons of carbon black feedstock consumed per metric ton of carbon black produced. The average natural gas
consumption factor for U.S. carbon black production is 32 1 normal cubic meters of natural gas consumed per metric
ton of carbon black produced. The amount of carbon contained in the primary and secondary feedstocks is
calculated by applying the respective carbon contents of the feedstocks to the respective levels of feedstock
consumption (EIA 2003, 2004).
Table 4-43: Carbon Black Feedstock (Primary Feedstock) and Natural Gas Feedstock
(Secondary Feedstock) Consumption (Thousand Metric Tons)
Activity 1990 2005 2008 2009 2010 2011 2012
Primary Feedstock 2,213 I 2,794 2,305 1,828 2,229 2,262 2,262
Secondary Feedstock 284 _ 359 _ 296 235 286 290 290
For the purposes of emission estimation, 100 percent of the primary carbon black feedstock is assumed to be derived
from petroleum refining byproducts. Carbon black feedstock derived from metallurgical (coal) coke production
(i.e., creosote oil) is also used for carbon black production; however, no data are available concerning the annual
consumption of coal-derived carbon black feedstock. Carbon black feedstock derived from petroleum refining
byproducts is assumed to be 90 percent elemental carbon (IPCC 2006). It is assumed that 100 percent of the tail gas
produced from the carbon black production process is combusted and that none of the tail gas is vented to the
4-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
atmosphere uncontrolled. The furnace black process is assumed to be the only process used for the production of
carbon black because of the lack of data concerning the relatively small amount of carbon black produced using the
acetylene black, thermal black, and lamp black processes. The carbon black produced from the furnace black
process is assumed to be 97 percent elemental carbon (Othmer et al. 1992, IPCC 2006).
Uncertainty and Time-Series Consistency
The CH4 emission factors used for petrochemical production are based on a limited number of studies. Using plant-
specific factors instead of default or average factors could increase the accuracy of the emission estimates; however,
such data were not available for the current publication. There may also be other significant sources of CH4 arising
from petrochemical production activities that have not been included in these estimates.
The results of the quantitative uncertainty analysis for the CC>2 emissions from carbon black production calculation
are based on feedstock consumption, import and export data, and carbon black production data. The composition of
carbon black feedstock varies depending upon the specific refinery production process, and therefore the assumption
that carbon black feedstock is 90 percent C gives rise to uncertainty. Also, no data are available concerning the
consumption of coal-derived carbon black feedstock, so CCh emissions from the utilization of coal-based feedstock
are not included in the emission estimate. In addition, other data sources indicate that the amount of petroleum-
based feedstock used in carbon black production may be underreported by the U.S. Census Bureau. Finally, the
amount of carbon black produced from the acetylene black, thermal black, and lamp black processes, although
estimated to be a small percentage of the total production, is not known. Therefore, there is some uncertainty
associated with the assumption that all of the carbon black is produced using the furnace black process.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-44. Petrochemical production
CO2 emissions were estimated to be between 2.6 and 4.5 Tg CO2 Eq. at the 95 percent confidence level. This
indicates a range of approximately 26 percent below to 27 percent above the emission estimate of 3.5 Tg CCh Eq.
Petrochemical production CH4 emissions were estimated to be between 2.8 and 3.4 Tg CCh Eq. at the 95 percent
confidence level. This indicates a range of approximately 10 percent below to 10 percent above the emission
estimate of 3.1 Tg CO2 Eq.
Table 4-44: Tier 2 Quantitative Uncertainty Estimates for Cm Emissions from Petrochemical
Production and COz Emissions from Carbon Black Production (Tg COz Eq. and Percent)
2012 Emission
Source Gas Estimate
(Tg COz Eq.)
Petrochemical
Production CO2 3.5
Petrochemical
Production CH4 3.1
Uncertainty Range Relative to Emission Estimate3
(Tg COz Eq.) (%)
Lower
Bound
2.6
2.8
Upper
Bound
4.5
3.4
Lower
Bound
-26%
-10%
Upper
Bound
+27%
+10%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Pending resources, a potential improvement to the inventory estimates for this source category would include the
derivation of country-specific emission factors, based on data reported under EPA's GHGRP which uses a method
similar to IPCC Tier 2 and 3 approaches. Using data elements reported under EPA's GHGRP, specifically emissions
and petrochemical production data (i.e., carbon black, ethylene, ethylene oxide, and acrylonitrile) that can be
aggregated from facility level to national level for its use, EPA will derive a country-specific emission factor for
estimating process emissions for each type of petrochemical produced. The new emission factors derived from
GHGRP data will replace the use of IPCC defaults, as currently described in the methodological section.
Additionally, acrylonitrile and ethylene oxide are chemical processes that are included in the IPCC petrochemical
Industrial Processes 4-43
-------
production source category, but have not been included in the U.S. emission estimates from this category due to a
prior lack of data. Data on production of these two chemicals are not available from public sources used to establish
the production and emissions from manufacture of the other petrochemical processes. However, information from
these processes and other petrochemical products are collected by EPA's GHGRP starting with calendar year 2010.
In order to provide estimates for the entire time series (i.e., 1990 through 2009), EPA will need to evaluate the
applicability of more recent GHGRP data to the full time series' estimates, and potentially research additional data
that could be utilized to calculate emissions from production of these chemicals. In implementing improvements
and integration of data from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in
national inventories will be relied upon.138
4.12 Titanium Dioxide Production (IPCC
Source Category 2B5)
Titanium dioxide (TiCh) is manufactured using one of two processes: the chloride process and the sulfate process.
The chloride process uses petroleum coke and chlorine as raw materials and emits process-related CCh. Emissions
from fuels consumed for energy purposes during the production of titanium dioxide are accounted for in the Energy
chapter. The chloride process is based on the following chemical reactions:
2FeTi03 + 7C12 + 3C -> 2TiCl4 + 2FeCl3 + 3C02
2TiCl4 +202 -^2Ti02 + 4C/2
The sulfate process does not use petroleum coke or other forms of carbon as a raw material and does not emit CCh.
The carbon in the first chemical reaction is provided by petroleum coke, which is oxidized in the presence of the
chlorine and FeTiOs (rutile ore) to form CC>2. Since 2004, all TiCh produced in the United States has been produced
using the chloride process, and a special grade of "calcined" petroleum coke is manufactured specifically for this
purpose.
The principal use of TiCh is as a pigment in white paint, lacquers, and varnishes; it is also used as a pigment in the
manufacture of paper, foods, plastics, and other products. In 2012, U.S. TiCh production totaled 1,300,000 metric
tons (USGS 2013b). There were a total 6 plants producing TiCh in the United States—2 located in Mississippi, and
single plants located in Delaware, Louisiana, Ohio, and Tennessee.
Emissions of CO2 in 2012 were l.VTgCC^Eq. (1,742 Gg), which represents an increase of 46 percent since 1990
(see Table 4-45).
Table 4-45: COz Emissions from Titanium Dioxide (Tg COz Eq. and Gg)
Year Tg CCh Eq. Gg
1990 1.2 1,195
2008
2009
2010
2011
2012
1.8
1.6
1.8
1.7
1.7
1,809
1,648
1,769
1,729
1,742
138
See.
4-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Methodology
Emissions of CO2 from TiO2 production were calculated by multiplying annual national TiO2 production by
chloride-process-specific emission factors using a Tier 1 approach provided in 2006IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC 2006). The Tier 1 equation is as follows:
Etd = EFtd x Qtd
where,
Etd = CO2 emissions from TiO2 production, metric tons
EFtd = Emission factor (chloride process), metric ton CO2/metric ton TiO2
Qtd = Quantity of TiO2 produced
Data were obtained for the total amount of TiO2 produced each year. For years prior to 2004, it was assumed that
TiO2 was produced using the chloride process and the sulfate process in the same ratio as the ratio of the total U.S.
production capacity for each process. As of 2004, the last remaining sulfate-process plant in the United States
closed; therefore, 100 percent of post-2004 production uses the chloride process (USGS 2005). The percentage of
production from the chloride process is estimated at 100 percent since 2004. An emission factor of 1.34 metric tons
CO2/metric ton TiO2 was applied to the estimated chloride-process production (IPCC 2006). It was assumed that all
TiO2 produced using the chloride process was produced using petroleum coke, although some TiO2 may have been
produced with graphite or other carbon inputs.
The emission factor for the TiO2 chloride process was taken from the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC 2006). Titanium dioxide production data and the percentage of total TiO2
production capacity that is chloride process for 1990 through 2010 (see Table 4-46:) were obtained through the
Minerals Yearbook: Titanium Annual Report (USGS 1991 through 2013a). Production data for 2012 was obtained
from the Minerals Commodity Summary: Titanium and Titanium Dioxide (USGS 2013b). Data on the percentage of
total TiO2 production capacity that is chloride process were not available for 1990 through 1993, so data from the
1994 USGS Minerals Yearbook were used for these years. Because a sulfate process plant closed in September
2001, the chloride process percentage for 2001 was estimated based on a discussion with Joseph Gambogi (2002).
By 2002, only one sulfate plant remained online in the United States and this plant closed in 2004 (USGS 2005).
Table 4-46: Titanium Dioxide Production (Gg)
Year Gg
1990 979
2008 1,350
2009 1,230
2010 1,320
2011 1,290
2012 1,300
Uncertainty and Time-Series Consistency
Each year, USGS collects titanium industry data for titanium mineral and pigment production operations. If TiO2
pigment plants do not respond, production from the operations is estimated on the basis of prior year production
levels and industry trends. Variability in response rates varies from 67 to 100 percent of TiO2 pigment plants over
the time series.
Although some TiO2 may be produced using graphite or other carbon inputs, information and data regarding these
practices were not available. Titanium dioxide produced using graphite inputs, for example, may generate differing
amounts of CO2 per unit of TiO2 produced as compared to that generated through the use of petroleum coke in
production. While the most accurate method to estimate emissions would be to base calculations on the amount of
Industrial Processes 4-45
-------
reducing agent used in each process rather than on the amount of TiO2 produced, sufficient data were not available
to do so.
As of 2004, the last remaining sulfate-process plant in the United States closed. Since annual TiO2 production was
not reported by USGS by the type of production process used (chloride or sulfate) prior to 2004 and only the
percentage of total production capacity by process was reported, the percent of total TiO2 production capacity that
was attributed to the chloride process was multiplied by total TiO2 production to estimate the amount of TiO2
produced using the chloride process. Finally, the emission factor was applied uniformly to all chloride-process
production, and no data were available to account for differences in production efficiency among chloride-process
plants. In calculating the amount of petroleum coke consumed in chloride-process TiO2 production, literature data
were used for petroleum coke composition. Certain grades of petroleum coke are manufactured specifically for use
in the TiO2 chloride process; however, this composition information was not available.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-47: Titanium dioxide
consumption CO2 emissions were estimated to be between 1.5 and 2.0 Tg CO2 Eq. at the 95 percent confidence
level. This indicates a range of approximately 12 percent below and 13 percent above the emission estimate of 1.7
Tg CO2 Eq.
Table 4-47: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Titanium
Dioxide Production (Tg COz Eq. and Percent)
Source Gas 2012 Emission Estimate Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Titanium Dioxide Production CCh 1/7 1.5 2.0 -12% +13%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Production data for 2011 were updated relative to the previous Inventory based on recently published data in the
USGS Minerals Yearbook: Titanium 2013 (USGS 2013b). This resulted in a 9 percent decrease in 2011 CO2
emissions from TiO2 production relative to the previous report.
Planned Improvements
Pending resources, a potential improvement to the Inventory estimates for this source category would include the
derivation of country-specific emission factors, based on data reported under EPA's GHGRP. Using data elements
reported under EPA's GHGRP, specifically emissions and titanium production data that can be aggregated at the
national level for its use, a country-specific emission factor for estimating process emissions will be derived. The
emission factor will be derived by aggregating annual facility-level process line data on annual titanium dioxide
production and facility level emissions, Information on titanium dioxide production is collected by EPA's GHGRP
starting with calendar year 2010. In order to provide estimates for the entire time series (i.e., 1990 through 2009),
the applicability of more recent GHGRP data to previous years' estimates will need to be evaluated, and additional
data that could be utilized in the calculations for this source category may need to be researched. In implementing
improvements and integration of data from EPA's GHGRP, the latest guidance from the IPCContheuse of facility-
level data in national inventories will be relied upon.139
In addition, the planned improvements include researching the significance of titanium-slag production in electric
139
See.
4-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
furnaces and synthetic-rutile production using the Becher process in the United States. Significant use of these
production processes will be included in future estimates.
4.13 Carbon Dioxide Consumption (IPCC
Source Category 2B5)
CO2 is used for a variety of commercial applications, including food processing, chemical production, carbonated
beverage production, and refrigeration, and is also used in petroleum production for enhanced oil recovery (EOR).
Carbon dioxide used for EOR is injected into the underground reservoirs to increase the reservoir pressure to enable
additional petroleum to be produced. For the most part, CO2 used in non-EOR applications will eventually be
released to the atmosphere, and for the purposes of this analysis CO2 used in commercial applications other than
EOR is assumed to be emitted to the atmosphere. Carbon dioxide used in EOR applications is discussed in the
Energy Chapter under "Carbon Capture and Storage, including Enhanced Oil Recovery" and is not discussed in this
section.
CO2 is produced from naturally occurring CO2 reservoirs, as a byproduct from the energy and industrial production
processes (e.g., ammonia production, fossil fuel combustion, ethanol production), and as a byproduct from the
production of crude oil and natural gas, which contain naturally occurring CO2 as a component. Only COa produced
from naturally occurring CO2 reservoirs and used in industrial applications other than EOR is included in this
analysis. Neither byproduct CO2 generated from energy nor industrial production processes nor CO2 separated from
crude oil and natural gas are included in this analysis for a number of reasons. Carbon dioxide captured from
biogenic sources (e.g., ethanol production plants) is not included in the inventory. Carbon dioxide captured from
crude oil and gas production is used in EOR applications and is therefore reported in the Energy Chapter. Any CO2
captured from industrial or energy production processes (e.g., ammonia plants, fossil fuel combustion) and used in
non-EOR applications is assumed to be emitted to the atmosphere. The CO2 emissions from such capture and use
are therefore accounted for under Ammonia Production, Fossil Fuel Combustion, or other appropriate source
category.140
CO2 is produced as a byproduct of crude oil and natural gas production. This CO2 is separated from the crude oil
and natural gas using gas processing equipment, and may be emitted directly to the atmosphere, or captured and
reinjected into underground formations, used for EOR, or sold for other commercial uses. A further discussion of
CO2 used in EOR is described in the Energy Chapter under the text box titled "Carbon Dioxide Transport, Injection,
and Geological Storage." The only CO2 consumption that is accounted for in this analysis is CO2 produced from
naturally-occurring CO2 reservoirs that is used in commercial applications other than EOR.
There are currently three facilities, one in Mississippi (Jackson Dome) and two in New Mexico (Bravo Dome and
West Bravo Dome), producing CO2 from naturally-occurring CO2 reservoirs for use in both EOR and in other
commercial applications (e.g., chemical manufacturing, food production). A fourth facility in Colorado (McCallum
Dome) is producing CO2 from naturally occurring CO2 reservoirs for commercial applications only. There are other
naturally-occurring CO2 reservoirs, mostly located in the western United States, that produce CO2, but they are only
producing CO2 for EOR applications, not for other commercial applications (Allis et al. 2000). Carbon dioxide
production from these facilities is discussed in the Energy Chapter.
In 2012, the amount of CO2 produced by the Colorado, Mississippi, and New Mexico facilities for commercial
applications and subsequently emitted to the atmosphere was 1.8 Tg CO2Eq. (1,815 Gg) (see Table 4-48). This is a
decrease of 2 percent from the previous year and an increase of 28 percent since 1990. This increase was largely
due to an in increase in production at the Mississippi facility, despite the low percentage (9 percent) of the facility's
total reported production that was used for commercial applications in 2012.
140 There are currently four known electric power plants operating in the U.S. that capture CCh for use as food-grade CCh or
other industrial processes; however, insufficient data prevents estimating emissions from these activities as part of CCh
Consumption.
Industrial Processes 4-47
-------
Table 4-48: COz Emissions from COz Consumption (Tg COz Eq. and Gg)
Year Tg CO2 Eq.
1990
1,416
Methodology
CO2 emission estimates for 1990 through 2012 were based on production data for the four facilities currently
producing CC>2 from naturally-occurring CCh reservoirs for use in non-EOR applications. Some of the CCh
produced by these facilities is used for EOR and some is used in other commercial applications (e.g., chemical
manufacturing, food production). It is assumed that 100 percent of the CO2 production used in commercial
applications other than EOR is eventually released into the atmosphere.
CO2 production data and the percentage of production that was used for non-EOR applications for the Jackson
Dome, Mississippi facility were obtained from Advanced Resources International (ARI2006, 2007) for 1990 to
2000 and from the Annual Reports of Denbury Resources (Denbury Resources 2002 through 2013) for 2001 to 2012
(see Table 4-49). Denbury Resources reported the average CO2 production in units of MMCF CO2 per day for 2001
through 2012 and reported the percentage of the total average annual production that was used for EOR. Production
from 1990 to 2000 was set equal to 2001 production. Carbon dioxide production data for the Bravo Dome, New
Mexico facilities were obtained from ARI for 1990 through 2010. Data for the West Bravo Dome facility was only
available for 2009 and 2010. Since 2012 CO2 production was not available for Bravo Dome facilities, 2010 data was
used as a proxy for 2012, and the percentage of total production that was used for non-EOR applications in 2012
was obtained from the GHGRP Flight Tool.141 The percentage of total production that was used for non-EOR
applications for the Bravo Dome facilities for 1990 through 2011 were obtained from New Mexico Bureau of
Geology and Mineral Resources (Broadhead 2003 and New Mexico Bureau of Geology and Mineral Resources
2006). Production data for the McCallum Dome, Colorado facility were obtained from the Colorado Oil and Gas
Conservation Commission (COGCC) for 1999 through 2012 (COGCC 2013). Production data for 1990 to 1998 and
percentage of production used for EOR were assumed to be the same as for 1999.
Table 4-49: COz Production (Gg COz) and the Percent Used for Non-EOR Applications
Year Jackson Dome, MS
CCh Production
(Gg) (% Non-EOR)
Bravo Dome, NM
CCh Production
(Gg)(% Non-
EOR)
West Bravo Dome,
NMCCh
Production
(Gg) (% Non-EOR)
McCallum Dome,
CO
CO2 Production
(Gg) (% Non-EOR)
1990
1,353(100%)
63 (1%
0.07(100%)
2008
2009
2010
2011
2012
1,724(14%)
1,716(13%)
2,145 (13%)
1,754(9%)
1,782(9%)
56 (1%)
46 (1%)
48 (1%)
48 (1%)
+
+
20 (1%)
9 (1%)
9 (1%)
+
0.07(100%)
0.02 (100%)
51 (100%)
33 (100%)
33 (100%)
+ Does not exceed 0%.
141 EPA's Facility Level Information on Greenhouse Gases Tool available online at .
4-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Uncertainty and Time-Series Consistency
Uncertainty is associated with the number of facilities that are currently producing €62 from naturally occurring
CO2 reservoirs for commercial uses other than EOR, and for which the €62 emissions are not accounted for
elsewhere. Research indicates that there are only two such facilities, which are in New Mexico and Mississippi;
however, additional facilities may exist that have not been identified. In addition, it is possible that CCh recovery
exists in particular production and end-use sectors that are not accounted for elsewhere. Such recovery may or may
not affect the overall estimate of CO2 emissions from that sector depending upon the end use to which the recovered
CO2 is applied. Further research is required to determine whether CO2 is being recovered from other facilities for
application to end uses that are not accounted for elsewhere.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-50. Carbon dioxide
consumption CCh emissions for 2012 were estimated to be between 1.1 and 2.6 Tg €62 Eq. at the 95 percent
confidence level. This indicates a range of approximately 39 percent below to 40 percent above the emission
estimate of 1.8 Tg CO2 Eq.
Table 4-50: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from COz
Consumption (Tg COz Eq. and Percent)
Source Gas 2012 Emission Estimate Uncertainty Range Relative to Emission Estimate3
(Tg CCh Eq.) (Tg CQ2 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
CO2 Consumption CCh L8 U 2.6 -39% +40%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Relative to the previous Inventory, the 2010 and 2011 €62 consumption data for the McCallum Dome facility in
Colorado was corrected after a unit conversion error was identified. These revised 2010 and 2011 estimates result in
an annual increase in €62 emissions of approximately 0.05 Tg for those two years.
Planned Improvements
Future improvements involve evaluating and analyzing data reported under EPA's GHGRP that would be useful to
improve the emission estimates for the Carbon Dioxide Consumption source category. Particular attention will be
made to ensure time series consistency of the emission estimates presented in future Inventory reports, consistent
with IPCC and UNFCCC guidelines. This is required as the facility-level reporting data from EPA's GHGRP, with
the program's initial requirements for reporting of emissions in calendar year 2010, are not available for all
inventory years (i.e., 1990 through 2009) as required for this Inventory. In implementing improvements and
integration of data from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in
national inventories will be relied upon.142
142
See.
Industrial Processes 4-49
-------
4.14 Phosphoric Acid Production (IPCC
Source Category 2B5)
Phosphoric acid (H3PO4) is a basic raw material used in the production of phosphate-based fertilizers. Phosphoric
acid production from natural phosphate rock is a source of CCh emissions, due to the chemical reaction of the
inorganic carbon (calcium carbonate) component of the phosphate rock.
Phosphate rock is mined in Florida, North Carolina, Idaho, Utah, and other areas of the United States and is used
primarily as a raw material for phosphoric acid production.
The composition of natural phosphate rock varies depending upon the location where it is mined. Natural phosphate
rock mined in the United States generally contains inorganic carbon in the form of calcium carbonate (limestone)
and also may contain organic carbon.
The calcium carbonate component of the phosphate rock is integral to the phosphate rock chemistry. Phosphate
rock can also contain organic carbon that is physically incorporated into the mined rock but is not an integral
component of the phosphate rock chemistry.
The phosphoric acid production process involves chemical reaction of the calcium phosphate
component of the phosphate rock with sulfuric acid (H2SO4) and recirculated phosphoric acid (H3PO4) (EFMA
2000). However, the generation of CCh is due to the associated limestone-sulfuric acid reaction, as shown below:
CaC03 + H2S04 + H20 -> CaS04 • 2H20 + C02
Total U.S. phosphate rock production sold or used in 2012 was 26.6 million metric tons (USGS 2013).
Approximately 80 percent of domestic phosphate rock production was mined in Florida and North Carolina (8 mines
total), while the remaining 20 percent of production was mined in Idaho and Utah (5 mines total). Total imports of
phosphate rock in 2012 were 2.9 million metric tons (USGS 2013). Most of the imported phosphate rock (70
percent) is from Morocco, with the remaining 30 percent being from Peru (USGS 2013). All phosphate rock mining
companies are vertically integrated with fertilizer plants that produce phosphoric acid located near the mines. Some
additional phosphoric acid production facilities are located in Texas, Louisiana, and Mississippi that used imported
phosphate rock.
Over the 1990 to 2012 period, domestic production has decreased by nearly 47 percent. Total CCh emissions from
phosphoric acid production were 1.1 Tg CCh Eq. (1,101 Gg) in 2012 (see Table 4-51). Consumption of phosphate
rock was estimated to have been lower in 2012 compared with 201 1, owing to the lower seasonal demand in the first
quarter of the year, which resulted in the temporary closure of some fertilizer plants (USGS 2013a).
Table 4-51: COz Emissions from Phosphoric Acid Production (Tg COz Eq. and Gg)
Year Tg CCh Eq. Gg
1990 1.6 1,586
2008
2009
2010
2011
2012
1.2
1.0
1.1
1.2
1.1
1,177
1,016
1,130
1,199
1,101
Methodology
CO2 emissions from production of phosphoric acid from phosphate rock are estimated by multiplying the average
amount of inorganic carbon (expressed as CCh) contained in the natural phosphate rock as calcium carbonate by the
4-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
amount of phosphate rock that is used annually to produce phosphoric acid, accounting for domestic production and
net imports for consumption. The estimation methodology is as follows:
where,
Cpr
CO2 emissions from phosphoric acid production, metric tons
Average amount of carbon (expressed as CCh) in natural phosphate rock, metric ton
metric ton phosphate rock
Quantity of phosphate rock used to produce phosphoric acid
The CO2 emissions calculation methodology is based on the assumption that all of the inorganic carbon (calcium
carbonate) content of the phosphate rock reacts to CCh in the phosphoric acid production process and is emitted with
the stack gas. The methodology also assumes that none of the organic carbon content of the phosphate rock is
converted to CC>2 and that all of the organic carbon content remains in the phosphoric acid product.
From 1993 to 2004, the USGSMineral Yearbook: Phosphate Rock disaggregated phosphate rock mined annually in
Florida and North Carolina from phosphate rock mined annually in Idaho and Utah, and reported the annual
amounts of phosphate rock exported and imported for consumption (see Table 4-52). For the years 1990 through
1992, and 2005 through 2012, only nationally aggregated mining data was reported by USGS. For the years 1990,
1991, and 1992, the breakdown of phosphate rock mined in Florida and North Carolina, and the amount mined in
Idaho and Utah, are approximated using average share of U.S. production in those states from 1993 to 2004 data.
For the years 2005 through 2012, the same approximation method is used, but the share of U.S. production in those
states data were obtained from the USGS commodity specialist for phosphate rock (USGS 2012). Data for domestic
sales or consumption of phosphate rock, exports of phosphate rock (primarily from Florida and North Carolina), and
imports of phosphate rock for consumption for 1990 through 2012 were obtained from USGS Minerals Yearbook:
Phosphate Rock (USGS 1994 through 2013). From 2004 through 2012, the USGS reported no exports of phosphate
rock from U.S. producers (USGS 2005 through 2013).
The carbonate content of phosphate rock varies depending upon where the material is mined. Composition data for
domestically mined and imported phosphate rock were provided by the Florida Institute of Phosphate Research
(FIPR 2003). Phosphate rock mined in Florida contains approximately 1 percent inorganic carbon, and phosphate
rock imported from Morocco contains approximately 1.46 percent inorganic carbon. Calcined phosphate rock
mined in North Carolina and Idaho contains approximately 0.41 percent and 0.27 percent inorganic carbon,
respectively (see Table 4-53).
Carbonate content data for phosphate rock mined in Florida are used to calculate the CO2 emissions from
consumption of phosphate rock mined in Florida and North Carolina (80 percent of domestic production) and
carbonate content data for phosphate rock mined in Morocco are used to calculate CCh emissions from consumption
of imported phosphate rock. The CCh emissions calculation is based on the assumption that all of the domestic
production of phosphate rock is used in uncalcined form. As of 2006, the USGS noted that one phosphate rock
producer in Idaho produces calcined phosphate rock; however, no production data were available for this single
producer (USGS 2006). The USGS confirmed that no significant quantity of domestic production of phosphate rock
is in the calcined form (USGS 2012b).
Table 4-52: Phosphate Rock Domestic Consumption, Exports, and Imports (Gg)
Location/Year
1990
2005
U.S. Domestic
Consumption*
FL and NC
IDandUT
Exports—FL and NC
Imports
49,800
42,494
7,306
6,240
451
Total U.S.
Consumption
2008
2009
2010
2011
2012
28,900 25,500 28,100
23,120 20,400 22,480
5,780 5,100 5,620
2,750
2,000
2,400
28,600
22,880
5,720
3,350
26,600
21,280
5,320
2,850
44,011
37,830
31,650 27,500 30,500 31,950 29,450
+ Assumed equal to zero.
Industrial Processes 4-51
-------
Table 4-53: Chemical Composition of Phosphate Rock (percent by weight)
Composition
Total Carbon (as C)
Inorganic Carbon (as C)
Organic Carbon (as C)
Inorganic Carbon (as CCh)
Central
Florida
1.60
1.00
0.60
3.67
North
Florida
1.76
0.93
0.83
3.43
North Carolina
(calcined)
0.76
0.41
0.35
1.50
Idaho
(calcined)
0.60
0.27
0.00
1.00
Morocco
1.56
1.46
0.10
5.00
Source: FIPR 2003
Uncertainty and Time-Series Consistency
Phosphate rock production data used in the emission calculations were developed by the USGS through monthly and
semiannual voluntary surveys of the active phosphate rock mines during 2012. For previous years in the time series,
USGS provided the data disaggregated regionally; however, beginning in 2006, only total U.S. phosphate rock
production was reported. Regional production for 2012 was estimated based on regional production data from
previous years and multiplied by regionally-specific emission factors. There is uncertainty associated with the
degree to which the estimated 2012 regional production data represents actual production in those regions. Total
U.S. phosphate rock production data are not considered to be a significant source of uncertainty because all the
domestic phosphate rock producers report their annual production to the USGS. Data for exports of phosphate rock
used in the emission calculation are reported by phosphate rock producers and are not considered to be a significant
source of uncertainty. Data for imports for consumption are based on international trade data collected by the U.S.
Census Bureau. These U.S. government economic data are not considered to be a significant source of uncertainty.
An additional source of uncertainty in the calculation of COa emissions from phosphoric acid production is the
carbonate composition of phosphate rock, the composition of phosphate rock varies depending upon where the
material is mined, and may also vary over time. The inventory relies on one study (FIPR 2003) of chemical
composition of the phosphate rock; limited data is available beyond this study. Another source of uncertainty is the
disposition of the organic carbon content of the phosphate rock. A representative of the FIPR indicated that in the
phosphoric acid production process, the organic carbon content of the mined phosphate rock generally remains in
the phosphoric acid product, which is what produces the color of the phosphoric acid product (FIPR 2003a).
Organic carbon is therefore not included in the calculation of CC>2 emissions from phosphoric acid production.
A third source of uncertainty is the assumption that all domestically-produced phosphate rock is used in phosphoric
acid production and used without first being calcined. Calcination of the phosphate rock would result in conversion
of some of the organic carbon in the phosphate rock into CCh. However, according to air permit information
available to the public, at least one facility has calcining units permitted for operation (NCDENR 2013).
Finally, USGS indicated that approximately 7 percent of domestically-produced phosphate rock is used to
manufacture elemental phosphorus and other phosphorus-based chemicals, rather than phosphoric acid (USGS
2006). According to USGS, there is only one domestic producer of elemental phosphorus, in Idaho, and no data
were available concerning the annual production of this single producer. Elemental phosphorus is produced by
reducing phosphate rock with coal coke, and it is therefore assumed that 100 percent of the carbonate content of the
phosphate rock will be converted to COa in the elemental phosphorus production process. The calculation for CCh
emissions is based on the assumption that phosphate rock consumption, for purposes other than phosphoric acid
production, results in CC>2 emissions from 100 percent of the inorganic carbon content in phosphate rock, but none
from the organic carbon content.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-54. Phosphoric acid production
CO2 emissions were estimated to be between 0.9 and 1.3 Tg CCh Eq. at the 95 percent confidence level. This
indicates a range of approximately 19 percent below and 21 percent above the emission estimate of 1.1 Tg CCh Eq.
Table 4-54: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Phosphoric
Acid Production (Tg COz Eq. and Percent)
2012 Emission
Source Gas Estimate Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (Tg C02 Eq.) (%)
4-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Lower Upper Lower Upper
Bound Bound Bound Bound
Phosphoric Acid Production CCh LI 0.9 1_3 -19% +21%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Pending resources, a potential improvement to the inventory estimates for this source category would include
updating the inorganic carbon content of phosphate rock based on data reported under EPA's GHGRP. This new
inorganic carbon content factor would be applied to regional phosphate rock consumption aggregated from facility
level reports in the methodology, replacing use of USGS national-level data for 2010 and onward. Information from
phosphoric acid producers is now collected by EPA's GHGRP starting with calendar year 2010. In order to provide
estimates for the entire time series (i.e. 1990 through 2009), EPA will need to evaluate applicability of more recent
GHGRP data to previous years' estimates and potentially research additional data that could be utilized in the
calculations for this source category. In implementing improvements and integration of data from EPA's GHGRP,
the latest guidance from the IPCC on the use of facility-level data in national inventories will be relied upon.143
4.15 Iron and Steel Production (IPCC Source
Category 2C1) and Metallurgical Coke
Production
Iron and steel production is a multi-step process that generates process-related emissions of CCh and CH4 as raw
materials are refined into iron and then transformed into crude steel. Emissions from conventional fuels (e.g., natural
gas, fuel oil, etc.) consumed for energy purposes during the production of iron and steel are accounted for in the
Energy chapter.
Iron and steel production includes six distinct production processes: coke production, sinter production, direct
reduced iron (DRI) production, pig iron production, electric arc furnace (EAF) steel production, and basic oxygen
furnace (EOF) steel production. The number of production processes at a particular plant is dependent upon the
specific plant configuration. In addition to the production processes mentioned above, CCh is also generated at iron
and steel mills through the consumption of process byproducts (e.g., blast furnace gas, coke oven gas, etc.) used for
various purposes including heating, annealing, and electricity generation. Process byproducts sold for use as
synthetic natural gas are deducted and reported in the Energy chapter. In general, CCh emissions are generated in
these production processes through the reduction and consumption of various carbon-containing inputs (e.g., ore,
scrap, flux, coke byproducts, etc.). In addition, fugitive CH4 emissions are also generated by the coke production,
sinter production, and pig iron production processes.
Currently, there are between 15 and 20 integrated iron and steel steelmaking facilities that utilize BOFs to refine and
produce steel from iron and more than 100 steelmaking facilities that utilize EAFs to produce steel primarily from
recycled ferrous scrap. In addition, there are 18 cokemaking facilities, of which 7 facilities are co-located with
integrated iron and steel facilities. Nearly 62 percent of the raw steel produced in the United States is produced in
one of seven states: Alabama, Arkansas, Indiana, Kentucky, Mississippi, Ohio, and Tennessee.
143
See.
Industrial Processes 4-53
-------
Total production of crude steel in the United States between 2000 and 2008 ranged from a low of 99,320,000 tons to
a high of 109,880,000 tons (2001 and 2004, respectively). Due to the decrease in demand caused by the global
economic downturn (particularly from the automotive industry), crude steel production in the United States sharply
decreased to 65,459,000 tons in 2009. In 2010, crude steel production rebounded to 88,731,000 tons as economic
conditions improved and then continued to increase to 95,237,000 tons in 2011 and 97,770,000 tons in 2012 (AISI
2013). As of 2012, the United States was the third largest producer of raw steel in the world, behind China and
Japan, accounting for approximately 6 percent of world production in 2012 (USGS 2012).
The majority of CO2 emissions from the iron and steel production process come from the use of coke in the
production of pig iron and from the consumption of other process byproducts, with lesser amounts emitted from the
use of flux and from the removal of carbon from pig iron used to produce steel.
According to the 2006IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006), the production of
metallurgical coke from coking coal is considered to be an energy use of fossil fuel and the use of coke in iron and
steel production is considered to be an industrial process source. Therefore, the Guidelines suggest that emissions
from the production of metallurgical coke should be reported separately in the Energy source, while emissions from
coke consumption in iron and steel production should be reported in the industrial process source. However, the
approaches and emission estimates for both metallurgical coke production and iron and steel production are both
presented here because the activity data used to estimate emissions from metallurgical coke production have
significant overlap with activity data used to estimate iron and steel production emissions. In addition, some
byproducts (e.g., coke oven gas, etc.) of the metallurgical coke production process are consumed during iron and
steel production, and some byproducts of the iron and steel production process (e.g., blast furnace gas, etc.) are
consumed during metallurgical coke production. Emissions associated with the consumption of these byproducts are
attributed at the point of consumption. Emissions associated with the use of conventional fuels (e.g., natural gas, fuel
oil, etc.) for electricity generation, heating and annealing, or other miscellaneous purposes downstream of the iron
and steelmaking furnaces are reported in the Energy chapter.
Metallurgical Coke Production
Emissions of CO2 and CH4 from metallurgical coke production in 2012 were 0.5 Tg CO2 Eq. (541 Gg) and less than
0.5 Tg CO2 Eq. (less than 0.05 Gg), respectively (see Table 4-55 and Table 4-56), totaling 0.5 Tg CO2 Eq.
Emissions decreased in 2012 from 2011 levels and have decreased overall since 1990. In 2012, domestic coke
production decreased by 2 percent from the previous year, and has decreased overall since 1990. Coke production
in 2012 was 27 percent lower than in 2000 and 45 percent below 1990. Overall, emissions from metallurgical coke
production have declined by 78 percent (1.9 Tg CO2 Eq.) from 1990 to 2012.
Table 4-55: COz and Cm Emissions from Metallurgical Coke Production (Tg COz Eq.)
Year
C02
CH4
Total
1990
2.5
+
2.5
2005
2.0
+
2.0
2008
2.3
+
2.3
2009
1.0
+
1.0
2010
2.1
+
2.1
2011
1.4
+
1.4
2012
0.5
+
0.5
+ Does not exceed 0.05 Tg CO2 Eq.
Table 4-56: COz and Cm Emissions from Metallurgical Coke Production (Gg)
Year 1990 2005 2008 2009 2010 2011 2012
C02 2,470 2,043 2,334 956 2,084 1,425 541
CH4 + + + + + + +_
+ Does not exceed 0.5 Gg
Iron and Steel Production
Emissions of CO2 and CH4 from iron and steel production in 2012 were 53.8 Tg CO2 Eq. (53,778 Gg) and 0.6 Tg
CO2 Eq. (29.3 Gg), respectively (see Table 4-57 through Table 4-60), totaling approximately 54.3 Tg CO2 Eq.
Emissions decreased in 2012 and have decreased overall since 1990 due to restructuring of the industry,
4-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
technological improvements, and increased scrap steel utilization. Carbon dioxide emission estimates include
emissions from the consumption of carbonaceous materials in the blast furnace, EAF, and EOF, as well as blast
furnace gas and coke oven gas consumption for other activities at the steel mill.
In 2012, domestic production of pig iron increased by 6 percent from 2011 levels. Overall, domestic pig iron
production has declined since the 1990s. Pig iron production in 2012 was 33 percent lower than in 2000 and 35
percent below 1990. Carbon dioxide emissions from steel production have increased by 24 percent (1.9 Tg CC>2 Eq.)
since 1990, while overall CCh emissions from iron and steel production have declined by 45 percent (43.5 Tg CC>2
Eq.) from 1990 to 2011.
Table 4-57: COz Emissions from Iron and Steel Production (Tg COz Eq.)
Year
Sinter Production
Iron Production
Steel Production
Other Activities*
Total
1990
2.4
47.6
8.0
39.3
97.3
2005
1.7
19.4B
9.4M
34.2
64.6
2008
1.3
25.6
8.4
29.1
64.5
2009
0.8
15.9
7.6
17.8
42.1
2010
1.0
19.1
9.2
24.3
53.7
2011
1.2
19.9
9.3
28.2
58.6
2012
1.2
12.6
9.9
30.2
53.8
Note: Totals may not sum due to independent rounding.
a Includes emissions from blast furnace gas and coke oven gas combustion for activities at the
steel mill other than consumption in blast furnace, EAFs, or BOFs.
Table 4-58: COz Emissions from Iron and Steel Production (Gg)
Year
Sinter Production
Iron Production
Steel Production
Other Activities a
Total
1990
2,448
47,650
7,958 •
39,256
97,311
2005
1,663
19,414
9,386
34,160
64,623
2008
1,299
25,622
8,422
29,146
64,488
2009
763
15,941
7,555
17,815
42,073
2010
1,045
19,109
9,248
24,260
53,662
2011
1,188
19,901
9,262
28,230
58,583
2012
1,159
12,551
9,873
30,195
53,778
Note: Totals may not sum due to independent rounding.
a Includes emissions from blast furnace gas and coke oven gas combustion for activities at the steel
mill other than consumption in blast furnace, EAFs, or BOFs.
Table 4-59: CH4 Emissions from Iron and Steel Production (Tg COz Eq.)
Year
Sinter Production
Iron Production
Total
1990
+
0.9
1.0
2005
+
0.7
0.7
2008
+
0.6
| 0.6
2009
+
0.4
0.4
2010
+
0.5
0.5
2011
+
0.6
0.6
2012
+
0.6
0.6
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 4-60: CH4 Emissions from Iron and Steel Production (Gg)
Year
Sinter Production
Iron Production
Total
1990
0.9
44.7
45.6
2005
0.6
33.5
34.1
2008
0.4
30.4
30.8
2009
0.3
17.1
17.4
2010
0.4
24.2
24.5
2011
0.4
27.2
27.6
2012
0.4
28.9
29.3
Note: Totals may not sum due to independent rounding.
Methodology
Emission estimates presented in this chapter are largely based on Tier 2 methodologies provided by the 2006IPCC
Guidelines for National Greenhouse Gas Inventories (IPCC 2006). These Tier 2 methodologies call for a mass
balance accounting of the carbonaceous inputs and outputs during the iron and steel production process and the
Industrial Processes 4-55
-------
metallurgical coke production process. Tier 1 methods are used for certain iron and steel production processes (i.e.,
sinter production and DRI production) for which available data are insufficient for utilizing a Tier 2 method.
The Tier 2 methodology equation is as follows:
44
-C02 —
X12
where,
ECo2 = Emissions from coke, pig iron, EAF steel, or EOF steel production, metric tons
a = Input material a
b = Output material b
Qa = Quantity of input material a, metric tons
Ca = Carbon content of material a, metric tons C/metric ton material
Qb = Quantity of output material b, metric tons
Cb = Carbon content of material b, metric tons C/metric ton material
44/12 = Stoichiometric ratio of CO2 to C
The Tier 1 methodology equations are as follows:
ESip = Qsx EFS:P
Ed,p = Qd X EFd,p
where,
ES)P = Emissions from sinter production process for pollutant/) (CO2 or CH4), metric ton
Qs = Quantity of sinter produced, metric tons
EFS>P = Emission factor for pollutant/) (CO2 or CH4), metric ton/>/metric ton sinter
Ed,P = Emissions from DRI production process for pollutant/) (CO2 or CH4), metric ton
Qa = Quantity of DRI produced, metric tons
EFd,P = Emission factor for pollutant/) (CO2 or CH4), metric ton/)/metric ton DRI
Metallurgical Coke Production
Coking coal is used to manufacture metallurgical (coal) coke that is used primarily as a reducing agent in the
production of iron and steel, but is also used in the production of other metals including zinc and lead (see Zinc
Production and Lead Production sections of this chapter). Emissions associated with producing metallurgical coke
from coking coal are estimated and reported separately from emissions that result from the iron and steel production
process. To estimate emission from metallurgical coke production, a Tier 2 method provided by the 2006IPCC
Guidelines for National Greenhouse Gas Inventories (IPCC 2006) was utilized. The amount of carbon contained in
materials produced during the metallurgical coke production process (i.e., coke, coke breeze, coke oven gas, and
coal tar) is deducted from the amount of carbon contained in materials consumed during the metallurgical coke
production process (i.e., natural gas, blast furnace gas, and coking coal). Light oil, which is produced during the
metallurgical coke production process, is excluded from the deductions due to data limitations. The amount of
carbon contained in these materials is calculated by multiplying the material-specific carbon content by the amount
of material consumed or produced (see Table 4-61). The amount of coal tar produced was approximated using a
production factor of 0.03 tons of coal tar per ton of coking coal consumed. The amount of coke breeze produced
was approximated using a production factor of 0.075 tons of coke breeze per ton of coking coal consumed. Data on
the consumption of carbonaceous materials (other than coking coal) as well as coke oven gas production were
available for integrated steel mills only (i.e., steel mills with co-located coke plants). Therefore, carbonaceous
material (other than coking coal) consumption and coke oven gas production were excluded from emission estimates
for merchant coke plants. Carbon contained in coke oven gas used for coke-oven underfiring was not included in
the deductions to avoid double-counting.
Table 4-61: Material Carbon Contents for Metallurgical Coke Production
Material kg C/kg
4-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Coal Tar 0.62
Coke 0.83
Coke Breeze 0.83
Coking Coal 0.73
Material kg C/GJ
Coke Oven Gas 12.1
Blast Furnace Gas 70.8
Source: IPCC 2006, Table 4.3. Coke Oven Gas and
Blast Furnace Gas, Table 1.3.
The production processes for metallurgical coke production results in fugitive emissions of CH4, which are emitted
via leaks in the production equipment, rather than through the emission stacks or vents of the production plants. The
fugitive emissions were calculated by applying Tier 1 emission factors (0. Ig CH4 per metric ton) taken from the
2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) for metallurgical coke production.
Data relating to the mass of coking coal consumed at metallurgical coke plants and the mass of metallurgical coke
produced at coke plants were taken from the Energy Information Administration (EIA), Quarterly Coal Report:
October through December (EIA 1998 through 2013d) (see Table 4-62). Data on the volume of natural gas
consumption, blast furnace gas consumption, and coke oven gas production for metallurgical coke production at
integrated steel mills were obtained from the American Iron and Steel Institute (AISI), Annual Statistical Report
(AISI 2004 through 2013a) and through personal communications with AISI (2008b) (see Table 4-63). The factor
for the quantity of coal tar produced per ton of coking coal consumed was provided by AISI (2008b). The factor for
the quantity of coke breeze produced per ton of coking coal consumed was obtained through Table 2-1 of the report,
Energy and Environmental Profile of the U. S. Iron and Steel Industry (DOE 2000). Data on natural gas
consumption and coke oven gas production at merchant coke plants were not available and were excluded from the
emission estimate. Carbon contents for coking coal, metallurgical coke, coal tar, coke oven gas, and blast furnace
gas were provided by the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006). The
carbon content for coke breeze was assumed to equal the carbon content of coke.
Table 4-62: Production and Consumption Data for the Calculation of COz and CH4 Emissions
from Metallurgical Coke Production (Thousand Metric Tons)
Source/Activity Data
Metallurgical Coke Production
Coking Coal Consumption at Coke Plants
Coke Production at Coke Plants
Coal Breeze Production
Coal Tar Production
1990
35,2691
25,0541
2,645 1
1,058
2005
21,259
15,167
1,594
638
2008 2009 2010 2011 2012
§20,022 13,904 19,135 19,445 18,825
14,194 10,109 13,628 13,989 13,764
1,502 1,043 1,435 1,458 1,412
601 417 574 583 565
Table 4-63: Production and Consumption Data for the Calculation of COz Emissions from
Metallurgical Coke Production (million ft3)
Source/Activity Data
Metallurgical Coke Production
Coke Oven Gas Production
Natural Gas Consumption
Blast Furnace Gas Consumption
1990
250,767
599|
24,602
2005
1 114,2131
2,996
4,460
2008 2009 2010 2011 2012
1 103,191 66,155 95,405 109,044 113,772
3,134 2,121 3,108 3,175 3,267
4,829 2,435 3,181 3,853 4,351
Iron and Steel Production
Emissions of CO2 from sinter production and direct reduced iron production were estimated by multiplying total
national sinter production and the total national direct reduced iron production by Tier 1 CO2 emission factors (see
Table 4-64). Because estimates of sinter production and direct reduced iron production were not available,
production was assumed to equal consumption.
Industrial Processes 4-57
-------
Table 4-64: COz Emission Factors for Sinter Production and Direct Reduced Iron Production
Material Produced Metric Ton
COi/Metric Ton
Sinter 0.2
Direct Reduced Iron 0/7
Source: IPCC 2006, Table 4.1.
To estimate emissions from pig iron production in the blast furnace, the amount of carbon contained in the produced
pig iron and blast furnace gas were deducted from the amount of carbon contained in inputs (i.e., metallurgical coke,
sinter, natural ore, pellets, natural gas, fuel oil, coke oven gas, and direct coal injection). The carbon contained in
the pig iron, blast furnace gas, and blast furnace inputs was estimated by multiplying the material-specific carbon
content by each material type (see Table 4-65). Carbon in blast furnace gas used to pre-heat the blast furnace air is
combusted to form CO2 during this process.
Emissions from steel production in EAFs were estimated by deducting the carbon contained in the steel produced
from the carbon contained in the EAF anode, charge carbon, and scrap steel added to the EAF. Small amounts of
carbon from direct reduced iron, pig iron, and flux additions to the EAFs were also included in the EAF calculation.
For BOFs, estimates of carbon contained in EOF steel were deducted from carbon contained in inputs such as
natural gas, coke oven gas, fluxes, and pig iron. In each case, the carbon was calculated by multiplying material-
specific carbon contents by each material type (see Table 4-65). For EAFs, the amount of EAF anode consumed
was approximated by multiplying total EAF steel production by the amount of EAF anode consumed per metric ton
of steel produced (0.002 metric tons EAF anode per metric ton steel produced [AISI 2008b]). The amount of flux
(e.g., limestone and dolomite) used during steel manufacture was deducted from the Other Process Uses of
Carbonates source category to avoid double-counting.
CO2 emissions from the consumption of blast furnace gas and coke oven gas for other activities occurring at the
steel mill were estimated by multiplying the amount of these materials consumed for these purposes by the material-
specific carbon content (see Table 4-65).
CO2 emissions associated with the sinter production, direct reduced iron production, pig iron production, steel
production, and other steel mill activities were summed to calculate the total CCh emissions from iron and steel
production (see Table 4-57 and Table 4-58).
Table 4-65: Material Carbon Contents for Iron and Steel Production
Material
Coke
Direct Reduced Iron
Dolomite
EAF Carbon Electrodes
EAF Charge Carbon
Limestone
Pig Iron
Steel
Material
Coke Oven Gas
Blast Furnace Gas
Source: IPCC 2006, Table 4.3.
Blast Furnace Gas, Table 1.3.
kgC/kg
0.83
0.02
0.13
0.82
0.83
0.12
0.04
0.01
kg C/GJ
12.1
70.8
Coke Oven Gas and
The production processes for sinter and pig iron result in fugitive emissions of CH4, which are emitted via leaks in
the production equipment, rather than through the emission stacks or vents of the production plants. The fugitive
emissions were calculated by applying Tier 1 emission factors taken from the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC 2006) for sinter production and the 1995 IPCC Guidelines (IPCC/UNEP/
OECD/IEA 1995) (see
) for pig iron production. The production of direct reduced iron also results in emissions of CH4 through the
consumption of fossil fuels (e.g., natural gas); however, these emissions estimates are excluded due to data
limitations.
4-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 4-66: CH4 Emission Factors for Sinter and Pig Iron Production
Material Produced Factor Unit
Pig Iron 6~9g CH4/kg
Sinter 0.07 kg CHVmetric ton
Source: Sinter (IPCC 2006, Table 4.2), Pig Iron (IPCC/UNEP/OECD/IEA
1995, Table 2.2)
Sinter consumption data were obtained from AISFs Annual Statistical Report (AISI 2004 through 2013a) and
through personal communications with AISI (2008b) (see Table 4-67). In general, direct reduced iron (DRI)
consumption data were obtained from the USGSMinerals Yearbook - Iron and Steel Scrap (USGS 1991 through
2012) and personal communication with the USGS Iron and Steel Commodity Specialist (Fenton 2013). However,
data for DRI consumed in EAFs were not available for the years 1990 and 1991. EAF DRI consumption in 1990
and 1991 was calculated by multiplying the total DRI consumption for all furnaces by the EAF share of total DRI
consumption in 1992. Also, data for DRI consumed in BOFs were not available for the years 1990 through 1993.
EOF DRI consumption in 1990 through 1993 was calculated by multiplying the total DRI consumption for all
furnaces (excluding EAFs and cupola) by the EOF share of total DRI consumption (excluding EAFs and cupola) in
1994.
The Tier 1 CCh emission factors for sinter production and direct reduced iron production were obtained through the
2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006). Data for pig iron production, coke,
natural gas, fuel oil, sinter, and pellets consumed in the blast furnace; pig iron production; and blast furnace gas
produced at the iron and steel mill and used in the metallurgical coke ovens and other steel mill activities were
obtained from AISI's Annual Statistical Report (AISI 2004 through 2013a) and through personal communications
with AISI (2008b) (see Table 4-68).
Data for EAF steel production, flux, EAF charge carbon, and natural gas consumption were obtained from AISI's
Annual Statistical Report (AISI 2004 through 2013a) and through personal communications with AISI (2013b and
2008b). The factor for the quantity of EAF anode consumed per ton of EAF steel produced was provided by AISI
(AISI 2008b). Data for EOF steel production, flux, natural gas, natural ore, pellet sinter consumption as well as
EOF steel production were obtained from AISI's Annual Statistical Report (AISI 2004 through 2013a) and through
personal communications with AISI (2008b). Data for EAF and EOF scrap steel, pig iron, and DRI consumption
were obtained from the USGSMinerals Yearbook-Iron and Steel Scrap (USGS 1991 through 2012). Data on coke
oven gas and blast furnace gas consumed at the iron and steel mill (other than in the EAF, EOF, or blast furnace)
were obtained from AISI's Annual Statistical Report (AISI 2004 through 2013a) and through personal
communications with AISI (2008b).
Data on blast furnace gas and coke oven gas sold for use as synthetic natural gas were obtained from EIA's Natural
Gas Annual 2011 (EIA 2012b). Carbon contents for direct reduced iron, EAF carbon electrodes, EAF charge
carbon, limestone, dolomite, pig iron, and steel were provided by the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC 2006). The carbon contents for natural gas, fuel oil, and direct injection coal
were obtained from EIA (2012c) and EPA (2010). Heat contents for the same fuels were obtained from EIA (1992,
2012a). Heat contents for coke oven gas and blast furnace gas were provided in Table 2-2 of the report Energy and
Environmental Profile of the U.S. Iron and Steel Industry (DOE 2000).
Table 4-67: Production and Consumption Data for the Calculation of COz and CH4 Emissions
from Iron and Steel Production (Thousand Metric Tons)
Source/Activity Data 1990 2005 2008 2009 2010 2011 2012
Sinter Production
Sinter Production 12,239 8,315 6,497 3,814 5,225 5,941 5,795
Direct Reduced Iron
Production
Direct Reduced Iron
Production 498H 962B 1,210 824 1,100 1,270 1,620
Pig Iron Production
Coke Consumption 24,946 13,832 14,251 8,572 10,883 11,962 9,571
3
Industrial Processes 4-59
-------
Pig Iron Production
Direct Injection Coal
Consumption
EAF Steel Production
EAF Anode and Charge
Carbon Consumption
Scrap Steel Consumption
Flux Consumption
EAF Steel Production
EOF Steel Production
Pig Iron Consumption
Scrap Steel Consumption
Flux Consumption
EOF Steel Production
49,669B
l,48sH
67
42,69 ll
319
33,51ll
47,307^
14,713|
57eB
43,973
37,222
2,573
1,127
46,600
695
52,194
34,400
11,400
582
42,705
33
2
1
50
52
,730
,578
,109
,500
680
,791
30,600
8
39
,890
431
,105
19,019
1,674
845
43,200
476
36,725
25,900
7,110
318
22,659
26,844
2,279
1,189
47,500
640
49,339
31,200
9,860
431
31,158
30
2
1
50
52
31
8
34
,228 32,063
,604 2,802
,257 1,318
,500 50,900
726 748
,10852,415
,30031,500
,800 8,350
454 476
,291 36,282
Table 4-68: Production and Consumption Data for the Calculation of COz Emissions from
Iron and Steel Production (million ft3 unless otherwise specified)
Source/Activity Data 1990 2005 2008 2009 2010 2011 2012
Pig Iron Production
Natural Gas Consumption 56,273
Fuel Oil Consumption
(thousand gallons) 163,397|
Coke Oven Gas
Consumption 22,033
Blast Furnace Gas
Production 1,439,380
EAF Steel Production
Natural Gas Consumption 15,905
EOF Steel Production
Coke Oven Gas
Consumption 3,851
Other Activities
Coke Oven Gas
Consumption 224,883
Blast Furnace Gas
Consumption 1,414,778
59,844
16,170
16,557
524
53,349 35,933 47,814 59,132 62,469
55,552 23,179 27,505 21,378 19,240
15,336 9,951 14,233 17,772 18,608
l,299,980Bl,104,674 672,486 911,1801,063,326 1,139,578
19,98sB 10,826 7,848 10,403 6,263 11,145
528
373
546
554
568
97,132| 87,327 55,831 80,626 90,718 94,596
1,295,520^1,099,845 670,051 907,9991,059,473 1,135,227
Uncertainty and Time-Series Consistency
The estimates of CCh and CH4 emissions from metallurgical coke production are based on material production and
consumption data and average carbon contents. Uncertainty is associated with the total U.S. coking coal
consumption, total U.S. coke production and materials consumed during this process. Data for coking coal
consumption and metallurgical coke production are from different data sources (EIA) than data for other
carbonaceous materials consumed at coke plants (AISI), which does not include data for merchant coke plants.
There is uncertainty associated with the fact that coal tar and coke breeze production were estimated based on coke
production because coal tar and coke breeze production data were not available. Since merchant coke plant data is
not included in the estimate of other carbonaceous materials consumed at coke plants, the mass balance equation for
CO2 from metallurgical coke production cannot be reasonably completed. Therefore, for the purpose of this
analysis, uncertainty parameters are applied to primary data inputs to the calculation (i.e., coking coal consumption
and metallurgical coke production) only.
The estimates of CCh emissions from iron and steel production are based on material production and consumption
data and average carbon contents. There is uncertainty associated with the assumption that direct reduced iron and
sinter consumption are equal to production. There is uncertainty associated with the assumption that all coal used
for purposes other than coking coal is for direct injection coal; some of this coal may be used for electricity
4-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
generation. There is also uncertainty associated with the carbon contents for pellets, sinter, and natural ore, which
are assumed to equal the carbon contents of direct reduced iron. For EAF steel production, there is uncertainty
associated with the amount of EAF anode and charge carbon consumed due to inconsistent data throughout the time
series. Also for EAF steel production, there is uncertainty associated with the assumption that 100 percent of the
natural gas attributed to "steelmaking furnaces" by AISI is process-related and nothing is combusted for energy
purposes. Uncertainty is also associated with the use of process gases such as blast furnace gas and coke oven gas.
Data are not available to differentiate between the use of these gases for processes at the steel mill versus for energy
generation (i.e., electricity and steam generation); therefore, all consumption is attributed to iron and steel
production. These data and carbon contents produce a relatively accurate estimate of CCh emissions. However,
there are uncertainties associated with each.
For the purposes of the CH4 calculation from iron and steel production it is assumed that all of the CH4 escapes as
fugitive emissions and that none of the CH4 is captured in stacks or vents. Additionally, the CO2 emissions
calculation is not corrected by subtracting the carbon content of the CH4, which means there may be a slight double
counting of carbon as both CCh and CH4.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-69 for metallurgical coke
production and iron and steel production. Total CC>2 emissions from metallurgical coke production and iron and
steel production were estimated to be between 45.4 and 63.7 Tg CCh Eq. at the 95 percent confidence level. This
indicates a range of approximately 16 percent below and 17 percent above the emission estimate of 54.3 Tg CC>2 Eq.
Total CH4 emissions from metallurgical coke production and iron and steel production were estimated to be between
0.5 and 0.8 Tg CCh Eq. at the 95 percent confidence level. This indicates a range of approximately 21 percent
below and 22 percent above the emission estimate of 0.6 Tg CCh Eq.
Table 4-69: Tier 2 Quantitative Uncertainty Estimates for COz and Cm Emissions from Iron
and Steel Production and Metallurgical Coke Production (Tg COz Eq. and Percent)
Uncertainty Range Relative to Emission
Source Gas 2012 Emission Estimate Estimate3
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Metallurgical Coke & Iron
and Steel Production
Metallurgical Coke & Iron
and Steel Production
C02
CH4
54.3
0.6
Lower
Bound
45.4
0.5
Upper
Bound
63.7
0.8
Lower
Bound
-16%
-21%
Upper
Bound
+17%
+22%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Future improvements involve evaluating and analyzing data reported under EPA's GHGRP that would be useful to
improve the emission estimates for the Iron and Steel Production source category. Particular attention would be
made to ensure time series consistency of the emissions estimates presented in future inventory reports, consistent
with IPCC and UNFCCC guidelines. This is required as the facility-level reporting data from EPA's GHGRP, with
the program's initial requirements for reporting of emissions in calendar year 2010, are not available for all
inventory years (i.e., 1990 through 2009) as required for this inventory. In implementing improvements and
integration of data from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in
national inventories will be relied upon.144
144 See.
Industrial Processes 4-61
-------
Additional improvements include accounting for emission estimates for the production of metallurgical coke to the
Energy chapter as well as identifying the amount of carbonaceous materials, other than coking coal, consumed at
merchant coke plants. Other potential improvements include identifying the amount of coal used for direct injection
and the amount of coke breeze, coal tar, and light oil produced during coke production. Efforts will also be made to
identify inputs for preparing Tier 2 estimates for sinter and direct reduced iron production, as well as identifying
information to better characterize emissions from the use of process gases and fuels within the Energy and Industrial
Processes chapters.
4.16 Ferroalloy Production (IPCC Source
Category 2C2)
Carbon dioxide and CH4 are emitted from the production of several ferroalloys. Ferroalloys are composites of iron
(Fe) and other elements such as silicon (Si), manganese (Mn), and chromium (Cr). Emissions from fuels consumed
for energy purposes during the production of ferroalloys are accounted for in the Energy chapter. Emissions from
the production of two types of ferrosilicon (25 to 55 percent and 56 to 95 percent silicon), silicon metal (96 to 99
percent silicon), and miscellaneous alloys (32 to 65 percent silicon) have been calculated. Emissions from the
production of ferrochromium and ferromanganese are not included here because of the small number of
manufacturers of these materials in the United States, and therefore, government information disclosure rules
prevent the publication of production data for these production facilities.
Similar to emissions from the production of iron and steel, CO2 is emitted when metallurgical coke is oxidized
during a high-temperature reaction with iron and the selected alloying element. Due to the strong reducing
environment, CO is initially produced, and eventually oxidized to CO2. A representative reaction equation for the
production of 50 percent ferrosilicon (FeSi) is given below:
Fe2O3 + 2SiO 2 + 7C -^ 2FeSi + 7CO
While most of the carbon contained in the process materials is released to the atmosphere as CO2, a percentage is
also released as CH4 and other volatiles. The amount of CH4 that is released is dependent on furnace efficiency,
operation technique, and control technology.
When incorporated in alloy steels, ferroalloys are used to alter the material properties of the steel. Ferroalloys are
used primarily by the iron and steel industry, and production trends closely follow that of the iron and steel industry.
Fewer than 10 facilities in the United States produce ferroalloys.
Emissions of CO2 from ferroalloy production in 2012 were 1.7 Tg CO2 Eq. (1,663 Gg) (see Table 4-70 and Table
4-71), which is a 23 percent reduction since 1990. Emissions of CH4 from ferroalloy production in 2012 were 0.01
Tg CO2 Eq. (less than 0.5 Gg), which is a 31 percent decrease since 1990.
Table 4-70: COz and Cm Emissions from Ferroalloy Production (Tg COz Eq.)
Year 1990 2005 2008 2009 2010 2011 2012
C02 2.2 1.4 1.6 1.5 1.7 1.7 1.7
CH4 + + + + + + +
Total 2.2 IA 1.6 1.5 1.7 1.7 1.7
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 4-71: COz and Cm Emissions from Ferroalloy Production (Gg)
Year 1990 2005 2008 2009 2010 2011 2012
CO2 2,152 1,392 1,599 1,469 1,663 1,663 1,663
CH4 1 + + + + + +
+ Does not exceed 0.5 Gg CO2 Eq.
4-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Methodology
Emissions of CCh and CH4 from ferroalloy production were calculated using a Tier 1 method from the 2006IPCC
Guidelines for National Greenhouse Gas Inventories (IPCC 2006) by multiplying annual ferroalloy production by
material-specific default emission factors provided by IPCC (2006). Default emission factors were used because
country-specific emission factors are not currently available.
For ferrosilicon alloys containing 25 to 55 percent silicon and miscellaneous alloys (including primarily magnesium-
ferrosilicon, but also including other silicon alloys) containing 32 to 65 percent silicon, an emission factor for 45
percent silicon was applied for CO2 (i.e., 2.5 metric tons CO2/metric ton of alloy produced) and an emission factor
for 65 percent silicon was applied for CH4 (i.e., 1 kg CH4/metric ton of alloy produced). Additionally, for
ferrosilicon alloys containing 56 to 95 percent silicon, an emission factor for 75 percent silicon ferrosilicon was
applied for both CC>2 and CH4 (i.e., 4 metric tons CO^metric ton alloy produced and 1 kg CH4/metric ton of alloy
produced, respectively). The emission factors for silicon metal equaled 5 metric tons CCh/metric ton metal
produced and 1.2 kg CHVmetric ton metal produced. It was assumed that 100 percent of the ferroalloy production
was produced using petroleum coke in an electric arc furnace process (IPCC 2006), although some ferroalloys may
have been produced with coking coal, wood, other biomass, or graphite carbon inputs. The amount of petroleum
coke consumed in ferroalloy production was calculated assuming that the petroleum coke used is 90 percent C and
10 percent inert material (Onder and Bagdoyan 1993).
Ferroalloy production data for 1990 through 2010 (see Table 4-72) were obtained from the USGS through personal
communications with the USGS Silicon Commodity Specialist (Corathers 2011, Corathers 2012) and through the
Minerals Yearbook: Silicon Annual Report (USGS 1996 through 2012). Due to the small number of ferroalloy
manufacturers in the United States and government information disclosure rules, USGS does not provide estimates
of ferrosilicon production for 2011 or 2012; therefore, 2010 production data are used as proxy in 2011 and 2012.
Likewise, because USGS does not provide estimates of silicon metal production for 2006 through 2011, 2005
production data are used. Until 1999, the USGS reported production of ferrosilicon containing 25 to 55 percent
silicon separately from production of miscellaneous alloys containing 32 to 65 percent silicon; however, beginning
in 1999, the USGS reported these as a single category. The composition data for petroleum coke was obtained from
Onder and Bagdoyan (1993).
Table 4-72: Production of Ferroalloys (Metric Tons)
Year Ferrosilicon Ferrosilicon Silicon Metal Misc. Alloys
25%-55% 56%-95% 32-65%
1990
321,385
109,566
145,744
72,442
2008
2009
2010
2011
2012
193,000
123,932
153,000
153,000
153,000
94,000
104,855
135,000
135,000
135,000
148,000
148,000
148,000
148,000
148,000
NA
NA
NA
NA
NA
NA (Not Available)
Uncertainty and Time-Series Consistency
Annual ferroalloy production is currently reported by the USGS in three broad categories: ferroalloys containing 25
to 55 percent silicon (including miscellaneous alloys), ferroalloys containing 56 to 95 percent silicon, and silicon
metal (through 2005 only). Silicon metal production values for 2006 through 2012 are assumed to be equal to the
2005 value reported by USGS (USGS did not report silicon metal production for 2006 through 2012). Ferrosilicon
production values for 2011 and 2012 are assumed to be equal to the 2010 value reported by USGS (USGS did not
report ferrosilicon production for 2011 and 2012). It was assumed that the IPCC emission factors apply to all of the
ferroalloy production processes, including miscellaneous alloys. Finally, production data for silvery pig iron (alloys
containing less than 25 percent silicon) are not reported by the USGS to avoid disclosing proprietary company data.
Emissions from this production category, therefore, were not estimated.
Industrial Processes 4-63
-------
Also, some ferroalloys may be produced using wood or other biomass as a primary or secondary carbon source
(carbonaceous reductants), information and data regarding these practices were not available. Emissions from
ferroalloys produced with wood or other biomass would not be counted under this source because wood-based
carbon is of biogenic origin.145 Even though emissions from ferroalloys produced with coking coal or graphite
inputs would be counted in national trends, they may be generated with varying amounts of CCh per unit of
ferroalloy produced. The most accurate method for these estimates would be to base calculations on the amount of
reducing agent used in the process, rather than the amount of ferroalloys produced. These data, however, were not
available, and are also often considered confidential business information.
Emissions of CH4 from ferroalloy production will vary depending on furnace specifics, such as type, operation
technique, and control technology. Higher heating temperatures and techniques such as sprinkle charging will
reduce CH4 emissions; however, specific furnace information was not available or included in the CH4 emission
estimates.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-73. Ferroalloy production CO2
emissions were estimated to be between 1.5 and 1.9 Tg CCh Eq. at the 95 percent confidence level. This indicates a
range of approximately 12 percent below and 12 percent above the emission estimate of 1.7 Tg CC>2 Eq. Ferroalloy
production CH4 emissions were estimated to be between a range of approximately 11 percent below and 11 percent
above the emission estimate of 0.01 Tg CC>2 Eq.
Table 4-73: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Ferroalloy
Production (Tg COz Eq. and Percent)
Source Gas 2012 Emission Estimate Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (Tg CCh Eq.) (%)
Ferroalloy Production
Ferroalloy Production
C02 1.7
CH4 +
Lower
Bound
1.5
+
Upper Lower
Bound Bound
1.9 -12%
+ -11%
Upper
Bound
+12%
+11%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
+ Does not exceed 0.05 Tg CO2 Eq.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
According to the 2006IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006), emission factors
are provided for a total of nine different ferroalloy types: four grades of ferrosilicon (FeSi) (i.e., 45, 65, 75, and 90
percent Si), two grades of ferromanganese (FeMn) (i.e., 1 and 7 percent C), silicomanganese (SiMn), ferrochromium
(FeCr), and silicon metal. However, due to the small number of ferroalloy manufacturers in the United States and
government information disclosure rules, the current availability of ferroalloy production data is quite limited (Tuck
2013). Additional research is being conducting to assess the feasibility of obtaining alternative activity data.
Future improvements involve evaluating and analyzing data reported under EPA's GHGRP that would be useful to
improve the emission estimates for the Ferroalloy Production source category. Particular attention would be made to
ensure time series consistency of the emissions estimates presented in future inventory reports, consistent with IPCC
and UNFCCC guidelines. This is required as the facility-level reporting data from EPA's GHGRP, with the
program's initial requirements for reporting of emissions in calendar year 2010, are not available for all inventory
years (i.e., 1990 through 2009) as required for this inventory. In implementing improvements and integration of data
145
Emissions and sinks of biogenic carbon are accounted for in the Land Use, Land-Use Change, and Forestry chapter.
4-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
from EPA's GHGRP, the latest guidance from the IPCC on the use of facility -level data in national inventories will
be relied upon.146
4.17 Aluminum Production (IPCC Source
Category 2C3) _
Aluminum is a light-weight, malleable, and corrosion-resistant metal that is used in many manufactured products,
including aircraft, automobiles, bicycles, and kitchen utensils. As of last reporting, the United States was the fourth
largest producer of primary aluminum, with approximately 5 percent of the world total (USGS 2013a). The United
States was also a major importer of primary aluminum. The production of primary aluminum — in addition to
consuming large quantities of electricity — results in process-related emissions of CO2 and two perfluorocarbons
(PFCs): perfluoromethane (CF4) and perfluoroethane (C2F6).
CO2 is emitted during the aluminum smelting process when alumina (aluminum oxide, A^Os) is reduced to
aluminum using the Hall-Heroult reduction process. The reduction of the alumina occurs through electrolysis in a
molten bath of natural or synthetic cryolite (NasAlFe). The reduction cells contain a carbon lining that serves as the
cathode. Carbon is also contained in the anode, which can be a carbon mass of paste, coke briquettes, or prebaked
carbon blocks from petroleum coke. During reduction, most of this carbon is oxidized and released to the
atmosphere as CO2.
Process emissions of CO2 from aluminum production were estimated to be 3.4 Tg CO2 Eq. (3,439 Gg) in 2012 (see
Table 4-74). The carbon anodes consumed during aluminum production consist of petroleum coke and, to a minor
extent, coal tar pitch. The petroleum coke portion of the total CO2 process emissions from aluminum production is
considered to be a non-energy use of petroleum coke, and is accounted for here and not under the CO2 from Fossil
Fuel Combustion source category of the Energy sector. Similarly, the coal tar pitch portion of these CO2 process
emissions is accounted for here.
Table 4-74: COz Emissions from Aluminum Production (Tg COz Eq. and Gg)
Year Tg CCh Eq. Gg
1990 6.8 6,831
2008
2009
2010
2011
2012
4.5
3.0
2.7
3.3
3.4
4,477
3,009
2,722
3,292
3,439
In addition to CO2 emissions, the aluminum production industry is also a source of PFC emissions. During the
smelting process, when the alumina ore content of the electrolytic bath falls below critical levels required for
electrolysis, rapid voltage increases occur, which are termed "anode effects." These anode effects cause carbon
from the anode and fluorine from the dissociated molten cryolite bath to combine, thereby producing fugitive
emissions of CF4 and C2F6. In general, the magnitude of emissions for a given smelter and level of production
depends on the frequency and duration of these anode effects. As the frequency and duration of the anode effects
increase, emissions increase.
Since 1990, emissions of CF4 and C2F6 have declined by 87 percent and 81 percent, respectively, to 2.0 Tg CO2 Eq.
of CF4 (0.31 Gg) and 0.5 Tg CO2 Eq. of C2F6 (0.056 Gg) in 2012, as shown in Table 4-75 and Table 4-76. This
146
See.
Industrial Processes 4-65
-------
decline is due both to reductions in domestic aluminum production and to actions taken by aluminum smelting
companies to reduce the frequency and duration of anode effects. These actions include technology and operational
changes such as employee training, use of computer monitoring, and changes in alumina feeding techniques. Since
1990, aluminum production has declined by 49 percent, while the combined CF4 and C^& emission rate (per metric
ton of aluminum produced) has been reduced by 73 percent. Emissions declined by approximately 15 percent
between 2011 and 2012 due to a reduction in both CF4 and C2p6 emissions per metric ton of aluminum produced.
Table 4-75: PFC Emissions from Aluminum Production (Tg COz Eq.)
Year
CF4
C2F6
Total
1990
15.
18.4
2005
2008
2009
2010
2011
2012
2.2
1.3
1.2
2.3
2.0
0.5
0.3
0.4
0.6
0.5
2.7
1.6
1.6
2.9
2.5
Note: Totals may not sum due to independent rounding.
Table 4-76: PFC Emissions from Aluminum Production (Gg)
Year CF4 C2F6
1990
2008
2009
2010
2011
2012
2.4
+ Does not exceed 0.05 Gg.
In 2012, U.S. primary aluminum production totaled approximately 2.1 million metric tons, a 4 percent increase from
2011 production levels (USAA 2013a). In 2012, five companies managed production at ten operational primary
aluminum smelters. Four smelters were closed for the entire year in 2012 (USGS 2013b). During 2012, monthly
U.S. primary aluminum production was greater in the first three quarters of 2012, but less in the October-December
quarter when compared to the corresponding quarters in 2011 (USAA2013a).
For 2013, total production was approximately 1.9 million metric tons compared to 2.1 million metric tons in 2012, a
6 percent decrease (USAA 2013b). Based on the decrease in production, process CCh and PFC emissions are likely
to be lower in 2013 compared to 2012 if there are no significant changes in process controls at operational facilities.
Methodology
Process CO2 and perfluorocarbon (PFC)—i.e., perfluoromethane (CF4) and perfluoroethane (C2F6)—emission
estimates from primary aluminum production for 2010, 2011, and 2012 are reported in the EPA's GHGRP database.
Under EPA's GHGRP, facilities began reporting primary aluminum production process emissions (for 2010) in
2011; as a result, GHGRP data (for 2010, 2011, and 2012) are available to be incorporated into the inventory.
EPA's GHGRP mandates that all facilities that contain an aluminum production process must report: CF4 and C2p6
emissions from anode effects in all prebake and Soderberg electrolysis cells, carbon dioxide (CCh) emissions from
anode consumption during electrolysis in all prebake and Soderberg cells, and all CCh emissions from onsite anode
baking. To estimate the process emissions, EPA's GHGRP uses the process-specific equations (and certain
technology-specific defaults) detailed in subpart F. These equations are based on the Tier 2/Tier 3 IPCC (2006)
4-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
methods for primary aluminum production, and Tier 1 methods when estimating missing data elements. It should be
noted that the same methods (i.e., IPCC 2006) were used for estimating the emissions prior to the availability of the
reported GHGRP data in the inventory.
Process CO2 Emissions from Anode Consumption and Anode Baking
CO2 emission estimates for the years prior to the introduction of EPA's GHGRP in 2010 were estimated with IPCC
(2006) methods, but individual facility reported data were combined with process-specific emissions modeling.
These estimates were based on information previously gathered from EPA's VAIP program, U.S. Geological Survey
(USGS) Mineral Commodity reviews, and The Aluminum Association (USAA) statistics, among other sources.
Since pre- and post-GHGRP estimates use the same methodology, emission estimates are comparable across the
time series.
Most of the CO2 emissions released during aluminum production occur during the electrolysis reaction of the carbon
anode, as described by the following reaction:
2A12O3 + 3C -» 4A1 + 3CC-2
For prebake smelter technologies, CO2 is also emitted during the anode baking process. These emissions can
account for approximately 10 percent of total process CO2 emissions from prebake smelters.
Depending on the availability of smelter-specific data, the CO2 emitted from electrolysis at each smelter was
estimated from: (1) the smelter's annual anode consumption, (2) the smelter's annual aluminum production and rate
of anode consumption (per ton of aluminum produced) for previous and/or following years, or, (3) the smelter's
annual aluminum production and IPCC default CO2 emission factors. The first approach tracks the consumption and
carbon content of the anode, assuming that all carbon in the anode is converted to CO2. Sulfur, ash, and other
impurities in the anode are subtracted from the anode consumption to arrive at a C consumption figure. This
approach corresponds to either the IPCC Tier 2 or Tier 3 method, depending on whether smelter-specific data on
anode impurities are used. The second approach interpolates smelter-specific anode consumption rates to estimate
emissions during years for which anode consumption data are not available. This approach avoids substantial errors
and discontinuities that could be introduced by reverting to Tier 1 methods for those years. The last approach
corresponds to the IPCC Tier 1 method (2006), and is used in the absence of present or historic anode consumption
data.
The equations used to estimate CO2 emissions in the Tier 2 and 3 methods vary depending on smelter type (IPCC
2006). For Prebake cells, the process formula accounts for various parameters, including net anode consumption,
and the sulfur, ash, and impurity content of the baked anode. For anode baking emissions, the formula accounts for
packing coke consumption, the sulfur and ash content of the packing coke, as well as the pitch content and weight of
baked anodes produced. For Sederberg cells, the process formula accounts for the weight of paste consumed per
metric ton of aluminum produced, and pitch properties, including sulfur, hydrogen, and ash content.
Through the VAIP, anode consumption (and some anode impurity) data have been reported for 1990, 2000, 2003,
2004, 2005, 2006, 2007, 2008, and 2009. Where available, smelter-specific process data reported under the VAIP
were used; however, if the data were incomplete or unavailable, information was supplemented using industry
average values recommended by IPCC (2006). Smelter-specific CO2 process data were provided by 18 of the 23
operating smelters in 1990 and 2000, by 14 out of 16 operating smelters in 2003 and 2004, 14 out of 15 operating
smelters in 2005, 13 out of 14 operating smelters in 2006, 5 out of 14 operating smelters in, 2007 and 2008, and 3
out of 13 operating smelters in 2009. For years where CO2 emissions data or CO2 process data were not reported by
these companies, estimates were developed through linear interpolation, and/or assuming representative (e.g.,
previously reported or industry default) values.
In the absence of any previous historical smelter specific process data (i.e., 1 out of 13 smelters in 2009, 1 out of 14
smelters in 2006, 2007, and 2008, 1 out of 15 smelters in 2005, and 5 out of 23 smelters between 1990 and 2003),
CO2 emission estimates were estimated using Tier 1 Sederberg and/or Prebake emission factors (metric ton of CO2
per metric ton of aluminum produced) from IPCC (2006).
Industrial Processes 4-67
-------
Process RFC Emissions from Anode Effects
Smelter-specific PFC emissions from aluminum production for 2010, 2011, and 2012 were reported to EPA under
its GHGRP. To estimate their PFC emissions and report them under EPA's GHGPJ3, smelters use an approach
identical to the Tier 3 approach in the 2006IPCC Guidelines. Specifically, they use a smelter-specific slope
coefficient as well as smelter-specific operating data to estimate an emission factor using the following equation:
PFC (CF4 or C2F6) kg/metric ton Al = S x (Anode Effect Minutes/Cell-Day)
where,
S = Slope coefficient ((kg PFC/metric ton Al)/(Anode Effect Minutes/Cell-Day))
(Anode Effect Minutes/Cell-Day) = (Anode Effect Frequency/Cell-Day) x Anode Effect Duration (minutes)
They then multiply this emission factor by aluminum production to estimate PFC emissions. All U.S. aluminum
smelters are required to report their emissions under EPA's GHGPJ3.
PFC emissions for the years prior to 2010 were estimated using the same equation, but the slope-factor used for
some smelters was technology-specific rather than smelter-specific, making the method a Tier 2 rather than a Tier 3
approach for those smelters. Emissions and background data were reported to EPA under the VAIP. For 1990
through 2009, smelter-specific slope coefficients were available and were used for smelters representing between 30
and 94 percent of U. S. primary aluminum production. The percentage changed from year to year as some smelters
closed or changed hands and as the production at remaining smelters fluctuated. For smelters that did not report
smelter-specific slope coefficients, IPCC technology-specific slope coefficients were applied (IPCC 2000, 2006).
The slope coefficients were combined with smelter-specific anode effect data collected by aluminum companies and
reported under the VAIP to estimate emission factors over time. For 1990 through 2009, smelter-specific anode
effect data were available for smelters representing between 80 and 100 percent of U.S. primary aluminum
production. Where smelter-specific anode effect data were not available, representative values (e.g., previously
reported or industry averages) were used.
For all smelters, emission factors were multiplied by annual production to estimate annual emissions at the smelter
level. For 1990 through 2009, smelter-specific production data were available for smelters representing between 30
and 100 percent of U.S. primary aluminum production. (For the years after 2000, this percentage was near the high
end of the range.) Production at non-reporting smelters was estimated by calculating the difference between the
production reported under VAIP and the total U.S. production supplied by USGS or USAA, and then allocating this
difference to non-reporting smelters in proportion to their production capacity. Emissions were then aggregated
across smelters to estimate national emissions.
Between 1990 and 2009, production data were provided under the VAIP by 21 of the 23 U.S. smelters that operated
during at least part of that period. For the non-reporting smelters, production was estimated based on the difference
between reporting smelters and national aluminum production levels (from USGS and USAA), with allocation to
specific smelters based on reported production capacities (from USGS).
National primary aluminum production data for 2012 were obtained via The Aluminum Association (USAA 2013a).
For 1990 through 2001, and 2006 (see Table 4-77) data were obtained from USGS, Mineral Industry Surveys:
Aluminum Annual Report (USGS 1995, 1998, 2000, 2001, 2002, 2007). For 2002 through 2005, and 2007 through
2011, national aluminum production data were obtained from the USAA's Primary Aluminum Statistics (USAA
2004, 2005, 2006, 2008, 2009, 2010, 2011, 2012).
4-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 4-77: Production of Primary Aluminum (Gg)
Year
1990
2008
2009
2010
2011
2012
4,048
2,659
1,727
1,727
1,986
2,070
Uncertainty and Time Series Consistency
Uncertainty was assigned to the CO2, CF4, and C2F6 emission values reported by each individual facility to EPA's
GHGRP. As previously mentioned, the methods for estimating emissions for EPA's GHGRP and this report are the
same, and follow the IPCC (2006) methodology. As a result, it was possible to assign uncertainty bounds (and
distributions) based on an analysis of the uncertainty associated with the facility-specific emissions estimated for
previous inventory years. Uncertainty surrounding the reported CO2, CF4, and C2F6 emission values were
determined to have a normal distribution with uncertainty ranges of ±6, ±16, and ±20 percent, respectively. A
Monte Carlo analysis was applied to estimate the overall uncertainty of the CO2, CF4, and C2F6 emission estimates
for the U.S. aluminum industry as a whole, and the results are provided below.
The results of this Tier 2 quantitative uncertainty analysis are summarized in Table 4-78. Aluminum production-
related CO2 emissions were estimated to be between 3.4 and 3.5 Tg CO2 Eq. at the 95 percent confidence level.
This indicates a range of approximately 2 percent below to 2 percent above the emission estimate of 3.4 Tg CO2 Eq.
Also, production-related CF4 emissions were estimated to be between 1.9 and 2.1 Tg CO2 Eq. at the 95 percent
confidence level. This indicates a range of approximately 6 percent below to 6 percent above the emission estimate
of 2.0 Tg CO2 Eq. Finally, aluminum production-related C2F6 emissions were estimated to be between 0.5 and 0.6
Tg CO2 Eq. at the 95 percent confidence level. This indicates a range of approximately 10 percent below to 10
percent above the emission estimate of 0.5 Tg CO2 Eq.
Table 4-78: Tier 2 Quantitative Uncertainty Estimates for COz and PFC Emissions from
Aluminum Production (Tg COz Eq. and Percent)
Source
Aluminum Production
Aluminum Production
Aluminum Production
Gas
C02
CF4
C2F6
2012 Emission
Estimate
(Tg C02 Eq.)
3.4
2.0
0.5
Uncertainty Range Relative to 2012 Emission Estimate3
(Tg COz Eq.) (%)
Lower
Bound
3.4
1.9
0.5
Upper
Bound
3.5
2.1
0.6
Lower
Bound
-2%
-6%
-10%
Upper
Bound
+2%
+6%
+10%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Industrial Processes 4-69
-------
4.18 Magnesium Production and Processing
(IPCC Source Category 2C4)
The magnesium metal production and casting industry uses sulfur hexafluoride (SF6) as a cover gas to prevent the
rapid oxidation of molten magnesium in the presence of air. Sulfur hexafluoride has been used in this application
around the world for more than twenty-five years. A dilute gaseous mixture of SF6 with dry air and/or CC>2 is blown
over molten magnesium metal to induce and stabilize the formation of a protective crust. A small portion of the SF6
reacts with the magnesium to form a thin molecular film of mostly magnesium oxide and magnesium fluoride. The
amount of SF6 reacting in magnesium production and processing is considered to be negligible and thus all SF6 used
is assumed to be emitted into the atmosphere. Although alternative cover gases, such as AM-cover™ (containing
HFC-134a), Novec™ 612 (FK-5-1-12) and dilute SCh systems can be used, many facilities in the United States are
still using traditional SF6 cover gas systems.
The magnesium industry emitted 1.71 Tg CC>2 Eq. (0.07 Gg) of SF6 in 2012, representing a decrease of
approximately 41 percent from 2011 emissions (See Table 4-79). The decrease can be attributed to a decrease in
consumption of primary magnesium for die casting and wrought casting in the United States (USGS 2012), and a
reduction in sand casting SF6 emissions between 2011 and 2012 as reported through EPA's GHGRP. The reduction
in sand casting SF6 emissions is likely due to decreased production from reporting facilities in 2012. The decrease in
SF6 emissions may also be attributed in part by continuing industry efforts to utilize SF6 alternatives, such as
Novec™612 and sulfur dioxide, to reduce greenhouse gas emissions.
Table 4-79: SFe Emissions from Magnesium Production and Processing (Tg COz Eq. and Gg)
Year Tg CCh Eq. Gg
1990
2008
2009
2010
2011
2012
1.9 +
1.7 +
2.2 +
2.9 +
1.7 +
+ Does not exceed 0.05 Tg CCh Eq.
Methodology
Emission estimates for the magnesium industry incorporate information provided by some industry participants in
EPA's SF6 Emission Reduction Partnership for the Magnesium Industry as well as emissions data reported through
subpart T (Magnesium Production and Processing) of the EPA's GHGRP. The Partnership started in 1999 and, in
2010, participating companies represented 100 percent of U.S. primary and secondary production and 16 percent of
the casting sector production (i.e., die, sand, permanent mold, wrought, and anode casting). Emissions for 1999
through 2010 from primary production, secondary production (i.e., recycling), and die casting were generally
reported by Partnership participants. Partners reported their SF6 consumption, which was assumed to be equivalent
to emissions. 2010 was the last reporting year under the Partnership. Emissions data for 2011 and 2012 were
obtained through EPA's GHGRP. Under the program, owners or operators of facilities that have a magnesium
production or casting process must report emissions from use of cover or carrier gases, which include SF6, HFC-
134a, FK 5-1-12 and CO2. Consequently, emissions from magnesium production and processing were estimated for
three time periods, depending on the source of the emissions data: 1990 through 1998, 1999 through 2010, and 2011
through 2012. The methodologies described below also make use of magnesium production data published by the
U.S. Geological Survey (USGS).
4-70 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
1990 through 1998
To estimate emissions for 1990 through 1998, industry emission factors were multiplied by the corresponding metal
production and consumption (casting) statistics from USGS.
Emission factors from 1990 through 1998 were based on a number of sources and assumptions. Emission factors for
primary production were available from U.S. primary producers for 1994 and 1995. The primary production
emission factors were 1.2 kg per metric ton for 1990 through 1993, and 1.1 kg per metric ton for 1994 through 1997.
The emission factor for secondary production from 1990 through 1998 was assumed to be constant at the 1999
average Partner value. Emission factor for die casting of 4.1 kg SF6 per metric ton was available for the mid-1990s
from an international survey (Gjestland & Magers 1996) that was used for years 1990 through 1996. For 1996
through 1998, the emission factor for die casting was assumed to decline linearly to the level estimated based on
Partner reports in 1999. This assumption is consistent with the trend in SF6 sales to the magnesium sector that is
reported in the RAND survey of major SF6 manufacturers, which shows a decline of 70 percent from 1996 to 1999
(RAND 2002). Sand casting emission factors for 1990 through 2001 were assumed to be the same as the 2002
emission factor. The emission factors for the other processes (i.e., permanent mold, wrought, and anode casting),
about which less is known, were assumed to remain constant at levels defined in Table 4-79.
1999 through 2010
The 1999 through 2010 emissions from primary and secondary production are based on information provided by
EPA's industry Partners. In some instances, there were years of missing Partner data. For these situations, emissions
were estimated through interpolation where possible, or by holding company-reported emissions (as well as
production) constant from the previous year.
The die casting emission estimates for 1999 through 2010 are also based on information supplied by industry
Partners. When a Partner was determined to be no longer in production, its metal production and emissions rates
were set to zero. Missing data on emissions or metal input was either interpolated or held constant at the last
available reported value. In 1999 and from 2008 through 2010, Partners did not account for all die casting tracked
by USGS, and, therefore, it was necessary to estimate the emissions of die casters who were not Partners. For 1999,
die casters who were not Partners were assumed to be similar to Partners who cast small parts. Due to process
requirements, these casters consume larger quantities of SF6 per metric ton of processed magnesium than casters that
process large parts. Consequently, emission estimates from this group of die casters were developed using an
average emission factor of 5.2 kg SF6 per metric ton of magnesium. This emission factor was developed using
magnesium production and SF6 usage data for the year 1999. For 2008 through 2010, the characteristics of the die
casters who were not Partners were not well known, and therefore the emission factor for these die casters was set
equal to 3.0 kg SF6 per metric ton of magnesium, the average of the emission factors reported over the same period
by the die casters who were Partners.
The emissions from other casting operations were estimated by multiplying emission factors (kg SF6 per metric ton
of metal produced or processed) by the amount of metal produced or consumed from USGS, with the exception of
some years for which Partner sand casting emissions data are available. The emission factors for sand casting
activities were acquired through the data reported by the Partnership for 2002 to 2006. For 2007 through 2010, the
sand casting Partner did not report and the reported emission factor from 2005 was applied to the Partner and to all
other sand casters.
The emission factors for primary production, secondary production and sand casting are not published to protect
company-specific production information. However, the emission factor for primary production has not risen above
the average 1995 Partner value of 1.1 kg SF6 per metric ton. The emission factors for the other industry sectors (i.e.,
permanent mold, wrought, and anode casting) were based on discussions with industry representatives. The
emission factors for casting activities are provided below in Table 4-80.
Table 4-80: SFe Emission Factors (kg SFe per metric ton of magnesium)
Year
1999
2000
2001
2002
Die Casting
2.14a
0.71
0.71
0.71
Permanent Mold
2
2
2
2
Wrought
1
1
1
1
Anodes
1
1
1
1
Industrial Processes 4-71
-------
2003
2004
2005
2006
2007
2008
2009
2010
0.81
0.79
0.77
0.88
0.64
1.18
2.43
2.95
2
2
2
2
2
2
2
2
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
a Weighted average that includes an estimated emission factor of 5.2 kg SF
-------
confidence level. This indicates a range of approximately 11 percent below to 12 percent above the 2012 emission
estimate of 1.7 Tg CCh Eq. The uncertainty estimates for 2012 are higher relative to the uncertainty reported in the
2011 inventory year which is due to the relatively large share of die casting not represented through EPA's GHGRP.
Table 4-81: Tier 2 Quantitative Uncertainty Estimates for SFe Emissions from Magnesium
Production and Processing (Tg COz Eq. and Percent)
Source
2012 Emission
Gas Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate3
(Tg COz Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Magnesium
Production
SFe 1.7
1.5 1.9 -11% +12%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence
interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
The USGS 2012 Mineral Yearbook for Magnesium showed a revision in its estimate of permanent mold and
wrought casting production of magnesium for 2011 in the United States, revising its previous estimate of 336 and
3,580 metric tons in 2011 to 193 and 3,720 metric tons, respectively.
The SF6 emissions estimation methodologies for the year 2011 for die casting, sand casting, and primary and
secondary production were also revised to incorporate newly available data from subpart T of EPA's GHGRP. The
emission estimation method required by subpart T of EPA's GHGRP is the same method that Partners use to
estimate emissions when reporting in previous Inventories. Therefore, the use of the new data did not create any
time series consistency issues.
For the 1999 through 2010 time period, a methodological change was introduced for die casting in situations where
Partners failed to report for a particular year or years. In the current Inventory, the missing emissions or activity data
were estimated though either interpolation or through extrapolation by holding the Partner's emissions and activity
constant. In previous Inventories, the missing data were estimated using an average industry growth rate.
Lastly, due to the methodological change above, the metal consumption levels estimated for die casting Partners fell
below those reported to and estimated by USGS from 2008 through 2012. This difference is not surprising because
USGS reporting and estimates account for a larger set of die casting facilities than do EPA estimates. To account
for emissions from the facilities that were not EPA Partners, the difference between the EPA and USGS estimates
was multiplied by an average emission factor, as described above.
Planned Improvements
In a future inventory report, emissions data for alternative cover gases and carrier gases (e.g., CCh) could be
incorporated, as this information is now available from EPA's GHGRP. The alternative cover gases have lower
GWPs than SF6, and tend to quickly degrade during their exposure to the molten metal. Magnesium producers and
processors have already begun using these cover gases starting in around 2006; because the amounts being used by
companies on the whole are low enough that they have a minor effect on the overall emissions from the industry,
and the data being collected from EPA's GHGRP for these cover gases is a relatively new type of information for
reporters to collect, these emissions are only being monitored and recorded at this time as opposed to being included
in Inventory estimates.
In addition, cover gas research conducted over the last decade has found that SF6 used for magnesium melt
protection can have degradation rates on the order of 20 percent in die casting applications (Bartos et al. 2007).
Current emission estimates assume (per the 2006IPCC Guidelines) that all SF6 utilized is emitted to the atmosphere.
Industrial Processes 4-73
-------
Additional research may lead to a revision of IPCC Guidelines to reflect this phenomenon and until such time,
developments in this sector will be monitored for possible application to the inventory methodology.
4.19 Zinc Production (IPCC Source Category
2C5)
Zinc production in the United States consists of both primary and secondary processes. Of the primary and
secondary processes used in the United States, only the electrothermic and Waelz kiln secondary processes result in
non-energy CO2 emissions (Viklund-White 2000). Emissions from fuels consumed for energy purposes during the
production of zinc are accounted for in the Energy chapter.
The majority of zinc produced in the United States is used for galvanizing. Galvanizing is a process where zinc
coating is applied to steel in order to prevent corrosion. Zinc is used extensively for galvanizing operations in the
automotive and construction industry. Zinc is also used in the production of zinc alloys and brass and bronze alloys
(e.g., brass mills, copper foundries, copper ingot manufacturing, etc.). Zinc compounds and dust are also used, to a
lesser extent, by the agriculture, chemicals, paint, and rubber industries.
Primary production in the United States is conducted through the electrolytic process, while secondary techniques
include the electrothermic and Waelz kiln processes, as well as a range of other metallurgical, hydrometallurgical,
and pyrometallurgical processes. Worldwide primary zinc production also employs a pyrometallurgical process
using the Imperial Smelting Furnace process; however, this process is not used in the United States (Sjardin 2003).
In the electrothermic process, roasted zinc concentrate and secondary zinc products enter a sinter feed where they
are burned to remove impurities before entering an electric retort furnace. Metallurgical coke is added to the electric
retort furnace as a carbon-containing reductant. This concentration step, using metallurgical coke and high
temperatures, reduces the zinc oxides and produces vaporized zinc, which is then captured in a vacuum condenser.
This reduction process also generates non-energy CCh emissions.
ZnO + C -> Zn(gas~) + C02 (Reaction 1)
ZnO +CO -*Zn(gas*) + C02 (Reaction 2)
In the Waelz kiln process, electric arc furnace (EAF) dust, which is captured during the recycling of galvanized
steel, enters a kiln along with a reducing agent (typically carbon-containing metallurgical coke). When kiln
temperatures reach approximately 1100-1200 °C, zinc fumes are produced, which are combusted with air entering
the kiln. This combustion forms zinc oxide, which is collected in a baghouse or electrostatic precipitator, and is then
leached to remove chloride and fluoride. The use of carbon-containing metallurgical coke in a high-temperature
fuming process results in non-energy CO2 emissions. Through this process, approximately 0.33 metric ton of zinc is
produced for every metric ton of EAF dust treated (Viklund-White 2000).
The only companies in the United States that use emissive technology to produce secondary zinc products are
Horsehead, PIZO, and Steel Dust Recycling. For Horsehead, EAF dust is recycled in Waelz kilns at their
Beaumont, TX; Calumet, IL; Palmerton, PA; Rockwood, TN; and Barnwell, SC facilities. These Waelz kiln
facilities produce intermediate zinc products (crude zinc oxide or calcine), most of which is transported to their
Monaca, PA facility where the products are smelted into refined zinc using electrothermic technology. Some of
Horsehead's intermediate zinc products that are not smelted at Monaca are instead exported to other countries
around the world (Horsehead 2010a). PIZO and Steel Dust Recycling recycle EAF dust into intermediate zinc
products using Waelz kilns, and then sell the intermediate products to companies who smelt it into refined products.
In 2012, U.S. primary and secondary refined zinc production were estimated to total 265,000 metric tons (USGS
2013), which was larger than 2011 levels, due to the increased demand for zinc at continuous galvanizing plants in
2012 (USGS 2013) (see Table). Zinc mine production decreased in 2012 compared to 2011 levels, primarily owing
to lower production in a zinc-lead mine in Alaska as a result of lower ore processing rates. Also, a zinc producing
mine in Idaho was temporarily idled in 2012 due to underground structural work. Primary zinc production (primary
slab zinc) slightly increased in 2012. The primary zinc production was lower in 2011 due to planned maintenance in
4-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
the third quarter at a zinc refinery in Tennessee. On the other hand, secondary zinc production in 2012 increased
relative to 2011 owing to an increase in production at a smelter in Pennsylvania (USGS 2013).
Emissions of CCh from zinc production in 2012 were estimated to be 1.4 Tg CCh Eq. (1,422 Gg) (see Table 4-83).
All 2012 CO2 emissions resulted from secondary zinc production processes. Emissions from zinc production in the
U.S. have increased overall since 1990 due to a gradual shift from non-emissive primary production to emissive
secondary production. In 2012, emissions were estimated to be 125 percent higher than they were in 1990.
Table 4-82: Zinc Production (Metric Tons)
Year
Primary
Secondary
1990
2005
262,704
191,120
95,708
156,000
2008
2009
2010
2011147
2012
125,000
94,000
120,000
110,000
114,000
161,000
109,000
129,000
138,000
147,000
Table 4-83: COz Emissions from Zinc Production (Tg COz Eq. and Gg)
Year Tg CCh Eq.
1990
0.6
2008
2009
2010
2011
2012
1.2
0.9
1.2
1.3
1.4
1,159
943
1,182
1,286
1,422
Methodology
The methods used to estimate non-energy CCh emissions from zinc production using the electrothermic primary
production and Waelz kiln secondary production processes are based on Tier 1 methods from the 2006IPCC
Guidelines for National Greenhouse Gas Inventories (IPCC 2006). The Tier 1 emission factors provided by IPCC
for Waelz kiln-based secondary production were derived from coke consumption factors and other data presented in
Vikland-White (2000). These coke consumption factors as well as other inputs used to develop the Waelz kiln
emission factors are shown below. IPCC does not provide an emission factor for electrothermic processes due to
limited information; therefore, the Waelz kiln-specific emission factors were also applied to zinc produced from
electrothermic processes.
For Waelz kiln-based production, IPCC recommends the use of emission factors based on EAF dust consumption, if
possible, rather than the amount of zinc produced since the amount of reduction materials used is more directly
dependent on the amount of EAF dust consumed. Since only a portion of emissive zinc production facilities
consume EAF dust, the emission factor based on zinc production is applied to the non-EAF dust consuming
facilities while the emission factor based on EAF dust consumption is applied to EAF dust consuming facilities.
147 2011 primary and secondary zinc production data were revised to reflect updated information in USGS, 2012 Minerals
Yearbook: Zinc [Advance Release]. This update did not result in a change in emissions.
Industrial Processes 4-75
-------
The Waelz kiln emission factor based on the amount of zinc produced was developed based on the amount of
metallurgical coke consumed for non-energy purposes per ton of zinc produced (i.e., 1.19 metric tons coke/metric
ton zinc produced) (Viklund-White 2000), and the following equation:
1.19 metric tons coke 0.85 metric tons C ^-61 metric tons CO 3.70 metric tons CO
EF,,
Waelz Kiln metric tons zinc metric tons coke metric tons C metric tons zinc
The Waelz kiln emission factor based on the amount of EAF dust consumed was developed based on the amount of
metallurgical coke consumed per ton of EAF dust consumed (i.e., 0.4 metric tons coke/metric ton EAF dust
consumed) (Viklund-White 2000), and the following equation:
0 A metric tons coke 0.85 metric tons C ^ 61 metric tons CO 1.24 metric tons CO
EAF Dust metric tons EAF dust metric tons coke metric tons C metric tons EAF Dust
The total amount of EAF dust consumed by Horsehead at their Waelz kilns was available from Horsehead financial
reports for years 2006 through 2012 (Horsehead 2007, 2008, 2010a, 2011, 2012, and 2013). Consumption levels for
1990 through 2005 were extrapolated using the percentage change in annual refined zinc production at secondary
smelters in the United States as provided by USGS Minerals Yearbook: Zinc (USGS 1995 through 2012). The EAF
dust consumption values for each year were then multiplied by the 1 .24 metric tons CO2/metric ton EAF dust
consumed emission factor to develop CO2 emission estimates for Horsehead' s Waelz kiln facilities.
The amount of EAF dust consumed and total production capacity were obtained from Steel Dust Recycling's facility
for 20 1 1 (Rowland 20 12). SDR's facility in Alabama underwent expansion in 20 1 1 to include a second unit (to be
operational in early- to mid-2012). SDR's facility has been operational since 2008. The amount of EAF dust
consumed by PIZO's facility in 2009, 2010, and 201 1 (the only years this facility has been in operation) and Steel
Dust Recycling's facility for 2008, 2009, and 2010 was not publicly available. Therefore, these consumption values,
excluding PIZO's 201 1 value, were estimated by calculating the 2008 through 2010 annual capacity utilization of
Horsehead' s Waelz kilns and multiplying this utilization ratio by the capacities of the PIZO and Steel Dust
Recycling facilities, which were available from the companies (Horsehead 2007, 2008, 2010a, 2010b, and 201 1;
PIZO 2012; Steel Dust Recycling LLC 2013). EAF dust consumption for PIZO's facility for 2011 was calculated by
applying the average annual capacity utilization rates for Horsehead and SDR (Grupo PROMAX) to PIZO's annual
capacity. (Horsehead 2012, Rowland 2012, PIZO 2012). The 1.24 metric tons CO2/metric ton EAF dust consumed
emission factor was then applied to PIZO's and Steel Dust Recycling's estimated EAF dust consumption to develop
CO2 emission estimates for those Waelz kiln facilities. The quantity of EAF dust consumed by SDR's Alabama
facility in 20 12 was requested; however, this information has still not been obtained. Therefore, the quantity of EAF
dust consumed by SDR in 2012 was assumed to be equal to the quantity consumed in 201 1.
Refined zinc production levels for Horsehead' s Monaca, PA facility (utilizing electrothermic technology) were
available from the company for years 2005 through 2012 (Horsehead 2008, 201 1, 2012, and 2013). Production
levels for 1990 through 2004 were extrapolated using the percentage changes in annual refined zinc production at
secondary smelters in the United States as provided by USGS Minerals Yearbook: Zinc (USGS 1995 through 2012).
The 3.70 metric tons CO^metric ton zinc emission factor was then applied to the Monaca facility's production
levels to estimate CO2 emissions for the facility. The Waelz kiln production emission factor was applied in this case
rather than the EAF dust consumption emission factor since Horsehead' s Monaca facility did not consume EAF
dust.
Uncertainty and Time-Series Consistency
The uncertainties contained in these estimates are two-fold, relating to activity data and emission factors used.
First, there is uncertainty associated with the amount of EAF dust consumed in the United States to produce
secondary zinc using emission-intensive Waelz kilns. The estimate for the total amount of EAF dust consumed in
Waelz kilns is based on (1) an EAF dust consumption value reported annually by Horsehead Corporation as part of
its financial reporting to the Securities and Exchange Commission (SEC), and (2) an EAF dust consumption value
obtained from the Waelz kiln facility operated in Alabama by Steel Dust Recycling LLC. Since actual EAF dust
consumption information is not available for PIZO's facility (2009-2010) and SDR's facility (2008-2010), the
4-76 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
amount is estimated by multiplying the EAF dust recycling capacity of the facility (available from the company's
Web site) by the capacity utilization factor for Horsehead Corporation (which is available from Horsehead's
financial reports). Also, the EAF dust consumption for PIZO's facility in 201 1 was estimated by multiplying the
average capacity utilization factor developed from Horsehead Corp. and SDK's annual capacity utilization rates by
PIZO's EAF dust recycling capacity. Therefore, there is uncertainty associated with the assumption used to estimate
PIZO and SDR' s annual EAF dust consumption values (except SDR' s EAF dust consumption in 20 1 1 which was
obtained from SDR's recycling facility in Alabama).
Second, there are uncertainties associated with the emission factors used to estimate CCh emissions from secondary
zinc production processes. The Waelz kiln emission factors are based on materials balances for metallurgical coke
and EAF dust consumed as provided by Viklund- White (2000). Therefore, the accuracy of these emission factors
depend upon the accuracy of these materials balances. Data limitations prevented the development of emission
factors for the electrothermic process. Therefore, emission factors for the Waelz kiln process were applied to both
electrothermic and Waelz kiln production processes. The results of the Tier 2 quantitative uncertainty analysis are
summarized in Table 4-84. Zinc production CC>2 emissions were estimated to be between 1.2 and 1.7 Tg CCh Eq. at
the 95 percent confidence level. This indicates a range of approximately 16 percent below and 17 percent above the
emission estimate of 1.4 Tg CCh Eq.
Table 4-84: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Zinc
Production (Tg COz Eq. and Percent)
Source Gas 2012 Emission Estimate Uncertainty Range Relative to Emission Estimate3
_ (Tg C02 Eq.) _ (Tg C02 Eq.) _ (^ _
_ Lower Bound Upper Bound Lower Bound Upper Bound
Zinc Production CO2 _ l_4 _ l_2 _ 1/7 _ -16% _ +17%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future improvements involve evaluating and analyzing data reported under EPA's GHGRP that would be useful to
improve the emission estimates for the Zinc Production source category. Particular attention would be made to
ensure time series consistency of the emissions estimates presented in future inventory reports, consistent with IPCC
and UNFCCC guidelines. This is required as the facility -level reporting data from EPA's GHGRP, with the
program's initial requirements for reporting of emissions in calendar year 2010, are not available for all inventory
years (i.e., 1990 through 2009) as required for this inventory. In implementing improvements and integration of data
from EPA's GHGRP, the latest guidance from the IPCC on the use of facility -level data in national inventories will
be relied upon.148
4.20 Lead Production (IPCC Source Category
2C5)
Lead production in the United States consists of both primary and secondary processes — both of which emit
(Sjardin 2003). Emissions from fuels consumed for energy purposes during the production of lead are accounted for
in the Energy chapter.
148
See.
Industrial Processes 4-77
-------
Primary production of lead through the direct smelting of lead concentrate produces CCh emissions as the lead
concentrates are reduced in a furnace using metallurgical coke (Sjardin 2003). Primary lead production, in the form
of direct smelting, occurs at a just a single smelter in Missouri. This primary lead smelter is expected to be closed by
the end of 2013 (USGS 2013).
Similar to primary lead production, CC>2 emissions from secondary production result when a reducing agent, usually
metallurgical coke, is added to the smelter to aid in the reduction process. Carbon dioxide emissions from secondary
production also occur through the treatment of secondary raw materials (Sjardin 2003). Secondary production
primarily involves the recycling of lead acid batteries at approximately 20 separate smelters located throughout the
United States. A total of 14 of these secondary smelters have annual capacities of 30,000 tons or more and were
collectively responsible for more than 99 percent of secondary lead production in 2012 (USGS 2013). Secondary
lead production has increased in the United States over the past decade while primary lead production has decreased.
In 2012, secondary lead production accounted for nearly 91 percent of total lead production.
U.S. primary lead production decreased by approximately 6 percent from 2011 to 2012, and has decreased by 73
percent since 1990 (USGS 1995 through 2013a, Guberman 2013). In 2012, U.S. secondary lead production
decreased from 2011 levels by approximately 2 percent, but has increased by 20 percent since 1990 (USGS 1995
through 2013 a, Guberman 2013).
In 2012, U.S. primary and secondary lead production totaled 1,221,000 metric tons (Guberman 2013). The resulting
emissions of CCh from 2012 production were estimated to be 0.5 Tg CCh Eq. (527 Gg) (see Table 4-85). The
majority of 2012 lead production is from secondary processes, which accounted for 95 percent of total 2012 CC>2
emissions. At last reporting, the United States was the third largest mine producer of lead in the world, behind
China and Australia, accounting for approximately 7 percent of world production in 2012 (USGS 2013).
Table 4-85: COz Emissions from Lead Production (Tg COz Eq. and Gg)
Year Tg CCh Eq. Gg
1990
2008
2009
2010
2011
2012
0.6
0.5
0.5
0.5
0.5
547
525
542
538
527
After a steady increase in total emissions from 1995 to 2000, total emissions have gradually decreased since 2000
but were still 2 percent greater in 2012 than in 1990. Although primary production has decreased significantly (73
percent since 1990), secondary production has increased by about 20 percent over the same time period. Since
secondary production is more emissions-intensive, the increase in secondary production since 1990 has resulted in a
net increase in emissions despite the sharp decrease in primary production (USGS 1995 through 2013a; Guberman
2013).
Methodology
The methods used to estimate emissions for lead production are based on Sjardin's work (Sjardin 2003) for lead
production emissions and Tier 1 methods from the 2006IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC 2006). The Tier 1 equation is as follows:
11 13 O.3 J. \J11\J VV 3.
C02 Emissions = (DS x EFa) + (S x EFb)
Where,
DS = Lead produced by direct smelting, metric ton
S = Lead produced from secondary materials
EFa, b = Applicable emission factor, metric tons CCVmetric ton product
4-78 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
For primary lead production using direct smelting, Sjardin (2003) and the IPCC (2006) provide an emission factor of
0.25 metric tons CCh/metric ton lead. For secondary lead production, Sjardin (2003) and IPCC (2006) provide an
emission factor of 0.25 metric tons CCVmetric ton lead for direct smelting, as well as an emission factor of 0.2
metric tons CCVmetric ton lead produced for the treatment of secondary raw materials (i.e., pretreatment of lead
acid batteries). Since the secondary production of lead involves both the use of the direct smelting process and the
treatment of secondary raw materials, Sjardin recommends an additive emission factor to be used in conjunction
with the secondary lead production quantity. The direct smelting factor (0.25) and the sum of the direct smelting and
pretreatment emission factors (0.45) are multiplied by total U.S. primary and secondary lead production,
respectively, to estimate CCh emissions.
The 1990 through 2012 activity data for primary and secondary lead production (see Table 4-86) were obtained from
the USGS through personal communications with the USGS Lead Commodity Specialist (Guberman 2013) and
through the USGS Mineral Yearbook: Lead (USGS 1995 through 2013 a).
Table 4-86: Lead Production (Metric Tons)
Year Primary Secondary
1990 404,000 922,000
2008
2009
2010
2011
2012
135,000
103,000
115,000
118,000
111,000
1,140,000
1,110,000
1,140,000
1,130,000
1,110,000
Uncertainty and Time-Series Consistency
Uncertainty associated with lead production relates to the emission factors and activity data used. The direct
smelting emission factor used in primary production is taken from Sjardin (2003) who averaged the values provided
by three other studies (Dutrizac et al. 2000, Morris et al. 1983, Ullman 1997). For secondary production, Sjardin
(2003) added a CCh emission factor associated with battery treatment. The applicability of these emission factors to
plants in the United States is uncertain. There is also a smaller level of uncertainty associated with the accuracy of
primary and secondary production data provided by the USGS.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-87. Lead production CO2
emissions were estimated to be between 0.5 and 0.6 Tg CCh Eq. at the 95 percent confidence level. This indicates a
range of approximately 14 percent below and 15 percent above the emission estimate of 0.5 Tg CC>2 Eq.
Table 4-87: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Lead
Production (Tg COz Eq. and Percent)
Source Gas 2012 Emission Estimate Uncertainty Range Relative to Emission Estimate3
(Tg CCh Eq.) (Tg CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Lead Production CCh 0.5 0.5 0.6 -14% +15%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Industrial Processes 4-79
-------
Planned Improvements
Future improvements involve evaluating and analyzing data reported under EPA's GHGRP that would be useful to
improve the emission estimates for the Lead Production source category. Particular attention would be made to
ensure time series consistency of the emissions estimates presented in future inventory reports, consistent with IPCC
and UNFCCC guidelines. This is required as the facility-level reporting data from EPA's GHGRP, with the
program's initial requirements for reporting of emissions in calendar year 2010, are not available for all inventory
years (i.e., 1990 through 2009) as required for this Inventory. In implementing improvements and integration of data
from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in national inventories will
be relied upon.149
4.21 HCFC-22 Production (IPCC Source
Category 2E1)
Trifluoromethane (HFC-23 or CHF3) is generated as a byproduct during the manufacture of chlorodifluoromethane
(HCFC-22), which is primarily employed in refrigeration and air conditioning systems and as a chemical feedstock
for manufacturing synthetic polymers. Between 1990 and 2000, U.S. production of HCFC-22 increased
significantly as HCFC-22 replaced chlorofluorocarbons (CFCs) in many applications. Between 2000 and 2007, U.S.
production fluctuated but generally remained above 1990 levels. In 2008 and 2009, U.S. production declined
markedly and has remained near 2009 levels since. Because HCFC-22 depletes stratospheric ozone, its production
for non-feedstock uses is scheduled to be phased out by 2020 under the U.S. Clean Air Act.150 Feedstock
production, however, is permitted to continue indefinitely.
HCFC-22 is produced by the reaction of chloroform (CHCls) and hydrogen fluoride (HF) in the presence of a
catalyst, SbCls. The reaction of the catalyst and HF produces SbClxFy, (where x + y = 5), which reacts with
chlorinated hydrocarbons to replace chlorine atoms with fluorine. The HF and chloroform are introduced by
submerged piping into a continuous-flow reactor that contains the catalyst in a hydrocarbon mixture of chloroform
and partially fluorinated intermediates. The vapors leaving the reactor contain HCFC-21 (CHC^F), HCFC-22
(CHC1F2), HFC-23 (CHF3), HC1, chloroform, and HF. The under-fluorinated intermediates (HCFC-21) and
chloroform are then condensed and returned to the reactor, along with residual catalyst, to undergo further
fluorination. The final vapors leaving the condenser are primarily HCFC-22, HFC-23, HC1 and residual HF. The
HC1 is recovered as a useful byproduct, and the HF is removed. Once separated from HCFC-22, the HFC-23 may
be released to the atmosphere, recaptured for use in a limited number of applications, or destroyed.
Three facilities produced HCFC-22 in the U. S. in 2012. Emissions of HFC-23 from this activity in 2012 were
estimated to be 4.3 Tg CCh Eq. (0.4 Gg) (see Table 4-88). This quantity represents a 38 percent decrease from 2011
emissions and an 88 percent decline from 1990 emissions. The decrease from 2011 emissions was caused by a 13
percent decrease in HCFC-22 production and a 28 percent decrease in the HFC-23 emission rate (kg HFC-23
emitted/kg HCFC-22 produced). The decline from 1990 emissions is due to a 31 percent decrease in HCFC-22
production and an 83 percent decrease in the HFC-23 emission rate since 1990. The decrease in the emission rate is
primarily attributable to five factors: (a) five plants that did not capture and destroy the HFC-23 generated have
ceased production of HCFC-22 since 1990, (b) one plant that captures and destroys the HFC-23 generated began to
produce HCFC-22, (c) one plant implemented and documented a process change that reduced the amount of HFC-23
generated, and (d) the same plant began recovering HFC-23, primarily for destruction and secondarily for sale, and
(e) another plant began destroying HFC-23.
Table 4-88: HFC-23 Emissions from HCFC-22 Production (Tg COz Eq. and Gg)
149 See.
150 As construed, interpreted, and applied in the terms and conditions of the Montreal Protocol on Substances that Deplete the
Ozone Layer. [42 U.S.C. §7671m(b), CAA §614]
4-80 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Year TgCChEq.
Gg
1990
36.4
2008
2009
2010
2011
2012
13.6
5.4
6.4
6.9
4.3
1
0.5
0.5
0.6
0.4
Methodology
To estimate HFC-23 emissions for five of the eight HCFC-22 plants that have operated in the United States since
1990, methods comparable to the Tier 3 methods in the 2006IPCC Guidelines for National Greenhouse Gas
Inventories (IPCC 2006) were used. Emissions for 2010 through 2012 were obtained through reports submitted by
U.S. HCFC-22 production facilities to EPA's GHGRP. EPA's GHGRP mandates that all HCFC-22 production
facilities report their annual emissions of HFC-23 from HCFC-22 production processes and HFC-23 destruction
processes. Previously, data were obtained by EPA through collaboration with an industry association that received
voluntarily reported HCFC-22 production and HFC-23 emissions annually from all U.S. HCFC-22 producers from
1990 through 2009. These emissions were aggregated and reported to EPA on an annual basis.
For the other three plants, the last of which closed in 1993, methods comparable to the Tier 1 method in the 2006
IPCC Guidelines were used. Emissions from these three plants have been calculated using the recommended
emission factor for unoptimized plants operating before 1995 (0.04 kg HCFC-23/kg HCFC-22 produced).
The five plants that have operated since 1994 measured concentrations of HFC-23 to estimate their emissions of
HFC-23. Plants using thermal oxidation to abate their HFC-23 emissions monitor the performance of their oxidizers
to verify that the HFC-23 is almost completely destroyed. Plants that release (or historically have released) some of
their byproduct HFC-23 periodically measure HFC-23 concentrations in the output stream using gas
chromatography. This information is combined with information on quantities of products (e.g., HCFC-22) to
estimate HFC-23 emissions.
To estimate 1990 through 2009 emissions, reports from an industry association were used that aggregated HCFC-22
production and HFC-23 emissions from all U.S. HCFC-22 producers and reported them to EPA (ARAP 1997, 1999,
2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010). To estimate 2010 through 2012 emissions,
facility-level data (including both HCFC-22 production and HFC-23 emissions) reported through the EPA's
GHGRP were analyzed. In 1997 and 2008, comprehensive reviews of plant-level estimates of HFC-23 emissions
and HCFC-22 production were performed (RTI1997; RTI2008). The 1997 and 2008 reviews enabled U.S. totals to
be reviewed, updated, and where necessary, corrected, and also for plant-level uncertainty analyses (Monte-Carlo
simulations) to be performed for 1990, 1995, 2000, 2005, and 2006. Estimates of annual U.S. HCFC-22 production
are presented in Table 4-89.
Table 4-89: HCFC-22 Production (Gg)
Year
1990
2008
2009
2010
2011
2012
126
91
101
110
96
Industrial Processes 4-81
-------
Uncertainty and Time-Series Consistency
The uncertainty analysis presented in this section was based on a plant-level Monte Carlo Stochastic Simulation for
2006. The Monte Carlo analysis used estimates of the uncertainties in the individual variables in each plant's
estimating procedure. This analysis was based on the generation of 10,000 random samples of model inputs from
the probability density functions for each input. A normal probability density function was assumed for all
measurements and biases except the equipment leak estimates for one plant; a log-normal probability density
function was used for this plant's equipment leak estimates. The simulation for 2006 yielded a 95-percent
confidence interval for U.S. emissions of 6.8 percent below to 9.6 percent above the reported total.
The relative errors yielded by the Monte Carlo Stochastic Simulation for 2006 were applied to the U.S. emission
estimate for 2012. The resulting estimates of absolute uncertainty are likely to be reasonably accurate because (1)
the methods used by the three plants to estimate their emissions are not believed to have changed significantly since
2006, and (2) although the distribution of emissions among the plants may have changed between 2006 and 2012
(because both HCFC-22 production and the HFC-23 emission rate declined significantly), the two plants that
contribute significantly to emissions were estimated to have similar relative uncertainties in their 2006 (as well as
2005) emission estimates. Thus, changes in the relative contributions of these two plants to total emissions are not
likely to have a large impact on the uncertainty of the national emission estimate.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-90. HFC-23 emissions from
HCFC-22 production were estimated to be between 4.0 and 4.8 Tg CC>2 Eq. at the 95 percent confidence level. This
indicates a range of approximately 7 percent below and 10 percent above the emission estimate of 4.3 Tg CO2 Eq.
Table 4-90: Quantitative Uncertainty Estimates for HFC-23 Emissions from HCFC-22
Production (Tg COz Eq. and Percent)
2012 Emission
Source Gas Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
HCFC-22 Production HFC-23 4.3
4.0 4.8 -7% +10%
a Range of emissions reflects a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
4.22 Substitution of Ozone Depleting
Substances (IPCC Source Category 2F)
Hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs) are used as alternatives to several classes of ozone-
depleting substances (ODSs) that are being phased out under the terms of the Montreal Protocol and the Clean Air
Act Amendments of 1990.151 Ozone depleting substances—chlorofluorocarbons (CFCs), halons, carbon
tetrachloride, methyl chloroform, and hydrochlorofluorocarbons (HCFCs)—are used in a variety of industrial
applications including refrigeration and air conditioning equipment, solvent cleaning, foam production, sterilization,
fire extinguishing, and aerosols. Although HFCs and PFCs are not harmful to the stratospheric ozone layer, they are
potent greenhouse gases. Emission estimates for HFCs and PFCs used as substitutes for ODSs are provided in Table
4-9 land Table 4-92.
151
[42 U.S.C § 7671, CAA Title VI]
4-82 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 4-91: Emissions of MFCs and PFCs from OPS Substitutes (Tg COz Eg.)
Gas
1990
2005
2008
2009
2010
2011
2012
HFC-23
HFC-32
HFC-125
HFC-134a
HFC-143a
HFC-236fa
CF4
Others*
Total
+ 1
+ 1
1
+ 1
0.3
0.3
+ 1
0.3 1
8.5
79.8
8.7 1
0.8 1
+ 1
5.6
103.8
+
1.3
14.3
87.9
11.1
0.9
+
6.7
122.2
+
1.7
17.3
90.0
12.6
0.9
+
7.0
129.6
+
2.5
22.2
89.7
14.7
0.9
+
7.4
137.5
+
3.2
26.6
86.1
16.8
0.9
+
7.8
141.5
+
4.1
31.7
82.8
18.9
0.9
+
8.2
146.8
+ Does not exceed 0.05 Tg CO2 Eq.
* Others include HFC-152a, HFC-227ea, HFC-245fa, HFC-43-10mee, C4Fio, and PFC/PFPEs, the latter being a proxy for a
diverse collection of PFCs and perfluoropolyethers (PFPEs) employed for solvent applications. For estimating purposes, the
GWP value used for PFC/PFPEs was based upon CeFn.
Note: Totals may not sum due to independent rounding.
Table 4-92:
Gas
HFC-23
HFC-32
HFC-125
HFC-134a
HFC-143a
HFC-236fa
CF4
Others*
Emissions
1990
+ |
of MFCs and
2005
1
505
3,053
61,362
2,290
125
2
M
PFCs from
2008
2
2,025
5,119
67,634
2,911
141
2
M
ODS
2009
2
2,613
6,178
69,224
3,325
144
2
Substitution
2010
2
3,856
7,930
68,998
3,861
146
3
M M
(Mg)
2011
2
4,935
9,511
66,234
4,412
147
3
M
2012
2
6,324
11,333
63,719
4,976
148
3
M
M (Mixture of Gases)
+ Does not exceed 0.5 Mg
* Others include HFC-152a, HFC-227ea, HFC-245fa, HFC-43-10mee, C4Fio, and PFC/PFPEs, the latter being a proxy for a
diverse collection of PFCs and perfluoropoly ethers (PFPEs) employed for solvent applications.
In 1990 and 1991, the only significant emissions of HFCs and PFCs as substitutes to ODSs were relatively small
amounts of HFC-152a—used as an aerosol propellant and also a component of the refrigerant blend R-500 used in
chillers—and HFC-134a in refrigeration end-uses. Beginning in 1992, HFC-134a was used in growing amounts as a
refrigerant in motor vehicle air-conditioners and in refrigerant blends such as R-404 A.152 In 1993, the use of HFCs
in foam production began, and in 1994 ODS substitutes for halons entered widespread use in the United States as
halon production was phased-out. In 1995, these compounds also found applications as solvents.
The use and subsequent emissions of HFCs and PFCs as ODS substitutes has been increasing from small amounts in
1990 to 146.8 Tg CO2 Eq. in 2012. This increase was in large part the result of efforts to phase out CFCs and other
ODSs in the United States. In the short term, this trend is expected to continue, and will likely continue over the
next decade as HCFCs, which are interim substitutes in many applications, are themselves phased-out under the
provisions of the Copenhagen Amendments to the Montreal Protocol. Improvements in the technologies associated
with the use of these gases and the introduction of alternative gases and technologies, however, may help to offset
this anticipated increase in emissions.
Table 4-93 presents emissions of HFCs and PFCs as ODS substitutes by end-use sector for 1990 through 2012 The
end-use sectors that contributed the most toward emissions of HFCs and PFCs as ODS substitutes in 2012 include
refrigeration and air-conditioning (128.2 Tg CO2 Eq., or approximately 87 percent), aerosols (9.9 Tg CO2 Eq., or
approximately 7 percent), and foams (6.3 Tg CO2 Eq., or approximately 4 percent). Within the refrigeration and air-
conditioning end-use sector, motor vehicle air-conditioning was the highest emitting end-use (58.5 Tg CO2 Eq.),
152 R.4Q4A contains HFC-125, HFC-143a, and HFC-134a.
Industrial Processes 4-83
-------
followed by refrigerated retail food and refrigerated transport. Each of the end-use sectors is described in more
detail below.
Table 4-93: Emissions of MFCs and PFCs from OPS Substitutes (Tg COz Eg.) by Sector
Sector 1990 2005 2007 2008 2009 2010 2011 2012
Refrigeration/Air
Conditioning + I 92.7 103.6 109.1 114.6 120.5 123.7 128.2
Aerosols 0.3 7.3 8.2 8.6 9.1 9.3 9.7 9.9
Foams + I 1.9 2.3 2.5 3.9 5.4 5.9 6.3
Solvents + I 1.3 1.3 1.3 1.3 1.3 1.4 1.4
Fire Protection + 0.5 0/7 0/7 0.8 0.9 0.9 1.0
Total 0.3 103.8 116.0 122.2 129.6 137.5 141.5 146.8
+ Does not exceed 0.05 Tg CO2 Eq.
Refrigeration/Air Conditioning
The refrigeration and air-conditioning sector includes a wide variety of equipment types that have historically used
CFCs or HCFCs. End-uses within this sector include motor vehicle air-conditioning, retail food refrigeration,
refrigerated transport (e.g., ship holds, truck trailers, railway freight cars), household refrigeration, residential and
small commercial air-conditioning and heat pumps, chillers (large comfort cooling), cold storage facilities, and
industrial process refrigeration (e.g., systems used in food processing, chemical, petrochemical, pharmaceutical, oil
and gas, and metallurgical industries). As the ODS phaseout is taking effect, most equipment is being or will
eventually be retrofitted or replaced to use HFC-based substitutes. Common HFCs in use today in refrigeration/air-
conditioning equipment are HFC-134a, R-410A153, R-404A, and R-507A154. These HFCs are emitted to the
atmosphere during equipment manufacture and operation (as a result of component failure, leaks, and purges), as
well as at servicing and disposal events.
Aerosols
Aerosol propellants are used in metered dose inhalers (MDIs) and a variety of personal care products and
technical/specialty products (e.g., duster sprays and safety horns). Many pharmaceutical companies that produce
MDIs—a type of inhaled therapy used to treat asthma and chronic obstructive pulmonary disease—have replaced
the use of CFCs with HFC-propellant alternatives. The earliest ozone-friendly MDIs were produced with HFC-
134a, but the industry has started to use HFC-227ea as well. Conversely, since the use of CFC propellants was
banned in 1978, most non-medical consumer aerosol products have not transitioned to HFCs, but to "not-in-kind"
technologies, such as solid roll-on deodorants and finger-pump sprays. The transition away from ODS in specialty
aerosol products has also led to the introduction of non-fluorocarbon alternatives (e.g., hydrocarbon propellants) in
certain applications, in addition to HFC-134a or HFC-152a. These propellants are released into the atmosphere as
the aerosol products are used.
Foams
CFCs and HCFCs have traditionally been used as foam blowing agents to produce polyurethane (PU), polystyrene,
polyolefin, and phenolic foams, which are used in a wide variety of products and applications. Since the Montreal
Protocol, flexible PU foams as well as other types of foam, such as polystyrene sheet, polyolefin, and phenolic
foam, have transitioned almost completely away from fluorocompounds, into alternatives such as CO2, methylene
chloride, and hydrocarbons. The majority of rigid PU foams have transitioned to HFCs—primarily HFC-134a and
HFC-245fa. Today, these HFCs are used to produce polyurethane appliance, PU commercial refrigeration, PU
spray, and PU panel foams—used in refrigerators, vending machines, roofing, wall insulation, garage doors, and
cold storage applications. In addition, HFC-152a, HFC-134a and CCh are used to produce polystyrene sheet/board
foam, which is used in food packaging and building insulation. Emissions of blowing agents occur when the foam is
manufactured as well as during the foam lifetime and at foam disposal, depending on the particular foam type.
153 R.41QA contains HFC-32 and HFC-125.
154 R-507A, also called R-507, contains HFC-125 and HFC-143a.
4-84 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Solvents
CFCs, methyl chloroform (1,1,1-trichloroethane orTCA), and to a lesser extent carbon tetrachloride (CC14) were
historically used as solvents in a wide range of cleaning applications, including precision, electronics, and metal
cleaning. Since their phaseout, metal cleaning end-use applications have primarily transitioned to non-fluorocarbon
solvents and not-in-kind processes. The precision and electronics cleaning end-uses have transitioned in part to high-
GWP gases, due to their high reliability, excellent compatibility, good stability, low toxicity, and selective solvency.
These applications rely on HFC-43-10mee, HFC-365mfc, HFC-245fa, and to a lesser extent, PFCs. Electronics
cleaning involves removing flux residue that remains after a soldering operation for printed circuit boards and other
contamination-sensitive electronics applications. Precision cleaning may apply to either electronic components or to
metal surfaces, and is characterized by products, such as disk drives, gyroscopes, and optical components, that
require a high level of cleanliness and generally have complex shapes, small clearances, and other cleaning
challenges. The use of solvents yields fugitive emissions of these HFCs and PFCs.
Fire Protection
Fire protection applications include portable fire extinguishers ("streaming" applications) that originally used halon
1211, and total flooding applications that originally used halon 1301, as well as some halon 2402. Since the
production and sale of halons were banned in the United States in 1994, the halon replacement agent of choice in the
streaming sector has been dry chemical, although HFC-236fa is also used to a limited extent. In the total flooding
sector, HFC-227ea has emerged as the primary replacement for halon 1301 in applications that require clean agents.
Other HFCs, such as HFC-23 and HFC-125, are used in smaller amounts. The majority of HFC-227ea in total
flooding systems is used to protect essential electronics, as well as in civil aviation, military mobile weapons
systems, oil/gas/other process industries, and merchant shipping. As fire protection equipment is tested or
deployed, emissions of these HFCs occur.
Methodology
A detailed Vintaging Model of ODS-containing equipment and products was used to estimate the actual—versus
potential—emissions of various ODS substitutes, including HFCs and PFCs. The name of the model refers to the
fact that it tracks the use and emissions of various compounds for the annual "vintages" of new equipment that enter
service in each end-use. The Vintaging Model predicts ODS and ODS substitute use in the United States based on
modeled estimates of the quantity of equipment or products sold each year containing these chemicals and the
amount of the chemical required to manufacture and/or maintain equipment and products over time. Emissions for
each end-use were estimated by applying annual leak rates and release profiles, which account for the lag in
emissions from equipment as they leak over time. By aggregating the data for 60 different end-uses, the model
produces estimates of annual use and emissions of each compound. Further information on the Vintaging Model is
contained in Annex 3.8.
Uncertainty and Time-Series Consistency
Given that emissions of ODS substitutes occur from thousands of different kinds of equipment and from millions of
point and mobile sources throughout the United States, emission estimates must be made using analytical tools such
as the Vintaging Model or the methods outlined in IPCC (2006). Though the model is more comprehensive than the
IPCC default methodology, significant uncertainties still exist with regard to the levels of equipment sales,
equipment characteristics, and end-use emissions profiles that were used to estimate annual emissions for the
various compounds.
The Vintaging Model estimates emissions from 60 end-uses. The uncertainty analysis, however, quantifies the level
of uncertainty associated with the aggregate emissions resulting from the top 21 end-uses, comprising over 95
percent of the total emissions, and 6 other end-uses. These 27 end-uses comprise 97 percent of the total emissions,
equivalent to 143.6 Tg CO2 Eq. In an effort to improve the uncertainty analysis, additional end-uses are added
annually, with the intention that over time uncertainty for all emissions from the Vintaging Model will be fully
characterized. Any end-uses included in previous years' uncertainty analysis were included in the current
Industrial Processes 4-85
-------
uncertainty analysis, whether or not those end-uses were included in the top 95 percent of emissions from ODS
Substitutes.
In order to calculate uncertainty, functional forms were developed to simplify some of the complex "vintaging"
aspects of some end-use sectors, especially with respect to refrigeration and air-conditioning, and to a lesser degree,
fire extinguishing. These sectors calculate emissions based on the entire lifetime of equipment, not just equipment
put into commission in the current year, thereby necessitating simplifying equations. The functional forms used
variables that included growth rates, emission factors, transition from ODSs, change in charge size as a result of the
transition, disposal quantities, disposal emission rates, and either stock for the current year or original ODS
consumption. Uncertainty was estimated around each variable within the functional forms based on expert
judgment, and a Monte Carlo analysis was performed. The most significant sources of uncertainty for this source
category include the emission factors for refrigerated transport, as well as the percent of non-MDI aerosol propellant
thatisHFC-152a.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-94. Substitution of ozone
depleting substances HFC and PFC emissions were estimated to be between 143.4 and 163.9 Tg CCh Eq. at the 95
percent confidence level. This indicates a range of approximately 0.14 percent below to 14.1 percent above the
emission estimate of 146.8Tg CCh Eq.
Table 4-94: Tier 2 Quantitative Uncertainty Estimates for HFC and PFC Emissions from ODS
Substitutes (Tg COz Eq. and Percent)
Source
Gases
2012 Emission
Estimate
(Tg C02 Eq.)a
Uncertainty Range Relative to Emission Estimate1"
(Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Substitution of Ozone
Depleting Substances
HFCs and
PFCs
146.8
143.4 163.9 -0.14% +14.1%
a 2012 emission estimates and the uncertainty range presented in this table correspond to selected end-uses within the aerosols,
foams, solvents, fire extinguishing agents, and refrigerants sectors that comprise 97 percent of total emissions, but not for other
remaining categories. Therefore, because the uncertainty associated with emissions from "other" ODS substitutes was not
estimated, they were excluded in the estimates reported in this table.
b Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
A review of the MVAC light-duty vehicle (LDV) and light-duty truck (LOT) end-uses led to revisions in the
assumed transition scenarios, stock and growth rate assumptions, and equipment lifetime. Updated annual sales and
registration data was used to update the installed base, annual growth rate, and lifetime for the MVAC end-uses. In
addition, although HFC-134a has been the dominant refrigerant in MVACs since the 1990s, an additional transition
to HFO-1234yf was added to the Vintaging Model beginning in 2012 to reflect a recent shift in new vehicles to
HFO-1234yf. Overall, these changes to the Vintaging Model increased GHG emissions on average by 7 percent
across the time series.
4.23 Semiconductor Manufacture (IPCC
Source Category 2F6)
The semiconductor industry uses multiple long-lived fluorinated greenhouse gases in plasma etching and plasma
enhanced chemical vapor deposition (PECVD) processes to produce semiconductor products. The gases most
commonly employed are trifluoromethane (HFC-23 or CHF3), perfluoromethane (CF4), perfluoroethane (C2F6),
4-86 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
nitrogen trifluoride (NF3), and sulfur hexafluoride (SFe), although other compounds such as perfluoropropane
and perfluorocyclobutane (c-C4F8) are also used. The exact combination of compounds is specific to the process
employed.
A single 300 mm silicon wafer that yields between 400 to 500 semiconductor products (devices or chips) may
require as many as, or more than 100 distinct fluorinated-gas-using process steps, principally to deposit and pattern
dielectric films. Plasma etching (or patterning) of dielectric films, such as silicon dioxide and silicon nitride, is
performed to provide pathways for conducting material to connect individual circuit components in each device.
The patterning process uses plasma-generated fluorine atoms, which chemically react with exposed dielectric film to
selectively remove the desired portions of the film. The material removed as well as undissociated fluorinated gases
flow into waste streams and, unless emission abatement systems are employed, into the atmosphere. PECVD
chambers, used for depositing dielectric films, are cleaned periodically using fluorinated and other gases. During
the cleaning cycle the gas is converted to fluorine atoms in plasma, which etches away residual material from
chamber walls, electrodes, and chamber hardware. Undissociated fluorinated gases and other products pass from the
chamber to waste streams and, unless abatement systems are employed, into the atmosphere. In addition to
emissions of unreacted gases, some fluorinated compounds can also be transformed in the plasma processes into
different fluorinated compounds which are then exhausted, unless abated, into the atmosphere. For example, when
C2F6 is used in cleaning or etching, CF4 is generated and emitted as a process by-product. Besides dielectric film
etching and PECVD chamber cleaning, much smaller quantities of fluorinated gases are used to etch poly silicon
films and refractory metal films like tungsten.
For 2012, total CCh weighted emissions of all fluorinated greenhouse gases by the U.S. semiconductor industry were
estimated to be 3.7 Tg CCh Eq. Combined emissions of all fluorinated greenhouse gases are presented in Table 4-95
and Table 4-96 below foryears 1990, 2005 and the period 2008 to 2012. The rapid growth of this industry and the
increasing complexity (growing number of layers) of semiconductor products led to an increase in emissions of 148
percent between 1990 and 1999, when emissions peaked at 7.2 Tg CCh Eq.155 The emissions growth rate began to
slow after 1999, and emissions declined by 48 percent between 1999 and 2012. Together, industrial growth and
adoption of emissions reduction technologies, including but not limited to abatement technologies, resulted in a net
increase in emissions of 28 percent between 1990 and 2012.
There was a sizable dip seen in emissions between 2008 and 2009, a 28 percent decrease, due to the slowed
economic growth, and hence production, during this time. The industry recovered and emissions rose between 2009
and 2010 by more than 29 percent and between 2010 and 2011 by 34 percent; a small reduction in emissions can be
seen between 2011 and 2012.
Table 4-95: RFC, HFC, and SFe Emissions from Semiconductor Manufacture (Tg COz Eq.)
Year 1990 2005 2008 2009 2010 2011 2012
CF4 0.7 0.9 1.0 0.9 0.9 1.3 1.2
C2F6 1.5 1.5 1.3 0.0 1.3 1.5 1.5
CsFs 0.0 0.1 0.1 0.1 0.0 0.2 0.1
C4F8 0.0 0.1 0.1 0.0 0.0 0.1 0.1
HFC-23 0.2 0.2 0.2 0.1 0.2 0.2 0.2
SF6 0.5 0.7 0.5 0.3 0.4 0.7 0.7
NF3* 0.0 0.4 05 04 0.4 0_3 0.3
Total 2.9 3.5 3.0 2.2 2.8 3.9 3.7
Note: Totals may not sum due to independent rounding.
* NFs emissions are presented for informational purposes, using the AR4 GWP of 17,200,
and are not included in totals.
Table 4-96: RFC, HFC, and SFe Emissions from Semiconductor Manufacture (Mg)
Year 1990 2005 2008 2009 2010 2011 2012
155 Complexity is a term denoting the circuit required to connect the active circuit elements (transistors) on a chip. Increasing
miniaturization, for the same chip size, leads to increasing transistor density, which, in turn, requires more complex
interconnections between those transistors. This increasing complexity is manifested by increasing the levels (i.e., layers) of
wiring, with each wiring layer requiring fluorinated gas usage for its manufacture.
Industrial Processes 4-87
-------
CF4 115 144 146 109 148 197 186
C2F6 160 162 138 94 119 164 167
C3F8 0 I 9 I 18 11 14 22 14
C4F8 0 I 12 6 4 4 8 7
HFC-23 15 14 15 12 15 13 14
SF6 22 31 19 14 17 29 28
NF3 3 I 24 27 21 23 20 20
Methodology
Emissions are based on data reported through Subpart I—Electronics Manufacture of EPA's GHGRP, Partner
reported emissions data received through the EPA's PFC Reduction/Climate Partnership, EPA's PFC Emissions
Vintage Model (PEVM)—a model that estimates industry emissions in the absence of emission control strategies
(Burton and Beizaie 2001)156, and estimates of industry activity (i.e., total manufactured layer area). The availability
and applicability of reported data from the EPA Partnership and EPA's GHGPJ3 differs across the 1990 through
2012 time series. Consequently, emissions from semiconductor manufacturing were estimated using five distinct
methods, one each for the periods 1990 through 1994, 1995 through 1999, 2000 through 2006, 2007 through 2010,
and 201 land 2012.
1990 through 1994
From 1990 through 1994, Partnership data was unavailable and emissions were modeled using the PEVM (Burton
and Beizaie 2001).157 The 1990 to 1994 emissions are assumed to be uncontrolled, since reduction strategies such as
chemical substitution and abatement were yet to be developed.
PEVM is based on the recognition that PFC emissions from semiconductor manufacturing vary with: (1) the number
of layers that comprise different kinds of semiconductor devices, including both silicon wafer and metal
interconnect layers, and (2) silicon consumption (i.e., the area of semiconductors produced) for each kind of device.
The product of these two quantities, Total Manufactured Layer Area (TMLA), constitutes the activity data for
semiconductor manufacturing. PEVM also incorporates an emission factor that expresses emissions per unit of
layer-area. Emissions are estimated by multiplying TMLA by this emission factor.
PEVM incorporates information on the two attributes of semiconductor devices that affect the number of layers: (1)
linewidth technology (the smallest manufactured feature size), 158 and (2) product type (discrete, memory or
logic).159 For each linewidth technology, a weighted average number of layers is estimated using VLSI product-
specific worldwide silicon demand data in conjunction with complexity factors (i.e., the number of layers per
Integrated Circuit (1C)) specific to product type (Burton and Beizaie 2001, ITRS 2007). PEVM derives historical
156 A Partner refers to a participant in the U.S. EPA PFC Reduction/Climate Partnership for the Semiconductor Industry.
Through a Memorandum of Understanding (MoU) with the EPA, Partners voluntarily reported their PFC emissions to the EPA
by way of a third party, which aggregated the emissions through 2010. For 2011, while no MOU existed, it was assumed that the
same companies that were Partners in 2010 were "Partners" in 2011 for purposes of estimating inventory emissions.
157 Various versions of the PEVM exist to reflect changing industrial practices. From 1990 to 1994 emissions estimates are from
PEVMvl.O, completed in September 1998. The emission factor used to estimate 1990 to 1994 emissions is an average of the
1995 and 1996 emissions factors, which were derived from Partner reported data for those years.
158 By decreasing features of Integrated Circuit components, more components can be manufactured per device, which increases
its functionality. However, as those individual components shrink it requires more layers to interconnect them to achieve the
functionality. For example, a microprocessor manufactured with the smallest feature sizes (65 nm) might contain as many as 1
billion transistors and require as many as 11 layers of component interconnects to achieve functionality, while a device
manufactured with 130 nm feature size might contain a few hundred million transistors and require 8 layers of component
interconnects (ITRS 2007).
159 Memory devices manufactured with the same feature sizes as microprocessors (a logic device) require approximately one-
half the number of interconnect layers, whereas discrete devices require only a silicon base layer and no interconnect layers
(ITRS 2007). Since discrete devices did not start using PFCs appreciably until 2004, they are only accounted for in the PEVM
emissions estimates from 2004 onwards.
4-88 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
consumption of silicon (i.e., square inches) by linewidth technology from published data on annual wafer starts and
average wafer size (VLSI Research, Inc. 2010).
The emission factor in PEVM is the average of four historical emission factors, each derived by dividing the total
annual emissions reported by the Partners for each of the four years between 1996 and 1999 by the total TMLA
estimated for the Partners in each of those years. Over this period, the emission factors varied relatively little (i.e.,
the relative standard deviation for the average was 5 percent). Since Partners are believed not to have applied
significant emission reduction measures before 2000, the resulting average emission factor reflects uncontrolled
emissions. The emission factor is used to estimate world uncontrolled emissions using publicly-available data on
world silicon consumption.
As it was assumed for this time period that there was no consequential adoption of fluorinated-gas-reducing
measures, a fixed distribution of fluorinated-gas use was assumed to apply to the entire U.S. industry to estimate
gas-specific emissions. This distribution was based upon the average fluorinated-gas purchases made by
semiconductor manufacturers during this period and the application of IPCC default emission factors for each gas
(Burton and Beizaie 2001).
1995 through 1999
For 1995 through 1999, total U.S. emissions were extrapolated from the total annual emissions reported by the
Partners (1995 through 1999). Partner-reported emissions are considered more representative (e.g., in terms of
capacity utilization in a given year) than PEVM estimated emissions, and are used to generate total U.S. emissions
when applicable. The emissions reported by the Partners were divided by the ratio of the total capacity of the plants
operated by the Partners and the total capacity of all of the semiconductor plants in the United States; this ratio
represents the share of capacity attributable to the Partnership. This method assumes that Partners and non-Partners
have identical capacity utilizations and distributions of manufacturing technologies. Plant capacity data is contained
in the World Fab Forecast (WFF) database and its predecessors, which is updated quarterly (Semiconductor
Equipment and Materials Industry 2011). Gas-specific emissions were estimated using the same method as for 1990
through 1994.
2000 through 2006
Emissions for the years 2000 through 2006—the period during which Partners began the consequential application
of PFC-reduction measures—were estimated using a combination of Partner-reported emissions and adjusted PEVM
modeled emissions. The emissions reported by Partners for each year were accepted as the quantity emitted from
the share of the industry represented by those Partners. Remaining emissions, those from non-Partners, were
estimated using PEVM and the method described above, with one change. To ensure time series consistency and to
reflect the increasing use of remote clean technology (which increases the efficiency of the production process while
lowering emissions of fluorinated greenhouse gases), the average non-Partner emission factor was assumed to begin
declining gradually during this period. Specifically, the non-Partner emission factor for each year was determined
by linear interpolation, using the end points of 1999 (the original PEVM emission factor) and 2011 (new emission
factor determined for non-Partners population based on GHGRP-reported data, described below).
The portion of the U.S. total attributed to non-Partners is obtained by multiplying PEVM's total U.S. emissions
figure by the non-Partner share of U. S. total silicon capacity for each year as described above. 16° Gas-specific
emissions from non-Partners were estimated using linear interpolation of gas-specific emission distribution of 1999
(assumed same as total US Industry in 1994) and 2011 (calculated from a subset of non-Partner facilities from
GHGRP reported emissions data). Annual updates to PEVM reflect published figures for actual silicon consumption
from VLSI Research, Inc., revisions and additions to the world population of semiconductor manufacturing plants,
160 This approach assumes that the distribution of linewidth technologies is the same between Partners and non-Partners. As
discussed in the description of the method used to estimate 2007 emissions, this is not always the case.
Industrial Processes 4-89
-------
and changes in 1C fabrication practices within the semiconductor industry (see ITRS 2008 and Semiconductor
Equipment and Materials Industry 2011). 16U62.163
2007 through 2010
For the years 2007 through 2010, emissions were also estimated using a combination of Partner reported emissions
and adjusted PEVM modeled emissions to provide estimates for non-Partners; however, two improvements were
made to the estimation method employed for the previous years in the time series. First, the 2007 through 2010
emission estimates account for the fact that Partners and non-Partners employ different distributions of
manufacturing technologies, with the Partners using manufacturing technologies with greater transistor densities and
therefore greater numbers of layers.164 Second, the scope of the 2007 through 2010 estimates was expanded relative
to the estimates for the years 2000 through 2006 to include emissions from research and development (R&D) fabs.
This additional enhancement was feasible through the use of more detailed data published in the World Fab
Forecast. PEVM databases were updated annually as described above. The published world average capacity
utilization for 2007 through 2010 was used for production fabs, while for R&D fabs a 20 percent figure was
assumed (SIA 2009).
In addition, publicly-available actual utilization data was used to account for differences in fab utilization for
manufacturers of discrete and 1C products for 2010 emissions for non-Partners. PEVM estimates were adjusted
using technology-weighted capacity shares that reflect the relative influence of different utilization. Gas-specific
emissions for non-Partners were estimated using the same method as for 2000 through 2006.
2011 and 2012
The fifth and final method for estimating emissions from semiconductor manufacturing covers the period 2011 and
2012, the first years after EPA's Partnership with the semiconductor industry ended in 2010. Manufacturers with the
total potential to emit 25,000 mt COa Eq. per year were required to report their emissions to the EPA. This
population of manufacturers included Partners of EPA's PFC Reduction/Climate Partnership as well as non-
Partners. The population of non-Partner facilities also included manufacturers that use GaAs technology in addition
to Si technology. Emissions from the population of manufacturers that were below the reporting threshold were also
161 Special attention was given to the manufacturing capacity of plants that use wafers with 300 mm diameters because the actual
capacity of these plants is ramped up to design capacity, typically over a 2-3 year period. To prevent overstating estimates of
partner-capacity shares from plants using 300 mm wafers, design capacities contained in WFW were replaced with estimates of
actual installed capacities for 2004 published by Citigroup Smith Barney (2005). Without this correction, the partner share of
capacity would be overstated, by approximately 5 percent. For perspective, approximately 95 percent of all new capacity
additions in 2004 used 300 mm wafers, and by year-end those plants, on average, could operate at approximately 70 percent of
the design capacity. For 2005, actual installed capacities were estimated using an entry in the World Fab Watch database (April
2006 Edition) called "wafers/month, 8-inch equivalent," which denoted the actual installed capacity instead of the fully-ramped
capacity. For 2006, actual installed capacities of new fabs were estimated using an average monthly ramp rate of 1100 wafer
starts per month (wspm) derived from various sources such as semiconductor fabtech, industry analysts, and articles in the trade
press. The monthly ramp rate was applied from the first-quarter of silicon volume (FQS V) to determine the average design
capacity over the 2006 period.
162 In 2006, the industry trend in co-ownership of manufacturing facilities continued. Several manufacturers, who are Partners,
now operate fabs with other manufacturers, who in some cases are also Partners and in other cases are not Partners. Special
attention was given to this occurrence when estimating the Partner and non-Partner shares of U.S. manufacturing capacity.
I63 Two versions of PEVM are used to model non-Partner emissions during this period. For the years 2000 to 2003 PEVM
v3.2.0506.0507 was used to estimate non-Partner emissions. During this time, discrete devices did not use PFCs during
manufacturing and therefore only memory and logic devices were modeled in the PEVM v3.2.0506.0507. From 2004 onwards,
discrete device fabrication started to use PFCs, hence PEVM v4.0.0701.0701, the first version of PEVM to account for PFC
emissions from discrete devices, was used to estimate non-Partner emissions for this time period.
164 EPA considered applying this change to years before 2007, but found that it would be difficult due to the large amount of
data (i.e., technology-specific global and non-Partner TMLA) that would have to be examined and manipulated for each year.
This effort did not appear to be justified given the relatively small impact of the improvement on the total estimate for 2007 and
the fact that the impact of the improvement would likely be lower for earlier years because the estimated share of emissions
accounted for by non-Partners is growing as Partners continue to implement emission-reduction efforts.
4-90 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
estimated for 2011 and 2012 using newly developed emission factors and estimates of 2011 and 2012 facility-
specific production. Inventory totals reflect the emissions from both populations.
Under EPA's GHGRP, semiconductor manufacturing facilities report emissions of fluorinated GHGs used in etch
and clean processes and as heat transfer fluids. They also report N2O emissions from CVD and other processes.
This data was aggregated, by gas, across all semiconductor manufacturing GHGRP reporters to calculate gas-
specific emissions for the GHGRP-reporting segment of the U.S. industry.
For the segment of the semiconductor industry that does not meet EPA's GHGRP reporting threshold, and for R&D
facilities which are not covered by EPA's GHGRP, emission estimates are based on new emission factors developed
by EPA for the fluorinated GHGs used in etch and CVD clean processes. The new emission factors (in units of
mass of COa Eq./TMLA) are based on the emissions reported by facilities under EPA's GHGRP and TMLA
estimates for these facilities from the World Fab Forecast (SEMI 2012 and SEMI 2013). In a refinement of the
method used in prior years to estimate emissions for the non-Partner population, different emission factors were
developed for different subpopulations of fabs. An analysis of the emission factors of reporting fabs showed that the
characteristics that had the largest impacts on emission factors were the technology (e.g., Si of GaAS) used at the
fab, whether the fab contained R&D activities, and whether the fab reported using point-of-use fluorinated
greenhouse gas abatement. For each of these groups, a population-specific emission factor was obtained using a
regression-through-the-origin (RTO) model: facility-reported aggregate emissions of seven fluorinated GHGs (CF4,
C2F6, CsFg, C4F8, CHF3, SF6 and NF3)165 were regressed against the corresponding TMLA. For each subpopulation,
the slope of the RTO model is the emission factor for that subpopulation. To estimate emissions from fabs that are
solely doing research and development (R&D) or are Pilot fabs (i.e., fabs that are excluded from subpart I reporting
requirements), emission factors were estimated based on GHGRP reporting fabs containing R&D activities. EPA
applied a scaling factor of 15 percent to the slope of the RTO model to estimate the emission factor applicable to the
non-reporting fabs that are only R&D or Pilot fabs. This was done as R&D activities lead to use of more PFCs for
development of chips that are not counted towards the final estimated TMLA. Hence, it is assumed that the fabs with
only R&D activities use 15 percent more PFCs per TMLA.
Non-reporting fabs were then broken out into similar subpopulations. Information on the technology and R&D
activities of non-reporting fabs was available through the WFF. Information on the use of point-of-use abatement
by non-reporting fabs was not available; thus, EPA conservatively assumed that non-reporting facilities did not use
point-of-use abatement. The appropriate emission factor was applied to the total TMLA of each subpopulation of
non-reporting facilities to estimate the CO26 emissions of that subpopulation.
Gas-specific, GWP-weighted emissions for each subpopulation of non-reporting facilities were estimated using the
corresponding reported distribution of gas-specific, GWP-weighted emissions from which the aggregate emission
factors were developed. Estimated in this manner, the non-reporting population accounted for 22 and 27 percent of
U.S. emissions in 2011 and 2012, respectively. The GHGRP-reported emissions and the calculated non-reporting
population emissions are summed to estimate the total emissions from semiconductor manufacturing.
The methodology used for 2011 and 2012 included, for the first time, emissions from facilities employing Si- and
GaAs-using technologies. The use of GaAs technology became evident via analysis of GHGRP emissions and WFF
data. However, no adjustment of pre-2011 emissions was made because (1) the use of these technologies appears
relatively new, (2) in the aggregate make a relatively small contribution to total industry emissions (i.e., 14 percent
in 2012), and (3) would require a large effort to retroactively adjust pre-2011 emissions.
Data Sources
GHGRP reporters estimated their emissions using a default emission factor method established by EPA. This
method is very similar to the Tier 2b Method in the 2006IPCC Guidelines, but it goes beyond that method by
establishing different default emission and by-product generation factors for different wafer sizes (i.e., 300mm vs.
150 and 200mm) and CVD clean subtypes (in situ thermal, in situ thermal, and remote plasma). Partners estimated
their emissions using a range of methods. It is assumed that most Partners used a method at least as accurate as the
IPCC's Tier 2a Methodology, recommended in the IPCC Guidelines for National Greenhouse Inventories (2006).
Estimates of operating plant capacities and characteristics for Partners and non-Partners were derived from the
165 Only seven gases were aggregated because inclusion of fluorinated GHGs that are not reported in the inventory results in
overestimation of emission factor that is applied to the various non-reporting subpopulations.
Industrial Processes 4-91
-------
Semiconductor Equipment and Materials Industry (SEMI) World Fab Forecast (formerly World Fab Watch)
database (1996 through 2013) (e.g., Semiconductor Materials and Equipment Industry, 2013). Actual capacity
utilizations for 2011 were obtained from Semiconductor International Capacity Statistics (SICAS) (SIA, 2012).
Estimates of the number of layers for each linewidth was obtained from International Technology Roadmap for
Semiconductors: 2011 Update (Burton and Beizaie 2001, ITRS 2007, ITRS 2008, ITRS 2011). PEVM utilized the
World Fab Forecast, SICAS, and ITRS, as well as a historical silicon consumption estimates published by VLSI.
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis of this source category was performed using the IPCC-recommended Tier 2
uncertainty estimation methodology, the Monte Carlo Stochastic Simulation technique. The equation used to
estimate uncertainty is:
Total Emissions (ET) = GHGRP Reported Emissions (ER) + Non-GHGRP Reporters Emissions (ENR)
where ER and ENR denote totals for the indicated subcategories of emissions.
The uncertainty in ET presented in Table 4-97 below results from the convolution of two distributions of emissions,
each reflecting separate estimates of possible values of ER and ENR. The approach and methods for estimating each
distribution and combining them to arrive at the reported 95 percent CI are described in the remainder of this
section.
The uncertainty estimate of ER, or GHGRP reported emissions, is developed based on gas-specific uncertainty
estimates of emissions for two representative model facilities, one processing 200 mm wafers and one processing
300 mm wafers. Uncertainties in emissions for each gas and model facility were developed during the assessment of
emission estimation methods for the subpart I GHGRP rulemaking in 2012 (see Technical Support for Modifications
to the Fluorinated Greenhouse Gas Emission Estimation Method Option for Semiconductor Facilities under Subpart
I, docket EPA-HQ-OAR-2011-0028).166 This analysis did not take into account the use of abatement. For the
model facility that processed 200 mm wafers, estimates of uncertainties at a 95 percent CI ranged from ±29 percent
for CsFg to ±10 percent for CF4. For the corresponding model 300 mm facility, estimates of the 95 percent CI ranged
from ±36 percent for C4p8 to ±16 percent for CF4. These gas and wafer-specific uncertainty estimates are applied for
facilities that did not abate emissions as reported under EPA's GHGRP.
For those facilities reporting abatement of emissions under EPA's GHGRP, estimates of uncertainties for the no
abatement model facilities are modified to reflect the use of full abatement (abatement of all gases from all cleaning
and etching equipment) and partial abatement. These assumptions used to develop uncertainties for the partial and
full abatement facilities are identical for 200 mm and 300 mm wafer processing facilities. For all facilities reporting
gas abatement, a triangular distribution of destruction or removal efficiency is assumed for each gas. For facilities
reporting partial abatement, the distribution of destruction efficiencies, for each gas, is assumed to be right
triangularly distributed. Consideration of abatement then resulted in four additional model facilities, two (model)
200 mm wafer-processing facilities (one fully and one partially abating each gas) and two (model) 300 mm wafer-
processing facilities (one fully and the other partially abating each gas). Gas-specific emission uncertainties were
estimated by convolving the distributions of unabated emissions with the appropriate distribution of abatement
efficiency for fully and partially abated facilities using a Montel Carlo simulation.
166 On November 13, 2013, EPA published a final rule revising subpart I (Electronics Manufacturing) of the GHGRP (78 FR
68162). The revised rule includes updated default emission factors and updated default destruction and removal efficiencies that
are slightly different from those that semiconductor manufacturers were required to use to report their 2012 emissions. The
uncertainty analyses that were performed during the development of the revised rule focused on these updated defaults, but are
expected to be reasonably representative of the uncertainties associated with the older defaults, particularly for estimates at the
country level. (They may somewhat underestimate the uncertainties associated with the older defaults at the facility level.) For
simplicity, the 2012 estimates are assumed to be unbiased although in some cases, the updated (and therefore more
representative) defaults are higher or lower than the older defaults. Multiple models and sensitivity scenarios were run for the
subpart I analysis. The uncertainty analysis presented here made use of the Input gas and wafer size model (Model 1) under the
following conditions: Year = 2010, f = 20, n = SIA3.
4-92 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The uncertainty in ER is obtained by mapping GHGRP-reported gas and wafer-specific emissions to one of the six
described model facilities, and then running a Monte Carlo simulation which results in the 95 percent CI for
GHGRP reporting facilities (ER).
The estimate of uncertainty in ENR entailed developing estimates of uncertainties for the emissions factors for each
non-reporting sub-category and the corresponding estimates of TMLA.
The uncertainty in TMLA depends on the uncertainty of two variables - an estimate of the uncertainty in the average
annual capacity utilization for each level of production of fabs (e.g., full scale or R&D production) and a
corresponding estimate of the uncertainty in the number of layers manufactured. For both variables, the distributions
of capacity utilizations and number of manufactured layers are assumed triangular for all categories of non-reporting
fabs. For production fabs the most probable utilization is assumed to be 89 percent, with the highest and lowest
utilization assumed to be 95 percent and 70 percent, respectively. The corresponding values for facilities that
manufacture discrete devices are, 84 percent, 95 percent, and 73 percent, respectively, while the values for
utilization for R&D facilities, are assumed to be 20 percent, 30 percent, and 10 percent, respectively. For the
triangular distributions that govern the number of possible layers manufactured, it is assumed the most probable
value is one layer less than reported in the ITRS; the smallest number varied by technology generation between one
and two layers less than given in the ITRS and largest number of layers corresponded to the figure given in the
ITRS.
The uncertainty bounds for the average capacity utilization and the number of layers manufactured are used as
inputs in a separate Monte Carlo simulation to estimate the uncertainty around the TMLA of both individual
facilities as well as the total non-reporting TMLA of each sub-population.
The uncertainty around the emission factors for each non-reporting category of facilities is dependent on the
uncertainty of the total emissions (MMTCChe units) and the TMLA of each reporting facility in that category. For
each subpopulation of reporting facilities, total emissions were regressed on TMLA (with an intercept forced to
zero) for 10,000 emissions and 10,000 TMLA values in a Monte Carlo simulation, which results in 10,000 total
regression coefficients (emission factors). The 2.5th and the 97.5th percentile of these emission factors are
determined and the bounds are assigned as the percent difference from the estimated emission factor.
For simplicity, the results of the Monte Carlo simulations on the bounds of the gas- and wafer size-specific
emissions as well as the TMLA and emission factors are assumed to be normally distributed and the uncertainty
bounds are assigned at 1.96 standard deviations around the estimated mean. The departures from normality were
observed to be small.
The final step in estimating the uncertainty in emissions of non-reporting facilities is convolving the distribution of
emission factors with the distribution of TMLA using Monte Carlo simulation.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-97, which is also obtained by
convolving—using Monte Carlo simulation—the distributions of emissions for each reporting and non-reporting
facility.. The emissions estimate for total U.S. PFC emissions from semiconductor manufacturing were estimated to
be between 3.6 and 3.9 Tg CO2 Eq. at a 95 percent confidence level. This range represents 5 percent below to 5
percent above the 2011 emission estimate of 3.7 Tg CO2 Eq. This range and the associated percentages apply to the
estimate of total emissions rather than those of individual gases. Uncertainties associated with individual gases will
be somewhat higher than the aggregate, but were not explicitly modeled.
Table 4-97: Tier 2 Quantitative Uncertainty Estimates for HFC, PFC, and SFe Emissions from
Semiconductor Manufacture (Tg COz Eq. and Percent)
Source
Semiconductor
Manufacture
2012 Emission
Gas Estimate3
(Tg C02 Eq.)
HFC,
PFC, and , 7
SF6
Uncertainty Range Relative to Emission Estimate1"
(Tg C02 Eq.) (%)
Lower
Bound0
3.6
Upper
Bound0
3.9
Lower
Bound
-5%
Upper
Bound
5%
a Because the uncertainty analysis covered all emissions (including NFs), the emission estimate presented here
does not match that shown in Table 4-95.
Industrial Processes 4-93
-------
b Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence
interval.
c Absolute lower and upper bounds were calculated using the corresponding lower and upper bounds in percentages.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Emissions for 2000 through 2010 were recalculated to ensure time-series consistency. No recalculation methods
were applied to emission estimates prior to 2000 because it is assumed that this data is representative of emissions
during that time. In previous inventories, non-Partner emissions were estimated based on data from the late 1990s
(i.e., the original PEVM emission factor). In looking at new industry emission factors using the GHGRP data as
compared to the PEVM emission factor, it is clear there has been a decrease in the amount of fluorinated gases
emitted per TMLA over the 2000 to 2011 time period. This is likely due to processes becoming more efficient and
the use of new technology, specifically remote chamber clean as opposed to traditional in-situ chamber cleans. The
non-Partner portion of total industry emissions was therefore recalculated as described under "2000 through 2006"
above. The use of remote chamber clean also introduces change is the types of process gas used. To adjust for the
shift in gas usage, gas-specific distribution for years 2000 to 2010 were also updated by interpolating between years
1999 and 2011 as described under "2000 through 2006" above. Additionally, the 2011 emission estimates were
revised to incorporate GHGRP data; the previous Inventory estimated 2011 emissions by extrapolating 2010 Partner
data and using PEVM for non-Partners.
Planned Improvements
This Inventory has estimates of seven fluorinated gases for semiconductor manufacturing. However, other
fluorinated gases (e.g., CsF8) are used in relatively smaller, but significant amounts. Previously, emissions data for
these other fluorinated gases was not reported through the EPA Partnership. Through EPA's GHGRP, these data, as
well as N2O and heat transfer fluid emission data, are available. Therefore, a point of consideration for future
inventories is the inclusion of other fluorinated gases, N2O, and emissions from heat transfer fluid (HTF) loss to the
atmosphere.
N2O is mainly used for the chemical vapor deposition process. Deposition is a fundamental step in the fabrication of
a variety of electronic devices. During deposition, layers of dielectric, barrier, or electrically conductive films are
deposited or grown on a wafer or other substrate. Chemical vapor deposition (CVD) enables the deposition of
dielectric or metal films. During the CVD process, gases that contain atoms of the material to be deposited react on
the wafer surface to form a thin film of solid material. Films deposited by CVD may be silicon oxide, single-layer
crystal epitaxial silicon, amorphous silicon, silicon nitride, dielectric anti-reflective coatings, low-k dielectric,
aluminum, titanium, titanium nitride, polysilicon, tungsten, refractory metals or silicides. Higher number of layers
means more deposition steps are required during the manufacturing stage, leading to more emissions. Emissions
from N2O usage can be estimated by developing an emission factor based on GHGRP-reported data per units of
TMLA, as is done with other F-GHGs. N2O may be the oxidizer of choice during deposition of silicon oxide films.
N2O may also be used in other manufacturing processes.
Fluorinated heat transfer fluids, of which some are liquid perfluorinated compounds, are used for temperature
control, device testing, cleaning substrate surfaces and other parts, and soldering in certain types of semiconductor
manufacturing production processes. Evaporation of these fluids is a source of fluorinated emissions (EPA 2006).
The GHGRP-reported HTF emissions along with WFF database could be used to develop emission factors for
identified subpopulations. Further research needs to be done to determine if the same subpopulations identified in
developing new emission factors for f-GHGs are applicable or new subpopulations have to be studied as HTFs are
used primarily by manufacturers of wafer size 300 mm and above.
Along with more emissions information for semiconductor manufacturing, EPA's GHGRP requires the reporting of
emissions from other types of electronics manufacturing, including micro-electro-mechanical systems, flat panel
displays, and photovoltaic cells. There currently are no flat panel displays, and photovoltaic cell manufacturing
4-94 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
facilities that are reporting to EPA's GHGRP, and five reporting MEMs manufacturers. The MEMs manufacturers
also report emissions from semiconductor manufacturing and do not distinguish between these two types of
manufacturing in their report; thus, emissions from MEMs manufacturers are included in the totals here. EPA may
consider including emissions from manufacturing of flat panel displays and photovoltaic cells in future inventories;
however, estimation methodologies would need to be developed.
4.24 Electrical Transmission and Distribution
(IPCC Source Category 2F7)
The largest use of SF6, both in the United States and internationally, is as an electrical insulator and interrupter in
equipment that transmits and distributes electricity (PxAND 2004). The gas has been employed by the electric power
industry in the United States since the 1950s because of its dielectric strength and arc-quenching characteristics. It
is used in gas-insulated substations, circuit breakers, and other switchgear. Sulfur hexafluoride has replaced
flammable insulating oils in many applications and allows for more compact substations in dense urban areas.
Fugitive emissions of SF6 can escape from gas-insulated substations and switchgear through seals, especially from
older equipment. The gas can also be released during equipment manufacturing, installation, servicing, and
disposal. Emissions of SF6 from equipment manufacturing and from electrical transmission and distribution systems
were estimated to be 6.0 Tg CCh Eq. (0.2 Gg) in 2012. This quantity represents a 77 percent decrease from the
estimate for 1990 (see Table 4-98 and Table 4-99). This decrease is believed to have two causes: a sharp increase in
the price of SF6 during the 1990s and a growing awareness of the environmental impact of SF6 emissions through
programs such as EPA's SF6 Emission Reduction Partnership for Electric Power Systems.
Table 4-98: SFe Emissions from Electric Power Systems and Electrical Equipment
Manufacturers (Tg COz Eq.)
Year
Electric Power
Systems
Electrical Equipment
Manufacturers
Total
1990
26.3
26.7
2008
2009
2010
2011
2012
7.2
6.9
6.4
5.9
4.8
1.2
0.6
0.8
1.3
1.2
8.4
7.5
7.2
7.2
6.0
Note: Totals may not sum due to independent rounding.
Table 4-99: SFe Emissions from Electric Power Systems and Electrical Equipment
Manufacturers (Gg)
Year
Emissions
1990
2008
2009
2010
2011
2012
0.4
0.3
0.3
0.3
0.3
Industrial Processes 4-95
-------
The estimates of emissions from Electrical Transmission and Distribution are comprised of emissions from electric
power systems and emissions from the manufacture of electrical equipment. The methodologies for estimating both
sets of emissions are described below.
This year's inventory, like the 1990-2011 inventory, incorporates emission estimates from electric power systems
reported through EPA's GHGRP. In the most recent year of reporting, utilities were required to submit reports for
2012 and resubmit reports for 2011 with additional data elements, including the decrease in SF6 inventory,
purchases of SF6, disbursements of SF6, and net increase in total nameplate capacity of equipment operated. This
allowed inclusion of GHGRP data on nameplate capacity and purchases in the inventory.167
1999 through 2012 Emissions from Electric Power Systems
Emissions from electric power systems from 1999 to 2012 were estimated based on: (1) reporting from utilities
participating in EPA's SF6 Emission Reduction Partnership for Electric Power Systems (Partners), which began in
1999; (2) reporting from utilities required to report under the EPA's GHGRP, which began in 2012 for emissions
occurring in 2011 (GHGRP-Only Reporters); and (3) the relationship between utilities' reported emissions and their
transmission miles as reported in the 2001, 2004, 2007, 2010, and 2013 Utility Data Institute (UDI) Directories of
Electric Power Producers and Distributors (UDI 2001, 2004, 2007, 2010, 2013), which was applied to the electric
power systems that do not report to EPA (Non-Reporters). (Transmission miles are defined as the miles of lines
carrying voltages above 34.5 kV).
Partners
Over the period from 1999 to 2012, Partner utilities, which for inventory purposes are defined as utilities that either
currently are or previously have been part of the Partnership, represented between 43 percent and 48 percent of total
U.S. transmission miles. Partner utilities estimated their emissions using a Tier 3 utility-level mass balance
approach (IPCC 2006). If a Partner utility did not provide data for a particular year, emissions were interpolated
between years for which data were available or extrapolated based on Partner-specific transmission mile growth
rates. In 2012, many Partners reported their emissions through EPA's GHGRP (discussed further below) rather than
through the Partnership. In 2012, approximately 0.7 percent of the total emissions attributed to Partner utilities were
reported through Partnership reports. Approximately 92 percent of the total emissions attributed to Partner utilities
were reported and verified through EPA's GHGRP. Partners without verified 2012 data accounted for
approximately 8 percent of the total emissions attributed to Partner utilities.168
GHGRP-Only Reporters
167 por GHGRP reporters, an end-of-year nameplate was calculated by summing the beginning of year nameplate capacity
(which excludes hermetically sealed-pressure equipment) and the net increase in nameplate capacity (which includes hermetically
sealed-pressure equipment). Although there are concerns with summing these two metrics due to their differential use of
hermetically-sealed pressure switchgear, this remains the best possible approach for ensuring time series consistency and using
an "end-of-year" nameplate capacity estimate.
168 It should be noted that data reported through the GHGRP must go through a verification process; only data verified as of
September 1, 2013 could be used in the emission estimates for 2011 and 2012. For Partners whose GHGRP data was not yet
verified, emissions were extrapolated based upon historical Partner-specific transmission mile growth rates, and those Partners
are included in the 'non-reporting Partners' category.
For electric power systems, verification involved a series of electronic range, completeness, and algorithm checks for each report
submitted. In addition, EPA manually reviewed the reported data and compared each facility's reported transmission miles with
the corresponding quantity in the UDI 2013 database (UDI 2013). In the first year of GHGRP reporting, EPA followed up with
reporters where the discrepancy between the reported miles and the miles published by UDI was greater than 10 percent, with a
goal to improve data quality. Only GHGRP data verified as of September 1,2013 was included in the emission estimates for
201 land 2012.
4-96 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
EPA's GHGRP requires users of SF6 in electric power systems to report emissions if the facility has a total SF6
nameplate capacity that exceeds 17,820 pounds. (This quantity is the nameplate capacity that would result in annual
SF6 emissions equal to 25,000 metric tons of CCh equivalent at the historical emission rate reported under the
Partnership.) As under the Partnership, electric power systems that report their SF6 emissions under EPA's GHGRP
are required to use the Tier 3 utility-level mass-balance approach. Many Partners began reporting their emissions
through EPA's GHGRP in 2012 because their nameplate capacity exceeded the reporting threshold. Partners who
did not report through EPA's GHGRP continued to report through the Partnership.
In addition, many non-Partners began reporting to EPA for the first time through its GHGRP in 2012. Non-Partner
emissions reported and verified under EPA's GHGRP were compiled to form a new category of reported data
(GHGRP-Only Reporters). GHGRP-Only Reporters accounted for 23 percent of U.S. transmission miles and 15
percent of estimated U.S. emissions from electric power system in 2012.169
Non-Reporters
Emissions from Non-Reporters (i.e., utilities other than Partners and GHGRP-Only Reporters) in every year since
1999 were estimated using the results of a regression analysis that correlated emissions from reporting utilities
(using verified data from both Partners and GHGRP-Only Reporters) with their transmission miles. 17° Two
equations were developed, one for "non-large" and one for "large" utilities (i.e., with fewer or greater than 10,000
transmission miles, respectively). The distinction between utility sizes was made because the regression analysis
showed that the relationship between emissions and transmission miles differed for non-large and large transmission
networks. As noted above, non-Partner emissions were reported to the EPA for the first time through its GHGRP in
2012 (representing 2011 emissions). This data was of particular interest because it provided insight into the
emission rate of non-Partners, which previously was assumed to be equal to the historical (1999) emission rate of
Partners for both large and non-large utilities.171 The availability of non-Partner emissions estimates allowed the
regression analysis to be modified for both groups. Specifically, emissions were estimated for Non-Reporters as
follows:
• Non-Reporters. 1999 to 2011: First, the 2011 emission rates (per kg nameplate capacity and per
transmission mile) reported by Partners and GHGRP-Only Reporters were reviewed to determine whether
there was a statistically significant difference between these two groups. Transmission mileage data for
2011 was reported through GHGRP, with the exception of transmission mileage data for Partners that did
not report through GHGRP, which was obtained from UDI. It was determined that there is no statistically
significant difference between the emission rates of Partners and GHGRP-Only reporters; therefore, Partner
and GHGRP-Only reported data for 2011 were combined to develop regression equations to estimate the
emissions of Non-Reporters for both "non-large" and "large" utilities. Historical emissions from Non-
Reporters for both "non-large" and "large" utilities were estimated by linearly interpolating between the
1999 regression coefficients (based on 1999 Partner data) and the 2011 regression coefficients.
• Non-Reporters. 2012: It was determined that there continued to be no statistically significant difference
between the emission rates reported by Partners and by GHGRP-Only Reporters. Therefore, the emissions
data from both groups were combined to develop regression equations for 2012.
o "Non-large " utilities (less than 10,000 transmission miles): The regression equation for "non-large"
utilities was developed based on the emissions reported by a subset of 47 Partner utilities and 50
169 It should also be noted that GHGRP-reported emissions from twelve facilities that did not have any associated transmission
miles were included in the emissions estimates for 2011. Emissions from these facilities comprise approximately 0.3 percent of
total reported and verified emissions. These facilities were not included in the development of the regression equations
(discussed further below). EPA is continuing to investigate whether or not these emissions are already implicitly accounted for in
the relationship between transmission miles and emissions, and whether to update the regression analysis to better capture
emissions from non-reporters that may have zero transmission miles.
170 In the United States, SFe is contained primarily in transmission equipment rated above 34.5 kV.
171 Partners in EPA's SFe Emission Reduction Partnership reduced their emissions by approximately 68 percent from 1999 to
2011 and 74 percent from 1999 to 2012.
Industrial Processes 4-97
-------
GHGRP-Only utilities (representing approximately 44 percent of total U.S. transmission miles for
utilities with fewer than 10,000 transmission miles). The regression equation for 2012 is:
Emissions (kg) = 0.23 x Transmission Miles
o "Large " utilities (more than 10,000 transmission miles): The regression equation was developed based
on the emissions reported by a subset of 13 Partner utilities and 7 non-Partner utilities (representing
approximately 88 percent of total U.S. transmission miles for utilities with greater than 10,000
transmission miles). The regression equation for 2012 is:
Emissions (kg) = 0.27 x Transmission Miles
Table 4-100 below shows the percentage of transmission miles covered by reporters (i.e., associated with reported
data) and the regression coefficient for both large and non-large reporters for 1999 (the first year data was reported),
2011 (the first year with GHGRP reported data), and 2012 (the most recent year of data).
Table 4-100 Transmission Mile Coverage and Regression Coefficients for Large and Non-
Large Utilities, Percent
Percentage of Miles
Covered by Reporters
Regression
Coefficient3
1999
Non-large Large
31 86
0.89 0.58
2011
Non-large Large
45 97
0.33 0.26
2012
Non-large Large
44 88
0.23 0.27
a Regression coefficient is defined as emissions (in kg) divided by transmission miles.
The coefficient for non-large utilities has dropped rather dramatically between 2011 and 2012 from 0.33 to 0.23 due
to a large decrease in Partner and GHGRP-only reported emissions, primarily from the largest emitters, and an
increase in transmission miles.
Data on transmission miles for each Non-Reporter for the years 2000, 2003, 2006, and 2009, and 2012 were
obtained from the 2001, 2004, 2007, 2010, and 2013 UDI Directories of Electric Power Producers and Distributors,
respectively (UDI 2001, 2004, 2007, 2010, 2013). The U.S. transmission system grew by over 25,000 miles
between 2000 and 2003 and by only 2,400 miles between 2003 and 2006. These periodic increases are assumed to
have occurred gradually. Therefore, transmission mileage was assumed to increase at an annual rate of 1.3 percent
between 2000 and 2003 and 0.1 percent between 2003 and 2006. This growth rate grew to 2.8 percent from 2006 to
2009 as transmission miles increased by 56,000 miles (approximately). The annual growth rate for 2009 through
2012 was calculated to be 2.7 percent as transmission miles grew by approximately 58,000 during this time period.
Total Industry Emissions
As a final step, total electric power system emissions from 1999 through 2012 were determined for each year by
summing the Partner reported and estimated emissions (reported data was available through the EPA's SF6 Emission
Reduction Partnership for Electric Power Systems), the GHGRP-Only reported emissions, and the non-reporting
utilities' emissions (determined using the regression equations).
1990 through 1998 Emissions from Electric Power Systems
Because most utilities participating in the Partnership reported emissions only for 1999 through 2011, modeling was
used to estimate SF6 emissions from electric power systems for the years 1990 through 1998. To perform this
modeling, U.S. emissions were assumed to follow the same trajectory as global emissions from this source during
the 1990 to 1999 period. To estimate global emissions, the RAND survey of global SF6 sales were used, together
with the following equation for estimating emissions, which is derived from the mass-balance equation for chemical
emissions (Volume 3, Equation 7.3) in the IPCC Guidelines for National Greenhouse Gas Inventories (IPCC
4-98 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
2006).172 (Although equation 7.3 of the IPCC Guidelines appears in the discussion of substitutes for ozone-
depleting substances, it is applicable to emissions from any long-lived pressurized equipment that is periodically
serviced during its lifetime.)
Emissions (kilograms SF6) = SF6 purchased to refill existing equipment (kilograms) + nameplate capacity of retiring
equipment (kilograms)173
Note that the above equation holds whether the gas from retiring equipment is released or recaptured; if the gas is
recaptured, it is used to refill existing equipment, thereby lowering the amount of SF6 purchased by utilities for this
purpose.
Gas purchases by utilities and equipment manufacturers from 1961 through 2003 are available from the RAND
(2004) survey. To estimate the quantity of SF6 released or recovered from retiring equipment, the nameplate
capacity of retiring equipment in a given year was assumed to equal 81.2 percent of the amount of gas purchased by
electrical equipment manufacturers 40 years previous (e.g., in 2000, the nameplate capacity of retiring equipment
was assumed to equal 81.2 percent of the gas purchased in 1960). The remaining 18.8 percent was assumed to have
been emitted at the time of manufacture. The 18.8 percent emission factor is an average of IPCC default SF6
emission rates for Europe and Japan for 1995 (IPCC 2006). The 40-year lifetime for electrical equipment is also
based on IPCC (2006). The results of the two components of the above equation were then summed to yield
estimates of global SF6 emissions from 1990 through 1999.
U.S. emissions between 1990 and 1999 are assumed to follow the same trajectory as global emissions during this
period. To estimate U.S. emissions, global emissions for each year from 1990 through 1998 were divided by the
estimated global emissions from 1999. The result was a time series of factors that express each year's global
emissions as a multiple of 1999 global emissions. Historical U.S. emissions were estimated by multiplying the
factor for each respective year by the estimated U.S. emissions of SF6 from electric power systems in 1999
(estimated to be 15.0 Tg CO2 Eq.).
Two factors may affect the relationship between the RAND sales trends and actual global emission trends. One is
utilities' inventories of SF6 in storage containers. When SF6 prices rise, utilities are likely to deplete internal
inventories before purchasing new SF6 at the higher price, in which case SF6 sales will fall more quickly than
emissions. On the other hand, when SF6 prices fall, utilities are likely to purchase more SF6 to rebuild inventories,
in which case sales will rise more quickly than emissions. This effect was accounted for by applying 3-year
smoothing to utility SF6 sales data. The other factor that may affect the relationship between the RAND sales trends
and actual global emissions is the level of imports from and exports to Russia and China. SF6 production in these
countries is not included in the RAND survey and is not accounted for in any another manner by RAND. However,
atmospheric studies confirm that the downward trend in estimated global emissions between 1995 and 1998 was real
(see the Uncertainty discussion below).
1990 through 2012 Emissions from Manufacture of Electrical Equipment
The 1990 to 2012 emission estimates for original equipment manufacturers (OEMs) were derived by assuming that
manufacturing emissions equal 10 percent of the quantity of SF6 provided with new equipment. The quantity of SF6
provided with new equipment was estimated based on statistics compiled by the National Electrical Manufacturers
Association (NEMA). These statistics were provided for 1990 to 2000; the quantities of SF6 provided with new
equipment for 2001 to 2012 were estimated using Partner reported data and the total industry SF6 nameplate
capacity estimate (147.7 Tg CO2 Eq. in 2012). Specifically, the ratio of new nameplate capacity to total nameplate
capacity of a subset of Partners for which new nameplate capacity data was available from 1999 to 2012 was
calculated. These ratios were then multiplied by the total industry nameplate capacity estimate for each year to
derive the amount of SF6 provided with new equipment for the entire industry. The 10 percent emission rate is the
average of the "ideal" and "realistic" manufacturing emission rates (4 percent and 17 percent, respectively)
172 Ideally, sales to utilities in the U.S. between 1990 and 1999 would be used as a model. However, this information was not
available. There were only two U.S. manufacturers of SFe during this time period, so it would not have been possible to conceal
sensitive sales information by aggregation.
173 Nameplate capacity is defined as the amount of SF
-------
identified in a paper prepared under the auspices of the International Council on Large Electric Systems (CIGRE) in
February 2002 (O'Connell et al. 2002).
Uncertainty and Time-Series Consistency
To estimate the uncertainty associated with emissions of SF6 from Electrical Transmission and Distribution,
uncertainties associated with four quantities were estimated: (1) emissions from Partners, (2) emissions from
GHGRP-Only Reporters, (3) emissions from Non-Reporters, and (4) emissions from manufacturers of electrical
equipment. A Monte Carlo analysis was then applied to estimate the overall uncertainty of the emissions estimate.
Total emissions from the SF6 Emission Reduction Partnership include emissions from both reporting (through the
Partnership or GHGRP) and non-reporting Partners. For reporting Partners, individual Partner-reported SF6 data
was assumed to have an uncertainty of 10 percent. Based on a Monte Carlo analysis, the cumulative uncertainty of
all Partner-reported data was estimated to be 2.5 percent. The uncertainty associated with extrapolated or
interpolated emissions from non-reporting Partners was assumed to be 20 percent.
For GHGRP-Only Reporters, reported SF6 data was assumed to have an uncertainty of 20 percent.174 Based on a
Monte Carlo analysis, the cumulative uncertainty of all GHGRP-Only reported data was estimated to be 5.2 percent.
There are two sources of uncertainty associated with the regression equations used to estimate emissions in 2012
from Non-Reporters: (1) uncertainty in the coefficients (as defined by the regression standard error estimate), and
(2) the uncertainty in total transmission miles for Non-Reporters. Uncertainties were also estimated regarding (1)
the quantity of SF6 supplied with equipment by equipment manufacturers, which is projected from Partner provided
nameplate capacity data and industry SF6 nameplate capacity estimates, and (2) the manufacturers' SF6 emissions
rate.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-101. Electrical Transmission
and Distribution SF6 emissions were estimated to be between 4.9 and 7.5 Tg COa Eq. at the 95 percent confidence
level. This indicates a range of approximately 18 percent below and 25 percent above the emission estimate of 6.0
Tg CO2 Eq.
Table 4-101: Tier 2 Quantitative Uncertainty Estimates for SFe Emissions from Electrical
Transmission and Distribution (Tg COz Eq. and Percent)
2012 Emission
Source Gas Estimate Uncertainty Range Relative to 2012 Emission Estimate3
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Upper
Lower Bound Upper Bound Lower Bound Bound
Electrical Transmission
and Distribution SFe 6.0 49 7.5 -18% +25%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
In addition to the uncertainty quantified above, there is uncertainty associated with using global SF6 sales data to
estimate U.S. emission trends from 1990 through 1999. However, the trend in global emissions implied by sales of
SF6 appears to reflect the trend in global emissions implied by changing SF6 concentrations in the atmosphere. That
is, emissions based on global sales declined by 29 percent between 1995 and 1998 (RAND 2004), and emissions
based on atmospheric measurements declined by 17 percent over the same period (Levin et al. 2010).
Several pieces of evidence indicate that U.S. SF6 emissions were reduced as global emissions were reduced. First,
the decreases in sales and emissions coincided with a sharp increase in the price of SF6 that occurred in the mid-
1990s and that affected the United States as well as the rest of the world. A representative from DILO, a major
manufacturer of SF6 recycling equipment, stated that most U.S. utilities began recycling rather than venting SF6
within two years of the price rise. Finally, the emissions reported by the one U.S. utility that reported its emissions
174 Uncertainty is assumed to be higher for the GHGRP-Only category, because 2011 is the first year that those utilities have
reported to EPA.
4-100 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
for all the years from 1990 through 1999 under the Partnership showed a downward trend beginning in the mid-
1990s.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
The historical emissions estimated for this source category have undergone significant revisions. First, in the
current inventory, SF6 emission estimates for the period 1990 through 2011 were updated relative to the previous
report based on revisions to interpolated and extrapolated non-reported Partner data and transmission mile data from
UDI. Second, the previously-described interpolation between 1999 and 2011 regression coefficients to estimate
emissions from non-reporting utilities with fewer than 10,000 transmission miles was updated using revised GHGRP
reports, which impacted historical estimates for the period 2000 through 2011. Third, the previously-described
interpolation between 1999 and 2011 regression coefficients to estimate emissions from non-reporting utilities with
greater than 10,000 transmission miles significantly impacted historical estimates for the period 2000 through 2011.
Previously, a conservative coefficient had been used to estimate non-Partner emissions for the period 2000 through
2011 that proved too high once GHGRP-reported data was analyzed for the 2011 reporting year.
Additionally, changes were made to the internal methodology for estimating Non-Reporter nameplate capacity. In
2012, nameplate capacity reported through GHGRP was accessible for the first time. Therefore, the nameplate of
GHGRP-Only Reporters could be separated from Non-Reporters. In order to do this, new leak rates were estimated
for Non-Reporters in 2011 and 2012 using Partner data, and interpolated back through 1999 to calculate Non-
Reporter nameplate capacity over the entire time series.
As a result of the above changes, SF6 emissions from electrical transmission and distribution decreased by 5 percent
for 2011 relative to the previous report.
Planned Improvements
EPA is exploring the use of OEM data from GHGRP subpart SS to use for future inventory reports instead of
estimating those emissions based on elements reported through subpart DD and Partner data. In implementing
improvements and integration of data from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-
level data in national inventories will be relied upon.175
Box 4-2: Potential Emission Estimates of MFCs, PFCs, and SF6
Emissions of HFCs, PFCs and SF6 from industrial processes can be estimated in two ways, either as potential
emissions or as actual emissions. Emission estimates in this chapter are "actual emissions," which are defined by
the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC/UNEP/OECD/IEA 1997) as
estimates that take into account the time lag between consumption and emissions. In contrast, "potential emissions"
are defined to be equal to the amount of a chemical consumed in a country, minus the amount of a chemical
recovered for destruction or export in the year of consideration. Potential emissions will generally be greater for a
given year than actual emissions, since some amount of chemical consumed will be stored in products or equipment
and will not be emitted to the atmosphere until a later date, if ever. Although actual emissions are considered to be
the more accurate estimation approach for a single year, estimates of potential emissions are provided for
informational purposes.
Separate estimates of potential emissions were not made for industrial processes that fall into the following
categories:
175
See.
Industrial Processes 4-101
-------
• By-product emissions. Some emissions do not result from the consumption or use of a chemical, but are
the unintended by-products of another process. For such emissions, which include emissions of CF4 and
C2F6 from aluminum production and of HFC-23 from HCFC-22 production, the distinction between
potential and actual emissions is not relevant.
• Potential emissions that equal actual emissions. For some sources, such as magnesium production and
processing, no delay between consumption and emission is assumed and, consequently, no destruction of
the chemical takes place. In this case, actual emissions equal potential emissions.
Table 4-102 presents potential emission estimates for HFCs and PFCs from the substitution of ozone depleting
substances, HFCs, PFCs, and SF6 from semiconductor manufacture, and SF6 from magnesium production and
processing and electrical transmission and distribution.176 Potential emissions associated with the substitution for
ozone depleting substances were calculated using the EPA's Vintaging Model. Estimates of HFCs, PFCs, and SF6
consumed by semiconductor manufacture were developed by dividing chemical-by-chemical emissions by the
appropriate chemical-specific emission factors from the IPCC Good Practice Guidance (Tier 2c). Estimates of CF4
consumption were adjusted to account for the conversion of other chemicals into CF4 during the semiconductor
manufacturing process, again using the default factors from the IPCC Good Practice Guidance. Potential SF6
emissions estimates for electrical transmission and distribution were developed using U.S. utility purchases of SF6
for electrical equipment. From 1999 through 2007, estimates were obtained from reports submitted by participants in
EPA's SF6 Emission Reduction Partnership for Electric Power Systems. U.S. utility purchases of SF6 for electrical
equipment from 1990 through 1998 were backcasted based on world sales of SF6 to utilities. Purchases of SF6 by
utilities were added to SF6 purchases by electrical equipment manufacturers to obtain total SF6 purchases by the
electrical equipment sector.
Table 4-102: 2012 Potential and Actual Emissions of HFCs, PFCs, and SFe from Selected
Sources (Tg CO2 Eg.)
Source
Substitution of Ozone Depleting Substances
Aluminum Production
HCFC-22 Production
Semiconductor Manufacture
Magnesium Production and Processing
Electrical Transmission and Distribution
Potential
272.4
NA
NA
12.92
1.7
15.7
Actual
146.8
2.5
4.3
3.7
1.7
6.0
- Not applicable.
4.25 Industrial Sources of Indirect
Greenhouse Gases
In addition to the main greenhouse gases addressed above, many industrial processes generate emissions of indirect
greenhouse gases. Total emissions of nitrogen oxides (NOX), carbon monoxide (CO), and non-CH4 volatile organic
compounds (NMVOCs) from non-energy industrial processes from 1990 to 2012 are reported in Table 4-103.
Table 4-103: NOX, CO, and NMVOC Emissions from Industrial Processes (Gg)
Gas/Source
NOx
Other Industrial Processes
Metals Processing
Chemical and Allied Product
1990
591
343 1
88
152 |
2005
566
434
60 1
55 |
2008
510
377
72
50
2009
488
356
69
48
2010
466
335
67
47
2011
444
315
64
45
2012
444
315
64
45
176 See Annex 5 for a discussion of sources of SFe emissions excluded from the actual emissions estimates in this report.
4-102 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Manufacturing
Storage and Transport
Miscellaneous*
CO
Metals Processing
Other Industrial Processes
Chemical and Allied Product
Manufacturing 1,073 189 166 161 156 152 152
Storage and Transport 69 97 16 19 21 24 24
Miscellaneous* 101 32 45 47 49 56 56
NMVOCs 2,422 I 1,982 1,548 1,544 1,540 1,538 1,538
Storage and Transport 1,352 1,293 1,082 1,090 1,099 1,107 1,107
Other Industrial Processes 364 414 329 318 308 298 298
Chemical and Allied Product
Manufacturing 575 213 80 77 74 72 72
Metals Processing 111 45 34 33 32 31 31
Miscellaneous* 20 17 I 24 25 26 30 30
* Miscellaneous includes the following categories: catastrophic/accidental release, other combustion,
health services, cooling towers, and fugitive dust. It does not include agricultural fires or slash/prescribed
burning, which are accounted for under the Field Burning of Agricultural Residues source.
Note: Totals may not sum due to independent rounding.
Methodology
Emission estimates for 1990 through 2012 were obtained from data published on the National Emission Inventory
(NEI) Air Pollutant Emission Trends web site (EPA 2013), and disaggregated based on EPA (2003). Emission
estimates for 2012 for non-EGU and non-mobile sources are held constant from 2011 in EPA (2013). Emissions
were calculated either for individual categories or for many categories combined, using basic activity data (e.g., the
amount of raw material processed) as an indicator of emissions. National activity data were collected for individual
categories from various agencies. Depending on the category, these basic activity data may include data on
production, fuel deliveries, raw material processed, etc.
Activity data were used in conjunction with emission factors, which together relate the quantity of emissions to the
activity. Emission factors are generally available from the EPA's Compilation of Air Pollutant Emission Factors,
AP-42 (EPA 1997). The EPA currently derives the overall emission control efficiency of a source category from a
variety of information sources, including published reports, the 1985 National Acid Precipitation and Assessment
Program emissions inventory, and other EPA databases.
Uncertainty and Time-Series Consistency
Uncertainties in these estimates are partly due to the accuracy of the emission factors and activity data used. A
quantitative uncertainty analysis was not performed.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Industrial Processes 4-103
-------
-------
5. Solvent and Other Product Use
Greenhouse gas emissions are produced as a by-product of various solvent and other product uses. In the United
States, emissions from Nitrous Oxide (N2O) Product Uses, the only source of greenhouse gas emissions from this
sector, accounted for less than 0.1 percent of total U.S. anthropogenic greenhouse gas emissions on a CO2 equivalent
basis in 2012 (see Table 5-1). Indirect greenhouse gas emissions also result from solvent and other product use, and
are presented in Table 5-5 in gigagrams (Gg).
Table 5-1: NzO Emissions from Solvent and Other Product Use
Gas/Source
N2O from Product Uses
Tg C02 Eq.
Gg
1990
4.4 1
14
2005
4.4
14 |
2008
4.4
14
2009
4.4
14
2010
4.4
14
2011
4.4
14
2012
4.4
14
5.1 Nitrous Oxide from Product Uses (IPCC
Source Category 3D)
N2O is a clear, colorless, oxidizing liquefied gas, with a slightly sweet odor which is used in a wide variety of
specialized product uses and applications. The amount of N2O that is actually emitted depends upon the specific
product use or application.
There are a total of three N2O production facilities currently operating in the United States (Ottinger 2014). N2O is
primarily used in carrier gases with oxygen to administer more potent inhalation anesthetics for general anesthesia,
and as an anesthetic in various dental and veterinary applications. The second main use of N2O is as a propellant in
pressure and aerosol products, the largest application being pressure-packaged whipped cream. Small quantities of
N2O also are used in the following applications:
• Oxidizing agent and etchant used in semiconductor manufacturing;
• Oxidizing agent used, with acetylene, in atomic absorption spectrometry;
• Production of sodium azide, which is used to inflate airbags;
• Fuel oxidant in auto racing; and
• Oxidizing agent in blowtorches used by jewelers and others (Heydorn 1997).
Production of N2O in 2012 was approximately 15 Gg (Table 5-2).
Table 5-2: N2O Production (Gg)
Year Gg
1990 16
Solvent and Other Product Use 5-1
-------
2008
2009
2010
2011
2012
15
15
15
15
15
N2O emissions were 4.4 Tg CO2 Eq. (14 Gg) in 2012 (Table 5-3). Production of N2O stabilized during the 1990s
because medical markets had found other substitutes for anesthetics, and more medical procedures were being
performed on an outpatient basis using local anesthetics that do not require N2O. The use of N2O as a propellant for
whipped cream has also stabilized due to the increased popularity of cream products packaged in reusable plastic
tubs (Heydorn 1997).
Table 5-3: NzO Emissions from NzO Product Usage (Tg COz Eq. and Gg)
Year Tg CCh Eq. Gg
1990 4.4 14
2008
2009
2010
2011
2012
4.4
4.4
4.4
4.4
4.4
14
14
14
14
14
Methodology
Emissions from N2O product uses were estimated using the following equation:
where,
p
-L^pU
P
a
Sa
ERa
N2O emissions from product uses, metric tons
Total U.S. production of N2O, metric tons
specific application
Share of N2O usage by application a
Emission rate for application a, percent
The share of total quantity of N2O usage by end use represents the share of national N2O produced that is used by
the specific subcategory (i.e., anesthesia, food processing, etc.). In 2012, the medical/dental industry used an
estimated 89.5 percent of total N2O produced, followed by food processing propellants at 6.5 percent. All other
categories combined used the remainder of the N2O produced. This subcategory breakdown has changed only
slightly over the past decade. For instance, the small share of N2O usage in the production of sodium azide has
declined significantly during the 1990s. Due to the lack of information on the specific time period of the phase-out
in this market subcategory, most of the N2O usage for sodium azide production is assumed to have ceased after
1996, with the majority of its small share of the market assigned to the larger medical/dental consumption
subcategory (Heydorn 1997). The N2O was allocated across the following categories: medical applications, food
processing propellant, and sodium azide production (pre-1996). A usage emissions rate was then applied for each
sector to estimate the amount of N2O emitted.
5-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Only the medical/dental and food propellant subcategories were estimated to release emissions into the atmosphere,
and therefore these subcategories were the only usage subcategories with emission rates. For the medical/dental
subcategory, due to the poor solubility of N2O in blood and other tissues, none of the N2O is assumed to be
metabolized during anesthesia and quickly leaves the body in exhaled breath. Therefore, an emission factor of 100
percent was used for this subcategory (IPCC 2006). For N2O used as a propellant in pressurized and aerosol food
products, none of the N2O is reacted during the process and all of the N2O is emitted to the atmosphere, resulting in
an emission factor of 100 percent for this subcategory (IPCC 2006). For the remaining subcategories, all of the N2O
is consumed/reacted during the process, and therefore the emission rate was considered to be zero percent (Tupman
2002).
The 1990 through 1992 N2O production data were obtained from SRI Consulting's Nitrous Oxide, North America
report (Heydorn 1997). N2O production data for 1993 through 1995 were not available. Production data for 1996
was specified as a range in two data sources (Heydorn 1997, Tupman 2002). In particular, for 1996, Heydorn
(1997) estimates N2O production to range between 13.6 and 18.1 thousand metric tons. Tupman (2003) provided a
narrower range (15.9 to 18.1 thousand metric tons) for 1996 that falls within the production bounds described by
Heydorn (1997). Tupman (2003) data are considered more industry-specific and current. Therefore, the midpoint of
the narrower production range was used to estimate N2O emissions for years 1993 through 2001 (Tupman 2003).
The 2002 and 2003 N2O production data were obtained from the Compressed Gas Association Nitrous Oxide Fact
Sheet and Nitrous Oxide Abuse Hotline (CGA 2002, 2003). These data were also provided as a range. For
example, in 2003, CGA (2003) estimates N2O production to range between 13.6 and 15.9 thousand metric tons. Due
to the unavailability of data, production estimates for years 2004 through 2012 were held constant at the 2003 value.
The 1996 share of the total quantity of N2O used by each subcategory was obtained from SRI Consulting's Nitrous
Oxide, North America report (Heydorn 1997). The 1990 through 1995 share of total quantity of N2Ousedby each
subcategory was kept the same as the 1996 number provided by SRI Consulting. The 1997 through 2001share of
total quantity of N2O usage by sector was obtained from communication with a N2O industry expert (Tupman 2002).
The 2002 and 2003 share of total quantity of N2O usage by sector was obtained from CGA (2002, 2003). Due to the
unavailability of data, the share of total quantity of N2O usage data for years 2004 through 2012 was assumed to
equal the 2003 value. The emissions rate for the food processing propellant industry was obtained from SRI
Consulting's Nitrous Oxide, North America report (Heydorn 1997), and confirmed by a N2O industry expert
(Tupman 2002). The emissions rate for all other subcategories was obtained from communication with a N2O
industry expert (Tupman 2002). The emissions rate for the medical/dental subcategory was obtained from the 2006
IPCC Guidelines.
Uncertainty and Time-Series Consistency
The overall uncertainty associated with the 2012 N2O emission estimate from N2O product usage was calculated
using the IPCC Guidelines for National Greenhouse Gas Inventories (2006) Tier 2 methodology. Uncertainty
associated with the parameters used to estimate N2O emissions include production data, total market share of each
end use, and the emission factors applied to each end use, respectively.
The results of this Tier 2 quantitative uncertainty analysis are summarized in Table 5-4. N2O emissions from N2O
product usage were estimated to be between 3.3 and 5.4 Tg CO2 Eq. at the 95 percent confidence level. This
indicates a range of approximately 24 percent below to 24 percent above the emission estimate of 4.4 Tg CO2 Eq.
Table 5-4: Tier 2 Quantitative Uncertainty Estimates for NzO Emissions from NzO Product
Usage (Tg COz Eq. and Percent)
Source Gas 2012 Emission Uncertainty Range Relative to Emission Estimate3
Estimate
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
N2O Product
Usage N2O 4.4
3.3 5.4 -24% +24%
1 Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence
interval.
Solvent and Other Product Use 5-3
-------
Furthermore, methodological recalculations were applied to the entire time-series to ensure time-series consistency
from 1990 through 2012. Details on the emission trends through time-series are described in more detail in the
Methodology section, above.
Planned Improvements
Planned improvements include a continued evaluation of alternative production statistics for cross verification, a
reassessment of N2O product use subcategories to accurately represent trends, investigation of production and use
cycles, and the potential need to incorporate a time lag between production and ultimate product use and resulting
release of N2O. Additionally, planned improvements include considering imports and exports of N2O for product
uses.
Future inventories will examine data from EPA's GHGPvP to improve the emission estimates for the N2O product
use subcategory. Particular attention will be made to ensure time series consistency, as the facility-level reporting
data from EPA's GHGRP are not available for all inventory years as reported in this Inventory.
5.2 Indirect Greenhouse Gas Emissions from
Solvent Use
The use of solvents and other chemical products can result in emissions of various ozone precursors (i.e., indirect
greenhouse gases).177 Non-CH4 volatile organic compounds (NMVOCs), commonly referred to as "hydrocarbons,"
are the primary gases emitted from most processes employing organic or petroleum based solvents. As some of
industrial applications also employ thermal incineration as a control technology, combustion by-products, such as
carbon monoxide (CO) and nitrogen oxides (NOX), are also reported with this source category. In the United States,
emissions from solvents are primarily the result of solvent evaporation, whereby the lighter hydrocarbon molecules
in the solvents escape into the atmosphere. The evaporation process varies depending on different solvent uses and
solvent types. The major categories of solvent uses include: degreasing, graphic arts, surface coating, other
industrial uses of solvents (i.e., electronics, etc.), dry cleaning, and non-industrial uses (i.e., uses of paint thinner,
etc.).
Total emissions of NOX, NMVOCs, and CO from 1990 to 2012 are reported in Table 5-5.
Table 5-5: Emissions of NOX, CO, and NMVOC from Solvent Use (Gg)
Activity 1990 2005 2008 2009 2010 2011 2012
NOx 1 3~| 4 3 2 1 F~
Surface Coating I I 3 I 4 3 2 1 1
Graphic Arts + I + I + + + + +
Degreasing + I + I + + + + +
Dry Cleaning + I + I + + + + +
Other Industrial Processes3 + I + I + + + + +
Non-Industrial Processes15 + I + I + + + + +
Other NA + I + + + + +
CO 5J2J65311
Surface Coating + I 2 I 6 5 3 1 1
177 Solvent usage in the United States also results in the emission of small amounts of hydrofluorocarbons (HFCs) and
hydrofluoroethers (HFEs), which are included under Substitution of Ozone Depleting Substances in the Industrial Processes
chapter.
5-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Other Industrial Processes3 4 I + I + + + + +
Dry Cleaning + I + I + + + + +
Degreasing + I + I + + + + +
Graphic Arts + I + I + + + + +
Non-Industrial Processes'5 + I + I + + + + +
Other NA + I + + + + +
NMVOCs 5,216 I 3,851 I 2,992 2,838 2,684 2,531 2,531
Surface Coating 2,289 I 1,578 1,226 1,163 1,100 1,037 1,037
Non-Industrial Processes15 1,724 1,446 1,123 1,066 1,008 950 950
Degreasing 675 280 218 207 196 184 184
Dry Cleaning 195 230 179 170 160 151 151
Graphic Arts 249 194 150 143 135 127 127
Other Industrial Processes3 85 88 68 65 61 58 58
Other + 36 28 26 25 24 24
a Includes rubber and plastics manufacturing, and other miscellaneous applications.
b Includes cutback asphalt, pesticide application adhesives, consumer solvents, and other miscellaneous
applications.
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.5 Gg.
NA: Not available
Methodology
Emissions were calculated by aggregating solvent use data based on information relating to solvent uses from
different applications such as degreasing, graphic arts, etc. Emission factors for each consumption category were
then applied to the data to estimate emissions. For example, emissions from surface coatings were mostly due to
solvent evaporation as the coatings solidify. By applying the appropriate solvent-specific emission factors to the
amount of solvents used for surface coatings, an estimate of emissions was obtained. Emissions of CO and NOX
result primarily from thermal and catalytic incineration of solvent-laden gas streams from painting booths, printing
operations, and oven exhaust.
Emission estimates for 1990 through 2012 were obtained from data published on the National Emission Inventory
(NEI) Air Pollutant Emission Trends web site (EPA 2013), and disaggregated based on EPA (2003). Emission
estimates for 2012 for non-EGU and non-mobile sources are held constant from 2011 in EPA (2013). Emissions
were calculated either for individual categories or for many categories combined, using basic activity data (e.g., the
amount of solvent purchased) as an indicator of emissions. National activity data were collected for individual
applications from various agencies.
Activity data were used in conjunction with emission factors, which together relate the quantity of emissions to the
activity. Emission factors are generally available from the EPA's Compilation of Air Pollutant Emission Factors,
AP-42 (EPA 1997). The EPA currently derives the overall emission control efficiency of a source category from a
variety of information sources, including published reports, the 1985 National Acid Precipitation and Assessment
Program emissions inventory, and other EPA databases.
Uncertainty and Time-Series Consistency
Uncertainties in these estimates are partly due to the accuracy of the emission factors used and the reliability of
correlations between activity data and actual emissions.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Solvent and Other Product Use 5-5
-------
-------
6. Agriculture
Agricultural activities contribute directly to emissions of greenhouse gases through a variety of processes. This
chapter provides an assessment of non-carbon-dioxide emissions from the following source categories: enteric
fermentation in domestic livestock, livestock manure management, rice cultivation, agricultural soil management,
and field burning of agricultural residues (see Figure 6-1). Carbon dioxide (CO2) emissions and removals from
agriculture-related land-use activities, such as liming of agricultural soils and conversion of grassland to cultivated
land, are presented in the Land Use, Land-Use Change, and Forestry chapter. Carbon dioxide emissions from on-
farm energy use are accounted for in the Energy chapter.
Figure 6-1: 2012 Agriculture Chapter Greenhouse Gas Emission Sources
Agricultural Soil Management
Enteric Fermentation
Manure Management
Rice Cultivation
Reid Burning of Agricultural Residues
Agriculture as a Portion of
all Emissions
25 50
75 100 125 150 175 200 225 250 275 300
Tg CQz Eq.
In 2012, the Agriculture sector was responsible for emissions of 526.3 teragrams of CO2 equivalents (Tg CO2 Eq.),
or 8. 1 percent of total U.S. greenhouse gas emissions. Methane (CH4) and nitrous oxide (N2O) were the primary
greenhouse gases emitted by agricultural activities. Methane emissions from enteric fermentation and manure
management represent 25.0 percent and 9.4 percent of total CH4 emissions from anthropogenic activities,
respectively. Of all domestic animal types, beef and dairy cattle were by far the largest emitters of CH4. Rice
cultivation and field burning of agricultural residues were minor sources of CH4. Agricultural soil management
activities such as fertilizer application and other cropping practices were the largest source of U.S. N2O emissions,
accounting for 74.8 percent. Manure management and field burning of agricultural residues were also small sources
of N2O emissions.
Table 6-1 and Table 6-2 present emission estimates for the Agriculture sector. Between 1990 and 2012, CH4
emissions from agricultural activities increased by 13.6 percent, while N2O emissions fluctuated from year to year,
but overall increased by 9.5 percent.
Agriculture 6-1
-------
Table 6-1: Emissions from Agriculture (Tg COz Eq.)
Gas/Source
1990
2005
2008
2009
2010
2011
Total
473.9
512.2
543.4
538.9
534.2
Note: Totals may not sum due to independent rounding.
Table 6-2: Emissions from Agriculture (Gg)
2012
CH4
Enteric Fermentation
Manure Management
Rice Cultivation
Field Burning of Agricultural Residues
N2O
Agricultural Soil Management
Manure Management
Field Burning of Agricultural Residues
177.3
137.9
31.5
7.7
0.3
296.6
282.1
14.4
0.1
197.7
142.5
47.6 1
7.5 1
0.2
314.5
297.3
17.1
0.1 M
206.5
147.0
51.5
7.8
0.3
336.9
319.0
17.8
0.1
204.
146.
50.
7.
0.
,7
1
5
,9
,2
334.2
316.4
17.
0.
,7
1
206.2
144.9
51.8
9.3
0.2
327.9
310.1
17.8
0.1
202.4
143.0
52.0
7.1
0.3
325.8
307.8
18.0
0.1
201.
141.
52,
,5
.0
.9
7.4
0,
324.
306,
18,
0,
.3
,7
.6
.0
.1
528.3 526.3
Gas/Source
CH4
Enteric Fermentation
Manure Management
Rice Cultivation
Field Burning of Agricultural Residues
N20
Agricultural Soil Management
Manure Management
Field Burning of Agricultural Residues
1990
8,445
6,566
1,499
366
13 1
957 1
910 1
46 1
+ |
2005
9,416
6,785
2,265
358 1
9 1
1,014
959
55 1
+ ^|
2008
9,835
6,999
2,452
370
13
1,087
1,029
57
+
2009
9,749
6,956
2,403
378
12
1,078
1,021
57
+
2010
9,820
6,898
2,466
444
11
1,058
1,000
57
+
2011
9,638
6,809
2,478
339
12
1,051
993
58
+
2012
9,597
6,714
2,519
351
12
1,047
989
58
+
+ Lessthan0.5Gg.
Note: Totals may not sum due to independent rounding.
6.1 Enteric Fermentation (IPCC Source
Category 4A)
Methane is produced as part of normal digestive processes in animals. During digestion, microbes resident in an
animal's digestive system ferment food consumed by the animal. This microbial fermentation process, referred to as
enteric fermentation, produces CH4 as a byproduct, which can be exhaled or eructated by the animal. The amount of
CH4 produced and emitted by an individual animal depends primarily upon the animal's digestive system, and the
amount and type of feed it consumes.
Ruminant animals (e.g., cattle, buffalo, sheep, goats, and camels) are the major emitters of CH4 because of their
unique digestive system. Ruminants possess a rumen, or large "fore-stomach," in which microbial fermentation
breaks down the feed they consume into products that can be absorbed and metabolized. The microbial
fermentation that occurs in the rumen enables them to digest coarse plant material that non-ruminant animals cannot.
Ruminant animals, consequently, have the highest CH4 emissions per unit of body mass among all animal types.
Non-ruminant animals (e.g., swine, horses, and mules and asses) also produce CH4 emissions through enteric
fermentation, although this microbial fermentation occurs in the large intestine. These non-ruminants emit
significantly less CH4 on a per-animal-mass basis than ruminants because the capacity of the large intestine to
produce CH4 is lower.
6-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
In addition to the type of digestive system, an animal's feed quality and feed intake also affect CH4 emissions. In
general, lower feed quality and/or higher feed intake leads to higher CH4 emissions. Feed intake is positively
correlated to animal size, growth rate, level of activity and production (e.g., milk production, wool growth,
pregnancy, or work). Therefore, feed intake varies among animal types as well as among different management
practices for individual animal types (e.g., animals in feedlots or grazing on pasture).
Methane emission estimates from enteric fermentation are provided in Table 6-3 and Table 6-4.Total livestock CH4
emissions in 2012 were 141.0 TgCChEq. (6,714 Gg). Beef cattle remain the largest contributor of CH4 emissions
from enteric fermentation, accounting for 71 percent in 2012. Emissions from dairy cattle in 2012 accounted for 25
percent, and the remaining emissions were from horses, sheep, swine, goats, American bison, mules and asses.
From 1990 to 2012, emissions from enteric fermentation have increased by 2.3 percent. While emissions generally
follow trends in cattle populations, over the long term there are exceptions as population decreases have been
coupled with production increases. For example, beef cattle emissions increased 0.6 percent from 1990 to 2012,
while beef cattle populations actually declined by 5 percent and beef production increased 14 percent (USD A 2013),
and while dairy emissions increased 6 percent over the entire time series, the population has declined by 2 percent
and milk production increased 36 percent (USDA 2013). This indicates that while emission factors per head are
increasing, emission factors per unit of product are going down. Generally, from 1990 to 1995 emissions increased
and then decreased from 1996 to 2004. These trends were mainly due to fluctuations in beef cattle populations and
increased digestibility of feed for feedlot cattle. Emissions generally increased from 2005 to 2007, as both dairy and
beef populations underwent increases and the literature for dairy cow diets indicated a trend toward a decrease in
feed digestibility for those years. Emissions decreased again from 2008 to 2012 as beef cattle populations again
decreased. Regarding trends in other animals, during the timeframe of this analysis, populations of sheep have
decreased 53 percent while horse populations have nearly doubled, with each annual increase ranging from about 2
to 9 percent. Goat and swine populations have increased 25 percent and 23 percent, respectively, during this
timeframe, though with some slight annual decreases. The population of American bison tripled, while mules and
asses have increased by a factor of five.
Table 6-3: CH4 Emissions from Enteric Fermentation (Tg COz Eq.)
Livestock Type
Beef Cattle
Dairy Cattle
Swine
Horses
Sheep
Goats
American Bison
Mules and Asses
Total
1990
100 0
0.8 1
1
+
137.9
2005
105.8
31.6
1.9 1
1.5 1
1.0 1
0.3 1
0.4 1
+
142.5
2008
107.5
34.1
2.1
1.6
1.0
0.3
0.3
0.1
147.0
2009
106.3
34.4
2.1
1.6
1.0
0.3
0.3
0.1
146.1
2010
105.4
34.1
2.0
1.6
0.9
0.3
0.3
0.1
144.9
2011
103.1
34.5
2.1
1.6
0.9
0.3
0.3
0.1
143.0
2012
100.6
35.0
2.1
1.7
0.9
0.3
0.3
0.1
141.0
Notes: Totals may not sum due to independent rounding.
+ Does not exceed 0.05 Tg CCh Eq.
Table 6-4: ChU Emissions from Enteric Fermentation (Gg)
Livestock Type
Beef Cattle
Dairy Cattle
Swine
Horses
Sheep
Goats
American Bison
Mules and Asses
Total
Note: Totals may
1990
4,763
1,574 1
6,566
2005 |
5,037
1,503
92
•
17 1
2 I
6,785 |
2008
5,119
1,622
101
74
48
16
16
3
6,999
2009
5,062
1,639
99
75
46
16
15
4
6,956
2010
5,019
1,626
97
77
45
16
15
4
6,898
2011
4,911
1,643
98
78
44
16
14
4
6,809
2012
4,789
1,668
100
79
43
16
14
5
6,714
not sum due to independent rounding.
Agriculture 6-3
-------
Livestock emission estimate methodologies fall into two categories: cattle and other domesticated animals. Cattle,
due to their large population, large size, and particular digestive characteristics, account for the majority of CH4
emissions from livestock in the United States. A more detailed methodology (i.e., IPCC Tier 2) was therefore
applied to estimate emissions for all cattle. Emission estimates for other domesticated animals (horses, sheep,
swine, goats, American bison, and mules and asses) were handled using a less detailed approach (i.e., IPCC Tier 1).
While the large diversity of animal management practices cannot be precisely characterized and evaluated,
significant scientific literature exists that provides the necessary data to estimate cattle emissions using the IPCC
Tier 2 approach. The Cattle Enteric Fermentation Model (CEFM), developed by EPA and used to estimate cattle
CH4 emissions from enteric fermentation, incorporates this information and other analyses of livestock population,
feeding practices, and production characteristics.
National cattle population statistics were disaggregated into the following cattle sub-populations:
• Dairy Cattle
o Calves
o Heifer Replacements
o Cows
• Beef Cattle
o Calves
o Heifer Replacements
o Heifer and Steer Stackers
o Animals in Feedlots (Heifers and Steer)
o Cows
o Bulls
Calf birth rates, end-of-year population statistics, detailed feedlot placement information, and slaughter weight data
were used to create a transition matrix that models cohorts of individual animal types and their specific emission
profiles. The key variables tracked for each of the cattle population categories are described in Annex 3.9. These
variables include performance factors such as pregnancy and lactation as well as average weights and weight gain.
Annual cattle population data were obtained from the U.S. Department of Agriculture's (USDA) National
Agricultural Statistics Service (NASS) QuickStats database (USDA 2013).
Diet characteristics were estimated by region for dairy, foraging beef, and feedlot beef cattle. These diet
characteristics were used to calculate digestible energy (DE) values (expressed as the percent of gross energy intake
digested by the animal) and CH4 conversion rates (Ym) (expressed as the fraction of gross energy converted to CH4)
for each regional population category. The IPCC recommends Ym ranges of 3.0± 1.0 percent for feedlot cattle and
6.5±1.0 percent for other well-fed cattle consuming temperate-climate feed types (IPCC 2006). Given the
availability of detailed diet information for different regions and animal types in the United States, DE and Ym
values unique to the United States were developed. The diet characterizations and estimation of DE and Ym values
were based on information from state agricultural extension specialists, a review of published forage quality studies
and scientific literature, expert opinion, and modeling of animal physiology.
The diet characteristics for dairy cattle were based on Donovan (1999) and an extensive review of nearly 20 years of
literature from 1990 through 2009. Estimates of DE were national averages based on the feed components of the
diets observed in the literature for the following year groupings: 1990-1993, 1994-1998, 1999-2003, 2004-2006,
2007, and 2008 onward.178 Base year Ym values by region were estimated using Donovan (1999). A ruminant
178 Due to inconsistencies in the 2003 literature values, the 2002 values were used for 2003, as well.
6-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
digestion model (COWPOLL, as selected in Kebreab et al. 2008) was used to evaluate Ym for each diet evaluated
from the literature, and a function was developed to adjust regional values over time based on the national trend.
Dairy replacement heifer diet assumptions were based on the observed relationship in the literature between dairy
cow and dairy heifer diet characteristics.
For feedlot animals, the DE and Ym values used for 1990 were recommended by Johnson (1999). Values for DE
and Ym for 1991 through 1999 were linearly extrapolated based on the 1990 and 2000 data. DE and Ym values for
2000 onwards were based on survey data in Galyean and Gleghorn (2001) and Vasconcelos and Galyean (2007).
For grazing beef cattle, Ym values were based on Johnson (2002), DE values for 1990 through 2006 were based on
specific diet components estimated from Donovan (1999), and DE values from 2007 onwards were developed from
an analysis by Archibeque (2011), based on diet information in Preston (2010) and USDA:APHIS:VS (2010).
Weight and weight gains for cattle were estimated from Holstein (2010), Doren et al. (1989), Enns (2008), Lippke et
al. (2000), Pinchack et al. (2004), Platter et al. (2003), Skogerboe et al. (2000), and expert opinion. See Annex 3.10
for more details on the method used to characterize cattle diets and weights in the United States.
Calves younger than 4 months are not included in emission estimates because calves consume mainly milk and the
IPCC recommends the use of a Ym of zero for all juveniles consuming only milk. Diets for calves aged 4 to 6
months are assumed to go through a gradual weaning from milk decreasing to 75 percent at 4 months, 50 percent at
age 5 months, and 25 percent at age 6 months. The portion of the diet made up with milk still results in zero
emissions. For the remainder of the diet, beef calf DE and Ym are set equivalent to those of beef replacement heifers,
while dairy calf DE is set equal to that of dairy replacement heifers and dairy calf Ym is provided at 4 and 7 months
of age by Soliva (2006). Estimates of Ym for 5 and 6 month old dairy calves are linearly interpolated from the values
provided for 4 and 7 months.
To estimate CH4 emissions, the population was divided into state, age, sub-type (i.e., dairy cows and replacements,
beef cows and replacements, heifer and steer stackers, heifers and steers in feedlots, bulls, beef calves 4 to 6 months,
and dairy calves 4 to 6 months), and production (i.e., pregnant, lactating) groupings to more fully capture differences
in CH4 emissions from these animal types. The transition matrix was used to simulate the age and weight structure
of each sub-type on a monthly basis in order to more accurately reflect the fluctuations that occur throughout the
year. Cattle diet characteristics were then used in conjunction with Tier 2 equations from IPCC (2006) to produce
CH4 emission factors for the following cattle types: dairy cows, beef cows, dairy replacements, beef replacements,
steer stackers, heifer stackers, steer feedlot animals, heifer feedlot animals, bulls, and calves. To estimate emissions
from cattle, monthly population data from the transition matrix were multiplied by the calculated emission factor for
each cattle type. More details are provided in Annex 3.9.
Emission estimates for other animal types were based on average emission factors representative of entire
populations of each animal type. Methane emissions from these animals accounted for a minor portion of total CH4
emissions from livestock in the United States from 1990 through 2012. Also, the variability in emission factors for
each of these other animal types (e.g., variability by age, production system, and feeding practice within each animal
type) is less than that for cattle. Annual livestock population data for sheep; swine; goats; horses; mules and asses;
and American bison were obtained for available years from USD ANAS S (USDA2013). Horse, goat and mule,
burro, and donkey population data were available for 1987, 1992, 1997, 2002, 2007 (USDA 1992, 1997, 2013); the
remaining years between 1990 and 2012 were interpolated and extrapolated from the available estimates (with the
exception of goat populations being held constant between 1990 and 1992 and 2007 through 2012). American bison
population estimates were available from USDA for 2002 and 2007 (USDA 2013) and from the National Bison
Association (1999) for 1997 through 1999. Additional years were based on observed trends from the National Bison
Association (1999), interpolation between known data points, and ratios extrapolation beyond 2007, as described in
more detail in Annex 3.9. Methane emissions from sheep, goats, swine, horses, American bison, and mules and
asses were estimated by using emission factors utilized in Crutzen et al. (1986, cited in IPCC 2006). These emission
factors are representative of typical animal sizes, feed intakes, and feed characteristics in developed countries. For
American bison the emission factor for buffalo was used and adjusted based on the ratio of live weights to the 0.75
power. The methodology is the same as that recommended by IPCC (2006).
See Annex 3.9 for more detailed information on the methodology and data used to calculate CH4 emissions from
enteric fermentation.
Agriculture 6-5
-------
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis for this source category was performed using the IPCC-recommended Tier 2
uncertainty estimation methodology based on a Monte Carlo Stochastic Simulation technique as described in ICF
(2003). These uncertainty estimates were developed for the 1990 through 2001 Inventory report (i.e., 2003
submission to the UNFCCC). There have been no significant changes to the methodology since that time;
consequently, these uncertainty estimates were directly applied to the 2012 emission estimates in this report.
A total of 185 primary input variables (177 for cattle and 8 for non-cattle) were identified as key input variables for
the uncertainty analysis. A normal distribution was assumed for almost all activity- and emission factor-related
input variables. Triangular distributions were assigned to three input variables (specifically, cow-birth ratios for the
three most recent years included in the 2001 model run) to ensure only positive values would be simulated. For
some key input variables, the uncertainty ranges around their estimates (used for inventory estimation) were
collected from published documents and other public sources; others were based on expert opinion and best
estimates. In addition, both endogenous and exogenous correlations between selected primary input variables were
modeled. The exogenous correlation coefficients between the probability distributions of selected activity-related
variables were developed through expert judgment.
The uncertainty ranges associated with the activity data-related input variables were plus or minus 10 percent or
lower. However, for many emission factor-related input variables, the lower- and/or the upper-bound uncertainty
estimates were over 20 percent. The results of the quantitative uncertainty analysis are summarized in Table 6-5.
Based on this analysis, enteric fermentation CH4 emissions in 2012 were estimated to be between 125.5 and 166.4
Tg CO2 Eq. at a 95 percent confidence level, which indicates a range of 11 percent below to 18 percent above the
2012 emission estimate of 141.0 Tg CO2 Eq. Among the individual cattle sub-source categories, beef cattle account
for the largest amount of CH4 emissions, as well as the largest degree of uncertainty in the emission estimates—due
mainly to the difficulty in estimating the diet characteristics for grazing members of this animal group. Among non-
cattle, horses represent the largest percent of uncertainty in the previous uncertainty analysis because the FAO
population estimates used for horses at that time had a higher degree of uncertainty than for the USDA population
estimates used for swine, goats, and sheep. The horse populations are now from the same USDA source as the other
animal types, and therefore the uncertainty range around horses is likely overestimated. Cattle calves, American
bison, mules and asses were excluded from the initial uncertainty estimate because they were not included in
emissions estimates at that time.
Table 6-5: Quantitative Uncertainty Estimates for Cm Emissions from Enteric Fermentation
(Tg COz Eq. and Percent)
Source
Gas
2012 Emission
Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate3' b' c
(Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Enteric Fermentation
CH4
141.0
125.5 166.4 -11% +18%
1 Range of emissions estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
b Note that the relative uncertainty range was estimated with respect to the 2001 emission estimates from the 2003
submission and applied to the 2012 estimates.
0 The overall uncertainty calculated in 2003, and applied to the 2012 emission estimate, did not include uncertainty
estimates for calves, American bison, and mules and asses. Additionally, for bulls the emissions estimate was based
on the Tier 1 methodology Since bull emissions are now estimated using the Tier 2 method, the uncertainty
surrounding their estimates is likely lower than indicated by the previous uncertainty analysis.
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section.
6-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
QA/QC and Verification
In order to ensure the quality of the emission estimates from enteric fermentation, the IPCC Tier 1 and Tier 2
Quality Assurance/Quality Control (QA/QC) procedures were implemented consistent with the U.S. QA/QC plan.
Tier 2 QA procedures included independent peer review of emission estimates. Recent updates to the forage portion
of the diet values for cattle made this the area of emphasis for QA/QC this year, with specific attention to the data
sources and comparisons of the current estimates with previous estimates.
In addition, over the past few years, particular importance has been placed on harmonizing the data exchange
between the enteric fermentation and manure management source categories. The current inventory submission now
utilizes the transition matrix from the CEFM for estimating cattle populations and weights for both source
categories, and the CEFM is used to output volatile solids and nitrogen excretion estimates using the diet
assumptions in the model in conjunction with the energy balance equations from the IPCC (2006). This approach
facilitates the QA/QC process for both of these source categories.
Recalculations Discussion
Calves 4-6 months were added to emission estimates for the first time in the current Inventory. The inclusion of
calves has increased emissions from beef cattle by approximately 3 percent per year. In addition, for the first time
calf populations for enteric fermentation were differentiated into dairy and beef calves. During this process, total
calf populations were updated slightly, so that the enteric fermentation calf populations differ an average of 0.9
percent per year from manure management calf populations. This issue will be resolved in the next inventory when
the manure management inventory uses updated calf population values from the CEFM. Additional recalculations
include the following:
• In the previous Inventory, aggregation in the 1992 feedlot cattle was linked incorrectly. This correction resulted
in a decrease in emissions for that year of 0.2 percent.
• The USD A published minor revisions in several categories that affected historical emissions estimated for cattle
in 2011, including dairy cow milk production for several states and cattle populations for January 1, 2012.
These changes had an insignificant impact on the overall results.
• Calves 4-6 months were added to emission estimates for the first time in the current Inventory. The inclusion of
calves has increased emissions from beef cattle by approximately 3 percent per year. In addition, for the first
time calf populations for enteric fermentation were differentiated into dairy and beef calves. During this
process, total calf populations were updated slightly, so that the enteric fermentation calf populations differ an
average of 0.9 percent per year from manure management calf populations.
• Horse population data was obtained for 1987 and 1992 from USDA census data, resulting in a change in
population estimates for 1990 through 1996. This resulted in an average decrease of 6.3 percent for those years
relative to the previous report.
• Populations of American bison and mules and asses were revised to extrapolate data beyond the 2007 census
based on a linear trend rather than following trends in bison slaughter and holding values constant. These
changes resulted in average decrease of 3.2 percent and increase of 31.4 percent, respectively, for those years.
Additionally, the name of this population group was revised from mules, burros, and donkeys to mules and
asses to be consistent with the CRF tables.
Planned Improvements
Continued research and regular updates are necessary to maintain an emissions inventory that reflects the current
base of knowledge. Future improvements for enteric fermentation could include some of the following options:
• Updating input variables that are from older data sources, such as beef births by month and beef cow lactation
rates;
Agriculture 6-7
-------
• Investigation of the availability of annual data for the DE and crude protein values of specific diet and feed
components for foraging and feedlot animals;
• Given the many challenges in characterizing dairy diets, further investigation may be conducted on additional
sources or methodologies for estimating DE for dairy;
• Assumptions about weights and weight gains for beef cows can be evaluated further such that trends beyond
2007 are updated, rather than held constant;
• Mature dairy cow weight is likely slightly overestimated, based on knowledge of the breeds of dairy cows in the
United States. The estimated weight for dairy cows (1,500 Ibs), based solely on Holstein cows, will be reduced
in future inventories;
• The possible updating to a Tier 2 methodology for other animal types (i.e., sheep, swine, goats, horses); and
• The investigation of methodologies and emission factors for including enteric fermentation emission estimates
from poultry.
• Recent changes that have been implemented to the CEFM warrant an assessment of the current uncertainty
analysis; therefore, a revision of the quantitative uncertainty surrounding emission estimates from this source
category will be initiated.
6.2 Manure Management (IPCC Source
Category 4B)
The treatment, storage, and transportation of livestock manure can produce anthropogenic CH4 and N2O emissions.
Methane is produced by the anaerobic decomposition of manure. Nitrous oxide emissions are produced through
both direct and indirect pathways. Direct N2O emissions are produced as part of the N cycle through the
nitrification and denitrification of the organic N in livestock dung and urine.179 There are two pathways for indirect
N2O emissions. The first is the result of the volatilization of N in manure (as NH3 and NOX) and the subsequent
deposition of these gases and their products (NH4+ and NOs") onto soils and the surface of lakes and other waters.
The second pathway is the runoff and leaching of N from manure to the groundwater below, in riparian zones
receiving drain or runoff water, or in the ditches, streams, rivers, and estuaries into which the land drainage water
eventually flows.
When livestock or poultry manure are stored or treated in systems that promote anaerobic conditions (e.g., as a
liquid/slurry in lagoons, ponds, tanks, or pits), the decomposition of the volatile solids component in the manure
tends to produce CH4. When manure is handled as a solid (e.g., in stacks or drylots) or deposited on pasture, range,
or paddock lands, it tends to decompose aerobically and produce little or no CH4. Ambient temperature, moisture,
and manure storage or residency time affect the amount of CH4 produced because they influence the growth of the
bacteria responsible for CH4 formation. For non-liquid-based manure systems, moist conditions (which are a
function of rainfall and humidity) can promote CH4 production. Manure composition, which varies by animal diet,
growth rate, and type, including the animal's digestive system, also affects the amount of CH4produced. In general,
the greater the energy content of the feed, the greater the potential for CH4 emissions. However, some higher-energy
feeds also are more digestible than lower quality forages, which can result in less overall waste excreted from the
animal.
The production of direct N2O emissions from livestock manure depends on the composition of the manure and urine,
the type of bacteria involved in the process, and the amount of oxygen and liquid in the manure system. For direct
179 Direct and indirect N2O emissions from dung and urine spread onto fields either directly as daily spread or after it is removed
from manure management systems (e.g., lagoon, pit, etc.) and from livestock dung and urine deposited on pasture, range, or
paddock lands are accounted for and discussed in the Agricultural Soil Management source category within the Agriculture
sector.
6-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
N2O emissions to occur, the manure must first be handled aerobically where ammonia (NH3) or organic N is
converted to nitrates and nitrites (nitrification), and then handled anaerobically where the nitrates and nitrites are
reduced to dinitrogen gas (N2), with intermediate production of N2O and nitric oxide (NO) (denitrification)
(Groffman et al. 2000). These emissions are most likely to occur in dry manure handling systems that have aerobic
conditions, but that also contain pockets of anaerobic conditions due to saturation. A very small portion of the total
N excreted is expected to convert to N2O in the waste management system (WMS). Indirect N2O emissions are
produced when nitrogen is lost from the system through volatilization (as NH3 or NOX) or through runoff and
leaching. The vast majority of volatilization losses from these operations are NH3. Although there are also some
small losses of NOX, there are no quantified estimates available for use, so losses due to volatilization are only based
on NH3 loss factors. Runoff losses would be expected from operations that house animals or store manure in a
manner that is exposed to weather. Runoff losses are also specific to the type of animal housed on the operation due
to differences in manure characteristics. Little information is known about leaching from manure management
systems as most research focuses on leaching from land application systems. Since leaching losses are expected to
be minimal, leaching losses are coupled with runoff losses and the runoff/leaching estimate provided in this chapter
does not account for any leaching losses.
Estimates of CH4 emissions in 2012 were 52.9 Tg CO2 Eq. (2,519 Gg); in 1990, emissions were 31.5 Tg CO2 Eq.
(1,499 Gg). This is a 68 percent increase in emissions from 1990. Emissions increased on average by 0.9 Tg CO2
Eq. (3.0 percent) annually over this period. The majority of this increase was from swine and dairy cow manure,
where emissions increased 53 and 115 percent, respectively. From 2011 to 2012, there was a 1.7 percent increase in
total CH4 emissions, mainly due to minor shifts in the animal populations and the resultant effects on manure
management system allocations.
Although the majority of managed manure in the United States is handled as a solid, producing little CH4, the
general trend in manure management, particularly for dairy and swine (which are both shifting towards larger
facilities), is one of increasing use of liquid systems. Also, new regulations controlling the application of manure
nutrients to land have shifted manure management practices at smaller dairies from daily spread systems to storage
and management of the manure on site. Although national dairy animal populations have generally been decreasing
since 1990, some states have seen increases in their dairy populations as the industry becomes more concentrated in
certain areas of the country and the number of animals contained on each facility increases. These areas of
concentration, such as California, New Mexico, and Idaho, tend to utilize more liquid-based systems to manage
(flush or scrape) and store manure. Thus the shift toward larger dairy and swine facilities has translated into an
increasing use of liquid manure management systems, which have higher potential CH4 emissions than dry systems.
This significant shift in both the dairy and swine industries was accounted for by incorporating state and WMS-
specific CH4 conversion factor (MCF) values in combination with the 1992, 1997, 2002, and 2007 farm-size
distribution data reported in the Census of Agriculture (USDA 2009a).
In 2012, total N2O emissions were estimated to be 18.0 Tg CO2 Eq. (58 Gg); in 1990, emissions were 14.4 Tg CO2
Eq. (46 Gg). These values include both direct and indirect N2O emissions from manure management. Nitrous oxide
emissions have remained fairly steady since 1990. Small changes in N2O emissions from individual animal groups
exhibit the same trends as the animal group populations, with the overall net effect that N2O emissions showed a 25
percent increase from 1990 to 2012 and a 0.1 percent increase from 2011 through 2012. Overall shifts toward liquid
systems have driven down the emissions per unit of nitrogen excreted.
Table 6-6 and Table 6-7 provide estimates of CH4 and N2O emissions from manure management by animal
category.
Table 6-6: CH4 and NzO Emissions from Manure Management (Tg COz Eq.)
Gas/Animal Type 1990
CH4a
Dairy Cattle
Beef Cattle
Swine
Sheep
Goats
Poultry
Horses
Agriculture 6-9
-------
American Bison
Mules and Asses
N20b
Dairy Cattle
Beef Cattle
Swine
Sheep
Goats
Poultry
Horses
American Bison
Mules and Asses
Total
,
14.4
5.3
1
0.1
NA
+
45.8
5.7
7.4
1.8
0.4
+
1.7
0.1
NA
+
64.7
+
+
17.8
5.8
7.8
2.0
0.4
+
1.7
0.1
NA
+
69.3
+
+
17.7
5.8
7.8
2.0
0.3
+
1.6
0.1
NA
+
68.2
+
+
17.8
5.9
7.8
1.9
0.3
+
1.6
0.1
NA
+
69.6
+
+
18.0
5.9
8.0
2.0
0.3
+
1.6
0.2
NA
+
70.0
+
+
18.0
6.0
7.9
2.0
0.3
+
1.6
0.2
NA
+
70.9
+ Less than 0.5 Gg.
aAccounts for CH4 reductions due to capture and destruction of CH4 at facilities using
anaerobic digesters.
Includes both direct and indirect N2O emissions.
Note: Totals may not sum due to independent rounding. American bison are maintained
entirely on unmanaged WMS; there are no American bison N2O emissions from managed
systems.
NA: Not available
Table 6-7: CH4 and NzO Emissions from Manure Management (Gg)
Gas/Animal Type 1990
CH4a
Dairy Cattle
Beef Cattle
Swine
Sheep
Goats
Poultry
Horses
American Bison
Mules and Asses
N2Ob
Dairy Cattle
Beef Cattle
Swine
Sheep
Goats
Poultry
Horses
American Bison
Mules and Asses
+ Lessthan0.5Gg.
aAccounts for CH4 reductions due to capture and destruction of CH4 at facilities using
anaerobic digesters.
Includes both direct and indirect N2O emissions.
Note: Totals may not sum due to independent rounding. American bison are maintained
entirely on unmanaged WMS; there are no American bison N2O emissions from managed
systems.
NA: Not available
6-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The methodologies presented in IPCC (2006) form the basis of the CH4 and N2O emission estimates for each animal
type. This section presents a summary of the methodologies used to estimate CH4 and N2O emissions from manure
management. See Annex 3.11 for more detailed information on the methodology and data used to calculate CH4 and
N2O emissions from manure management.
Methane Calculation Methods
The following inputs were used in the calculation of CH4 emissions:
• Animal population data (by animal type and state);
• Typical animal mass (TAM) data (by animal type);
• Portion of manure managed in each WMS, by state and animal type;
• Volatile solids (VS) production rate (by animal type and state or United States);
• Methane producing potential (B0) of the volatile solids (by animal type); and
• Methane conversion factors (MCF), the extent to which the CH4 producing potential is realized for each
type of WMS (by state and manure management system, including the impacts of any biogas collection
efforts).
Methane emissions were estimated by first determining activity data, including animal population, TAM, WMS
usage, and waste characteristics. The activity data sources are described below:
• Annual animal population data for 1990 through 2012 for all livestock types, except goats, horses, mules
and asses, and American bison were obtained from USDA National Agriculture Statistics Service (NASS).
For cattle, the USDA populations were utilized in conjunction with birth rates, detailed feedlot placement
information, and slaughter weight data to create the transition matrix in the Cattle Enteric Fermentation
Model (CEFM) that models cohorts of individual animal types and their specific emission profiles. The
key variables tracked for each of the cattle population categories are described in Section 6.1 and in more
detail in Annex 3.10. Goat population data for 1992, 1997, 2002, and 2007, horse and mule and ass
population data for 1987, 1992, 1997, 2002 and 2007, and American bison population for 2002 and 2007
were obtained from the Census of Agriculture (USDA 2009a). American bison population data for 1990-
1999 were obtained from the National Bison Association (1999).
• The TAM is an annual average weight that was obtained for animal types other than cattle from
information in USD A's Agricultural Waste Management Field Handbook (USDA 1996), the American
Society of Agricultural Engineers, Standard D384.1 (ASAE 1998) and others (Meagher 1986; EPA 1992;
Safley 2000; ERG 2003b; IPCC 2006; ERG 2010a). For a description of the TAM used for cattle, please
see section 6.1, Enteric Fermentation.
• WMS usage was estimated for swine and dairy cattle for different farm size categories using data from
USDA (USDA; APHIS 1996; Bush 1998; Ott 2000; USDA 2009a) and EPA (ERG 2000a; EPA 2002a;
2002b). For beef cattle and poultry, manure management system usage data were not tied to farm size but
were based on other data sources (ERG 2000a; USDA; APHIS 2000; UEP 1999). For other animal types,
manure management system usage was based on previous estimates (EPA 1992). American bison WMS
usage was assumed to be the same as not on feed (NOF) cattle, while mules and asses were assumed to be
the same as horses.
• VS production rates for all cattle except for calves were calculated by head for each state and animal type
in the CEFM. VS production rates by animal mass for all other animals were determined using data from
USD A's Agricultural Waste Management Field Handbook (USDA 1996, 2008 andERG2010b and2010c)
and data that was not available in the most recent Handbook were obtained from the American Society of
Agricultural Engineers, Standard D384.1 (ASAE 1998) or the 2006 IPCC Guidelines. American bison VS
production was assumed to be the same as NOF bulls.
Agriculture 6-11
-------
• The maximum CH4 producing capacity of the VS (B0) was determined for each animal type based on
literature values (Morris 1976; Bryant et al, 1976; Hashimoto 1981; Hashimoto 1984; EPA 1992; Hill
1982; Hill 1984).
• MCFs for dry systems were set equal to default IPCC factors based on state climate for each year (IPCC
2006). MCFs for liquid/slurry, anaerobic lagoon, and deep pit systems were calculated based on the
forecast performance of biological systems relative to temperature changes as predicted in the van't Hoff-
Arrhenius equation which is consistent with IPCC (2006) Tier 2 methodology.
• Data from anaerobic digestion systems with CH4 capture and combustion were obtained from the EPA
AgSTAR Program, including information presented in the AgSTAR Digest (EPA 2000, 2003, 2006) and the
AgSTAR project database (EPA 2012). Anaerobic digester emissions were calculated based on estimated
methane production and collection and destruction efficiency assumptions (ERG 2008).
• For all cattle except for calves, the estimated amount of VS (kg per animal-year) managed in each WMS
for each animal type, state, and year were taken from the CEFM, assuming American bison VS production
to be the same as NOF bulls. For animals other than cattle, the annual amount of VS (kg per year) from
manure excreted in each WMS was calculated for each animal type, state, and year. This calculation
multiplied the animal population (head) by the VS excretion rate (kg VS per 1,000 kg animal mass per
day), the TAM (kg animal mass per head) divided by 1,000, the WMS distribution (percent), and the
number of days per year (365.25).
The estimated amount of VS managed in each WMS was used to estimate the CH4 emissions (kg CH4 per year)
from each WMS. The amount of VS (kg per year) were multiplied by the maximum CH4 producing capacity of the
VS (B0) (m3 CH4 per kg VS), the MCF for that WMS (percent), and the density of CH4 (kg CH4 per m3 CH4). The
CH4 emissions for each WMS, state, and animal type were summed to determine the total U.S. CH4 emissions.
Nitrous Oxide Calculation Methods
The following inputs were used in the calculation of direct and indirect N2O emissions:
• Animal population data (by animal type and state);
• TAM data (by animal type);
• Portion of manure managed in each WMS (by state and animal type);
• Total Kjeldahl N excretion rate (Nex);
• Direct N2O emission factor (EFWMs);
• Indirect N2O emission factor for volitalization (EFvoiitaiization);
• Indirect N2O emission factor for runoff and leaching (EFrunoff/kach);
• Fraction of N loss from volitalization of NH3 and NOX (Fracgas); and
• Fraction of N loss from runoff and leaching I
N2O emissions were estimated by first determining activity data, including animal population, TAM, WMS usage,
and waste characteristics. The activity data sources (except for population, TAM, and WMS, which were described
above) are described below:
• Nex rates for all cattle except for calves were calculated by head for each state and animal type in the
CEFM. Nex rates by animal mass for all other animals were determined using data from USDA's
Agricultural Waste Management Field Handbook (USDA 1996, 2008 and ERG 2010b and 2010c) and data
from the American Society of Agricultural Engineers, Standard D384.1 (ASAE 1998) and IPCC (2006).
American bison Nex rates were assumed to be the same as NOF bulls.
• All N2O emission factors (direct and indirect) were taken from IPCC (2006). These data are appropriate
because they were developed using U.S. data.
• Country-specific estimates for the fraction of N loss from volatilization (Fracgas) and runoff and leaching
(Fracmnoff/ieach) were developed. Fracgas values were based on WMS-specific volatilization values as
estimated from EPA's National Emission Inventory - Ammonia Emissions from Animal Agriculture
Operations (EPA 2005). FraCnmoff/ieachmg values were based on regional cattle runoff data from EPA's
Office of Water (EPA 2002b; see Annex 3.1).
6-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
To estimate N2O emissions for cattle (except for calves) and American bison, the estimated amount of N excreted
(kg per animal-year) managed in each WMS for each animal type, state, and year were taken from the CEFM. For
calves and other animals, the amount of N excreted (kg per year) in manure in each WMS for each animal type,
state, and year was calculated. The population (head) for each state and animal was multiplied by TAM (kg animal
mass per head) divided by 1,000, the nitrogen excretion rate (Nex, in kg N per 1,000 kg animal mass per day), WMS
distribution (percent), and the number of days per year.
Direct N2O emissions were calculated by multiplying the amount of N excreted (kg per year) in each WMS by the
N2O direct emission factor for that WMS (EFwMs, in kg N2O-N per kg N) and the conversion factor of N2O-N to
N2O. These emissions were summed over state, animal, and WMS to determine the total direct N2O emissions (kg
of N2O per year).
Next, indirect N2O emissions from volatilization (kg N2O per year) were calculated by multiplying the amount of N
excreted (kg per year) in each WMS by the fraction of N lost through volatilization (Fractas) divided by 100, and the
emission factor for volatilization (EFvoiatiiization, in kg N2O per kg N), and the conversion factor of N2O-N to N2O.
Indirect N2O emissions from runoff and leaching (kg N2O per year) were then calculated by multiplying the amount
of N excreted (kg per year) in each WMS by the fraction of N lost through runoff and leaching (FraCmnoff/ieach)
divided by 100, and the emission factor for runoff and leaching (EFnmoff/ieach, in kg N2O per kg N), and the
conversion factor of N2O-N to N2O. The indirect N2O emissions from volatilization and runoff and leaching were
summed to determine the total indirect N2O emissions.
The direct and indirect N2O emissions were summed to determine total N2O emissions (kg N2O per year).
Uncertainty and Time-Series Consistency
An analysis (ERG 2003a) was conducted for the manure management emission estimates presented in the 1990
through 2001 Inventory report (i.e., 2003 submission to the UNFCCC) to determine the uncertainty associated with
estimating CH4 and N2O emissions from livestock manure management. The quantitative uncertainty analysis for
this source category was performed in 2002 through the IPCC-recommended Tier 2 uncertainty estimation
methodology, the Monte Carlo Stochastic Simulation technique. The uncertainty analysis was developed based on
the methods used to estimate CH4 and N2O emissions from manure management systems. A normal probability
distribution was assumed for each source data category. The series of equations used were condensed into a single
equation for each animal type and state. The equations for each animal group contained four to five variables
around which the uncertainty analysis was performed for each state. These uncertainty estimates were directly
applied to the 2012 emission estimates as there have not been significant changes in the methodology since that
time.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 6-8. Manure management CH4
emissions in 2012 were estimated to be between 43.4 and 63.5 Tg CO2 Eq. at a 95 percent confidence level, which
indicates a range of 18 percent below to 20 percent above the actual 2012 emission estimate of 52.9 Tg CO2 Eq. At
the 95 percent confidence level, N2O emissions were estimated to be between 15.1 and 22.4 Tg CO2 Eq. (or
approximately 16 percent below and 24 percent above the actual 2012 emission estimate of 18.0 Tg CO2 Eq.).
Table 6-8: Tier 2 Quantitative Uncertainty Estimates for CH4 and NzO (Direct and Indirect)
Emissions from Manure Management (Tg COz Eq. and Percent)
Source
Manure Management
Manure Management
Gas
CH4
N2O
2012 Emission
Estimate
(Tg C02 Eq.)
52.9
18.0
Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (%)
Lower Upper Lower
Bound Bound Bound
43.4 63.5 -18%
15.1 22.4 -16%
Upper
Bound
+20%
+24%
aRange of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Agriculture 6-13
-------
QA/QC and Verification
Tier 1 and Tier 2 QA/QC activities were conducted consistent with the U.S. QA/QC plan. Tier 2 activities focused
on comparing estimates for the previous and current inventories for N2O emissions from managed systems and CH4
emissions from livestock manure. All errors identified were corrected. Order of magnitude checks were also
conducted, and corrections made where needed. Manure N data were checked by comparing state-level data with
bottom up estimates derived at the county level and summed to the state level. Similarly, a comparison was made
by animal and WMS type for the full time series, between national level estimates for N excreted and the sum of
county estimates for the full time series.
Any updated data, including population, are validated by experts to ensure the changes are representative of the best
available U.S.-specific data. The U.S.-specific values for TAM, Nex, VS, B0, and MCF were also compared to the
IPCC default values and validated by experts. Although significant differences exist in some instances, these
differences are due to the use of U.S.-specific data and the differences in U.S. agriculture as compared to other
countries. The U.S. manure management emission estimates use the most reliable country-specific data, which are
more representative of U.S. animals and systems than the 2006 IPPC default values.
For additional verification, the implied CH4 emission factors for manure management (kg of CH4 per head per year)
were compared against the default 2006 IPCC values. Table 6-9 presents the implied emission factors of kg of CH4
per head per year used for the manure management emission estimates as well as the IPCC default emission factors.
The U.S. implied emission factors fall within the range of the 2006 IPCC default values, except in the case of sheep,
goats, and some years for horses and dairy cattle. The U.S. implied emission factors are greater than the 2006 IPCC
default value for those animals due to the use of U.S.-specific data for typical animal mass and VS excretion. There
is an increase in implied emission factors for dairy and swine across the time series. This increase reflects the dairy
and swine industry trend towards larger farm sizes; large farms are more likely to manage manure as a liquid and
therefore produce more CH4 emissions.
Table 6-9: 2006 IPCC Implied Emission Factor Default Values Compared with Calculated
Values for ChU from Manure Management (kg/head/year)
IPCC Default
Animal Type CH4 Emission Factors
(kg/head/year)
Dairy Cattle
Beef Cattle
Swine
Sheep
Goats
Poultry
Horses
Mules and Asses
American Bison
48-112
1-2
10-45
0.19-0.37
0.13-0.26
0.02-1.4
1.56-3.13
0.76-1.14
NA
Implied CH4 Emission Factors (kg/head/year)
1990
42.3
1.5
11.6
0.6
0.4
0.1
4.3
1.8
0.9
2005
81.2
1.6
15.0
0.6
i
2.0
0.9
2008
90.7
1.5
13.9
0.5
0.3
0.1
2.5
2.1
0.9
2009
89.6
1.5
13.6
0.5
0.3
0.1
2.5
2.1
0.9
2010
91.0
1.6
14.6
0.5
0.3
0.1
2.6
2.1
0.9
2011
92.0
1.6
14.3
0.5
0.3
0.1
2.6
2.1
0.9
2012
93.5
1.6
14.4
0.5
0.3
0.1
2.6
2.1
0.9
In addition, 2006 default IPCC emission factors for N2O were compared to the U.S. Inventory implied N2O emission
factors. Default N2O emission factors from the 2006 IPCC Guidelines were used to estimate N2O emission from
each WMS in conjunction with U.S.-specific Nex values. The implied emission factors differed from the U.S.
Inventory values due to the use of U.S.-specific Nex values and differences in populations present in each WMS
throughout the time series.
Recalculations Discussion
The CEFM produces population, VS and Nex data for cattle, excepting calves, that are used in the manure
management inventory. As a result, all changes to the CEFM described in Section 6.1 Enteric Fermentation
contributed to changes in the population, VS and Nex data used for calculating CH4 and N2O cattle emissions from
manure management. State animal populations were updated to reflect updated USDA NASS datasets. Population
changes occurred for poultry and swine in 2011. Changes also occurred for horses and mules and asses for 1990
6-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
through 1996 due to incorporation of older census data. VS for mules and asses was updated this year due to a
calculation error when the animal group was incorporated in 2011.
Planned Improvements
The uncertainty analysis will be updated in the future to more accurately assess uncertainty of emission calculations.
This update is necessary due to the extensive changes in emission calculation methodology, including estimation of
emissions at the WMS level and the use of new calculations and variables for indirect N2O emissions.
In the next Inventory report, the population, VS, and Nex values for calves calculated by the CEFM will be
incorporated into the manure management emission estimates. Calf populations will be differentiated into dairy and
beef calves so that populations between enteric fermentation and manure management will be equal. Also, the 2012
Agricultural Census data will also be incorporated into the inventory when it becomes available. These data will be
used to update animal population and WMS estimates.
6.3 Rice Cultivation (IPCC Source Category 4C)
Most of the world's rice, and all rice in the United States, is grown on flooded fields (Baicich 2013). When fields
are flooded, aerobic decomposition of organic material gradually depletes most of the oxygen present in the soil,
causing anaerobic soil conditions. Once the environment becomes anaerobic, CH4 is produced through anaerobic
decomposition of soil organic matter by methanogenic bacteria. As much as 60 to 90 percent of the CH4 produced is
oxidized by aerobic methanotrophic bacteria in the soil (some oxygen remains at the interfaces of soil and water, and
soil and root system) (Holzapfel-Pschorn et al. 1985, Sass et al. 1990). Some of the CH4 is also leached away as
dissolved CH4 in floodwater that percolates from the field. The remaining un-oxidized CH4 is transported from the
submerged soil to the atmosphere primarily by diffusive transport through the rice plants. Minor amounts of CH4
also escape from the soil via diffusion and bubbling through floodwaters.
The water management system under which rice is grown is one of the most important factors affecting CH4
emissions. Upland rice fields are not flooded, and therefore are not believed to produce CH4. In deepwater rice
fields (i.e., fields with flooding depths greater than one meter), the lower stems and roots of the rice plants are dead,
so the primary CH4 transport pathway to the atmosphere is blocked. The quantities of CH4 released from deepwater
fields, therefore, are believed to be significantly less than the quantities released from areas with shallower flooding
depths (Sass 2001). Some flooded fields are drained periodically during the growing season, either intentionally or
accidentally. If water is drained and soils are allowed to dry sufficiently, CH4 emissions decrease or stop entirely.
This is due to soil aeration, which not only causes existing soil CH4 to oxidize but also inhibits further CH4
production in soils. Rice in the United States is grown under continuously flooded, shallow water conditions; none
is grown under deepwater conditions (USD A 2012). Mid-season drainage does not occur except by accident (e.g.,
due to levee breach).
Other factors that influence CH4 emissions from flooded rice fields include fertilization practices (especially the use
of organic fertilizers), soil temperature, soil type, rice variety, and cultivation practices (e.g., tillage, seeding, and
weeding practices). The factors that determine the amount of organic material available to decompose under
anaerobic conditions (i.e., organic fertilizer use, soil type, rice variety180, and cultivation practices) are the most
important variables influencing the amount of CH4 emitted over the growing season. Soil temperature is known to
be an important factor regulating the activity of methanogenic bacteria, and therefore the rate of CH4 production.
However, although temperature controls the amount of time it takes to convert a given amount of organic material to
CH4, that time is short relative to a growing season, so the dependence of total emissions over an entire growing
season on soil temperature is weak. The application of synthetic fertilizers has also been found to influence CH4
emissions; in particular, both nitrate and sulfate fertilizers (e.g., ammonium nitrate and ammonium sulfate) appear to
inhibit CH4 formation.
180 The roots of rice plants shed organic material, which is referred to as "root exudate." The amount of root exudate produced by
a rice plant over a growing season varies among rice varieties.
Agriculture 6-15
-------
Rice is cultivated in seven states: Arkansas, California, Florida, Louisiana, Mississippi, Missouri, and Texas. Soil
types, rice varieties, and cultivation practices for rice vary from state to state, and even from farm to farm. However
most rice farmers recycle crop residues from the previous rice or rotational crop, which are left standing, disked, or
rolled into fields. Most farmers also apply synthetic fertilizer to their fields, usually urea. Nitrate and sulfate
fertilizers are not commonly used in rice cultivation in the United States. In addition, the climatic conditions of
southwest Louisiana, Texas, and Florida often allow for a second, or ratoon, rice crop. Ratoon crops are much less
common or non-existent in Arkansas, California, Mississippi, and Missouri. In 2012, Arkansas reported a larger-
than-usual ratoon crop because an early start to the planting season allowed more farmers to attempt a ratoon crop
(Hardke 2013). Methane emissions from ratoon crops have been found to be considerably higher than those from
the primary crop (Wang 2013). This second rice crop is produced from regrowth of the stubble after the first crop
has been harvested. Because the first crop's stubble is left behind in ratooned fields, and there is no time delay
between cropping seasons (which would allow the stubble to decay aerobically), the amount of organic material that
is available for anaerobic decomposition is considerably higher than with the first (i.e., primary) crop.
Rice cultivation is a small source of CH4 in the United States (Table 6-10 and Table 6-11). In 2012, CH4 emissions
from rice cultivation were 7.4 Tg CC>2 Eq. (351 Gg). Annual emissions fluctuated unevenly between the years 1990
and 2012, ranging from an annual decrease of 24 percent from 2010 and 2011 to an annual increase of 18 percent
from 2009 to 2010. There was an overall decrease of 16 percent between 1990 and 2006, due to an overall decrease
in primary crop area. However, emission levels increased again by 14 percent between 2006 and 2012 due to an
overall increase in total rice crop area. All states except Arkansas and Missouri reported a decrease in rice crop area
from 2011 to 2012. The factors that affect the rice acreage in any year vary from state to state and are typically the
result of weather phenomena (Baldwin et al. 2010).
Table 6-10: CH4 Emissions from Rice Cultivation (Tg COz Eq.)
State 1990 2005 2008 2009 2010 2011 2012
Primary 5.6 6.7 5.9 6.2 7.2 5.2 5.3
Arkansas 2.4 3.3 2.8 3.0 3.6 2.3 2.6
California 0.7 0.9 0.9 1.0 1.0 1.0 1.0
Florida + I + I + + + + +
Louisiana 1.1 1.1 0.9 0.9 1.1 0.8 0.8
Mississippi 0.5 0.5 0.5 0.5 0.6 0.3 0.3
Missouri 0.2 0.4 0.4 0.4 0.5 0.3 0.4
Oklahoma + I + I 0.0 0.0 0.0 0.0 0.0
Texas 0.7 0.4 0.3 0.3 0.4 0.4 0.3
Ratoon 2.1 0.8 1.9 1.8 2.1 1.9 2.1
Arkansas + I + I + + + +0.4
Florida + I + I + + + + +
Louisiana 1.1 0.5 1.2 1.1 1.4 1.0 1.1
Lexas 0.9 0.4 M 0.6 0.7 0.7 0.9 0.5
Total 7.7 7.5 7.8 7.9 9.3 7.1 7.4
+ Less than 0.05 Lg CO2 Eq.
Note: Lotals may not sum due to independent rounding.
Table 6-11: CH4 Emissions from Rice Cultivation (Gg)
State 1990 2005 2008 2009 2010 2011 2012
Primary 268 319 282 294 343 247 253
Arkansas 115 157 134 141 171 111 123
California 34 45 44 48 48 50 48
Florida I I I I 1 1 1 2 1
Louisiana 52 50 45 45 51 40 38
Mississippi 24 25 22 23 29 15 12
Missouri 8 I 21 19 19 24 12 17
Oklahoma + I + I + + + + +
Lexas 34 19 17 16 18 17 13
Ratoon 98 39 89 84 101 92 98
Arkansas + I I I + + + +20
Florida 2 | +| 1 2 2 2 2
6-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Louisiana
Texas
Total
59
29
358
370
+ Less than 0.5 Gg
Note: Totals may not sum due to independent rounding.
51
31
68
32
378
444
46
44
339
50
26
351
Methodology
IPCC Good Practice Guidance (GPG) (2000) recommends using harvested rice areas, and seasonally integrated
emission factors (i.e., emission factors for each commonly occurring set of rice production conditions in the country
developed from standardized field measurements representing the mix of different conditions that influence CH4
emissions in the area). To that end, the recommended GPG methodology and Tier 2 U.S.-specific seasonally
integrated emission factors derived from U.S. based rice field measurements were used. Following a literature
review of the most recent research on CH4 emissions from U.S. rice production, regional emission factors were
derived. California-specific winter flooded and non-winter flooded emission factors were applied to California rice
area harvested. Average U.S. seasonal emission factors were applied to Arkansas, Florida, Louisiana, Missouri,
Mississippi, and Texas as sufficient data to develop state-specific and/or daily emission factors were not available.
Seasonal emissions have been found to be much higher for ratooned crops than for primary crops, so emissions from
ratooned and primary areas are estimated separately using emission factors that are representative of the particular
growing season for those states where ratooning occurs. Within California, some rice crops are flooded during the
winter to prepare the fields for seedbeds for the next growing season, in addition to creating waterfowl habitat
(Young 2013); consequently, emissions from winter-flooded and non-winter flooded areas are also estimated using
separate emission factors. Winter flooded rice crops generate CH4 year round due to the anaerobic conditions the
winter flooding creates (EDF 2011). Thus for winter flooded rice crops in California, an annual CH4 emission factor
is used. For non-winter flooded California rice crops, a seasonal emission factor is applied. It has been found that up
to 50 percent of the year-round CH4 emissions in winter flooded rice crops will occur in the winter, but almost all of
the CH4 emissions from non-winter flooded rice crops occur during the growing season (Fitzgerald 2000). This
approach is consistent with IPCC (2000).
The harvested rice areas for the primary and ratoon crops in each state are presented in Table 6-12, and the ratooned
crop area as a percent of primary crop area is shown in Table 6-13. Primary crop areas for 1990 through 2012 for all
states except Florida and Oklahoma were taken from U.S. Department of Agriculture's Field Crops Final Estimates
1987-1992 (USDA 1994), Field Crops Final Estimates 1992-1997 (USDA 1998), Field Crops Final Estimates
1997-2002 (USDA 2003), and Crop Production Summary (USDA 2005 through 2013). Source data for non-USDA
sources of primary and ratoon harvest areas are shown in Table 6-14. California, Mississippi, Missouri, and
Oklahoma have not ratooned rice over the period 1990 through 2012 (Anderson 2008 through 2013; Beighley 2012;
Buehring 2009 through 2011; Guethle 1999 through 2010; Lee 2003 through 2007; Mutters 2002 through 2005;
Street 1999 through 2003; Walker 2005, 2007 through 2008).
Table 6-12: Rice Area Harvested (Hectares)
State/Crop
Arkansas
Primary
Ratoona
California
Florida
Primary
Ratoon
Louisiana
Primary
Ratoon
Mississippi
Missouri
Oklahoma
1990
485,633
159,854
4,978
2,489
220,558
66,16sB
101,174
32,376B
617
2005
2008
564,549
6
209,227
5,463
1,639
187,778
75,111
92,675
80,534
77
2009
594,901
6
225,010
5,664
2,266
187,778
65,722
98,341
80,939
-
2010
722,380
7
223,796
5,330
2,275
216,512
86,605
122,622
101,578
-
2011
467,017
5
234,723
8,212
2,311
169,162
59,207
63,942
51,801
-
2012
520,032
26,002
225,010
6,244
2,748
160,664
64,265
52,206
71,631
-
Agriculture 6-17
-------
Texas
Primary
Ratoon
Total Primary
Total Ratoon
Total
142,857
57,143
1,148,047
125,799
1,273,847
81,344
1 21,963
1,366,228
1 50,245
1,416,473
69,607
36,892
1,209,911
113,648
1,323,559
68,798
39,903
1,261,431
107,897
1,369,328
76,083
41,085
1,468,300
129,971
1,598,271
72,845
56,091
1,067,702
117,613
1,185,315
54,229
33,080
1,090,016
126,094
1,216,111
a Arkansas ratooning occurred only in 1998,1999, and 2005 through 2012, with particularly high ratoon rates in
2012.
"-" No reported value
Note: Totals may not sum due to independent rounding.
Table 6-13: Ratooned Area as Percent of Primary Growth Area
State
1990 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Arkansas +l+ + + + + + + + 0-1% + + + + + + 5%
Florida 50% 50% 50% 65% 41% 60% 54% 100% 77% 0% 28% 30% 30% 40% 43% 28% 44%
Louisiana 30% 30% 30% 30% 40% 30% 15% 35% 30% 13% 20% 35% 40% 35% 40% 35% 40%
Texas 40% 40% 40% 40% 50% 40% 37% 38% 35% 27% 39% 36% 53% 58% 54% 77% 61%
+ Indicates ratooning less than 0.1 percent of primary growth area.
Table 6-14: Non-USDA Data Sources for Rice Harvest Information (Citation Year)
State/Crop
Arkansas -
Ratoon
Florida -
Primary
Florida -
Ratoon
Louisiana -
Ratoon
Oklahoma -
Primary
Texas -
Ratoon
1990 2000 2001 2002 2003
Scheuneman
(1999-2001)
Scheuneman
(1999-2001)
Bollich (2000)
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Wilson (2002 -2007, 2009 -
Deren Kirstein
(2002) (2003)
Deren T,. , . ,~nn~ Canten
(2002) KlrSte?1nnn(42°°3- s
' (2005)
Klosterboer( 1999 -2003)
Linscombe (1999, 2001
Lee
(2003-2007)
2012) Hardke
J (2013)
Gonzales(2006-2013)
Kirstein (2006)
Gonzales(2006-2013)
-2013)
Anderson
(2008-2013)
Stansel Texas Ag Experiment Station
(2004,2005 (2006-2013)
To determine what CH4 emission factors should be used for the primary and ratoon crops, CH4 flux information
from rice field measurements in the United States was collected. Experiments that involved atypical or
nonrepresentative management practices (e.g., the application of nitrate or sulfate fertilizers, or other substances
believed to suppress CH4 formation), as well as experiments in which measurements were not made over an entire
flooding season or floodwaters were drained mid-season, were excluded from the analysis. The remaining
experimental results were then sorted by state, season (i.e., primary and ratoon), flooding practices, and type of
fertilizer amendment (i.e., no fertilizer added, organic fertilizer added, and synthetic and organic fertilizer added).
Eleven California-specific primary crop experimental results were added for California rice emissions this year.
These California-specific studies were selected because they met the criteria of experiments on primary crops with
added synthetic and organic fertilizer, without residue burning, and without winter flooding (Bossio 1999; Fitzgerald
et al. 2000). The seasonal emission rates estimated in these studies were averaged to derive a seasonal emission
factor for California's primary, non-winter flooded rice crop. Similarly, separate California-specific studies meeting
the same criteria, (i.e., primary crops with added synthetic and organic fertilizer, without residue burning) but with
winter flooding (Bossio 1999; Fitzgerald et al. 2000; McMillan et al. 2007) were averaged to derive an annual
6-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
emission factor for California's primary, winter-flooded rice crop. Approximately 60 percent of California's rice
crop is winter-flooded (Environmental Defense Fund, Inc. 2011), therefore the California-specific winter flooded
emission factor was applied to 60 percent of the California rice area harvested and the California-specific non-winter
flooded emission factor was applied to the 40 percent of the California rice area harvested. The resultant seasonal
emission factor for the California non-winter flooded crop is 133 kg CHVhectare-season, and the annual emission
factor for the California winter-flooded crop is 266 kg CH4/hectare-season.
For the remaining states, a non-California U.S. seasonal emission factor was derived by averaging seasonal
emissions rates from primary crops with added synthetic and organic fertilizer (Byrd 2000; Kongchum 2005; Rogers
et al. 2011; Sass et al. 1991a, 1991b, 2002a, 2002b; Yao 2000). The seasonal emissions rates from ratoon crops with
added synthetic fertilizer (Lindau and Bollich 1993; Lindau et al. 1995) were averaged to derive a seasonal emission
factor for the ratoon crop. The resultant seasonal emission factor for the primary crop is 237 kg CH4/hectare-season,
and the resultant emission factor for the ratoon crop is 780 kg CH4/hectare-season.
Box 6-1: Comparison of the U.S. Inventory Seasonal Emission Factors and IPCC (1996) Default Emission Factor
Emissions from rice production were estimated using a Tier 2 methodology consistent with IPCC (2000) Good
Practice Guidance. Default emission factors using experimentally determined seasonal CH4 emissions from U.S.
rice fields for both primary and ratoon crops were derived from a literature review. The 1996 IPCC Guidelines
default seasonal emission factors are compared because a U.S.-specific seasonal emission factor is provided instead
of the global daily emission factor provided in the 2006 IPCC guidelines, and the standard global seasonal emission
factor provided in the IPCC Good Practice Guidance (2000). As explained above, four different emission factors
were calculated: 1) a seasonal California-specific rate without winter flooding (133 kg CHVhectare-season), 2) an
annual California specific-rate with winter flooding (266 kg CH4/hectare-season), 3) a seasonal non-California
primary crop rate (237 kg CHVhectare-season), and 4) a seasonal non-California ratoon crop rate (780 kg
CH4/hectare-season). These emission factors represent averages across rice field measurements representing typical
water management practices and synthetic and organic amendment application practices in the United States
according to regional experts (Anderson 2013; Beighly 2012; Fife 2011; Gonzalez 2013; Linscombe 2013;
Vayssieres 2013; Wilson 2012). The IPCC (1996) default factor for U.S. (i.e., Texas) rice production of both
primary and ratoon crops is 250 kg CHVhectare-season .This default value is based on a study by Sass and Fisher
(1995) which reflects a growing season in Texas of approximately 275 days. Data results in the evaluated studies
were provided as seasonal emission factors; therefore, neither daily emission factors nor growing season length was
estimated. Some variability within season lengths in the evaluated studies is assumed. The Tier 2 emission factors
used here represent rice cultivation practices specific to the United States. For comparison, the 2012 U.S. emissions
from rice production are 7.4 Tg CCh Eq. using the four U.S.-specific emission factors for both primary and ratoon
crops and 6.4 Tg CC>2 Eq. using the IPCC (1996) emission factor.
Table 6-15: Non-California Seasonal Emission Factors (kg Cm/ha-season)
Primary
Low
High
Mean
61
500
237
Ratoon
Low 481
High 1490
Mean 780
Table 6-16: California Emission Factors (kg ChU/ha)
Winter Flooded
(Annual)3
Low 131
High 369
Mean 266
Non-Winter
Flooded
(Seasonal)1"
Low 62
High 221
Mean 133
1 Percentage of CA rice crop winter flooded: 60 percent
b Percentage of CA rice crop not winter flooded: 40 percent
Agriculture 6-19
-------
Uncertainty and Time-Series Consistency
The largest uncertainty in the calculation of CH4 emissions from rice cultivation is associated with the emission
factors. Seasonal emissions, derived from field measurements in the United States, vary by more than one order of
magnitude. This inherent variability is due to differences in cultivation practices, particularly fertilizer type,
amount, and mode of application; differences in cultivar type; and differences in soil and climatic conditions. A
portion of this variability is accounted for by separating primary from ratooned areas. However, even within a
cropping season or a given management regime, measured emissions may vary significantly. Of the experiments
used to derive the emission factors applied here, primary emissions ranged from 61 to 500 kg CH^hectare-season
and ratoon emissions ranged from 481 to 1,490 kg CH^hectare-season. The uncertainty distributions around the
California winter flooding, California non-winter flooding, non-California primary, and ratoon emission factors
were derived using the distributions of the relevant emission factors available in the literature and described above.
Variability around the rice emission factor means was not normally distributed for any crops, but rather skewed,
with a tail trailing to the right of the mean. A lognormal statistical distribution was, therefore, applied in the Tier 2
Monte Carlo analysis.
Other sources of uncertainty include the primary rice-cropped area for each state, percent of rice-cropped area that is
ratooned, the length of the growing season, and the extent to which flooding outside of the normal rice season is
practiced. Expert judgment was used to estimate the uncertainty associated with primary rice-cropped area for each
state at 1 to 5 percent, and a normal distribution was assumed. Uncertainties were applied to ratooned area by state,
based on the level of reporting performed by the state. Within California, the uncertainty associated with the
percentage of rice fields that are winter flooded was estimated at plus and minus 20 percent. No uncertainty
estimates were calculated for the practice of flooding outside of the normal rice season outside of California because
CH4 flux measurements have not been undertaken over a sufficient geographic range or under a broad enough range
of representative conditions to account for this source in the emission estimates or its associated uncertainty.
To quantify the uncertainties for emissions from rice cultivation, a Monte Carlo (Tier 2) uncertainty analysis was
performed using the information provided above. The results of the Tier 2 quantitative uncertainty analysis are
summarized in Table 6-17. Rice cultivation CH4 emissions in 2012 were estimated to be between 3.57 and 14.47 Tg
CO2 Eq. at a 95 percent confidence level, which indicates a range of 52 percent below to 96 percent above the actual
2012 emission estimate of 7.38 Tg CO2 Eq.
Table 6-17: Tier 2 Quantitative Uncertainty Estimates for Cm Emissions from Rice
Cultivation (Tg COz Eq. and Percent)
Source Gas 2012 Emission
Estimate
(Tg C02 Eq.)
Rice Cultivation CH4 7.38
Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (%)
Lower
Bound
3.57
Upper
Bound
14.47
Lower
Bound
-52%
Upper
Bound
+96%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence
interval.
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A source-specific QA/QC plan for rice cultivation was developed and implemented. This effort included a Tier 1
analysis, as well as portions of a Tier 2 analysis. The Tier 2 procedures focused on comparing trends across years,
states, and cropping seasons to attempt to identify any outliers or inconsistencies. No problems were found.
6-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Recalculations Discussion
An updated literature review of rice emission factor estimates was conducted for the current Inventory, resulting in
an updated set of regional rice emission factors. In the previous Inventory, two U.S. average emission factors were
applied to rice area harvested—one for the primary crop (210 kg CHVhectare-season) and one for the ratoon crop
(780 kg CH4/hectare-season). The updated emission factors, based on the recent literature, replace the primary crop
emission factor with two California-specific emission factors based on flooding practices and an updated non-
California primary crop emission factor of 237 kg CH4/hectare-season. The new emission factors were applied
across the full time series, as they represent the same assumptions about rice cultivation practices. The change in
emission factors resulted, on average, in an 8.3 percent increase in emissions from 1990 to 2011.
Planned Improvements
A planned improvement for the 1990 through 2013 Inventory will be the expansion of the California specific rice
emission factors to include an emission factor for the period prior to the passage of the Air Resources Board (ARE)
Mandate phasing out rice residue burning. This non-flooded residue burned emission factor will take into account
the phase down of rice straw burning that occurred in California from 1990 to 2002. During this time period, the
percentage of acres burned annually decreased from 75 percent in 1992 to 13 percent in 2002 (California Air
Resources Board 2003). California studies that include rice burning on non-flooded lands will be used to develop the
pre-2002 rice burning emission factor, and further research will be conducted to determine the percentage of winter
flooded acres to which the current California winter flooded emission factor will be applied. This new time series
dependent emission factor will be applied to non-flooded burned acres during the 1990 through 2002 time period to
capture the significant change in the percentage of rice acreage burned due to the California ARE Mandate.
Following 2002, the current methodology and emission factors will be applied.
Another possible future improvement is to create additional state- or region-specific emission factors for rice
cultivation. This prospective improvement would likely not take place for another 2 to 3 years, because the analyses
needed for it are currently taking place.
6.4 Agricultural Soil Management (IPCC Source
Category 4D)
Nitrous oxide is produced naturally in soils through the microbial processes of nitrification and denitrification.181 A
number of agricultural activities increase mineral N availability in soils, thereby increasing the amount available for
nitrification and denitrification, and ultimately the amount of N2O emitted. These activities increase soil mineral N
either directly or indirectly (see Figure 6-2). Direct increases occur through a variety of management practices that
add or lead to greater release of mineral N to the soil, including fertilization; application of managed livestock
manure and other organic materials such as sewage sludge; deposition of manure on soils by domesticated animals
in pastures, rangelands, and paddocks (PRP) (i.e., by grazing animals and other animals whose manure is not
managed); production of N-fixing crops and forages; retention of crop residues; and drainage of organic soils in
croplands and grasslands (i.e., soils with a high organic matter content, otherwise known as Histosols).182 Other
181 Nitrification and denitrification are driven by the activity of microorganisms in soils. Nitrification is the aerobic microbial
oxidation of ammonium (NH4+) to nitrate (NOs"), and denitrification is the anaerobic microbial reduction of nitrate to N2. Nitrous
oxide is a gaseous intermediate product in the reaction sequence of denitrification, which leaks from microbial cells into the soil
and then into the atmosphere. Nitrous oxide is also produced during nitrification, although by a less well-understood mechanism
(Nevison 2000).
182 Drainage of organic soils in former wetlands enhances mineralization of N-rich organic matter, thereby increasing N2O
emissions from these soils.
Agriculture 6-21
-------
agricultural soil management activities, including irrigation, drainage, tillage practices, and fallowing of land, can
influence N mineralization in soils, and thereby affect direct emissions. Mineral N is also made available in soils
through decomposition of soil organic matter and plant litter, as well as asymbiotic fixation of N from the
atmosphere, and these processes are influenced by agricultural management through impacts on moisture and
temperature regimes in soils.183 The N mineralization from decomposition of soil organic matter and also
asymbiotic N fixation are included based on the recommendation from the IPCC (2006) for complete accounting of
management impacts on greenhouse gas emissions, as discussed in the Methodology section. Indirect emissions of
N2O occur through two pathways: (1) volatilization and subsequent atmospheric deposition of applied/mineralized
N, and (2) surface runoff and leaching of applied/mineralized N into groundwater and surface water.184 Direct
emissions from agricultural lands (i.e., cropland and grassland as defined in Chapter 7, Land Representation Section)
are included in this section, while direct emissions from forest lands and settlements are presented in the Land Use,
Land-Use Change, and Forestry chapter. However, indirect N2O emissions from all land-uses (cropland, grassland,
forest lands, and settlements) are reported in this section.
183 Asymbiotic N fixation is the fixation of atmospheric N2 by bacteria living in soils that do not have a direct relationship with
plants.
184 These processes entail volatilization of applied or mineralized N as NHs and NOX, transformation of these gases within the
atmosphere (or upon deposition), and deposition of the N primarily in the form of participate NH4+, nitric acid (HNOs), and NOX.
6-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure 6-2: Sources and Pathways of N that Result in NzO Emissions from Agricultural Soil
Management
Sources and Pathways of N that Result in N20 Emissions from Agricultural Soil Management
N Volatilization
Synthetic N Fertilizers
Synthetic N fertilizer applied to soi
Organic
Amendments
Includes both com martial and
non-co,mTnercisl fertilizers (i.e.,
animal manure compost
Urine and Dung from
Grazing Animals
e deposited on pasture* rang
nd paddock
ncludes above- and belowground
residues forali crops (non-N and N-
ixing (and from perennial forage
crops and pastures followingrenewal
Mineralization of
Soil Organic Matter
includes N converted to mineral form
upon decomposition of soil organic
Asymbiotic Fixation
Fixation of atmospheric N2 by bacteria
living insoiisthatdo not have a direct
relationship with plants
This graphic illustrates the sources and pathways of nitrogen that result
in direct and indirect N20 emissions from soils using the methodologies
described in this Inventory. Emission pathways are shown with arrows.
On the lower right-hand side is a cut-away view of a representative
section of a managed soil; histosol cultivation is represented here.
Agriculture 6-23
-------
Agricultural soils produce the majority of N2O emissions in the United States. Estimated emissions from this source
in 2012 were 306.6 Tg CO2 Eq. (989 Gg N2O) (see Table 6-18 and Table 6-19). Annual N2O emissions from
agricultural soils fluctuated between 1990 and 2012, although overall emissions were 8.7 percent higher in 2012
than in 1990. Year-to-year fluctuations are largely a reflection of annual variation in weather patterns, synthetic
fertilizer use, and crop production. On average, cropland accounted for approximately 61 percent of total direct
emissions, while grassland accounted for approximately 39 percent. The percentages for indirect emissions are
approximately 76 percent for croplands, 22 percent for grasslands, and the remaining 2 percent is from forest lands
and settlements. Estimated direct and indirect N2O emissions by sub-source category are shown in Table 6-20 and
Table 6-21.
Table 6-18: NzO Emissions from Agricultural Soils (Tg COz Eq.)
Activity
Direct
Cropland
Grassland
Indirect (All Land-
Use Types)
Cropland
Grassland
Forest Land
Settlements
Total
+ Less than 0.05 Tg CO2
1990
240.7
155.1
85.6
41.4 1
31.6
9.5
+
0.4
282.1
Eq.
Table 6-19: NzO Emissions from
Activity
Direct
Cropland
Grassland
Indirect (All Land-Use
Types)
Cropland
Grassland
Forest Land
Settlements
Total
1990
776
500 1
276 1
134 1
102 1
31 1
0 1
1
910
2005
253.3
162.8
90.5
44.0 1
32.7
10.6 1
0.1 1
0.6 •
297.3
2008
269.5
166.5
103.0
49.5
38.2
10.6
0.1
0.6
319.0
Agricultural Soils
2005
817
525
292
142
105
34
+ 1
2
959
2008
869
537
332
160
123
34
+
2
1,029
2009
267.6
165.2
102.5
48.8
37.6
10.4
0.1
0.6
316.4
(Gg)
2009
863
533
331
157
121
34
+
2
1,021
2010
264.0
162.1
101.9
46.1
35.1
10.2
0.1
0.6
310.1
2010
852
523
329
149
113
33
+
2
1,000
2011
261.9
161.0
100.9
45.8
35.2
9.9
0.1
0.6
307.8
2011
845
519
325
148
114
32
+
2
993
2012
260.9
159.8
101.1
45.7
34.9
10.2
0.1
0.6
306.6
2012
842
515
326
147
112
33
+
2
989
+ Lessthan0.5GgN2O
6-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 6-20: Direct NzO Emissions from Agricultural Soils by Land Use Type and N Input Type
(Tg CQ2 Eg.)
Activity
Cropland
Mineral Soils
Synthetic Fertilizer
Organic
Amendment15
Residue Na
Mineralization and
Asymbiotic
Fixation
Organic Soils"
Grassland
Mineral Soils
Synthetic Fertilizer
PRP Manure
Managed Manure
Sewage Sludge
Residue Nc
Mineralization and
Asymbiotic Fixation
Total
1990
155.1
150.4
65.5
14.0
3.9
67.0
4.7
85.6
85.6
0.5
24.5
0.3
0.3
2.0
58.2
240.7
2005
162.8
158.7
65.8
15.3
4.8
72.9
4.1
90.5
90.5
1.0
25.5
0.3
0.5
2.4
60.8
253.3
H 2008
166.
162.
69,
15,
4,
72,
4.
103.
103.
1
26,
0,
0,
2,
72,
269.
,5
,5
.5
.8
.6
.5
0
0
0
.0
.6
.3
.5
.6
.0
,5
2009
165.2
161.1
69.0
15.7
4.6
71.8
4.0
102.5
102.5
1.0
26.3
0.3
0.5
2.6
71.9
267.6
2010
162
158,
68
15
4
69
4
101
101
1
25
0
0
2
71
264,
.1
.1
.6
.4
.5
.5
.0
.9
.9
.0
.8
.3
.5
.6
.7
.0
2011
161.0
157.0
67.4
15.5
4.5
69.6
4.0
100.9
100.9
1.0
25.0
0.3
0.6
2.5
71.5
261.9
2012
159.8
155.7
67.3
15.5
4.4
68.5
4.0
101.1
101.1
0.9
25.4
0.3
0.6
2.5
71.3
260.9
a Cropland residue N inputs include N in unharvested legumes as well as crop residue N.
b Organic amendment inputs include managed manure amendments, daily spread manure amendments, and
commercial organic fertilizers (i.e., dried blood, dried manure, tankage, compost, and other).
c Grassland residue N inputs include N in ungrazed legumes as well as ungrazed grass residue N
d Accounts for managed manure and daily spread manure amendments that are applied to grassland soils.
e Includes drainage of organic soils for both cropland and grasslands.
Table 6-21: Indirect NzO Emissions from all Land-Use Types (Tg COz Eq.)
Activity
1990
2005
2008 2009 2010 2011 2012
Cropland
Volatilization & Atm.
Deposition
Surface Leaching & Run-Off
Grassland
Volatilization & Atm.
Deposition
Surface Leaching & Run-Off
Forest Land
Volatilization & Atm.
Deposition
Surface Leaching & Run-Off
Settlements
Volatilization & Atm.
Deposition
Surface Leaching & Run-Off
31.6
Total
+ Less than 0.05 Tg CO2 Eq.
38.2 37.6 35.1 35.2 34.9
15.3
22.3
10.4
5.5
5.0
0.1
0.1
0.6
0.2
0.4
15.3
19.8
10.2
5.4
4.8
0.1
0.1
0.6
0.2
0.4
15.5
19.8
9.9
5.3
4.5
0.1
0.1
0.6
0.2
0.4
15.4
19.5
10.2
5.4
4.8
0.1
0.1
0.6
0.2
0.4
49.5 48.8 46.1 45.8 45.7
Figure 6-3 and Figure 6-6 show regional patterns in direct N2O emissions, and also show N losses from
volatilization, leaching, and runoff that lead to indirect N2O emissions. Annual emissions and N losses in 2012 are
shown for the Tier 3 Approach only.
Agriculture 6-25
-------
Direct N2O emissions from croplands tend to be high in the Corn Belt (Illinois, Iowa, Indiana, Ohio, southern and
western Minnesota, eastern and southern Nebraska, in addition to eastern South Dakota and North Dakota), where a
large portion of the land is used for growing highly fertilized corn and N-fixing soybean crops (Figure 6-3). New
York, Pennsylvania, Michigan and Wisconsin also have relatively high production of corn and soybeans. Direct
emissions are high in Kansas, Missouri and Texas, primarily from irrigated cropping in western Texas, dryland
wheat in Kansas, and hay cropping in eastern Texas and Missouri. Direct emissions are low in many parts of the
eastern United States because a small portion of land is cultivated, and also low in many western states where
rainfall and access to irrigation water are limited.
Direct emissions (Tg CCh Eq./state/year) from grasslands are highest in the central and western United States
(Figure 6-4) where a high proportion of the land is used for cattle grazing. Most areas in the Great Lake states, the
Northeast, and Southeast have moderate to low emissions because the total amount of grassland is much lower than
in the central and western United States, however, emissions from these areas tend to be higher on a per unit area
basis compared to other areas of the country.
Indirect emissions from croplands and grasslands (Figure 6-5 and Figure 6-6) show patterns similar to direct
emissions because the factors that control direct emissions (N inputs, weather, soil type) also influence indirect
emissions. However, there are some exceptions, because the processes that contribute to indirect emissions (NOs"
leaching, N volatilization) do not respond in exactly the same manner as the processes that control direct emissions
(nitrification and denitrification). For example, coarser-textured soils facilitate relatively high indirect emissions in
Florida grasslands due to high rates of N volatilization and NOs" leaching, even though they have only moderate
rates of direct N2O emissions.
Figure 6-3: Crops, Annual Direct NzO Emissions Estimated Using the Tier 3 DAYCENT Model,
1990-2012 (Tg CO2 Eq./year)
6-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure 6-4: Grasslands, Annual Direct NzO Emissions Estimated Using the Tier 3 DAYCENT
Model, 1990-2012 (Tg COz Eq./year)
Figure 6-5: Crops, Average Annual N Losses Leading to Indirect NzO Emissions Estimated
Using the Tier 3 DAYCENT Model, 1990-2012 (Gg N/year)
Agriculture 6-27
-------
Figure 6-6: Grasslands, Average Annual N Losses Leading to Indirect NzO Emissions
Estimated Using the Tier 3 DAYCENT Model, 1990-2012 (Gg N/year)
Methodology
The 2006IPCC Guidelines (IPCC 2006) divide the Agricultural Soil Management source category into five
components: (1) direct emissions due to N additions to cropland and grassland mineral soils, including synthetic
fertilizers, sewage sludge applications, crop residues, organic amendments, and biological N fixation associated with
planting of legumes on cropland and grassland soils; (2) direct emissions from soil organic matter mineralization
due to land use and management change, (3) direct emissions from drainage of organic soils in croplands and
grasslands; (4) direct emissions from soils due to the deposition of manure by livestock on PRP grasslands; and (5)
indirect emissions from soils and water due to N additions and manure deposition to soils that lead to volatilization,
leaching, or runoff of N and subsequent conversion to N2O.
The United States has adopted recommendations from IPCC (2006) on methods for agricultural soil management.
These recommendations include (1) estimating the contribution of N from crop residues to indirect soil N2O
emissions; (2) adopting a revised emission factor for direct N2O emissions to the extent that Tier 1 methods are used
in the Inventory (described later in this section); (3) removing double counting of emissions from N-fixing crops
associated with biological N fixation and crop residue N input categories; (4) using revised crop residue statistics to
compute N inputs to soils based on harvest yield data to the extent that Tier 1 methods are used in the Inventory; (5)
accounting for indirect as well as direct emissions from N made available via mineralization of soil organic matter
and litter, in addition to asymbiotic fixation (i.e., computing total emissions from managed land); (6) reporting all
emissions from managed lands because management affects all processes leading to soil N2O emissions; and (7)
estimating emissions associated with land use and management change which can significantly change the N
mineralization rates from soil organic matter.185 One recommendation from IPCC (2006) that has not been
185 N inputs from asymbiotic N fixation are not directly addressed in 2006 IPCC Guidelines, but are a component of the total
emissions from managed lands and are included in the Tier 3 approach developed for this source.
6-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
completely adopted is the accounting of emissions from pasture renewal, which involves occasional plowing to
improve forage production. Pastures are replanted occasionally in rotation with annual crops, and this practice is
represented in the Inventory. However, renewal of pasture that is not occasionally rotated with annual crops is
uncommon in the United States, and is not estimated.
Direct N2O Emissions
The methodology used to estimate direct emissions from agricultural soil management in the United States is based
on a combination of IPCC Tier 1 and 3 approaches. A Tier 3 process-based model (DAYCENT) was used to
estimate direct emissions from a variety of crops that are grown on mineral soils on mineral (i.e., non-organic) soils,
including alfalfa hay, barley, corn, cotton, dry beans, grass hay, grass-clover hay, oats, onions, peanuts, potatoes,
rice, sorghum, soybeans, sugar beets, sunflowers, tomatoes, and wheat; as well as the direct emissions from non-
federal grasslands with the exception of sewage sludge amendments (Del Grosso et al. 2010). The Tier 3 approach
has been specifically designed and tested to estimate N2O emissions in the United States, accounting for more of the
environmental and management influences on soil N2O emissions than the IPCC Tier 1 method (see Box 6-2 for
further elaboration). Moreover, the Tier 3 approach allows for the inventory to address direct N2O emissions and
soil C stock changes from mineral cropland soils in a single analysis. Carbon and N dynamics are linked in plant-
soil systems through biogeochemical processes of microbial decomposition and plant production (McGill and Cole
1981). Coupling the two source categories (i.e., agricultural soil C and N2O) in a single inventory analysis ensures
that there is a consistent activity data and treatment of the processes, and interactions are taken into account between
C and N cycling in soils.
The Tier 3 approach is based on the cropping and land use histories recorded in the USDA National Resources
Inventory (NRI) survey (USDA-NRCS 2009). The NRI is a statistically-based sample of all non-federal land, and
includes 380,956 points in agricultural land for the conterminous United States that are included in the Tier 3
methods.186 Each point is associated with an "expansion factor" that allows scaling of N2O emissions from NRI
points to the entire country (i.e., each expansion factor represents the amount of area with the same land-
use/management history as the sample point). Land-use and some management information (e.g., crop type, soil
attributes, and irrigation) were originally collected for each NRI point on a 5-year cycle beginning in 1982. For
cropland, data were collected for 4 out of 5 years in the cycle (i.e., 1979-1982, 1984-1987, 1989-1992, and 1994-
1997). In 1998, the NRI program began collecting annual data, and data are currently available through 2007.
Box 6-2: Tier 1 vs. Tier 3 Approach for Estimating N2O Emissions
The IPCC (2006) Tier 1 approach is based on multiplying activity data on different N inputs (e.g., synthetic
fertilizer, manure, N fixation, etc.) by the appropriate default IPCC emission factors to estimate N2O emissions on
an input-by-input basis. The Tier 1 approach requires a minimal amount of activity data, readily available in most
countries (e.g., total N applied to crops); calculations are simple; and the methodology is highly transparent. In
contrast, the Tier 3 approach developed for this Inventory employs a process-based model (i.e., DAYCENT) that
represents the interaction of N inputs and the environmental conditions at specific locations. Consequently, the Tier
3 approach produces more accurate estimates; it accounts more comprehensively for land-use and management
impacts and their interaction with environmental factors (i.e., weather patterns and soil characteristics), which will
enhance or dampen anthropogenic influences. However, the Tier 3 approach requires more detailed activity data
(e.g., crop-specific N amendment rates), additional data inputs (e.g., daily weather, soil types, etc.), and considerable
computational resources and programming expertise. The Tier 3 methodology is less transparent, and thus it is
critical to evaluate the output of Tier 3 methods against measured data in order to demonstrate the adequacy of the
method for estimating emissions (IPCC 2006). Another important difference between the Tier 1 and Tier 3
approaches relates to assumptions regarding N cycling. Tier 1 assumes that N added to a system is subject to N2O
emissions only during that year and cannot be stored in soils and contribute to N2O emissions in subsequent years.
This is a simplifying assumption that is likely to create bias in estimated N2O emissions for a specific year. In
186 ]\JRJ points were classified as agricultural if under grassland or cropland management between 1990 and 2007. There are
another 148,731 NRI survey points that are cropland) and are not included in the Tier 3 analysis. The soil N2O emissions
associated with these points are estimated with the IPCC Tier 1 method.
Agriculture 6-29
-------
contrast, the process-based model used in the Tier 3 approach includes the legacy effect of N added to soils in
previous years that is re-mineralized from soil organic matter and emitted as N2O during subsequent years.
The Tier 1IPCC (2006) methodology was used to estimate (1) direct emissions from crops on mineral soils that are
not simulated by DayCent (e.g., tobacco, sugarcane, orchards, vineyards, and other crops); (2) direct emissions from
Pasture/Range/Paddock on federal grasslands, which were not estimated with the Tier 3 DAYCENT model; and (3)
direct emissions from drainage of organic soils in croplands and grasslands.
Tier 3 Approach for Mineral Cropland Soils
The DAYCENT biogeochemical model (Parton et al. 1998; Del Grosso et al. 2001, 2011) was used to estimate
direct N2O emissions from mineral cropland soils that are managed for production of a wide variety of crops based
on the cropping histories in the National Resources Inventory (USDA-NRCS 2009). The crops include alfalfa hay,
barley, corn, cotton, dry beans, grass hay, grass-clover hay, oats, onions, peanuts, potatoes, rice, sorghum, soybeans,
sugar beets, sunflowers, tomatoes, and wheat. Crops simulated by DAYCENT are grown on approximately 93
percent of total cropland area in the United States. For agricultural systems in the central region of the United
States, crop production for key crops (i.e., corn, soybeans, sorghum, cotton and wheat) is simulated with NASA-
CASA production algorithm (Potter et al. 1993; Potter et al. 2007) using the MODIS Enhanced Vegetation Index
(EVI) products, MOD13Q1 and MYD13Q1, with a pixel resolution of 250m. A prediction algorithm was developed
to estimate EVI (Gurung et al. 2009) for gap-filling during years over the inventory time series when EVI data were
not available (e.g., data from the MODIS sensor were only available after 2000 following the launch of the Aqua
and Terra Satellites; see Annex 3.11 for more information). DAYCENT also simulated soil organic matter
decomposition, greenhouse gas fluxes, and key biogeochemical processes affecting N2O emissions.
DAYCENT was used to estimate direct N2O emissions due to mineral N available from the following sources: (1)
the application of synthetic fertilizers; (2) the application of livestock manure; (3) the retention of crop residues and
subsequent mineralization of N during microbial decomposition (i.e., leaving residues in the field after harvest
instead of burning or collecting residues); and (4) mineralization of soil organic matter, in addition to asymbiotic
fixation. Note that commercial organic fertilizers are addressed with the Tier 1 method because county-level
application data would be needed to simulate applications in DAYCENT, and currently data are only available at the
national scale. The third and fourth sources are generated internally by the DAYCENT model.
Synthetic fertilizer data were based on fertilizer use and rates by crop type for different regions of the United States
that were obtained primarily from the USDA Economic Research Service Cropping Practices Survey (USDA-ERS
1997, 2011) with additional data from other sources, including the National Agricultural Statistics Service (NASS
1992, 1999, 2004). Frequency and rates of livestock manure application to cropland during 1997 were estimated
from data compiled by the USDA Natural Resources Conservation Service (Edmonds et al. 2003), and then adjusted
using county-level estimates of manure available for application in other years. The adjustments were based on
county-scale ratios of manure available for application to soils in other years relative to 1997 (see Annex 3.12 for
further details). Greater availability of managed manure N relative to 1997 was assumed to increase the area
amended with manure, while reduced availability of manure N relative to 1997 was assumed to reduce the amended
area. Data on the county-level N available for application were estimated for managed systems based on the total
amount of N excreted in manure minus N losses during storage and transport, and including the addition of N from
bedding materials. Nitrogen losses include direct N2O emissions, volatilization of ammonia and NOX, runoff and
leaching, and poultry manure used as a feed supplement. For unmanaged systems, it is assumed that no N losses or
additions occur prior to the application of manure to the soil. More information on livestock manure production is
available in the Manure Management Section 6.2 and Annex 3.11.
The IPCC approach considers crop residue N and N mineralized from soil organic matter as activity data. However,
they are not treated as activity data in DAYCENT simulations because residue production, symbiotic N fixation
(e.g., legumes), mineralization of N from soil organic matter, and asymbiotic N fixation are internally generated by
the model as part of the simulation. In other words, DAYCENT accounts for the influence of symbiotic N fixation,
mineralization of N from soil organic matter and crop residue retained in the field, and asymbiotic N fixation on
N2O emissions, but these are not model inputs. The DAYCENT simulations also accounted for the approximately 3
percent of all crop residues that were assumed to be burned based on state inventory data (ILENR 1993; Oregon
6-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Department of Energy 1995;Noller 1996; Wisconsin Department of Natural Resources 1993; Cibrowski 1996), and
therefore N2O emissions were reduced by 3 percent from crop residues to account for the burning.
Additional sources of data were used to supplement the mineral N (USDAERS 1997, 2011), livestock manure
(Edmonds et al. 2003), and land-use information (USDA-NRCS 2009). The Conservation Technology Information
Center (CTIC 2004) provided annual data on tillage activity with adjustments for long-term adoption of no-till
agriculture (Towery 2001). Tillage data has an influence on soil organic matter decomposition and subsequent soil
N2O emissions. The time series of tillage data began in 1989 and ended in 2004, so further changes in tillage
practices since 2004 are not currently captured in the inventory. Daily weather data were used as an input in the
model simulations, based on gridded weather data at a 32 km scale from the North America Regional Reanalysis
Product (NARR) (Mesinger et al. 2006). Soil attributes were obtained from the Soil Survey Geographic Database
(SSURGO) (Soil Survey Staff 2011).
Each NRI point was run 100 times as part of the uncertainty assessment, yielding a total of over 18 million
simulations for the analysis. Soil N2O emission estimates from DAYCENT were adjusted using a structural
uncertainty estimator accounting for uncertainty in model algorithms and parameter values (Del Grosso et al. 2010).
Soil N2O emissions and 95 percent confidence intervals were estimated for each year between 1990 and 2007, but
emissions from 2008 to 2012 were assumed to be similar to 2007 because no additional activity data are currently
available from the NRI for the latter years.
Nitrous oxide emissions from managed agricultural lands are the result of interactions among anthropogenic
activities (e.g., N fertilization, manure application, tillage) and other driving variables, such as weather and soil
characteristics. These factors influence key processes associated with N dynamics in the soil profile, including
immobilization of N by soil microbial organisms, decomposition of organic matter, plant uptake, leaching, runoff,
and volatilization, as well as the processes leading to N2O production (nitrification and denitrification). It is not
possible to partition N2O emissions into each anthropogenic activity directly from model outputs due to the
complexity of the interactions (e.g., N2O emissions from synthetic fertilizer applications cannot be distinguished
from those resulting from manure applications). To approximate emissions by activity, the amount of mineral N
added to the soil for each of these sources was determined and then divided by the total amount of mineral N that
was made available in the soil according to the DAYCENT model. The percentages were then multiplied by the
total of direct N2O emissions in order to approximate the portion attributed to key practices. This approach is only
an approximation because it assumes that all N made available in soil has an equal probability of being released as
N2O, regardless of its source, which is unlikely to be the case (Delgado et al., 2009). However, this approach allows
for further disaggregation of emissions by source of N, which is valuable for reporting purposes and is analogous to
the reporting associated with the IPCC (2006) Tier 1 method, in that it associates portions of the total soil N2O
emissions with individual sources of N.
Tier 1 Approach for Mineral Cropland Soils
The IPCC (2006) Tier 1 methodology was used to estimate direct N2O emissions for mineral cropland soils that are
managed for production of crop types not simulated by DAYCENT, such as tobacco, sugarcane, and millet. For the
Tier 1 Approach, estimates of direct N2O emissions from N applications were based on mineral soil N that was
made available from the following practices: (1) the application of synthetic commercial fertilizers; (2) application
of managed manure and non-manure commercial organic fertilizers; and (3) the retention of above- and below-
ground crop residues in agricultural fields (i.e., crop biomass that is not harvested). Non-manure commercial
organic amendments were not included in the DAYCENT simulations because county-level data were not
available.187 Consequently, commercial organic fertilizer, as well as additional manure that was not added to crops
in the DAYCENT simulations, were included in the Tier 1 analysis. The influence of land-use change on soil N2O
emissions in the Tier 1 approach has not been addressed in this analysis, but is a planned improvement. The
following sources were used to derive activity data:
187 Commercial organic fertilizers include dried blood, tankage, compost, and other, but the dried manure and sewage sludge is
removed from the dataset in order to avoid double counting with other datasets that are used for manure N and sewage sludge.
Agriculture 6-31
-------
• A process-of-elimination approach was used to estimate synthetic N fertilizer additions for crops not
simulated by DAYCENT, because little information exists on their fertilizer application rates. The total
amount of fertilizer used on farms has been estimated at the count- level by the USGS from sales records
(Ruddy et al. 2006), and these data were aggregated to obtain state-level N additions to farms. For 2002
through 2012, state-level fertilizer for on-farm use is adjusted based on annual fluctuations in total U.S.
fertilizer sales (AAPFCO 1995 through 2012).188 After subtracting the portion of fertilizer applied to crops
and grasslands simulated by DAYCENT (see Tier 3 Approach for Cropland Mineral Soils Section and
Grasslands Section for information on data sources), the remainder of the total fertilizer used on farms was
assumed to be applied to crops that were not simulated by DAYCENT.
• Similarly, a process-of-elimination approach was used to estimate manure N additions for crops that were
not simulated by DAYCENT because little information exists on application rates for these crops. The
amount of manure N applied in the Tier 3 approach to crops and grasslands was subtracted from total
manure N available for land application (see Tier 3 Approach for Cropland Mineral Soils Section and
Grasslands Section for information on data sources), and this difference was assumed to be applied to crops
that are not simulated by DAYCENT.
• Commercial organic fertilizer additions were based on organic fertilizer consumption statistics, which were
converted to units of N using average organic fertilizer N content (TVA 1991 through 1994; AAPFCO
1995 through 2011). Commercial fertilizers do include some manure and sewage sludge, but the amounts
are removed from the commercial fertilizer data to avoid double counting with the manure N dataset
described above and the sewage sludge amendment data discussed later in this section.
• Crop residue N was derived by combining amounts of above- and below-ground biomass, which were
determined based on crop production yield statistics (USDA 1994, 1998, 2003, 2005, 2006, 2008, 2009,
2010a), dry matter fractions (IPCC 2006), linear equations to estimate above-ground biomass given dry
matter crop yields from harvest (IPCC 2006), ratios of below-to-above-ground biomass (IPCC 2006), and
N contents of the residues (IPCC 2006).
The total increase in soil mineral N from applied fertilizers and crop residues was multiplied by the IPCC (2006)
default emission factor to derive an estimate of direct N2O emissions using the Tier 1 Approach.
Drainage of Organic Soils in Croplands and Grasslands
The IPCC (2006) Tier 1 methods were used to estimate direct N2O emissions due to drainage of organic soils in
croplands or grasslands at a state scale. State-scale estimates of the total area of drained organic soils were obtained
from the National Resources Inventory (NRI) (USDA-NRCS 2009) using soils data from the Soil Survey
Geographic Database (SSURGO) (Soil Survey Staff 2011). Temperature datafromDaly etal. (1994, 1998) were
used to subdivide areas into temperate and tropical climates using the climate classification from IPCC (2006).
Annual data were available between 1990 and 2007. Emissions are assumed to be similar to 2007 from 2008 to
2012 because no additional activity data are currently available from the NRI for the latter years. To estimate annual
emissions, the total temperate area was multiplied by the IPCC default emission factor for temperate regions, and the
total tropical area was multiplied by the IPCC default emission factor for tropical regions (IPCC 2006).
Direct N2O Emissions from Grassland Soils
As with N2O from croplands, the Tier 3 process-based DAYCENT model and Tier 1 method described in IPCC
(2006) were combined to estimate emissions from non-federal grasslands and Pasture/Range/Paddock manure N
additions for federal grasslands, respectively. Grasslands include pastures and rangelands used for grass forage
production, where the primary use is livestock grazing. Rangelands are typically extensive areas of native
188 Values were not available for 2012 so a "least squares line" statistical extrapolation using the previous 5 years of data is used
to arrive at an approximate value.
6-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
grasslands that are not intensively managed, while pastures are often seeded grasslands, possibly following tree
removal, which may or may not be improved with practices such as irrigation and interseeding legumes.
DAYCENT was used to simulate N2O emissions from NRI survey locations (USDA-NRCS 2009) on non-federal
grasslands resulting from manure deposited by livestock directly onto pastures and rangelands (i.e., PRP manure), N
fixation from legume seeding, managed manure amendments (i.e., manure other than PRP manure such as Daily
Spread), and synthetic fertilizer application. Other N inputs were simulated within the DAYCENT framework,
including N input from mineralization due to decomposition of soil organic matter and N inputs from senesced grass
litter, as well as asymbiotic fixation of N from the atmosphere. The simulations used the same weather, soil, and
synthetic N fertilizer data as discussed under the Tier 3 Approach for Mineral Cropland Soils section. Managed
manure N amendments to grasslands were estimated from Edmonds et al. (2003) and adjusted for annual variation
using data on the availability of managed manure N for application to soils, according to methods described in the
Manure Management section (Section 6.2) and Annex 3.11. Biological N fixation is simulated within DAYCENT,
and therefore was not an input to the model.
Manure N deposition from grazing animals in Pasture/Range/Paddock systems (i.e., PRP manure) is another key
input of N to grasslands. The amounts of PRP manure N applied on non-federal grasslands for each NRI point were
based on amount of N excreted by livestock in PRP systems. The total amount of N excreted in each county was
divided by the grassland area to estimate the N input rate associated with PRP manure. The resulting input rates
were used in the DAYCENT simulations. DAYCENT simulations of non-federal grasslands accounted for
approximately 68 percent of total PRP manure N in aggregate across the country. The remainder of the PRP manure
N in each state was assumed to be excreted on federal grasslands, and the N2O emissions were estimated using the
IPCC (2006) Tier 1 method with IPCC default emission factors. Sewage sludge was assumed to be applied on
grasslands because of the heavy metal content and other pollutants in human waste that limit its use as an
amendment to croplands. Sewage sludge application was estimated from data compiled by EPA (1993, 1999, 2003),
McFarland (2001), and NEBRA (2007). Sewage sludge data on soil amendments to agricultural lands were only
available at the national scale, and it was not possible to associate application with specific soil conditions and
weather at the county scale. Therefore, DAYCENT could not be used to simulate the influence of sewage sludge
amendments on N2O emissions from grassland soils, and consequently, emissions from sewage sludge were
estimated using the IPCC (2006) Tier 1 method.
Grassland area data were consistent with the Land Representation reported in Section 7.1 for the conterminous
United States. Data were obtained from the U.S. Department of Agriculture National Resources Inventory (NRI)189
and the U.S. Geological Survey (USGS) National Land Cover Dataset, which were reconciled with the Forest
Inventory and Analysis Data.190 The area data for pastures and rangeland were aggregated to the county level to
estimate non-federal and federal grassland areas.191
N2O emissions for the PRP manure N deposited on federal grasslands and applied sewage sludge N were estimated
using the Tier 1 method by multiplying the N input by the appropriate emission factor. Emissions from manure N
were estimated at the state level and aggregated to the entire country, but emissions from sewage sludge N were
calculated exclusively at the national scale.
As previously mentioned, each NRI point was simulated 100 times as part of the uncertainty assessment, yielding a
total of over 18 million simulation runs for the analysis. Soil N2O emission estimates from DAYCENT were
adjusted using a structural uncertainty estimator accounting for uncertainty in model algorithms and parameter
values (Del Grosso et al. 2010). Soil N2O emissions and 95 percent confidence intervals were estimated for each
year between 1990 and 2007, but emissions from 2008 to 2012 were assumed to be similar to 2007 because no
additional activity data are currently available from the NRI for the latter years.
189 USDA-NRCS 2009, Nusser and Goebel 1997, .
190 porest Inventory and Analysis Data, .
191 NLCD, Vogelman et al. 2001, .
Agriculture 6-33
-------
Total Direct N2O Emissions from Cropland and Grassland Soils
Annual direct emissions from the Tier 1 and 3 approaches for cropland mineral soils, from drainage and cultivation
of organic cropland soils, and from grassland soils were summed to obtain the total direct N2O emissions from
agricultural soil management (see Table 6-18 and Table 6-19).
Indirect IM2O Emissions
This section describes the methods used for estimating indirect soil N2O emissions from all land-use types (i.e.,
croplands, grasslands, forest lands, and settlements). Indirect N2O emissions occur when mineral N made available
through anthropogenic activity is transported from the soil either in gaseous or aqueous forms and later converted
into N2O. There are two pathways leading to indirect emissions. The first pathway results from volatilization of N
as NOX and NH3 following application of synthetic fertilizer, organic amendments (e.g., manure, sewage sludge),
and deposition of PRP manure. N made available from mineralization of soil organic matter and residue, including
N incorporated into crops and forage from symbiotic N fixation, and input of N from asymbiotic fixation also
contributes to volatilized N emissions. Volatilized N can be returned to soils through atmospheric deposition, and a
portion of the deposited N is emitted to the atmosphere as N2O. The second pathway occurs via leaching and runoff
of soil N (primarily in the form of NOs") that was made available through anthropogenic activity on managed lands,
mineralization of soil organic matter and residue, including N incorporated into crops and forage from symbiotic N
fixation, and inputs of N into the soil from asymbiotic fixation. The NOs" is subject to denitrification in water
bodies, which leads to N2O emissions. Regardless of the eventual location of the indirect N2O emissions, the
emissions are assigned to the original source of the N for reporting purposes, which here includes croplands,
grasslands, forest lands, and settlements.
Indirect N2O Emissions from Atmospheric Deposition of Volatilized N from Managed Soils
As in the direct emissions calculation, the Tier 3 DAYCENT model and IPCC (2006) Tier 1 methods were
combined to estimate the amount of N that was volatilized and eventually emitted as N2O. DAYCENT was used to
estimate N volatilization for land areas whose direct emissions were simulated with DAYCENT (i.e., most
commodity and some specialty crops and most grasslands). The N inputs included are the same as described for
direct N2O emissions in the Tier 3 Approach for Cropland Mineral Soils Section and Grasslands Section. Nitrogen
volatilization for all other areas was estimated using the Tier 1 method and default IPCC fractions for N subject to
volatilization (i.e., N inputs on croplands not simulated by DAYCENT, PRP manure N excreted on federal
grasslands, sewage sludge application on grasslands). The Tier 1 method and default fractions were also used to
estimate N subject to volatilization from N inputs on settlements and forest lands (see the Land Use, Land-Use
Change, and Forestry chapter). For the volatilization data generated from both the DAYCENT and Tier 1
approaches, the IPCC (2006) default emission factor was used to estimate indirect N2O emissions occurring due to
re-deposition of the volatilized N (Table 6-21).
Indirect N2O Emissions from Leaching/Runoff
As with the calculations of indirect emissions from volatilized N, the Tier 3 DAYCENT model and IPCC (2006)
Tier 1 method were combined to estimate the amount of N that was subject to leaching and surface runoff into water
bodies, and eventually emitted as N2O. DAYCENT was used to simulate the amount of N transported from lands in
the Tier 3 Approach. N transport from all other areas was estimated using the Tier 1 method and the IPCC (2006)
default factor for the proportion of N subject to leaching and runoff. This N transport estimate includes N
applications on croplands that were not simulated by DAYCENT, sewage sludge amendments on grasslands, PRP
manure N excreted on federal grasslands, and N inputs on settlements and forest lands. For both the DAYCENT
Tier 3 and IPCC (2006) Tier 1 methods, nitrate leaching was assumed to be an insignificant source of indirect N2O
in cropland and grassland systems in arid regions as discussed in IPCC (2006). In the United States, the threshold
for significant nitrate leaching is based on the potential evapotranspiration (PET) and rainfall amount, similar to
IPCC (2006), and is assumed to be negligible in regions where the amount of precipitation plus irrigation does not
exceed 80 percent of PET. For leaching and runoff data estimated by the Tier 3 and Tier 1 approaches, the IPCC
(2006) default emission factor was used to estimate indirect N2O emissions that occur in groundwater and
waterways (Table 6-21).
6-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Uncertainty and Time-Series Consistency
Uncertainty was estimated for each of the following five components of N2O emissions from agricultural soil
management: (1) direct emissions simulated by DAYCENT; (2) the components of indirect emissions (N volatilized
and leached or runoff) simulated by DAYCENT; (3) direct emissions approximated with the IPCC (2006) Tier 1
method; (4) the components of indirect emissions (N volatilized and leached or runoff) approximated with the IPCC
(2006) Tier 1 method; and (5) indirect emissions estimated with the IPCC (2006) Tier 1 method. Uncertainty in
direct emissions, which account for the majority of N2O emissions from agricultural management, as well as the
components of indirect emissions calculated by DAYCENT were estimated with a Monte Carlo Analysis,
addressing uncertainties in model inputs and structure (i.e., algorithms and parameterization) (Del Grosso et al.
2010). Uncertainties in direct emissions calculated with the IPCC (2006) Tier 1 method, the proportion of
volatilization and leaching or runoff estimated with the IPCC (2006) Tier 1 method, and indirect N2O emissions
were estimated with a simple error propagation approach (IPCC 2006). Uncertainties from the Tier 1 and Tier 3
(i.e., DAYCENT) estimates were combined using simple error propagation (IPCC 2006). Additional details on the
uncertainty methods are provided in Annex 3.11. The combined uncertainty for direct soil N2O emissions ranged
from 17 percent below to 28 percent above the 2012 emissions estimate of 260.9 Tg CO2 Eq., and the combined
uncertainty for indirect soil N2O emissions ranged from 45 percent below to 151 percent above the 2012 estimate of
45.7TgCO2Eq.
Table 6-22: Quantitative Uncertainty Estimates of NzO Emissions from Agricultural Soil
Management in 2012 (Tg COz Eq. and Percent)
Source Gas
Direct Soil N2O Emissions N2O
Indirect Soil N2O Emissions N2O
2012 Emission Uncertainty Range Relative to Emission Estimate
Estimate
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Lower Upper Lower
Bound Bound Bound
260.9 215.4 334.4 -17%
45.7 25.3 114.5 -45%
Upper
Bound
28%
151%
Note: Due to lack of data, uncertainties in managed manure N production, PRP manure N production, other organic fertilizer
amendments, and sewage sludge amendments to soils are currently treated as certain; these sources of uncertainty will be included in
future Inventories.
Additional uncertainty is associated with no estimation of N2O emissions for croplands and grasslands in Hawaii
and Alaska, with the exception of drainage for organic soils in Hawaii. Agriculture is not extensive in either state so
the emissions are likely to be small compared to the conterminous United States.
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section
above.
QA/QC and Verification
DAYCENT results for N2O emissions and NOs" leaching were compared with field data representing various
cropland and grassland systems, soil types, and climate patterns (Del Grosso et al. 2005, Del Grosso et al. 2008), and
further evaluated by comparing to emission estimates produced using the IPCC (2006) Tier 1 method for the same
sites. Nitrous oxide measurement data were available for 24 sites in the United States, 5 in Europe, and one in
Australia, representing over 60 different combinations of fertilizer treatments and cultivation practices. DAYCENT
estimates of N2O emissions were closer to measured values at most sites compared to the IPCC Tier 1 estimate
(Figure 6-7). In general, IPCC Tier 1 methodology tends to over-estimate emissions when observed values are low
and under-estimate emissions when observed values are high, while DAYCENT estimates are less biased.
DAYCENT accounts for key site-level factors (weather, soil characteristics, and management) that are not addressed
in the IPCC Tier 1 Method, and thus the model is better able to represent the variability in N2O emissions. Nitrate
leaching data were available for four sites in the United States, representing 12 different combinations of fertilizer
Agriculture 6-35
-------
amendments/tillage practices. DAYCENT does have a tendency to under-estimate very high N2O emission rates;
estimates are increased to correct for this bias based on a statistical model derived from the comparison of model
estimates to measurements (See Annex 3.11 for more information). Regardless, the comparison demonstrates that
DAYCENT provides relatively high predictive capability for N2O emissions and NOs" leaching, and is an
improvement over the IPCC Tier 1 method.
Figure 6-7: Comparison of Measured Emissions at Field Sites and Modeled Emissions Using
the DAYCENT Simulation Model and IPCC Tier 1 Approach.
9 -,
8
t. 7
>• 6 -
jz ^
9,3
2 2 -
1 -
D Measured
1
m DayCent
• IPCC
Hi ,-EL rfl rfl II
C C *; W w *; *;
1_ i_ TO VI tl CD CD
O o • ^ Q LLJ * 5
|| |
o s 5
o
l/l
If
1
fa
^ "
">
f
0
u
—
s
n
1
-
n
,
1 1 1 1
'-•.;.- "fo
C u M —
*"• ~ Q ~c"
B ">•
s S.
— ^>
r^ O
§
1
-
_
I
n
n
n
i i;
j.l
1 1 1 1 1 1 1 1
ojiE Q.OO 22 ^*- u u w) w) ^
f | 1 0 H < £
1 I H
^ jr ^
2
^
Spreadsheets containing input data and probability distribution functions required for DAYCENT simulations of
croplands and grasslands and unit conversion factors were checked, as were the program scripts that were used to
run the Monte Carlo uncertainty analysis. Links between spreadsheets were checked, updated, and corrected when
necessary. Spreadsheets containing input data, emission factors, and calculations required for the Tier 1 approach
were checked and an error was found relating to residue N inputs. Some crops that were simulated by DAYCENT
were also included in the Tier 1 method. To correct this double-counting of N inputs, residue inputs from crops
simulated by DAYCENT were removed from the Tier 1 calculations.
Recalculations Discussion
Methodological recalculations in the current Inventory were associated with the following improvements: 1) Driving
the DAYCENT simulations with input data for the excretion of C and N onto Pasture/Range/Paddock based on
national livestock population data instead being internally generated by the DAYCENT model (note that revised
total PRP N additions increased from 6.9 to 7.2 Tg N on average); 2) expanding the number of experimental study
sites used to quantify model uncertainty for direct N2O emissions and bias correction; 3) refining the temperature
algorithm that is used for simulating crop production and carbon inputs to the soil in the DAYCENT biogeochemical
model; and (4) recalculation of Tier 2 organic soil N2O emissions using annual data from the NRI rather than
estimating emissions for every 5 years and holding emissions constant between the years. These changes resulted in
an increase in emissions of approximately 23 per cent on average relative to the previous Inventory and a decrease in
the upper bound of the 95 percent confidence interval for direct N2O emissions from 40 to 29 percent. The
6-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
differences are mainly due to the refinement of temperature algorithm in the model and expansion of the number of
field studies used to develop the statistical function for estimating uncertainty in the model structure and parameters.
In particular, additional studies showed very high N2O emissions during some years that were not captured by
DAYCENT. This resulted in a relatively large adjustment in a portion of the DAYCENT simulated N2O emissions
to capture the high N2O emission rates.
Several planned improvements are underway. The first is to update the time series of land use and management data
from the USDA National Resources Inventory so that it is extended from 2008 through 2010. Fertilization and
tillage activity data will also be updated as part of this improvement. The remote-sensing based data on the
Enhanced Vegetation Index will be extended through 2010 in order to use the EVI data to drive crop production in
DAYCENT. The update will extend the time series of activity data for the Tier 2 and 3 analyses through 2010, and
incorporate latest changes in agricultural production for the United States.
Second, improvements are planned for the DAYCENT biogeochemical model. Model structure will be improved
with a better representation of plant phenology, particularly senescence events following grain filling in crops, such
as wheat. In addition, crop parameters associated with temperature effects on plant production will be further
improved in DAYCENT with additional model calibration.
Experimental study sites will continue to be added for quantifying model structural uncertainty. Studies that have
continuous (daily) measurements of N2O (e.g., Scheer et al. 2013) will be given priority because they provide more
robust estimates of annual emissions compared to studies that sample trace gas emissions weekly or less frequently.
Another planned improvement is to account for the use of fertilizers formulated with nitrification inhibitors in
addition to slow-release fertilizers (e.g., polymer-coated fertilizers). Field data suggests that nitrification inhibitors
and slow-release fertilizers reduce N2O emissions significantly. The DAYCENT model can represent nitrification
inhibitors and slow-release fertilizers, but accounting for these in national simulations is contingent on testing the
model with a sufficient number of field studies and collection of activity data about the use of these fertilizers.
An improvement is also underway to simulate crop residue burning in the DAYCENT based on the amount of crop
residues burned according to the data that is used in the Field Burning of Agricultural Residues source category
(Section 6.5). The methodology for Field Burning of Agricultural Residues was significantly updated recently, but
the new estimates of crop residues burned have not been incorporated into the Agricultural Soil Management source.
Moreover, the data have only been used to reduce the N2O after DAYCENT simulations in the current Inventory,
but the planned improvement is to drive the simulations with burning events based on the new spatial data that is
used in Section 6.5.
Also, the treatment of N excretion from Pasture, Range and Paddock manure in both the Manure Management and
Agricultural Soil Management sections will be reconciled to ensure consistency in the next version of the
Inventory. Currently some managed manure, in addition to daily spread as noted in the methodology section, is
included in the Pasture, Range and Paddock manure for Agricultural Soil Management resulting in minor
differences.
All of these improvements are expected to be completed for the 1990 through 2013 Inventory report. However, the
time line may be extended if there are insufficient resources to fund all or part of these planned improvements.
Alaska and Hawaii are not included in the current Inventory for agricultural soil management, with the exception of
N2O emissions from drained organic soils in croplands and grasslands for Hawaii. Some minor crops that should be
in the Tier 1 analysis are also missing from the analysis, which will be added as a planned improvement. A planned
improvement over the next two years is to add these states into the Inventory analysis.
Agriculture 6-37
-------
6.5 Field Burning of Agricultural Residues (IPCC
Source Category 4F)
Farming activities produce large quantities of agricultural crop residues, and farmers use or dispose of these residues
in a variety of ways. For example, agricultural residues can be left on or plowed into the field; collected and used as
fuel, animal bedding material, supplemental animal feed, or construction material; composted and then applied to
soils; landfilled; or, as discussed in the chapter, burned in the field. Field burning of crop residues is not considered
a net source of CO2, because the C released to the atmosphere as CO2 during burning is assumed to be reabsorbed
during the next growing season. Crop residue burning is, however, a net source of CH4, N2O, CO, and NOX, which
are released during combustion.
Field burning of agricultural residues is not a common method of disposal in the United States. In the United States,
the primary crop types whose residues may be burned are corn, cotton, lentils, rice, soybeans, sugarcane, and wheat
(McCarty 2009). In 2012, CH4 and N2O emissions from field burning of agricultural residues were 0.3 Tg CO2 Eq.
(12 Gg) and 0.1 Tg. CO2 Eq. (0.3 Gg), respectively. Annual emissions from this source over the period 1990 to
2012 have remained relatively constant, averaging approximately 0.2 Tg CO2 Eq. (12 Gg) of CH4 and 0.1 Tg CO2
Eq. (0.3 Gg) of N2O (see Table 6-23 and Table 6-24).
Table 6-23: CH4 and NzO Emissions from Field Burning of Agricultural Residues (Tg COz Eq.)
Gas/Crop Type 1990 2005 2008 2009 2010 2011 2012
CH4 0.3 0.2 0.3 0.2 0.2 0.3 0.3
Soybeans + I + I + + + + +
Sugarcane 0.1 + I + + + + +
Wheat 0.1 0.1 0.1 0.1 0.1 0.1 0.1
N2O 0.1 0.1 0.1 0.1 0.1 0.1 0.1
Soybeans + I + I + + + + +
Sugarcane + I + I + + + + +
Wheat + + + + + + j_
Total 0.4 0.3 0.4 0.4 0.3 0.4 0.4
+ Less than 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 6-24: CH4, NzO, CO, and NOX Emissions from Field Burning of Agricultural Residues (Gg)
Gas/Crop Type 1990 2005 2008 2009 2010 2011 2012
CH4 13 9~| 13 12 11 12 12~
Com I I I I 1 2 2 2 2
Cotton + + I + + + + +
Lentils + I + I + + + + +
Rice 2l 2l 2 2 2 2 2
Soybeans I I I I 1 1 1 1 1
Sugarcane 3 I I I 2 2 2 2 2
Wheat 6 I 4 I 6 5 5 5 5
N2O + I + I + + + + +
Com + I + I + + + + +
Cotton + I + I + + + + +
Lentils + I + I + + + + +
Rice +| +| + + + + +
6-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Soybeans
Sugarcane
Wheat
CO
NOx
247
8
241
8
255
8
253
8
+ Lessthan0.5TgCO2Eq.
Note: Totals may not sum due to independent rounding.
Methodology
The Tier 2 methodology used for estimating greenhouse gas emissions from field burning of agricultural residues is
consistent with IPCC (2006) (for more details, see Box 6-3). In order to estimate the amounts of C and N released
during burning, the following equation was used:
C or N released = £ for all crop types and states
AB
CAH x CP x RCR x DMF x BE x CE x (FC or FN)
where,
Area Burned (AB)
Crop Area Harvested (CAH)
Crop Production (CP)
Residue/Crop Ratio (RCR)
Dry Matter Fraction (DMF)
Fraction of C or N (FC or FN)
Burning Efficiency (BE)
Combustion Efficiency (CE)
Total area of crop burned, by state
Total area of crop harvested, by state
Annual production of crop in Gg, by state
Amount of residue produced per unit of crop production, by state
Amount of dry matter per unit of biomass for a crop
Amount of C or N per unit of dry matter for a crop
The proportion of prefire fuel biomass consumed192
The proportion of C or N released with respect to the total amount of C or N
available in the burned material, respectively
Crop production and area harvested were available by state and year from USD A (2012) for all crops (except rice in
Florida and Oklahoma, as detailed below). The amount C or N released was used in the following equation to
determine the CH4, CO, N2O and NOX emissions from the field burning of agricultural residues:
CH4 and CO, or N2O and NOX Emissions from Field Burning of Agricultural Residues =
C or N Released x ER for C or N x CF
where,
Emissions Ratio (ER) = g CH4-C or CO-C/g C released, or g N2O-N or NOx-N/g N released
Conversion Factor (CF) = conversion, by molecular weight ratio, of CH4-C to C (16/12), or CO-C to C (28/12),
or N2O-N to N (44/28), or NOX-N to N (30/14)
Emissions from Burning of Agricultural Residues were calculated using a Tier 2 methodology that is based on
IPCC/UNEP/OECD/IEA (1997) and incorporates crop- and country-specific emission factors and variables. The
equation varies slightly in form from the one presented in the IPCC (2006) guidelines, but both equations rely on the
same underlying variables. The IPCC (2006) equation was developed to be broadly applicable to all types of
biomass burning, and, thus, is not specific to agricultural residues. IPCC (2006) default factors are provided only
for four crops (wheat, corn, rice, and sugarcane), while this Inventory analyzes emissions from seven crops. A
comparison of the methods and factors used in (1) the current Inventory and (2) the default IPCC (2006) approach
was undertaken in the 1990 through 2009 Inventory report to determine the magnitude of the difference in overall
estimates resulting from the two approaches. The IPCC (2006) approach was not used because crop-specific
192 In IPCC/UNEP/OECD/IEA (1997), the equation for C or N released contains the variable 'fraction oxidized in burning.'
This variable is equivalent to (burning efficiency * combustion efficiency).
Agriculture 6-39
-------
emission factors for N2O were not available for all crops, therefore the crop specific methodology provided in the
IPCC/UNEP/OECD/IEA (1997) approach was used.
The IPCC (2006) default approach resulted in 12 percent higher emissions of CH4 and 25 percent higher emissions
of N2O than the estimates in the 1990 through 2009 Inventory. It is reasonable to maintain the current methodology,
since the IPCC (2006) defaults are only available for four crops and are worldwide average estimates, while current
estimates are based on U.S.-specific, crop-specific, published data.
Crop production data for all crops except rice in Florida and Oklahoma were taken from USDA's QuickStats service
(USDA 2013). Rice production and area data for Florida and Oklahoma, which are not collected by USD A, were
estimated separately. Average primary and ratoon rice crop yields for Florida (Schueneman and Deren 2002) were
applied to Florida acreages (Schueneman 1999, 2001; Deren 2002; Kirstein 2003, 2004; Cantens 2004, 2005;
Gonzalez 2007 through 2013), and rice crop yields for Arkansas (USDA 2013) were applied to Oklahoma
acreages193 (Lee 2003 through 2006; Anderson 2008 through 2013). The production data for the crop types whose
residues are burned are presented in Table 6-25. Crop weight by bushel was obtained from Murphy (1993).
The fraction of crop area burned was calculated using data on area burned by crop type and state194 from McCarty
(2010) for corn, cotton, lentils, rice, soybeans, sugarcane, and wheat.195 McCarty (2010) used remote sensing data
from Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate area burned by crop. State-level area
burned data were divided by state-level crop area harvested data to estimate the percent of crop area burned by crop
for each state. The average fraction of area burned by crop across all states is shown in Table 6-26. All crop area
harvested data were from USDA (2013), except for rice acreage in Florida and Oklahoma, which is not measured by
USDA (Schueneman 1999, 2001; Deren 2002; Kirstein 2003, 2004; Cantens 2004, 2005; Gonzalez 2007 through
2013; Lee 2003 through 2006; Anderson 2008 through 2013). Data on crop area burned were only available from
McCarty (2010) for the years 2003 through 2007. For other years in the time series, the percent area burned was set
equal to the average 5 year percent area burned, based on data availability and inter-annual variability. This average
was taken at the crop and state level. Table 6-26 shows these percent area estimates aggregated for the United States
as a whole, at the crop level. State-level estimates based on state-level crop area harvested and burned data were also
prepared, but are not presented here.
All residue/crop product mass ratios except sugarcane and cotton were obtained from Strehler and Stiitzle (1987).
The datum for sugarcane is from Kinoshita (1988) and that of cotton from Huang et al. (2007). The residue/crop
ratio for lentils was assumed to be equal to the average of the values for peas and beans. Residue dry matter
fractions for all crops except soybeans, lentils, and cotton were obtained from Turn et al. (1997). Soybean and lentil
dry matter fractions were obtained from Strehler and Stiitzle (1987); the value for lentil residue was assumed to
equal the value for bean straw. The cotton dry matter fraction was taken from Huang et al. (2007). The residue C
contents and N contents for all crops except soybeans and cotton are from Turn et al. (1997). The residue C content
for soybeans is the IPCC default (IPCC/UNEP/OECD/IEA 1997). The N content of soybeans is from Barnard and
Kristoferson (1985). The C and N contents of lentils were assumed to equal those of soybeans. The C and N
contents of cotton are from Lachnicht et al. (2004). These data are listed in Table 6-27. The burning efficiency was
assumed to be 93 percent, and the combustion efficiency was assumed to be 88 percent, for all crop types, except
sugarcane (EPA 1994). For sugarcane, the burning efficiency was assumed to be 81 percent (Kinoshita 1988) and
the combustion efficiency was assumed to be 68 percent (Turn et al. 1997). Emission ratios and conversion factors
for all gases (see Table 6-28) were taken from the Revised 1996 IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997).
Table 6-25: Agricultural Crop Production (Gg of Product)
Crop 1990 2005 2008 2009 2010 2011 2012
193 Rice production yield data are not available for Oklahoma, so the Arkansas values are used as a proxy.
194 Alaska and Hawaii were excluded.
195 McCarty (2009) also examined emissions from burning of Kentucky bluegrass and a general "other crops/fallow" category,
but USDA crop area and production data were insufficient to estimate emissions from these crops using the methodology
employed in the Inventory. McCarty (2009) estimates that approximately 18 percent of crop residue emissions result from
burning of the Kentucky bluegrass and "other" categories.
6-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Coma
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
201,534
3,376 1
40 1
7,114
52,416
25,525
74,292
282,263
5,201
238
10,132
83,507
24,137
57,243
307,142
2,790
109
9,272
80,749
25,041
68,016
332,549
2,654
265
9,972
91,417
27,608
60,366
316,165
3,942
393
11,027
90,605
24,821
60,062
313,949
3,391
215
8,389
84,192
26,512
54,413
273,832
3,770
240
9,048
82,055
29,193
61,755
1 Corn for grain (i.e., excludes com for silage).
Table 6-26: U.S. Average Percent Crop Area Burned by Crop (Percent)
State
1990
2005
2008
2009
2010 2011
2012
Com
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
1%
1%
9%
37%
3%
1 %
8%
38%
3%
1%
1 %
10%
40%
3%
1%
1%
9%
37%
3%
+ Less than 0.5 percent
Table 6-27: Key Assumptions for Estimating Emissions from Field Burning of Agricultural
Residues
Crop
Corn
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
Table 6-28:
Gas
CH4:C
CO:C
N2O:N
NOX:N
Residue/Crop
Ratio
1.0
1.6
2.0
1.4
2.1
0.2
1.3
Greenhouse
Dry Matter C Fraction
Fraction
0.91
0.90
0.85
0.91
0.87
0.62
0.93
0.448
0.445
0.450
0.381
0.450
0.424
0.443
Gas Emission Ratios
N Fraction Burning Combustion
Efficiency Efficiency
(Fraction) (Fraction)
0.006
0.012
0.023
0.007
0.023
0.004
0.006
and Conversion
0.93
0.93
0.93
0.93
0.93
0.81
0.93
Factors
0.88
0.88
0.88
0.88
0.88
0.68
0.88
Emission Ratio Conversion Factor
0.005a
0.060a
0.007b
0.121b
16/12
28/12
44/28
30/14
a Mass of C compound released (units of C) relative to
mass of total C released from burning (units of C).
b Mass of N compound released (units of N) relative to
mass of total N released from burning (units of N).
Uncertainty and Time-Series Consistency
Due to data and time limitations, uncertainty resulting from the fact that emissions from burning of Kentucky
bluegrass and "other" residues are not included in the emissions estimates was not incorporated into the uncertainty
analysis. The results of the Tier 2 Monte Carlo uncertainty analysis are summarized in Table 6-29. Methane
emissions from field burning of agricultural residues in 2012 were estimated to be between 0.15 and 0.36 Tg CCh
Eq. at a 95 percent confidence level. This indicates a range of 41percent below and 42 percent above the 2012
Agriculture 6-41
-------
emission estimate of 0.25 Tg CCh Eq.196 Also at the 95 percent confidence level, N2O emissions were estimated to
be between 0.07 and 0.14 Tg CCh Eq., or approximately 30 percent below and 32 percent above the 2012 emission
estimate of 0.10 Tg CO2 Eq.
Table 6-29: Tier 2 Quantitative Uncertainty Estimates for Cm and NzO Emissions from Field
Burning of Agricultural Residues (Tg COz Eq. and Percent)
Source Gas 2012 Emission
Estimate
(Tg C02 Eq.)
Field Burning of Agricultural Residues CFLi 0.25
Field Burning of Agricultural Residues N2O 0.10
Uncertainty Range Relative to Emission
Estimate3
(Tg C02 Eq.) (%)
Lower
Bound
0.15
0.07
Upper
Bound
0.36
0.14
Lower
Bound
-41%
-30%
Upper
Bound
42%
32%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A source-specific QA/QC plan for field burning of agricultural residues was implemented. This effort included a
Tier 1 analysis, as well as portions of a Tier 2 analysis. The Tier 2 procedures focused on comparing trends across
years, states, and crops to attempt to identify any outliers or inconsistencies. For some crops and years in Florida
and Oklahoma, the total area burned as measured by McCarty (2010) was greater than the area estimated for that
crop, year, and state by Gonzalez (2004-2008) and Anderson (2007) for Florida and Oklahoma, respectively, leading
to a percent area burned estimate of greater than 100 percent. In such cases, it was assumed that the percent crop
area burned for that state was 100 percent.
Recalculations Discussion
The current Inventory was updated to incorporate state-level estimates of percentage of crop area burned. This
represents an improvement on the previous methodology, which used state-level percentage burned data to generate
a national average due to uncertainty analysis constraints. In addition, the crop production data for 2011 and 2012
were updated relative to the previous report using data from USDA (2013). Rice cultivation data for Florida and
Oklahoma, which are not reported by USDA, were updated for 2012 through communications with state experts.
Overall, these improvements resulted in an average increase in emissions of 14.4 percent from 1990 through 2011.
Emissions increased the most for 1996 (31.3 percent), and decreased in 2003 (-2.8 percent), the only year in which
emissions decreased. These changes are due almost entirely to the methodology updates and applying percentage of
crop area burned at the state level. The changes in crop production values had a negligible impact on emissions.
Planned Improvements
Further investigation will be conducted into inconsistent area burned data from Florida and Oklahoma as mentioned
in the QA/QC and verification section, and attempts will be made to revise or further justify the assumption of 100
percent of area burned for those crops and years where the estimated percent area burned exceeded 100 percent. The
availability of useable area harvested and other data for bluegrass and the "other crops" category in McCarty (2010)
will also be investigated in order to try to incorporate these emissions into the estimate. More crop area burned data
are becoming available and will be analyzed for incorporation into the next Inventory report.
196 This value of 0.25 Tg CCh is rounded and reported as 0.3 Tg CCh in Table 6-21 and the text discussing Table 6-21. For the
uncertainty calculations, the value of 0.25 Tg CCh was used to allow for more precise uncertainty ranges.
6-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
7. Land Use, Land-Use Change, and
Forestry
This chapter provides an assessment of the net greenhouse gas flux resulting from the uses and changes in land types
and forests in the United States.197 The Intergovernmental Panel on Climate Change 2006 Guidelines for National
Greenhouse Gas Inventories (IPCC 2006) recommends reporting fluxes according to changes within and
conversions between certain land-use types termed forest land, cropland, grassland, and settlements (as well as
wetlands). The greenhouse gas flux from Forest Land Remaining Forest Land is reported using estimates of
changes in forest carbon (C) stocks, non-carbon dioxide (CCh) emissions from forest fires, and the application of
synthetic fertilizers to forest soils. The greenhouse gas flux from agricultural lands (i.e., cropland and grassland)
that is reported in this chapter includes changes in organic C stocks in mineral and organic soils due to land use and
management, and emissions of CCh due to the application of crushed limestone and dolomite to managed land (i.e.,
soil liming) and urea fertilization. Fluxes are reported for four agricultural land use/land-use change categories:
Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land
Converted to Grassland. Fluxes resulting from Settlements Remaining Settlements include those from urban trees
and soil fertilization. Landfilled yard trimmings and food scraps are accounted for separately under Other.
The estimates in this chapter, with the exception of CCh fluxes from wood products and urban trees, and CCh
emissions from liming and urea fertilization, are based on activity data collected at multiple-year intervals, which
are in the form of forest, land-use, and municipal solid waste surveys. Carbon dioxide fluxes from forest C stocks
(except the wood product components) and from agricultural soils (except the liming component) are calculated on
an average annual basis from data collected in intervals ranging from 1 to 10 years. The resulting annual averages
are applied to years between surveys. Calculations of non-CCh emissions from forest fires are based on forest COa
flux data. For the landfilled yard trimmings and food scraps source, periodic solid waste survey data were
interpolated so that annual storage estimates could be derived. This flux has been applied to the entire time series,
and periodic U.S. census data on changes in urban area have been used to develop annual estimates of CO2 flux.
Land use, land-use change, and forestry activities in 2012 resulted in a net C sequestration of 979.3 Tg CO2 Eq.
(267.1 Tg C) (Table 7-1 and Table 7-2). This represents an offset of approximately 15.0 percent of total U.S. CO2
emissions. Total land use, land-use change, and forestry net C sequestration increased by approximately 17.8
percent between 1990 and 2012.198 This increase was primarily due to an increase in the rate of net C accumulation
in forest C stocks. Net C accumulation in Forest Land Remaining Forest Land, Land Converted to Grassland, and
Settlements Remaining Settlements increased, while net C accumulation in Cropland Remaining Cropland,
Grassland Remaining Grassland, and Landfilled Yard Trimmings and Food Scraps slowed over this period, and
emissions from Land Converted to Cropland decreased.
197 jjjg term "flux" is used here to encompass both emissions of greenhouse gases to the atmosphere, and removal of C from the
atmosphere. Removal of C from the atmosphere is also referred to as "carbon sequestration."
198 Carbon sequestration estimates are net figures. The C stock in a given pool fluctuates due to both gains and losses. When
losses exceed gains, the C stock decreases, and the pool acts as a source. When gains exceed losses, the C stock increases, and
the pool acts as a sink; also referred to as net C sequestration.
Land Use, Land-Use Change, and Forestry 7-1
-------
Table 7-1: Net COz Flux from Carbon Stock Changes in Land Use, Land-Use Change, and
Forestry (Tg COz Eq.)
Sink Category
Forest Land Remaining Forest Landa
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Settlements Remaining Settlements15
Other (Landfilled Yard Trimmings and
Food Scraps)
1990
(704.6)
(51.9)
26.9
(9.6)
(7.3)
(60.4)
(24.2)
2005
(927.2)
(29.1)
20.9
5.6
(8.3)
(80.5)
(12.0)
2008
(871.0)
(29.8)
16.8
6.8
(8.7)
(83.9)
(11.2)
2009
(849.4)
(29.2)
16.8
6.8
(8.7)
(85.0)
(12.9)
2010
(855.7)
(27.6)
16.8
6.7
(8.6)
(86.1)
(13.6)
2011
(867.1)
(27.5)
16.8
6.7
(8.6)
(87.3)
(13.5)
2012
(866.5)
(26.5)
16.8
6.7
(8.5)
(88.4)
(13.0)
Total
(831.1)
(1,030.7)
(981.0) (961.6) (968.0) (980.3) (979.3)
Note: Parentheses indicate net sequestration. Totals may not sum due to independent rounding.
a Estimates include C stock changes on both Forest Land Remaining Forest Land and Land Converted to Forest Land.
b Estimates include C stock changes on both Settlements Remaining Settlements and Land Converted to Settlements.
Table 7-2: Net COz Flux from Carbon Stock Changes in Land Use, Land-Use Change, and
Forestry (Tg C)
Sink Category
Forest Land Remaining Forest Landa
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Settlements Remaining Settlements15
Other (Landfilled Yard Trimmings and
Food Scraps)
1990
(192.2)
(14.2)
7.3
(2.6) 1
(2.0)
(16.5)
(6.6) 1
1 2005
(252.9)
(7.9)
5.7
1.5
(2.3)
(22.0)
1 (3.3)
2008
(237.6)
(8.1)
4.6
1.8
(2.4)
(22.9)
(3.0)
2009
(231.6)
(8.0)
4.6
1.8
(2.4)
(23.2)
(3.5)
2010
(233.4)
(7.5)
4.6
1.8
(2.4)
(23.5)
(3.7)
2011
(236.5)
(7.5)
4.6
1.8
(2.3)
(23.8)
(3.7)
2012
(236.3)
(7.2)
4.6
1.8
(2.3)
(24.1)
(3.6)
Total
(226.7)
(281.1)
(267.5) (262.3) (264.0) (267.4) (267.1)
Note: 1 Tg C = 1 teragram C = 1 million metric tons C. Parentheses indicate net sequestration. Totals may not sum due to
independent rounding.
a Estimates include C stock changes on both Forest Land Remaining Forest Land and Land Converted to Forest Land.
Estimates include C stock changes on both Settlements Remaining Settlements and Land Converted to Settlements.
Emissions from Land Use, Land-Use Change, and Forestry are shown in Table 7-3 and Table 7-4. Liming of
agricultural soils and urea fertilization in 2012 resulted in CO2 emissions of 7.4 Tg CO2 Eq. (7,381 Gg). Lands
undergoing peat extraction (i.e., Peatlands Remaining Peatlands) resulted in CO2 emissions of 0.8 Tg CO2 Eq. (830
Gg), and nitrous oxide (N2O) emissions of less than 0.05 Tg CO2 Eq. The application of synthetic fertilizers to
forest soils in 2012 resulted in direct N2O emissions of 0.4 Tg CO2 Eq. (1 Gg). Direct N2O emissions from fertilizer
application to forest soils have increased by 455 percent since 1990, but still account for a relatively small portion of
overall emissions. Additionally, direct N2O emissions from fertilizer application to settlement soils in 2012
accounted for 1.5 Tg CO2 Eq. (5 Gg). This represents an increase of 48 percent since 1990. Forest fires in 2012
resulted in methane (CH4) emissions of 15.3 Tg CO2 Eq. (727 Gg), and in N2O emissions of 12.5 Tg CO2 Eq. (40
Gg).
Table 7-3: Emissions from Land Use, Land-Use Change, and Forestry (Tg COz Eq.)
Source Category 1990 2005 2008 2009 2010 2011 2012
C02
Cropland Remaining Cropland: Liming
of Agricultural Soils
Cropland Remaining Cropland Urea
Fertilization
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands
CH4
Forest Land Remaining Forest Land:
Forest Fires
8.1
4.7
2.4
1.0
2.5
2.5
8.9
4.3
3.5
1.1
8.1
8.1
9.6
5.0
3.6
1.0
8.7
8.7
8.3
3.7
3.6
1.1
5.8
5.8
9.6
4.8
1.0
4.7
4.7
3.9
4.0
0.9
14.0
14.0
8.2
3.9
3.4
0.8
15.3
15.3
7-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
N20
Forest Land Remaining Forest Land:
Forest Fires
Forest Land Remaining Forest Land:
Forest Soilsa
Settlements Remaining Settlements:
Settlement Soilsb
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands
3.1
2.0
0.1
1.0
8.4
6.6
0.4
1.5
9.0
7.1
0.4
1.5
6.5
4.7
0.4
1.4
5.7
3.9
0.4
1.5
13.3
11.4
0.4
1.5
14.3
12.5
0.4
1.5
Total
13.7
25.5
27.3
20.5
20.0
36.0
37.8
+ Less than 0.05 Tg CO2 Eq.
Note: These estimates include direct emissions only. Indirect N2O emissions are reported in the Agriculture chapter. Totals
may not sum due to independent rounding.
a Estimates include emissions from N fertilizer additions on both Forest Land Remaining Forest Land, and Land Converted to
Forest Land, but not from land-use conversion.
b Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements, and Land Converted to
Settlements, but not from land-use conversion
Table 7-4: Emissions from Land Use, Land-Use Change, and Forestry (Gg)
Source Category
C02
Cropland Remaining
; Cropland:
k
Liming
1990
8,117
4,667
2005
8,933
4,349 1
2008
9,630
5,025
2009
8,313
3,669
2010
9,573
4,784
2011
8,783
3,871
2012
8,211
3,939
Cropland Remaining Cropland Urea
Fertilization
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands
CH4
Forest Land Remaining Forest Land:
Forest Fires
N2O
Forest Land Remaining Forest Land:
Forest Fires
Forest Land Remaining Forest Land:
Forest Soilsa
Settlements Remaining Settlements:
Settlement Soilsb
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands
2,417
3,504
3,613
3,555
3,780 3,993 3,441
992
416
416
29
23
1,089
275
275
21
15
1,010
225
225
18
12
919
664
664
43
37
830
727
727
46
40
+ Emissions are less than 0.5 Gg
Note: These estimates include direct emissions only. Indirect N2O emissions are reported in the Agriculture chapter. Totals
may not sum due to independent rounding.
a Estimates include emissions from N fertilizer additions on both Forest Land Remaining Forest Land, and Land Converted to
Forest Land, but not from land-use conversion.
b Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements, and Land Converted to
Settlements, but not from land-use conversion.
Box 7-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks
In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emissions
inventories, the emissions and sinks presented in this report are organized by source and sink categories and
calculated using internationally-accepted methods provided by the Intergovernmental Panel on Climate Change
(IPCC).199 Additionally, the calculated emissions and sinks in a given year for the United States are presented in a
199
See .
Land Use, Land-Use Change, and Forestry 7-3
-------
common manner in line with the UNFCCC reporting guidelines for the reporting of inventories under this
international agreement.200 The use of consistent methods to calculate emissions and sinks by all nations providing
their inventories to the UNFCCC ensures that these reports are comparable. In this regard, U.S. emissions and sinks
reported in this Inventory report are comparable to emissions and sinks reported by other countries. Emissions and
sinks provided in this Inventory do not preclude alternative examinations, but rather this Inventory report presents
emissions and sinks in a common format consistent with how countries are to report inventories under the
UNFCCC. The report itself follows this standardized format, and provides an explanation of the IPCC methods
used to calculate emissions and sinks, and the manner in which those calculations are conducted.
7.1 Representation of the United States Land
Base
A national land-use categorization system that is consistent and complete, both temporally and spatially, is needed in
order to assess land use and land-use change status and the associated greenhouse gas fluxes over the inventory time
series. This system should be consistent with IPCC (2006), such that all countries reporting on national greenhouse
gas fluxes to the UNFCCC should (1) describe the methods and definitions used to determine areas of managed and
unmanaged lands in the country, (2) describe and apply a consistent set of definitions for land-use categories over
the entire national land base and time series (i.e., such that increases in the land areas within particular land-use
categories are balanced by decreases in the land areas of other categories unless the national land base is changing),
and (3) account for greenhouse gas fluxes on all managed lands. The IPCC (2006, Vol. IV, Chapter 1) consider all
anthropogenic GHG emissions and removals associated with land use and management to occur on managed land,
and all emissions and removals on managed land should be reported based on this guidance (See IPCC 2010 for
further discussion). Consequently, managed land serves as a proxy for anthropogenic emissions and removals. This
proxy is intended to provide a practical framework for conducting an inventory, even though some of the GHG
emissions and removals on managed land are influenced by natural processes that may or may not be interacting
with the anthropogenic drivers. Guidelines for factoring out natural emissions and removals may be developed in the
future, but currently the managed land proxy is considered the most practical approach for conducting an inventory
in this sector (IPCC 2010). The implementation of such a system helps to ensure that estimates of greenhouse gas
fluxes are as accurate as possible, and does allow for potentially subjective decisions in regards to subdividing
natural and anthropogenic driven emissions. This section of the Inventory has been developed in order to comply
with this guidance.
Three databases are used to track land management in the United States and are used as the basis to classify United
States land area into the thirty-six IPCC land-use and land-use change categories (Table 7-6) (IPCC 2006). The
primary databases are the U.S. Department of Agriculture (USDA) National Resources Inventory (NRI)201 and the
USDA Forest Service (USFS) Forest Inventory and Analysis (FIA)202 Database. The Multi-Resolution Land
Characteristics Consortium (MRLC) National Land Cover Dataset (NLCD)203 is also used to identify land uses in
regions that were not included in the NRI or FIA.
The total land area included in the United States Inventory is 936 million hectares across the 50 states.204
Approximately 867 million hectares of this land base is considered managed, which has not changed over the time
series of the Inventory (Table 7-6). In 2012, the United States had a total of 304 million hectares of managed Forest
200 See.
201 NRI data is available at .
202 pjA data js available at .
203 NLCD data is available at http://www.mrlc.gov/ and MRLC is a consortium of several US government agencies.
204 jjjg current jancj representation does not include areas from United States territories, but there are planned improvements to
include these regions in future reports.
7-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Land (3.5 percent increase since 1990), 159 million hectares of Cropland (6.6 percent decrease since 1990), 292
million hectares of managed Grassland (3.1 percent decrease since 1990), 43 million hectares of managed Wetlands
(3.9 percent decrease since 1990), 51 million hectares of Settlements (31 percent increase since 1990), and 19
million hectares of managed Other Land (Table 7-6). Wetlands are not differentiated between managed and
unmanaged and are reported solely as managed. Some wetlands would be considered unmanaged, and a future
planned improvement will include a differentiation between managed and unmanaged wetlands using guidance in
the 2013 Supplement to the 2006 Guidelines for National Greenhouse Gas Inventories: Wetlands. In addition, C
stock changes are not currently estimated for the entire land base, which leads to discrepancies between the managed
land area data presented here and in the subsequent sections of the NIR (e.g., Grassland Remaining
Grassland).205'206 Planned improvements are under development to account for C stock changes on all managed
land (e.g., federal grasslands) and ensure consistency between the total area of managed land in the land
representation description and the remainder of the NIR.
Dominant land uses vary by region, largely due to climate patterns, soil types, geology, proximity to coastal regions,
and historical settlement patterns, although all land-uses occur within each of the fifty states (Table 7-1). Forest
Land tends to be more common in the eastern states, mountainous regions of the western United States, and Alaska.
Cropland is concentrated in the mid-continent region of the United States, and Grassland is more common in the
western United States. Wetlands are fairly ubiquitous throughout the United States, though they are more common
in the upper Midwest and eastern portions of the country. Settlements are more concentrated along the coastal
margins and in the eastern states.
Table 7-5: Managed and Unmanaged Land Area by Land Use Categories for all 50 States
(thousands of hectares)
Land Use Categories
Managed Lands
Forest
Croplands
Grasslands
Settlements
Wetlands
Other
Unmanaged Lands
Forest
Croplands
Grasslands
Settlements
Wetlands
Other
Total Land Areas
Forest
Croplands
Grasslands
Settlements
Wetlands
Other
1990
866,933
293,647
170,307
301,125
38,670
44,396
18,789
69,498
14,565
0
39,675
0
0
15,258
936,431
308,212
170,307
340,800
38,670
44,396
34,047
2005
866,932
300,365
159,950
294,284
49,658
43,828
18,847
69,499
14,565
0
39,676
0
0
15,259
936,431
314,930
159,950
333,959
49,658
43,828
34,106
2008
866
302
159
292
50
43
18
69
14
39
15
936
316
159
332
50
43
34
,932
,045
,096
,881
,610
,303
,997
,499
,565
0
,676
0
0
,259
,431
,610
,096
,556
,610
,303
,256
2009
866,932
302,535
159,088
292,575
50,603
43,146
18,985
69,499
14,565
0
39,676
0
0
15,259
936,431
317,100
159,088
332,250
50,603
43,146
34,243
2010
866,932
303,026
159,081
292,266
50,597
42,989
18,972
69,499
14,565
0
39,676
0
0
15,259
936,431
317,591
159,081
331,942
50,597
42,989
34,231
2011
866,932
303,517
159,074
291,958
50,592
42,832
18,960
69,499
14,565
0
39,676
0
0
15,259
936,431
318,082
159,074
331,633
50,592
42,832
34,219
2012
866
304
159
291
50
42
18
69
14
39
,932
,007
,067
,649
,586
,675
,948
,499
,565
0
,676
0
0
15,259
936
318
159
331
50
42
34
,431
,572
,067
,325
,586
,675
,207
205 C stock changes are not estimated for approximately 75 million hectares of Grassland Remaining Grassland. See specific
land-use sections for further discussion on gaps in the inventory of C stock changes, and discussion about planned improvements
to address the gaps in the near future
206 These "managed area" discrepancies also occur in the Common Reporting Format (CRF) tables submitted to the UNFCCC.
Land Use, Land-Use Change, and Forestry 7-5
-------
Table 7-6: Land Use and Land-Use Change for the United States Managed Land Base for all
50 States (thousands of hectares)
Land Use & Land-Use Change
Categories3 1990
2005
Total Forest Land
FF
CF
GF
WF
SF
OF
Total Cropland
CC
FC
GC
we
SC
oc
Total Grassland
GG
FG
CG
WG
SG
OG
Total Wetlands
WW
FW
CW
GW
SW
OW
Total Settlements
SS
FS
CS
GS
WS
OS
Total Other Land
OO
FO
CO
GO
WO
SO
Grand Total
293,647
288,535
1,118
3,425 1
66 1
104
398
170,307
154,840
1,118
13,583
156 1
431
180
301,125
290,917
1,611
7,898 1
238 1
111
349
44,396
43,747
140 1
132
343
0
33
38,670
34,129
1,787
l,343l
l,353l
3
55
18,789
17,756
1821
331
454
63
2!
866,933
300,365
288,061
2,651
7,823
256
372
1,201
159,950
143,072
675
15,067
193
688
253
294,284
275,170
2,990
14,598
408
274
844
43,828
42,320
393
366
696
10
43
49,658
35,264
6,111
3,625
4,430
31
198
18,847
16,628
538
645
896
119
21
866,932
2008
302,045
290,557
2,444
7,301
263
387
1,094
159,096
143,874
568
13,580
174
669
231
292,881
275,172
2,723
13,558
329
267
832
43,303
41,868
380
345
662
10
39
50,610
36,335
6,089
3,518
4,436
30
201
18,997
16,707
569
703
895
102
20
866,932
2009
302,535
291,041
2,445
7,302
264
388
1,097
159,088
143,867
567
13,580
174
669
231
292,575
274,922
2,721
13,505
328
267
832
43,146
41,714
379
344
661
10
38
50,603
36,329
6,089
3,518
4,436
30
201
18,985
16,695
569
703
895
102
20
866,932
2010
303,026
291,525
2,445
7,304
265
388
1,099
159,081
143,861
567
13,580
174
669
231
292,266
274,670
2,719
13,451
328
267
831
42,989
41,559
377
344
661
10
38
50,597
36,323
6,089
3,518
4,436
30
201
18,972
16,683
569
703
895
102
20
866,932
2011
303,517
292,010
2,445
7,305
265
389
1,101
159,074
143,855
567
13,580
174
669
231
291,958
274,418
2,716
13,397
328
267
831
42,832
41,405
375
344
661
10
37
50,592
36,318
6,089
3,518
4,436
30
201
18,960
16,671
569
703
894
102
20
866,932
2012
304,007
292,495
2,446
7,306
266
390
1,104
159,067
143,848
566
13,580
174
669
231
291,649
274,166
2,714
13,343
328
267
831
42,675
41,250
374
343
661
10
37
50,586
36,312
6,089
3,518
4,436
30
201
18,948
16,659
569
703
894
102
20
866,932
7-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
aThe abbreviations are "F" for Forest Land, "C" for Cropland, "G" for Grassland, "W" for Wetlands, "S" for Settlements, and
"O" for Other Lands. Lands remaining in the same land use category are identified with the land use abbreviation given
twice (e.g., "FF" is Forest Land Remaining Forest Land), and land use change categories are identified with the previous land
use abbreviation followed by the new land use abbreviation (e.g., "CF" is Cropland Converted to Forest Land).
Notes: All land areas reported in this table are considered managed. A planned improvement is underway to deal with an
exception for wetlands, which based on the definitions for the current United States Land Representation Assessment includes
both managed and unmanaged lands. United States Territories have not been classified into land uses and are not included in
the United States Land Representation Assessment. See Planned Improvements for discussion on plans to include territories
in future Inventories. In addition, C stock changes are not currently estimated for the entire land base, which leads to
discrepancies between the managed land area data presented here and in the subsequent sections of the NIR.
Land Use, Land-Use Change, and Forestry 7-7
-------
Figure 7-1. Percent of Total Land Area for each State in the General Land-Use Categories for
2012.
Croplands
Forest Lands
Grasslands
Other Lands
n-
n 10 - 30
• 30-50
Settlements
Wetlands
n 10 - 30
• 30 - 50
7-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
IPCC Approaches for Representing Land Areas
IPCC (2006) describes three approaches for representing land areas. Approach 1 provides data on the total area for
each individual land-use category, but does not provide detailed information on changes of area between categories
and is not spatially explicit other than at the national or regional level. With Approach 1, total net conversions
between categories can be detected, but not the individual changes (i.e., additions and/or losses) between the land-
use categories that led to those net changes. Approach 2 introduces tracking of individual land-use changes between
the categories (e.g., Forest Land to Cropland, Cropland to Forest Land, Grassland to Cropland), using surveys or
other forms of data that do not provide location data on specific parcels of land. Approach 3 extends Approach 2 by
providing location data on specific parcels of land, such as maps, along with the land-use history. The three
approaches are not presented as hierarchical tiers and are not mutually exclusive.
According to IPCC (2006), the approach or mix of approaches selected by an inventory agency should reflect
calculation needs and national circumstances. For this analysis, the NRI, FIA, and the NLCD have been combined
to provide a complete representation of land use for managed lands. These data sources are described in more detail
later in this section. NRI and FIA are Approach 2 data sources that do not provide spatially-explicit representations
of land use and land use conversions, even though land use and land use conversions are tracked explicitly at the
survey locations. NRI and FIA data can only be aggregated and used to develop a land use conversion matrix for a
political or ecologically-defined region. NLCD is a spatially-explicit time series of land-use data, and therefore
Approach 3. Lands are treated as remaining in the same category (e.g., Cropland Remaining Cropland) if a land-use
change has not occurred in the last 20 years. Otherwise, the land is classified in a land-use change category based on
the current use and most recent use before conversion to the current use (e.g., Cropland Converted to Forest Land).
Definitions of Land Use in the United States
Managed and Unmanaged Land
The United States definitions of managed and unmanaged lands are similar to the basic IPCC (2006) definition of
managed land, but with some additional elaboration to reflect national circumstances. Based on the following
definitions, most lands in the United States are classified as managed:
• Managed Land: Land is considered managed if direct human intervention has influenced its condition.
Direct intervention occurs mostly in areas accessible to human activity and includes altering or maintaining
the condition of the land to produce commercial or non-commercial products or services; to serve as
transportation corridors or locations for buildings, landfills, or other developed areas for commercial or
non-commercial purposes; to extract resources or facilitate acquisition of resources; or to provide social
functions for personal, community or societal objectives where these areas are readily accessible to
society.207
• Unmanaged Land: All other land is considered unmanaged. Unmanaged land is largely comprised of areas
inaccessible to society due to the remoteness of the locations. Though these lands may be influenced
indirectly by human actions such as atmospheric deposition of chemical species produced in industry or
CO2 fertilization, they are not influenced by a direct human intervention.208
207 Wetlands are an exception to this general definition, because these lands, as specified by IPCC (2006), are only considered
managed if they are created through human activity, such as dam construction, or the water level is artificially altered by human
activity. Distinguishing between managed and unmanaged wetlands is difficult due to limited data availability. Wetlands are not
characterized by use within the NRI. Therefore, unless wetlands are managed for cropland or grassland, it is not possible to
know if they are artificially created or if the water table is managed based on the use of NRI data. As a result, all wetlands are
reported as managed. See the Planned Improvements section of the Inventory for work being done to refine the Wetland area
estimates.
208 There are some areas, such as Forest Land and Grassland in Alaska that are classified as unmanaged land due to the
remoteness of their location.
Land Use, Land-Use Change, and Forestry 7-9
-------
In addition, land that is previously managed remains in the managed land base for 20 years before re-classifying the
land as unmanaged in order to account for legacy effects of management on C stocks.
Land-Use Categories
As with the definition of managed lands, IPCC (2006) provides general non-prescriptive definitions for the six main
land-use categories: Forest Land, Cropland, Grassland, Wetlands, Settlements and Other Land. In order to reflect
national circumstances, country-specific definitions have been developed, based predominantly on criteria used in
the land-use surveys for the United States. Specifically, the definition of Forest Land is based on the FIA definition
of forest,209 while definitions of Cropland, Grassland, and Settlements are based on the NRI.210 The definitions for
Other Land and Wetlands are based on the IPCC (2006) definitions for these categories.
• Forest Land: A land-use category that includes areas at least 36.6m wide and 0.4 ha in size with at least 10
percent cover (or equivalent stocking) by live trees of any size, including land that formerly had such tree
cover and that will be naturally or artificially regenerated. Forest land includes transition zones, such as
areas between forest and non-forest lands that have at least 10 percent cover (or equivalent stocking) with
live trees and forest areas adjacent to urban and built-up lands. Roadside, streamside, and shelterbelt strips
of trees must have a crown width of at least 36.6m and continuous length of at least 110.6 m to qualify as
forest land. Unimproved roads and trails, streams, and clearings in forest areas are classified as forest if
they are less than 36.6 m wide or 0.4 ha in size; otherwise they are excluded from Forest Land and
classified as Settlements. Some tree-covered areas are not considered forest land, such as fruit orchards in
agricultural production settings that are considered part of Croplands, or tree-covered areas in urban
settings, such as city parks that are classified as Settlements (Smith et al. 2009).
• Cropland: A land-use category that includes areas used for the production of adapted crops for harvest; this
category includes both cultivated and non-cultivated lands.21 1 Cultivated crops include row crops or close-
grown crops and also hay or pasture in rotation with cultivated crops. Non-cultivated cropland includes
continuous hay, perennial crops (e.g., orchards) and horticultural cropland. Cropland also includes land
with alley cropping and windbreaks,212 as well as lands in temporary fallow or enrolled in conservation
reserve programs (i.e., set-asides213), as long as these areas do not meet the Forest Land criteria. Roads
through Cropland, including interstate highways, state highways, other paved roads, gravel roads, dirt
roads, and railroads are excluded from Cropland area estimates and are, instead, classified as Settlements.
• Grassland: A land-use category on which the plant cover is composed principally of grasses, grass-like
plants (i.e., sedges and rushes), forbs, or shrubs suitable for grazing and browsing, and includes both
pastures and native rangelands.214 This includes areas where practices such as clearing, burning, chaining,
and/or chemicals are applied to maintain the grass vegetation. Savannas, some wetlands and deserts, in
addition to tundra are considered Grassland.215 Woody plant communities of low forbs and shrubs, such as
mesquite, chaparral, mountain shrub, and pinyon-juniper, are also classified as Grassland if they do not
meet the criteria for Forest Land. Grassland includes land managed with agroforestry practices such as
silvipasture and windbreaks, assuming the stand or woodlot does not meet the criteria for Forest Land.
209 See .
210 See < http://www.nrcs.usda.gov/wps/portal/nrcs/site/national/home>.
211 A minor portion of Cropland occurs on federal lands, and is not currently included in the C stock change inventory. A
planned improvement is underway to include these areas in future C inventories.
212 Currently, there is no data source to account for biomass C stock change associated with woody plant growth and losses in
alley cropping systems and windbreaks in cropping systems, although these areas are included in the cropland land base.
213 A set-aside is cropland that has been taken out of active cropping and converted to some type of vegetative cover, including,
for example, native grasses or trees.
214 Grasslands on federal lands are included in the managed land base, but C stock changes are not estimated on these lands.
Federal grassland areas have been assumed to have negligible changes in C due to limited land use and management change, but
planned improvements are underway to further investigate this issue and include these areas in future C inventories.
215 IPCC (2006) guidelines do not include provisions to separate desert and tundra as land categories.
7-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Roads through Grassland, including interstate highways, state highways, other paved roads, gravel roads,
dirt roads, and railroads are excluded from Grassland and are, instead, classified as Settlements.
• Wetlands: A land-use category that includes land covered or saturated by water for all or part of the year, in
addition to the areas of lakes, reservoirs and rivers. Managed Wetlands are those where the water level is
artificially changed, or were created by human activity. Certain areas that fall under the managed Wetlands
definition are included in other land uses based on the IPCC guidance, including Cropland (drained
wetlands for crop production and also systems that are flooded for most or just part of the year, such as rice
and cranberry production), Grassland (drained wetlands dominated by grass cover), and Forest Land
(including drained or undrained forested wetlands).
• Settlements: A land-use category representing developed areas consisting of units of 0.25 acres (0.1 ha) or
more that includes residential, industrial, commercial, and institutional land; construction sites; public
administrative sites; railroad yards; cemeteries; airports; golf courses; sanitary landfills; sewage treatment
plants; water control structures and spillways; parks within urban and built-up areas; and highways,
railroads, and other transportation facilities. Also included are tracts of less than 10 acres (4.05 ha) that may
meet the definitions for Forest Land, Cropland, Grassland, or Other Land but are completely surrounded by
urban or built-up land, and so are included in the settlement category. Rural transportation corridors
located within other land uses (e.g., Forest Land, Cropland, and Grassland) are also included in
Settlements.
• Other Land: A land-use category that includes bare soil, rock, ice, and all land areas that do not fall into
any of the other five land-use categories, which allows the total of identified land areas to match the
managed land base. Following the guidance provided by the IPCC (2006), C stock changes are not
estimated for Other Lands because these areas are largely devoid of biomass, litter and soil C pools.
Land-Use Data Sources: Description and Application to United
States Land Area Classification
United States Land-Use Data Sources
The three main sources for land use data in the United States are the NRI, FIA, and the NLCD (Table 7-7). These
data sources are combined to account for land use in all 50 states. FIA and NRI data are used when available for an
area because the surveys contain additional information on management, site conditions, crop types, biometric
measurements and other data from which to estimate C stock changes on those lands. If NRI and FIA data are not
available for an area, however, then the NLCD product is used to represent the land use.
Table 7-7: Data sources used to determine land use and land area for the Conterminous
United States, Hawaii and Alaska
NRI FIA NLCD
Forests
Conterminous United
States
Non-Federal
Federal
Hawaii
Non-Federal
Federal
Alaska
Non-Federal
Federal
Croplands, Grasslands, Other Lands, Settlements, and Wetlands
Conterminous United
States
Non-Federal •
Land Use, Land-Use Change, and Forestry 7-11
-------
Federal
Hawaii
Non-Federal
Federal
Alaska
Non-Federal
Federal
National Resources Inventory
For the Inventory, the NRI is the official source of data on all land uses on non-federal lands in the conterminous
United States and Hawaii (except forest land), and is also used as the resource to determine the total land base for
the conterminous United States and Hawaii. The NRI is a statistically-based survey conducted by the USDA Natural
Resources Conservation Service and is designed to assess soil, water, and related environmental resources on non-
federal lands. The NRI has a stratified multi-stage sampling design, where primary sample units are stratified on the
basis of county and township boundaries defined by the United States Public Land Survey (Nusser and Goebel
1997). Within a primary sample unit (typically a 160-acre [64.75 ha] square quarter-section), three sample points
are selected according to a restricted randomization procedure. Each point in the survey is assigned an area weight
(expansion factor) based on other known areas and land-use information (Nusser and Goebel 1997). The NRI
survey utilizes data derived from remote sensing imagery and site visits in order to provide detailed information on
land use and management, particularly for croplands and grasslands, and is used as the basis to account for C stock
changes in agricultural lands (except federal Grasslands). The NRI survey was conducted every 5 years between
1982 and 1997, but shifted to annualized data collection in 1998. The land use between 5 year periods from 1982
and 1997 are assumed to be the same for a five year time period if the land use is the same at the beginning and end
of the five year period (Note: most of the data has the same land use at the beginning and end of the 5 year periods).
If the land use had changed during a five year period, then the change is assigned at random to one of the five years.
For crop histories, years with missing data are estimated based on the sequence of crops grown during years
preceding and succeeding a missing year in the NRI history. This gap-filling approach allows for development of a
full time series of land use data for non-federal lands in the conterminous United States and Hawaii. This Inventory
incorporates data through 2007 from the NRI.
Forest Inventory and Assessment
The FIA program, conducted by the USFS, is another statistically-based survey for the conterminous United States,
and the official source of data on Forest Land area and management data for the Inventory in this region of the
country. FIA engages in a hierarchical system of sampling, with sampling categorized as Phases 1 through 3, in
which sample points for phases are subsets of the previous phase. Phase 1 refers to collection of remotely-sensed
data (either aerial photographs or satellite imagery) primarily to classify land into forest or non-forest and to identify
landscape patterns like fragmentation and urbanization. Phase 2 is the collection of field data on a network of
ground plots that enable classification and summarization of area, tree, and other attributes associated with forest
land uses. Phase 3 plots are a subset of Phase 2 plots where data on indicators of forest health are measured. Data
from all three phases are also used to estimate C stock changes for forest land. Historically, FIA inventory surveys
have been conducted periodically, with all plots in a state being measured at a frequency of every 5 to 14 years. A
new national plot design and annual sampling design was introduced by FIA about ten years ago. Most states,
though, have only recently been brought into this system. Annualized sampling means that a portion of plots
throughout each state is sampled each year, with the goal of measuring all plots once every 5 years. See Annex 3.13
to see the specific survey data available by state. The most recent year of available data varies state by state (range
of most recent data is from 2002 through 2012).
National Land Cover Dataset
7-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Though NRI provides land-area data for both federal and non-federal lands in the conterminous United States and
Hawaii, it only includes land-use data on non-federal lands, and FIA only records data for forest land.216
Consequently, major gaps exist when the datasets are combined, such as federal grassland operated by the Bureau of
Land Management (BLM), USD A, and National Park Service, as well as Alaska.217 The NLCD is used as a
supplementary database to account for land use on federal lands that are not included in the NRI and FIA databases.
The NLCD land-cover classification scheme, available for 1992, 2001, and 2006, has been applied over the
conterminous United States (Homer et al. 2007), and also for Alaska and Hawaii in 2001. For the conterminous
United States, the NLCD Land Cover Change Products for 2001 and 2006 were used in order to represent both land
use and land-use change for federal lands (Fry et al. 2011, Homer et al. 2007). The NLCD products are based
primarily on Landsat Thematic Mapper imagery. The NLCD contains 21 categories of land-cover information,
which have been aggregated into the IPCC land-use categories, and the data are available at a spatial resolution of
30 meters. The federal land portion of the NLCD was extracted from the dataset using the federal land area
boundary map from the National Atlas (U.S. Department of Interior 2005). This map represents federal land
boundaries in 2005, so as part of the analysis, the federal land area was adjusted annually based on the NRI federal
land area estimates (i.e., land is periodically transferred between federal and non-federal ownership). Consequently,
the portion of the land base categorized with NLCD data varied from year to year, corresponding to an increase or
decrease in the federal land base. The NLCD is strictly a source of land-cover information, however, and does not
provide the necessary site conditions, crop types, and management information from which to estimate C stock
changes on those lands.
Another step in the analysis is to address gaps as well as overlaps in the representation of the United States land base
between the Agricultural Carbon Stock inventory (Cropland Remaining Cropland, Land Converted to Cropland,
Grassland Remaining Grassland, Land Converted to Grassland) and Forest Land Carbon Stock inventory (Forest
Land Remaining Forest Land and Land Converted to Forest Land), which are based on the NRI and FIA databases,
respectively. NRI and FIA have different criteria for classifying forest land and sampling designs, leading to
discrepancies in the resulting estimates of Forest Land area on non-federal land in the conterminous United States.
Similarly, there are discrepancies between the NLCD and FIA data for defining and classifying Forest Land on
federal lands. In addition, dependence exists between the Forest Land area and the amount of land designated as
other land uses in both the NRI and the NLCD, such as the amount of Grassland, Cropland, and Wetlands, relative
to the Forest Land area. This results in inconsistencies among the three databases for estimated Forest Land area, as
well as for the area estimates for other land-use categories. FIA is the main database for forest statistics, and
consequently, the NRI and NLCD were adjusted to achieve consistency with FIA estimates of Forest Land in the
conterminous United States. The adjustments were made at a state-scale, and it was assumed that the majority of the
discrepancy in forest area was associated with an under- or over-prediction of Grassland and Wetland area in the
NRI and NLCD due to differences in Forest Land definitions. Specifically, the Forest Land area for a given state
according to the NRI and NLCD was adjusted to match the FIA estimates of Forest Land for non-federal and federal
land in Forest Lands Remaining Forest Lands, respectively. In a second step, corresponding increases or decreases
were made in the area estimates of Grassland and Wetland from the NRI and NLCD, Grasslands Remaining
Grasslands and Wetlands Remaining Wetlands, in order to balance the change in forest area, and therefore not
change the overall amount of managed land within an individual state. The adjustments were based on the
proportion of land within each of these land-use categories at the state level, (i.e., a higher proportion of Grassland
led to a larger adjustment in Grassland area).
As part of Quality Assurance /Quality Control (QA/QC), the land base derived from the NRI, FIA and NLCD was
compared to the Topologically Integrated Geographic Encoding and Referencing (TIGER) survey (U.S. Census
Bureau 2010). The U.S. Census Bureau gathers data on the United States population and economy, and has a
database of land areas for the country. The land area estimates from the U.S. Census Bureau differ from those
provided by the land-use surveys used in the Inventory because of discrepancies in the reporting approach for the
census and the methods used in the NRI, FIA, and NLCD. The area estimates of land-use categories, based on NRI,
FIA, and NLCD, are derived from remote sensing data instead of the land survey approach used by the U.S. Census
216 FIA does collect some data on non-forest land use, but these are held in regional databases versus the national database. The
status of these data is being investigated.
217 The FIA and NRI survey programs also do not include United States Territories with the exception of non-federal lands in
Puerto Rico, which are included in the NRI survey. Furthermore, NLCD does not include coverage for all United States
Territories.
Land Use, Land-Use Change, and Forestry 7-13
-------
Survey. More importantly, the U.S. Census Survey does not provide a time series of land-use change data or land
management information. Consequently, the U.S. Census Survey was not adopted as the official land area estimate
for the Inventory. Rather, the NRI, FIA and NLCD datasets were adopted because this database provides full
coverage of land area and land use for the conterminous United States, Alaska and Hawaii, in addition to
management and other data relevant for the inventory. Regardless, the total difference between the U.S. Census
Survey and the combined NRI, FIA and NLCD data is about 22 million hectares for the total United States land base
of about 936 million hectares currently included in the Inventory, or a 2.4 percent difference. Much of this
difference is associated with open waters in coastal regions and the Great Lakes, which is included in the Census.
Managed Land Designation
Lands are designated as managed in the United States based on the definitions provided earlier in this section. In
order to apply the definitions in an analysis of managed land, the following criteria are used:
• All croplands and settlements are designated as managed so only grassland, forest land or other
lands may be designated as unmanaged land;218
• All forest lands with active fire protection are considered managed;
• All grasslands are considered managed at a county scale if there are livestock in the county;219
• Other areas are considered managed if accessible based on the proximity to roads and other
transportation corridors, and/or infrastructure; and
• Lands that were previously managed but subsequently classified as unmanaged remain in the
managed land base for 20 years following the conversion to account for legacy effects of management on C
stocks.
These criteria will be expanded in the future as other data sources become available, such as national datasets on
mining and resource extraction.
The analysis of managed lands is conducted using a geographic information system. Lands that are used for crop
production or settlements are determined from the NLCD (Fry et al. 2011, Homer et al. 2007). Lands with active
fire management are determined from maps of federal and state management plans from the National Atlas (U.S.
Department of Interior 2005) and Alaska Interagency Fire Management Council (1998). It is noteworthy that all
forest lands in the conterminous United States have active fire protection, and are therefore designated as managed
regardless of accessibility or other criteria. The designation of grasslands as managed is determined based on
USDA-NASS livestock population data at the county scale (U.S. Department of Agriculture 2011). Accessibility is
evaluated based on a 10km buffer surrounding road and train transportation networks using the ESRI Data and Maps
product (ESRI 2008), and a 10km buffer surrounding settlements using NLCD. The resulting managed land area is
overlaid on the NLCD to estimate the area of managed land by land use for both federal and non-federal lands. The
remaining land represents the unmanaged land base.
Approach for Combining Data Sources
The managed land base in the United States has been classified into the thirty-six IPCC land-use categories using
definitions developed to meet national circumstances, while adhering to IPCC (2006). 22° In practice, the land was
initially classified into a variety of land-use categories within the NRI, FIA and NLCD datasets, and then aggregated
into the thirty-six broad land use and land-use-change categories identified in IPCC (2006). All three datasets
provide information on forest land areas in the conterminous United States, but the area data from FIA serve as the
official dataset for estimating forest land use areas in the conterminous United States. Therefore, the NRI and
NLCD data are modified at the state scale to match the FIA forest land areas, and any change is reflected in an
218 A planned improvement is underway to deal with an exception for wetlands which includes both managed and unmanaged
lands based on the definitions for the current United States Land Representation Assessment.
219 Assuming all grasslands are grazed in a county with livestock is a conservation assumption about human impacts on
grasslands. Currently, detailed information on grazing at sub-county scales is not available for the United States to make a finer
delineation of managed land.
220 Definitions are provided in the previous section.
7-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
increase or decrease in grassland and wetland area (See section United States Land Use Data Sources for more
information). The modified NRI data are then aggregated to provide the land use and land use change data for non-
federal lands in the conterminous United States, and the modified NLCD data are aggregated to provide the land use
and land use change data for federal lands. Data for all land uses in Hawaii are based on NRI for non-federal lands
and on NLCD for federal lands. Land use data in Alaska are based solely on the NLCD data (Table 7-7). The result
is land use and land use change data for the conterminous United States, Hawaii, and Alaska.221
A summary of the details on the approach used to combine data sources for each land use are described below.
• Forest Land: Both non-federal and federal forest lands in both the continental United States and coastal
Alaska are covered by FIA. FIA is used as the basis for both Forest Land area data as well as to estimate C
stocks and fluxes on Forest Land. Interior Alaska is not currently surveyed by FIA so forest land in Alaska
is evaluated with 2001 NLCD. NRI is being used in the current report to provide Forest Land areas on non-
federal lands in Hawaii, but FIA data will be collected in Hawaii in the future.
• Cropland: Cropland is classified using the NRI, which covers all non-federal lands within 49 states
(excluding Alaska), including state and local government-owned land as well as tribal lands. NRI is used
as the basis for both Cropland area data as well as to estimate C stocks and fluxes on Cropland. NLCD
2001 is used to determine Cropland area in Alaska.
• Grassland: Grassland on non-federal lands is classified using the NRI within 49 states (excluding Alaska),
including state and local government-owned land as well as tribal lands. NRI is used as the basis for both
Grassland area data as well as to estimate C stocks and fluxes on Grassland. Grassland on federal Bureau
of Land Management lands, Department of Defense lands, National Parks and within USFS lands are
covered by the NLCD. NLCD is used to estimate the areas of federal and non-federal grasslands in Alaska.
• Wetlands: NRI captures wetlands on non-federal lands within 49 states (excluding Alaska), while federal
wetlands and wetlands in Alaska are covered by the NLCD. This currently includes both managed and
unmanaged wetlands as no database has yet been applied to make this distinction. See Planned
Improvements for details.
• Settlements: The NRI captures non-federal settlement area in 49 states (excluding Alaska). If areas of
Forest Land or Grassland under 10 acres (4.05 ha) are contained within settlements or urban areas, they are
classified as Settlements (urban) in the NRI database. If these parcels exceed the 10 acre (4.05 ha)
threshold and are Grassland, they will be classified as such by NRI. Regardless of size, a forested area is
classified as non-forest by FIA if it is located within an urban area. Settlements on federal lands and in
Alaska are covered by NLCD.
• Other Land: Any land not falling into the other five land categories and, therefore, categorized as Other
Land is classified using the NRI for non-federal areas in the 49 states (excluding Alaska) and NLCD for the
federal lands and Alaska.
Some lands can be classified into one or more categories due to multiple uses that meet the criteria of more than one
definition. However, a ranking has been developed for assignment priority in these cases. The ranking process is
from highest to lowest priority, in the following manner:
Settlements > Cropland > Forest Land > Grassland > Wetlands > Other Land
Settlements are given the highest assignment priority because they are extremely heterogeneous with a mosaic of
patches that include buildings, infrastructure and travel corridors, but also open grass areas, forest patches, riparian
areas, and gardens. The latter examples could be classified as Grassland, Forest Land, Wetlands, and Cropland,
respectively, but when located in close proximity to settlement areas they tend to be managed in a unique manner
compared to non-settlement areas. Consequently, these areas are assigned to the Settlements land-use category.
Cropland is given the second assignment priority, because cropping practices tend to dominate management
activities on areas used to produce food, forage or fiber. The consequence of this ranking is that crops in rotation
with pasture will be classified as Cropland, and land with woody plant cover that is used to produce crops (e.g.,
orchards) is classified as Cropland, even though these areas may meet the definitions of Grassland or Forest Land,
221 Only one year of data are currently available for Alaska so there is no information on land use change for this state.
Land Use, Land-Use Change, and Forestry 7-15
-------
respectively. Similarly, Wetlands are considered Croplands if they are used for crop production, such as rice or
cranberries. Forest Land occurs next in the priority assignment because traditional forestry practices tend to be the
focus of the management activity in areas with woody plant cover that are not croplands (e.g., orchards) or
settlements (e.g., housing subdivisions with significant tree cover). Grassland occurs next in the ranking, while
Wetlands and Other Land complete the list.
The assignment priority does not reflect the level of importance for reporting greenhouse gas emissions and
removals on managed land, but is intended to classify all areas into a single land use. Currently, the IPCC does not
make provisions in the guidelines for assigning land to multiple uses. For example, a Wetland is classified as Forest
Land if the area has sufficient tree cover to meet the stocking and stand size requirements. Similarly, Wetlands are
classified as Cropland if they are used for crop production, such as rice or cranberries, or as Grassland if they are
composed principally of grasses, grass-like plants (i.e., sedges and rushes), forbs, or shrubs suitable for grazing and
browsing. Regardless of the classification, emissions from these areas are included in the Inventory if the land is
considered managed and presumably impacted by anthropogenic activity in accordance with the guidance provided
in IPCC (2006).
Recalculations Discussion
Relative to the previous Inventory, new data were incorporated from FIA on forestland areas, which were used to
make minor adjustments to the time series. FIA conducts a survey of plots annually so that each plot is visited every
5 years (Note: some states have not initiated the annual sampling regime, as discussed previously). Consequently,
the time series is updated each year as new data are collected over the 5 year cycles.
Planned Improvements
Preliminary land use area data by land-use category are provided in Box 7-2: Preliminary Estimates of Land Use in
United States Territories for the United States Territories. A key planned improvement is to fully incorporate land-
use data from these areas into the Inventory. Fortunately, most of the managed land in the United States is included
in the current land-use statistics, but a complete accounting is a key goal for the near future. Data sources will also
be evaluated for representing land use on federal and non-federal lands in United States territories.
Box 7-2: Preliminary Estimates of Land Use in United States Territories
Several programs have developed land cover maps for United States Territories using remote sensing imagery,
including the Gap Analysis program, Caribbean Land Cover project, National Land Cover dataset, USFS Pacific
Islands Imagery Project, and the NOAA Coastal Change Analysis Program. These products were reviewed and
evaluated for use in the national inventory as a step towards implementing a planned improvement to include United
States Territories in the land representation for the Inventory. Recommendations are to use the NOAA Coastal
Change Analysis Program (C-CAP) Regional Land Cover Database for the smaller island Territories (U.S. Virgin
Islands, Guam, Northern Marianas Islands, and American Samoa) because this program is an ongoing program that
will be continually updated and also has reasonable accuracy. The C-CAP product does not cover the entire territory
of Puerto Rico so the NLCD was used for this area. The final selection of a land cover product for these Territories
is still under discussion. Results are presented below (in hectares). The total land area of all United States
Territories is 1.05 million hectares, representing 0.1 percent of the total land base for the United States.
Table 7-8: Total Land Area (Hectares) by Land Use Category for United States Territories.
Cropland
Forest
Grasslands
Other
Settlements
Wetlands
Total
Puerto Rico
19,712
404,004
299,714
5,502
130,330
24,525
883,788
U.S. Virgin
Islands
138
13,107
12,148
1,006
7,650
4,748
38,796
Guam
236
24,650
15,449
1,141
11,146
1,633
54,255
Northern
Marianas
Islands
289
25,761
13,636
5,186
3,637
260
48,769
American
Samoa
389
15,440
1,830
298
1,734
87
19,777
Total
20,764
482,962
342,777
13,133
154,496
31,252
1,045,385
7-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Additional work will be conducted to reconcile differences in Forest Land estimates between the NRI and FIA,
evaluating the assumption that the majority of discrepancies in Forest Land areas are associated with an over- or
under-estimation of Grassland and Wetland area. In some regions of the United States, a discrepancy in Forest Land
areas between NRI and FIA may be associated with an over- or under-prediction of other land uses. This
improvement would include an analysis designed to develop region-specific adjustments.
There are also other databases that may need to be reconciled with the NRI and NLCD datasets, particularly for
Settlements. Urban area estimates, used to produce C stock and flux estimates from urban trees, are currently based
on population data (1990 and 2000 U.S. Census data). Using the population statistics, "urban clusters" are defined
as areas with more than 500 people per square mile. The USFS is currently moving ahead with an urban forest
inventory program so that urban forest area estimates will be consistent with FIA forest area estimates outside of
urban areas, which would be expected to reduce omissions and overlap of forest area estimates along urban
boundary areas.
Once approved by the UNFCCC, new guidance in the "2013 Supplement to the 2006 Guide lines for National
Greenhouse Gas Inventories: Wetlands" will be implemented in the Inventory. This will likely have implications
for the classification of managed and unmanaged wetlands in the Inventory report. More detailed wetlands datasets
will also be evaluated and integrated into the analysis in order to implement the new guidance.
The implementation criteria for managed land will also be expanded in the future, particularly in regard to inclusion
of areas managed for mining and petroleum extraction. This criterion will have an impact on the managed land base
in Alaska although there will still be large tracts of unmanaged land in this region with virtually no direct influence
on GHG emissions from human activity.
7.2 Forest Land Remaining Forest Land
Changes in Forest Carbon Stocks (IPCC Source Category 5A1)
For estimating carbon (C) stocks or stock change (flux), C in forest ecosystems can be divided into the following
five storage pools (IPCC 2003):
• Aboveground biomass, which includes all living biomass above the soil including stem, stump, branches,
bark, seeds, and foliage. This category includes live understory.
• Belowground biomass, which includes all living biomass of coarse living roots greater than 2 mm diameter.
• Dead wood, which includes all non-living woody biomass either standing, lying on the ground (but not
including litter), or in the soil.
• Litter, which includes the litter, fumic, and humic layers, and all non-living biomass with a diameter less
than 7.5 cm at transect intersection, lying on the ground.
• Soil organic C (SOC), including all organic material in soil to a depth of 1 meter but excluding the coarse
roots of the aboveground pools.
In addition, there are two harvested wood pools necessary for estimating C flux:
• Harvested wood products (HWP) in use.
• HWP in solid waste disposal sites (SWDS).
Carbon is continuously cycled among these storage pools and between forest ecosystems and the atmosphere as a
result of biological processes in forests (e.g., photosynthesis, respiration, decomposition, and disturbances such as
fires or pest outbreaks) and anthropogenic activities (e.g., harvesting, thinning, and replanting). As trees
photosynthesize and grow, C is removed from the atmosphere and stored in living tree biomass. As trees die and
otherwise deposit litter and debris on the forest floor, C is released to the atmosphere and also is transferred to the
soil by organisms that facilitate decomposition.
Land Use, Land-Use Change, and Forestry 7-17
-------
The net change in forest C is not equivalent to the net flux between forests and the atmosphere because timber
harvests do not cause an immediate flux of all vegetation C to the atmosphere. Instead, harvesting transfers a
portion of the C stored in wood to a "product pool." Once in a product pool, the C is emitted over time as CCh when
the wood product combusts or decays. The rate of emission varies considerably among different product pools. For
example, if timber is harvested to produce energy, combustion releases C immediately. Conversely, if timber is
harvested and used as lumber in a house, it may be many decades or even centuries before the lumber decays and C
is released to the atmosphere. If wood products are disposed of in SWDS, the C contained in the wood may be
released many years or decades later, or may be stored almost permanently in the SWDS.
This section quantifies the net changes in C stocks in the five forest C pools and two harvested wood pools. The
basic methodology for determining carbon stock and stock-change relies on the extensive inventories of U.S. forest
lands, and improvement in these inventories over time are reflected in the estimates (Heath et al. 2011, Heath 2012).
The net change in stocks for each pool is estimated, and then the changes in stocks are summed over all pools to
estimate total net flux. The focus on C implies that all C-based greenhouse gases are included, and the focus on
stock change suggests that specific ecosystem fluxes do not need to be separately itemized in this report. Changes in
C stocks from disturbances, such as forest fires, are implicitly included in the net changes. For instance, an
inventory conducted after fire counts only the trees that are left. The change between inventories thus accounts for
the C changes due to fires; however, it may not be possible to attribute the changes to the disturbance specifically.
Similarly, changes in C stocks from natural disturbances, such as wildfires, pest outbreaks, and storms, are implicitly
accounted for in the forest inventory approach; however, they are highly variable from year to year. Wildfire events
are typically the most severe but other natural disturbance events can result in large C stock losses that are time- and
location- specific. The IPCC (2003) recommends reporting C stocks according to several land-use types and
conversions, specifically Forest Land Remaining Forest Land and Land Converted to Forest Land. Research is
ongoing to track C across a matrix of land-uses and land-use changes. Until such time that reliable and
comprehensive estimates of C across the land-use matrix can be produced, net changes in all forest-related land,
including non-forest land converted to forest and forests converted to non-forest, are reported here.
Forest C storage pools, and the flows between them via emissions, sequestration, and transfers, are shown in Figure
7-2. In the figure, boxes represent forest C storage pools and arrows represent flows between storage pools or
between storage pools and the atmosphere. Note that the boxes are not identical to the storage pools identified in
this chapter. The storage pools identified in this chapter have been refined in this graphic to better illustrate the
processes that result in transfers of C from one pool to another, and emissions to as well as uptake from the
atmosphere.
7-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure 7-2: Forest Sector Carbon Pools and Flows
Forest Sector Carbon Pools and Flows
Combustion from
forest fires (carbon
dioxide, methane)
DeeomposUon Methane
Raring
and
Utilization
Legend
Carbon Pool
Carbon transfer or flux
Combustion
Source: Heath etal. 2003
Approximately 33 percent (304 million hectares) of the U.S. land area is estimated to be forested (Smith et al. 2009).
The current forest C inventory includes an estimated 278 million hectares in the conterminous 48 states (USDA
Forest Service 2013a, 2013b) that are considered managed and are included in this inventory. An additional 6
million hectares of southeast and south central Alaskan forest are inventoried and are included here. Some
differences exist in forest land defined in Smith et al. (2009) and the forest land included in this report, which is
based on the USDA Forest Service (2013b) forest land definition. Survey data are not yet available from Hawaii
and a large portion of interior Alaska, but estimates of these areas are included in Smith et al. (2009). Updated
survey data for central and western forest land in both Oklahoma and Texas have only recently become available,
and these forests contribute to overall C stock reported below. While Hawaii and U.S. territories have relatively
small areas of forest land and thus may not influence the overall C budget substantially, these regions will be added
to the C budget as sufficient data become available. Agroforestry systems are also not currently accounted for in the
inventory, since they are not explicitly inventoried by either the FIA program of the USDA Forest Service or the
NRI of the USDA Natural Resources Conservation Service (Perry et al. 2005).
An estimated 68 percent (208 million hectares) of U.S. forests in Alaska and the conterminous United States are
classified as timberland, meaning they meet minimum levels of productivity and have not been removed from
production. Nine percent of Alaskan forests and 81 percent of forests in the conterminous United States are
classified as timberlands. Of the remaining non-timberland forests, 30 million hectares are reserved forest lands
(withdrawn by law from management for production of wood products) and 66 million hectares are lower
productivity forest lands (Smith et al. 2009). Historically, the timberlands in the conterminous 48 states have been
more frequently or intensively surveyed than other forest lands.
Estimates of forest land area declined by approximately 10 million hectares over the period from the early 1960s to
the late 1980s. Since then, forest area has increased by about 12 million hectares (Smith et al. 2009). Current trends
in forest area represent an estimated average annual increase of 0.2 percent. In addition to the increase in forest
area, the major influences on the current net C flux from forest land are management activities and the ongoing
impacts of previous land-use changes. These activities affect the net flux of C by altering the amount of C stored in
forest ecosystems. For example, intensified management of forests that leads to an increased rate of growth may
Land Use, Land-Use Change, and Forestry 7-19
-------
increase the eventual biomass density of the forest, thereby increasing the uptake of C.222 Though harvesting forests
removes much of the aboveground C, on average the estimated volume of annual net growth nationwide is about 72
percent higher than the volume of annual removals on timberlands (Smith et al. 2009). The reversion of cropland to
forest land increases C storage in biomass, forest floor, and soils. The net effects of forest management and the
effects of land-use change involving forest land are captured in the estimates of C stocks and fluxes presented in this
chapter.
In the United States, improved forest management practices, the regeneration of previously cleared forest areas, and
timber harvesting and use have resulted in net uptake (i.e., net sequestration) of C each year from 1990 through
2012. The rate of forest clearing begun in the 17th century following European settlement had slowed by the late
19th century. Through the later part of the 20th century many areas of previously forested land in the United States
were allowed to revert to forests or were actively reforested. The impacts of these land-use changes still influence C
fluxes from these forest lands. More recently, the 1970s and 1980s saw a resurgence of federally-sponsored forest
management programs (e.g., the Forestry Incentive Program) and soil conservation programs (e.g., the Conservation
Reserve Program), which have focused on tree planting, improving timber management activities, combating soil
erosion, and converting marginal cropland to forests. In addition to forest regeneration and management, forest
harvests have also affected net C fluxes. Because most of the timber harvested from U.S. forests is used in wood
products, and many discarded wood products are disposed of in SWDS rather than by incineration, significant
quantities of C in harvested wood are transferred to long-term storage pools rather than being released rapidly to the
atmosphere (Skog and Nicholson 1998, Skog 2008). The size of these long-term C storage pools has increased
during the last century with the question arising as to how long the U.S. forests can remain a net C sink (Woodall et
al. 2013).
Changes in C stocks in U.S. forests and harvested wood were estimated to account for net sequestration of 866 Tg
CO2Eq. (236 Tg C) in 2012 (Table 7-9, Table 7-10, and Table 7-11). In addition to the net accumulation of C in
harvested wood pools, sequestration is a reflection of net forest growth and increasing forest area over this period.
Overall, estimates of average C in forest ecosystem biomass (aboveground and belowground) increased from 54 to
62 Mg C/ha between 1990 and 2013 (see Annex 3-13 for average C densities by specific regions and forest types).
Continuous, regular annual surveys are not available over the period for each state; therefore, estimates for non-
survey years were derived by interpolation between known data points. Survey years vary from state to state, and
national estimates are a composite of individual state surveys. Therefore, changes in sequestration over the interval
1990 to 2012 are the result of the sequences of new inventories for each state. C in forest ecosystem biomass had
the greatest effect on total change through increases in C density and total forest land. Management practices that
increase C stocks on forest land, as well as afforestation and reforestation efforts, influence the trends of increased C
densities in forests and increased forest land in the United States.
Annual net additions to HWP carbon stock are about the same for 2012 as in 2011. Additions to solid-wood
products in use increased a little with further recovery of the housing market, but additions to paper products in use
declined. Net additions to products in use for 2012 is less than 15 percent of the level of net additions to product in
use in 2007—prior to the recession. Additions to landfills have been relatively stable over time.
222 The term "biomass density" refers to the mass of live vegetation per unit area. It is usually measured on a dry-weight basis.
Dry biomass is 50 percent C by weight.
7-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 7-9: Estimated Net Annual Changes in C Stocks (Tg COz/yr) in Forest and Harvested
Wood Pools
Carbon Pool
Forest
Aboveground Biomass
Belowground Biomass
Dead Wood
Litter
Soil Organic Carbon
Harvested Wood
Products in Use
SWDS
Total Net Flux
1990
(572.8)
(354.5)
(69.2)
(50.6)
(24.0)
(74.5)
(131.8)
(64.8)
(67.0)
(704.6)
2005
(824.4)
(442.0) 1
(87.0) 1
(64.7) 1
(46.1) 1
(184.5)
(102.8) 1
(43.1) 1
(59.7)
(927.2)
• 2008
(795.2)
(435.0)
(86.4)
(73.4)
(51.2)
(149.3)
(75.8)
(13.3)
(62.5)
(871.0)
2009
(795.2)
(435.0)
(86.4)
(73.4)
(51.2)
(149.3)
(54.1)
6.7
(60.9)
(849.4)
2010
(796.4)
(435.0)
(86.4)
(74.5)
(51.2)
(149.3)
(59.3)
1.2
(60.5)
(855.7)
2011
(800.0)
(435.0)
(86.4)
(78.2)
(51.2)
(149.3)
(67.1)
(5.8)
(61.2)
(867.1)
2012
(800.0)
(435.0)
(86.4)
(78.2)
(51.2)
(149.3)
(66.5)
(4.9)
(61.6)
(866.5)
Note: Forest C stocks do not include forest stocks in U.S. territories, Hawaii, a portion of managed forests in
Alaska, or trees on non-forest land (e.g., urban trees, agroforestry systems). Parentheses indicate net C
sequestration (i.e., a net removal of C from the atmosphere). Total net flux is an estimate of the actual net flux
between the total forest C pool and the atmosphere. Forest area estimates are based on interpolation and
extrapolation of inventory data as described in the text and in Annex 3.13. Harvested wood estimates are based
on results from annual surveys and models. Totals may not sum due to independent rounding.
Table 7-10: Estimated Net Annual Changes in C Stocks (Tg C/yr) in Forest and Harvested
Wood Pools
Carbon Pool
Forest
Aboveground Biomass
Belowground Biomass
Dead Wood
Litter
Soil Organic C
Harvested Wood
Products in Use
SWDS
Total Net Flux
1990
(156.2)
(96.7)
(18.9)
(13.8)
(6.5)
(20.3)
(35.9)
(17.7)
(18.3)
(192.2)
2005
(224.8)
(120.6)
(23.7)
(17.6)
(12.6)
(50.3)
(28.0)
(11.7)
(16.3)
(252.9)
2008
(216.9)
(118.6)
(23.6)
(20.0)
(14.0)
(40.7)
(20.7)
(3.6)
(17.0)
(237.6)
2009
(216.9)
(118.6)
(23.6)
(20.0)
(14.0)
(40.7)
(14.8)
1.8
(16.6)
(231.6)
2010
(217.2)
(118.6)
(23.6)
(20.3)
(14.0)
(40.7)
(16.2)
0.3
(16.5)
(233.4)
2011
(218.2)
(118.6)
(23.6)
(21.3)
(14.0)
(40.7)
(18.3)
(1.6)
(16.7)
(236.5)
2012
(218.2)
(118.6)
(23.6)
(21.3)
(14.0)
(40.7)
(18.1)
(1.3)
(16.8)
(236.3)
Note: Forest C stocks do not include forest stocks in U.S. territories, Hawaii, a portion of managed lands in
Alaska, or trees on non-forest land (e.g., urban trees, agroforestry systems). Parentheses indicate net C
sequestration (i.e., a net removal of C from the atmosphere). Total net flux is an estimate of the actual net
flux between the total forest C pool and the atmosphere. Harvested wood estimates are based on results from
annual surveys and models. Totals may not sum due to independent rounding.
Stock estimates for forest and harvested wood C storage pools are presented in Table 7-11. Together, the estimated
aboveground live and forest soil pools account for a large proportion of total forest C stocks. The estimated C
stocks summed for non-soil pools increased over time. Therefore, the estimated C sequestration was greater than C
emissions from forests, as discussed above. When FIA plot data are viewed in a spatial context, the imputed C
density of individual forest ecosystem pools is highly variable across the diverse ecosystems of the United States
(Wilson et al. 2013), indicating the technical hurdles to accurate accounting.
Table 7-11: Estimated Forest area (1,000 ha) and C Stocks (Tg C) in Forest and Harvested
Wood Pools
1990
2005
2008
2009
2010
2011
2012
2013
Forest Area (1000 ha)
Carbon Pools (Tg C)
Forest
Aboveground Biomass
Belowground Biomass
275,399 282,583
284,345 284,858 285,371 285,884 286,397 286,910
38,967
12,318
2,437
41,377
13,915
2,751
42,038
14,272
2,822
42,255
14,391
2,846
42,472
14,510
2,869
42,689
14,628
2,893
42,907
14,747
2,916
43,126
14,866
2,940
Land Use, Land-Use Change, and Forestry 7-21
-------
Dead Wood
Litter
Soil Organic C
Harvested Wood
Products in Use
SWDS
Total C Stock
2,147
4,897
17,168
1,859
1,231
628
40,826
2,404
4,946
17,361
2,325
1,435
890
43,701
2,461
4,986
17,497
2,410
1,469
940
44,448
2,481
5,000
17,538
2,430
1,473
958
44,686
2,501
5,014
17,578
2,445
1,471
974
44,917
2
5
17
2
1
45
,521
,028
,619
,461
,471
991
,151
2,542
5,042
17,660
2,480
1,472
1,007
45,387
2,564
5,056
17,700
2,498
1,474
1,024
45,623
Note: Forest area estimates include portions of managed forests in Alaska for which survey data are available. Forest C
stocks do not include forest stocks in U.S. territories, Hawaii, a large portion of Alaska, or trees on non-forest land (e.g.,
urban trees, agro forestry systems). Wood product stocks include exports, even if the logs are processed in other countries,
and exclude imports. Forest area estimates are based on interpolation and extrapolation of inventory data as described in
Smith et al. (2010) and in Annex 3.13. Harvested wood estimates are based on results from annual surveys and models.
Totals may not sum due to independent rounding. Inventories are assumed to represent stocks as of January 1 of the
inventory year. Flux is the net annual change in stock. Thus, an estimate of flux for 2006 requires estimates of C stocks
for 2006 and 2007.
Figure 7-3: Estimates of Net Annual Changes in C Stocks for Major C Pools
25 n
e- -25 -
I
3 -75 H
-125
-175 -
-225 -
-275
Ha ivested Wood
Soil
Forest, Nonsoil
Total Net Change
8
rsJfNJrNfMrNrslrslrMpJrMno
7-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure 7-4: Forest Ecosystem Carbon Density Imputed from Forest Inventory Plots,
Conterminous U.S., 2001-2009
CD-
Figure 7-4 shows A) total forest ecosystem carbon, B) aboveground live trees, C) standing dead trees, D) litter, and
E) soil organic carbon (Wilson et al. 2013).
Box 7-3: CO? Emissions from Forest Fires
As stated previously, the forest inventory approach implicitly accounts for emissions due to disturbances such as
forest fires, because only C remaining in the forest is estimated. Net C stock change is estimated by subtracting
Land Use, Land-Use Change, and Forestry 7-23
-------
consecutive C stock estimates. A forest fire disturbance removes C from the forest. The inventory data on which
net C stock estimates are based already reflect this C loss. Therefore, estimates of net annual changes in C stocks
for U.S. forestland already account for CC>2 emissions from forest fires occurring in the lower 48 states as well as in
the proportion of Alaska's managed forest land captured in this Inventory. Because it is of interest to quantify the
magnitude of CCh emissions from fire disturbance, these estimates are highlighted here, using the full extent of
available data. Non-CCh greenhouse gas emissions from forest fires are also quantified in a separate section below.
The IPCC (2003) methodology and IPCC (2006) default combustion factor for wildfire were employed to estimate
CO2 emissions from forest fires. See the explanation in Annex 3.13 for more details on the methodology used to
estimate CCh emissions from forest fires. Carbon dioxide emissions for wildfires and prescribed fires in the lower
48 states and wildfires in Alaska in 2012 were estimated to be 242.7 Tg CCVyr. This amount is masked in the
estimate of net annual forest C stock change for 2012 because this net estimate accounts for the amount sequestered
minus any emissions.
Table 7-12: Estimates of COz (Tg/yr) Emissions for the Lower 48 States and Alaska
Year
CCh emitted from
Wildfires in
Lower 48 States
(Tg/yr)
CCh emitted from
Prescribed Fires
in Lower 48 States
(Tg/yr)
CCh emitted from
Wildfires in
Alaska (Tg/yr)
Total CCh emitted
(Tg/yr)
1990
32.6
39.7
2008
2009
2010
2011
2012
123.4
71.2
55.4
204.5
226.2
15.6
20.5
19.7
17.3
16.6
+ 139.0
+ 91.6
+ 75.1
+ 221.8
+ 242.7
+ Does not exceed 0.05 Tg CCh Eq.
Note that these emissions have already been accounted for in the estimates of net annual changes in C
stocks, which account for the amount sequestered minus any emissions.
Methodology and Data Sources
The methodology described herein is consistent with IPCC (2003, 2006) and IPCC/UNEP/OECD/IEA (1997).
Forest ecosystem C stocks and net annual C stock change were determined according to stock-difference methods,
which involved applying C estimation factors to forest inventory data and interpolating between successive
inventory-based estimates of C stocks. Harvested wood C estimates were based on factors such as the allocation of
wood to various primary and end-use products as well as half-life (the time at which half of the amount placed in use
will have been discarded from use) and expected disposition (e.g., product pool, SWDS, combustion). An overview
of the different methodologies and data sources used to estimate the C in forest ecosystems or harvested wood
products is provided here. See Annex 3.13 for details and additional information related to the methods and data.
Forest Ecosystem Carbon from Forest Inventory
Forest ecosystem stock and flux estimates are based on the stock-difference method and calculations for all
estimates are in units of C. Separate estimates were made for the five IPCC C storage pools described above. All
estimates were based on data collected from the extensive array of permanent forest inventory plots in the United
States as well as models employed to fill gaps in field data (USDA Forest Service 2013b, 2013c). Carbon
conversion factors were applied at the disaggregated level of each inventory plot and then appropriately expanded to
population estimates. A combination of tiers as outlined by IPCC (2006) was used. The Tier 3 biomass C values
were calculated from forest inventory tree-level data. The Tier 2 dead organic and soil C pools were based on
7-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
empirical or process models from the inventory data. All C conversion factors are specific to regions or individual
states within the United States, which were further classified according to characteristic forest types within each
region.
The first step in developing forest ecosystem estimates is to identify useful inventory data and resolve any
inconsistencies among datasets. Forest inventory data were obtained from the FIA program (Prayer and Furnival
1999, USDA Forest Service 2013b). Inventories include data collected on permanent inventory plots on forest lands
and were organized as a number of separate datasets, each representing a complete inventory, or survey, of an
individual state at a specified time. 223 Many of the more recent annual inventories reported for states were
represented as "moving window" averages, which means that a portion—but not all—of the previous year's
inventory is updated each year (USDA Forest Service 2013d). Forest C calculations were organized according to
these state surveys, and the frequency of surveys varies by state. All available data sets were identified for each
state starting with pre-1990 data, and all unique surveys were identified for stock and change calculations. Since C
stock change is based on differences between successive surveys within each state, accurate estimates of net C flux
thus depend on consistent representation of forest land between these successive inventories. In order to achieve
this consistency from 1990 to the present, states were sometimes subdivided into sub-state areas where the sum of
sub-state inventories produces the best whole-state representation of C change as discussed in Smith et al. (2010).
The principal FIA datasets employed are freely available for download at USDA Forest Service (2013b) as the
Forest Inventory and Analysis Database (FIADB) Version 5.1.6 (USDA Forest Service 2013c). However, to achieve
consistent representation (spatial and temporal), three other general sources of past FIA data were included as
necessary. First, older FIA plot- and tree-level data—not in the current FIADB format—were used if available.
Second, Resources Planning Act Assessment (RPA) databases, which are periodic, plot-level only, summaries of
state inventories, were used to provide the data at or before 1990. Finally, an additional forest inventory data source
used was the Integrated Database (IDE), which is a compilation of periodic forest inventory data from the 1990s for
California, Oregon, and Washington (Waddell and Hiserote 2005). These IDE data were identified by Heath et al.
(2011) as the most appropriate non-FIADB sources for these states and were included in this inventory. See USDA
Forest Service (2013a) for information on current and older data as well as additional FIA Program features. A
detailed list of the specific forest inventory data used in this inventory is included in Annex 3.13.
Forest C stocks were estimated from inventory data by a collection of conversion factors and models (Birdsey and
Heath 1995, Birdsey and Heath 2001, Heath et al. 2003, Smith et al. 2004, Smith et al. 2006), which have been
formalized in an FIADB-to-C calculator (Smith et al. 2010). The conversion factors and model coefficients were
categorized by region and forest type, and forest C stock estimates were calculated from application of these factors
at the scale of FIA inventory plots. The results were estimates of C density (Mg C per hectare) for six forest
ecosystem pools: live trees, standing dead trees, understory vegetation, down dead wood, forest floor, and soil
organic matter. The six C pools used in the FIADB-to-C calculator were aggregated to the five C pools defined by
IPCC (2006): aboveground biomass, belowground biomass, dead wood, litter, and soil organic matter. The live-tree
and understory C were pooled as biomass, and standing dead trees and down dead wood were pooled as dead wood,
in accordance with IPCC (2006).
Once plot-level C stocks were calculated as C densities on Forest Land Remaining Forest Land for the five IPCC
(2006) reporting pools, the stocks were expanded to population estimates according to methods appropriate to the
respective inventory data (for example, see Bechtold and Patterson (2005)). These expanded C stock estimates were
summed to state or sub-state total C stocks. Annualized estimates of C stocks were developed by using available
FIA inventory data and interpolating or extrapolating to assign a C stock to each year in the 1990 through 2013 time
series. Flux, or net annual stock change, was estimated by calculating the difference in stocks between two
successive years and applying the appropriate sign convention; net increases in ecosystem C were identified as
negative flux. By convention, inventories were assigned to represent stocks as of January 1 of the inventory year; an
estimate of flux for 1996 required estimates of C stocks for 1996 and 1997, for example. Additional discussion of
the use of FIA inventory data and the C conversion process is in Annex 3.13.
Carbon in Biomass
223 porest jancj in fljg United States includes land that is at least 10 percent stocked with trees of any size. Timberland is the most
productive type of forest land, which is on unreserved land and is producing or capable of producing crops of industrial wood.
Land Use, Land-Use Change, and Forestry 7-25
-------
Live tree C pools include aboveground and belowground (coarse root) biomass of live trees with diameter at
diameter breast height (dbh) of at least 2.54 cm at 1.37 m above the forest floor. Separate estimates were made for
above- and below-ground biomass components. If inventory plots included data on individual trees, tree C was
based on Woodall et al. (201 la), which is also known as the component ratio method (CRM), and is a function of
volume, species, and diameter. An additional component of foliage, which was not explicitly included in Woodall et
al. (201 la), was added to each tree following the same CRM method. Some of the older forest inventory data in use
for these estimates did not provide measurements of individual trees. Examples of these data include plots with
incomplete or missing tree data or the RPA plot-level summaries. The C estimates for these plots were based on
average densities (metric tons C per hectare) obtained from plots of more recent surveys with similar stand
characteristics and location. This applies to less than 5 percent of the forest land inventory-plot-to-C conversions
within the 193 state-level surveys utilized here.
Understory vegetation is a minor component of biomass, which is defined as all biomass of undergrowth plants in a
forest, including woody shrubs and trees less than 2.54 cm dbh. In the current inventory, it was assumed that 10
percent of total understory C mass is belowground. Estimates of C density were based on information in Birdsey
(1996) and biomass estimates from Jenkins et al. (2003). Understory frequently represented over 1 percent of C in
biomass, but its contribution rarely exceeded 2 percent of the total.
Carbon in Dead Organic Matter
Dead organic matter was initially calculated as three separate pools—standing dead trees, down dead wood, and
litter—with C stocks estimated from sample data or modeled. The standing dead tree C pools include aboveground
and belowground (coarse root) mass and include trees of at least 12.7 cm dbh. Calculations followed the basic
method applied to live trees (Woodall et al. 201 la) with additional modifications to account for decay and structural
loss (Domke etal. 2011, Harmon etal. 2011). Similar to the situation with live tree data, some of the older forest
inventory data did not provide sufficient data on standing dead trees to make accurate population-level estimates.
The C estimates for these plots were based on average densities (metric tons C per hectare) obtained from plots of
more recent surveys with similar stand characteristics and location. This applied to 23 percent of the forest land
inventory-plot-to-C conversions within the 193 state-level surveys utilized here. Down dead wood estimates are
based on measurement of a subset of FIA plots for downed dead wood (Domke et al. 2013, Woodall and Monleon
2008, Woodall et al. 2013). Down dead wood is defined as pieces of dead wood greater than 7.5 cm diameter, at
transect intersection, that are not attached to live or standing dead trees. This includes stumps and roots of harvested
trees. To facilitate the downscaling of downed dead wood C estimates from the state-wide population estimates to
individual plots, downed dead wood models specific to regions and forest types within each region are used. Litter
C is the pool of organic C (also known as duff, humus, and fine woody debris) above the mineral soil and includes
woody fragments with diameters of up to 7.5 cm. Estimates are based on equations of Smith and Heath (2002).
Carbon in Forest Soil
Soil organic C includes all organic material in soil to a depth of 1 meter but excludes the coarse roots of the biomass
or dead wood pools. Estimates of SOC were based on the national STATSGO spatial database (USDA 1991),
which includes region and soil type information. Soil organic C determination was based on the general approach
described by Amichev and Galbraith (2004). Links to FIA inventory data were developed with the assistance of the
USDA Forest Service FIA Geospatial Service Center by overlaying FIA forest inventory plots on the soil C map.
This method produced mean SOC densities stratified by region and forest type group. It did not provide separate
estimates for mineral or organic soils but instead weighted their contribution to the overall average based on the
relative amount of each within forest land. Thus, forest SOC is a function of species and location, and net change
also depends on these two factors as total forest area changes. In this respect, SOC provides a country-specific
reference stock for 1990 through the present, but it does not reflect the effects of past land use.
Harvested Wood Carbon
Estimates of the HWP contribution to forest C sinks and emissions (hereafter called "HWP Contribution") were
based on methods described in Skog (2008) using the WOODCARB II model. These methods are based on IPCC
(2006) guidance for estimating HWP C. IPCC (2006) provides methods that allow for reporting of HWP
Contribution using one of several different accounting approaches: production, stock change and atmospheric flow,
as well as a default method that assumes there is no change in HWP C stocks (see Annex 3.13 for more details about
7-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
each approach). The United States used the production accounting approach to report HWP Contribution. Under
the production approach, C in exported wood was estimated as if it remains in the United States, and C in imported
wood was not included in inventory estimates. Though reported U.S. HWP estimates are based on the production
approach, estimates resulting from use of the two alternative approaches, the stock change and atmospheric flow
approaches, are also presented for comparison (see Annex 3.13). Annual estimates of change were calculated by
tracking the additions to and removals from the pool of products held in end uses (i.e., products in use such as
housing or publications) and the pool of products held in solid waste disposal sites (SWDS). Emissions from HWP
associated with wood biomass energy are not included in this accounting—a net of zero sequestration and emissions
as they are a part of energy accounting (see Chapter 3).
Solidwood products added to pools include lumber and panels. End-use categories for solidwood include single and
multifamily housing, alteration and repair of housing, and other end-uses. There is one product category and one
end-use category for paper. Additions to and removals from pools were tracked beginning in 1900, with the
exception that additions of softwood lumber to housing began in 1800. Solidwood and paper product production
and trade data were taken from USDA Forest Service and other sources (Hair and Ulrich 1963; Hair 1958; USDC
Bureau of Census; 1976; Ulrich, 1985, 1989; Steer 1948; AF&PA 2006a 2006b; Howard 2003, 2007). Estimates for
disposal of products reflected the change over time in the fraction of products discarded to SWDS (as opposed to
burning or recycling) and the fraction of SWDS that were in sanitary landfills versus dumps.
There are five annual HWP variables that were used in varying combinations to estimate HWP Contribution using
any one of the three main approaches listed above. These are:
(1 A) annual change of C in wood and paper products in use in the United States,
(IB) annual change of C in wood and paper products in SWDS in the United States,
(2A) annual change of C in wood and paper products in use in the United States and other countries where
the wood came from trees harvested in the United States,
(2B) annual change of C in wood and paper products in SWDS in the United States and other countries
where the wood came from trees harvested in the United States,
(3) C in imports of wood, pulp, and paper to the United States,
(4) C in exports of wood, pulp and paper from the United States, and
(5) C in annual harvest of wood from forests in the United States.
The sum of variables 2 A and 2B yielded the estimate for HWP Contribution under the production accounting
approach. A key assumption for estimating these variables was that products exported from the United States and
held in pools in other countries have the same half-lives for products in use, the same percentage of discarded
products going to SWDS, and the same decay rates in SWDS as they would in the United States.
Uncertainty and Time Series Consistency
A quantitative uncertainty analysis placed bounds on current flux for forest ecosystems as well as C in harvested
wood products through Monte Carlo Stochastic Simulation of the Methods described above and probabilistic
sampling of C conversion factors and inventory data. See Annex 3.13 for additional information. The 2012 net
annual change for forest C stocks was estimated to be between -999 and -735 Tg CO2 Eq. at a 95 percent confidence
level. This includes a range of -932 to -669 Tg CO2 Eq. in forest ecosystems and -84 to -51 Tg CO2 Eq. for HWP.
Land Use, Land-Use Change, and Forestry 7-27
-------
Table 7-13: Tier 2 Quantitative Uncertainty Estimates for Net COz Flux from Forest Land
Remaining Forest Land: Changes in Forest C Stocks (Tg COz Eq. and Percent)
Source
Forest Ecosystem
Harvested Wood Products
Total Forest
Gas
C02
CO2
C02
2012 Flux
Estimate
(Tg C02 Eq.)
(800.0)
(66.5)
(866.5)
Uncertainty Range Relative to Flux Estimate a
(Tg COz Eq.) (%)
Lower
Bound
(932.3)
(84.4)
(999.3)
Upper
Bound
(668.8)
(50.8)
(734.9)
Lower
Bound
-16.5
-26.9
-15.3
Upper
Bound
+16.4
+23.5
+15.2
Note: Parentheses indicate negative values or net sequestration.
a Range of flux estimates predicted by Monte Carlo stochastic simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
As discussed above, the FIA program has conducted consistent forest surveys based on extensive statistically-based
sampling of most of the forest land in the conterminous United States, dating back to 1952. The FIA program
includes numerous quality assurance and quality control (QA/QC) procedures, including calibration among field
crews, duplicate surveys of some plots, and systematic checking of recorded data. Because of the statistically-based
sampling, the large number of survey plots, and the quality of the data, the survey databases developed by the FIA
program form a strong foundation for C stock estimates. Field sampling protocols, summary data, and detailed
inventory databases are archived and are publicly available on the Internet (USDA Forest Service 2013d).
Many key calculations for estimating current forest C stocks based on FIA data were developed to fill data gaps in
assessing forest C and have been in use for many years to produce national assessments of forest C stocks and stock
changes (see additional discussion and citations in the Methodology section above and in Annex 3.13). General
quality control procedures were used in performing calculations to estimate C stocks based on survey data. For
example, the derived C datasets, which include inventory variables such as areas and volumes, were compared to
standard inventory summaries such as the forest resource statistics of Smith et al. (2009) or selected population
estimates generated from FIADB 5.1.6, which are available at an FIA internet site (USDA Forest Service 2013b).
Agreement between the C datasets and the original inventories is important to verify accuracy of the data used.
Finally, C stock estimates were compared with previous Inventory report estimates to ensure that any differences
could be explained by either new data or revised calculation methods (see the "Recalculations" discussion, below).
Estimates of the HWP variables and the HWP contribution under the production accounting approach use data from
U.S. Census and USDA Forest Service surveys of production and trade. Factors to convert wood and paper to units
C are based on estimates by industry and Forest Service published sources. The WOODCARB II model uses
estimation methods suggested by IPCC (2006). Estimates of annual C change in solid wood and paper products in
use were calibrated to meet two independent criteria. The first criterion is that the WOODCARB II model estimate
of C in houses standing in 2001 needs to match an independent estimate of C in housing based on U.S. Census and
USDA Forest Service survey data. Meeting the first criterion resulted in an estimated half-life of about 80 years for
single family housing built in the 1920s, which is confirmed by other U.S. Census data on housing. The second
criterion is that the WOODCARB II model estimate of wood and paper being discarded to SWDS needs to match
EPA estimates of discards each year over the period 1990 to 2000 (EPA 2006). These criteria help reduce
uncertainty in estimates of annual change in C in products in use in the United States and, to a lesser degree, reduce
uncertainty in estimates of annual change in C in products made from wood harvested in the United States. In
addition, WOODCARB II landfill decay rates have been validated by ensuring that estimates of CH4 emissions from
landfills based on EPA (2006) data are reasonable in comparison to CH4 estimates based on WOODCARB II
landfill decay rates.
7-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Recalculations Discussion
Methods for forest inventory-to-carbon conversion and calculations of stock and stock-change remain unchanged
from the previous Inventory (EPA 2013). Updates to the annual forest inventories for many states were the source
of changes in the forest ecosystem carbon stocks and stock-change estimates relative to the previous year's report.
Data for two states—New Mexico and Alaska—affected the classification of forestland used to compile sub-state
stocks. Annual data became available for New Mexico, and in order to maintain consistent definitions with older
forest inventories, the non-National Forest forestland was reclassified as timberland and non-timberland. Alaska
sub-state classifications were renamed with the reserved forestlands pooled to a single classification. In addition,
the periodic Alaska forest inventory—nominal year of 2003—became available in the current FIADB 5.1.6. See
Annex 3.13 for specifics of inventories in use, including the modification to sub-state classifications for New
Mexico and Alaska. The estimate of annual change in HWP C stock and total C stock in HWP were revised
downward by small amounts for selected years back to 1998. This was mostly due to changes in the amount of
pulpwood used for paper and composite panel products back to 2003. All the adjustments were made as a result of
corrections in the database of forest products statistics used to prepare the estimates (Howard and Westby 2013).
The greatest change was to estimates of carbon added to paper products in use. The estimate of total C stored in
HWP in 2011 decreased by less than 0.1 percent from the estimate reported in the previous Inventory. The estimates
of HWP annual change were revised downward by small amounts back to 2003 for selected years due to changes in
the amounts of pulpwood used for paper and composite panels as published in the primary database used to prepare
the estimates (Howard 2013). The changes result in a reduction of less than 0.1 percent in the estimated total C
stored in HWP for the start of 2013.
Planned Improvements
Reliable estimates of forest C across the diverse ecosystems/industries of the United States require a high level of
investment in both annual monitoring and associated analytical techniques. Development of improved
monitoring/reporting techniques is a continuous process that occurs simultaneously with annual NGHGI
submissions. Only when forest C monitoring techniques are thoroughly vetted are they adopted as part of the
NGHGI. Planned improvements can be broadly assigned to the following categories: pool estimation techniques,
land use and land use change, and field inventories.
In an effort to reduce the uncertainty associated with the estimation of individual forest C pools, the empirical data
and associated models for each pool is being evaluated for potential improvement (Woodall 2012). In the 1990
through 2010 Inventory report, the approach to tree volume/biomass estimation was evaluated and refined (Domke
et al. 2012). In the 1990 through 2011 Inventory report, the standing dead tree C simulation model was replaced
with a nationwide inventory and associated empirical estimation techniques (Woodall et al. 2012, Domke et al.
2011, Harmon et al. 2011). In the current Inventory report, the downed dead tree C simulation model was refined
with a nearly nationwide field inventory (Woodall et al. 2013, Domke et al. 2013). The exact timing of future pool
estimation refinements is dependent on the vetting of current research outcomes. Research is underway to use a
national inventory of forest litter and SOC (Woodall et al. 201 Ib) to refine the estimation of these pools. It is
expected that improvements to litter estimation will be incorporated into either the 1990-2013 Inventory report or
the 1990-2014 Inventory report followed by SOC estimation improvements. Components of other pools, such as C
in belowground biomass and understory vegetation (Russell et al. In Review), are being explored but may require
additional investment in field inventories before improvements can be realized with NGHGI submissions.
Despite a consistent nationwide field inventory of forests that is measured annually, additional research advances are
needed to attain a complete, consistent, and accurate time series of annual land-use and land-use change matrices
from 1990 to the present report year. Lines of research have been initiated to explore techniques for bringing
together disparate sets of land use information (e.g., forest versus croplands) that rely on remotely sensed imagery
from the 1980s to the present (NASA CMS 2013). These lines of research are expected to require at least three
years for completion with subsequent time needed for application to future NGHGI submission. In an effort to align
the definition of forests with the international community (FAO 2010) and potentially expand the forest inventory
domain to an all vegetation inventory on all lands (e.g., woodlands and settlements), it is expected that the 1990
through 2013 Inventory report will exclude forest inventory plots that occur in shrub lands (i.e., woodlands where
minimum tree heights are not attained) from the forest land use category (reason why forest area estimates may
diverge between Smith et al. 2009 and those in this report). Inventory plots excluded from forest land use
accounting (potentially millions of hectares but with low C density; e.g., west Texas) may be used to inform C
Land Use, Land-Use Change, and Forestry 7-29
-------
monitoring in other land uses. This represents a future opportunity to refine land use accounting associated with
vegetation in land uses beyond forests such as grasslands (where woodlands/shrublands are a type), wetlands, and
settlements.
The foundation of forest C accounting is the annual forest inventory system. The ongoing annual surveys by the
FIA Program are expected to improve the precision of forest C estimates as new state surveys become available
(USDA Forest Service 2013b), particularly in western states. The annual surveys will eventually include all states.
As of July 11, 2013, two states are not yet reporting any data from the annualized sampling design of FIA: Hawaii
and Wyoming. Estimates for Wyoming are currently based on older, periodic data. Hawaii and U.S. territories will
also be included when appropriate forest C data are available. In addition, the more intensive sampling of fine
woody debris, litter, and SOC on some of the permanent FIA plots continues and will substantially improve
resolution of C pools (i.e., greater sample intensity; Westfall et al. 2013) at the plot level for all U.S. forest land as
this information becomes available (Woodall et al. 201 Ib). Increased sample intensity of some C pools, refined
managed land delineation of Alaska's forests, and using annualized sampling data as it becomes available for those
states currently not reporting are planned for future submissions.
Non-COz Emissions from Forest Fires
Emissions of non-CO2 gases from forest fires were estimated using the default IPCC (2003) methodology
incorporating default IPCC (2006) emissions factors and combustion factor for wildfires. Emissions from this
source in 2012 were estimated to be 15.3 Tg CO2 Eq. of CH4 and 12.5 Tg CO2 Eq. of N2O, as shown in Table 7-14
and Table 7-15. The estimates of non-CO2 emissions from forest fires account for wildfires in the lower 48 states
and Alaska as well as prescribed fires in the lower 48 states.
Table 7-14: Estimated Non-COz Emissions from Forest Fires (Tg COz Eq.) for U.S. Forests
Gas
CH4
N2O
Total
1990
2.5
2.0
4.5
2005
8.1
6.6
14.7
2008
8.7
7.1
15.9
2009
5.8
4.7
10.5
2010
4.7
3.9
8.6
2011
14.0
11.4
25.3
2012
15.3
12.5
27.7
Note: Calculated based on C emission estimates in Changes in Forest Carbon Stocks and default factors in
IPCC (2006).
Table 7-15: Estimated Non-COz Emissions from Forest Fires (Gg Gas) for U.S. Forests
Gas 1990 2005 2008 2009 2010 2011 2012~
CH4 119 386 416 275 225 664 727
N2Q 7 21 23 15 12 37 40
Note: Calculated based on C emission estimates in Changes in Forest Carbon Stocks and default factors in
IPCC (2006).
Methodology
The IPCC (2003) Tier 2 default methodology was used to calculate C and CO2 emissions from forest fires.
However, more up-to-date default emission factors from IPCC (2006) were converted into gas-specific emission
ratios and incorporated into the methodology to calculate non-CO2 emissions from C emissions. Estimates of
CH4 and N2O emissions were calculated by multiplying the total estimated CO2 emitted from forest burned by the
gas-specific emissions ratios. CO2 emissions were estimated by multiplying total C emitted (Table 7-16) by the C to
CO2 conversion factor of 44/12 and by 92.8 percent, which is the estimated proportion of C emitted as CO2 (Smith
2008a). The equations used to calculate CH4 and N2O emissions were:
CH4 Emissions = (C released) x 92.8% x (44/12) x (CH4 to C02 emission ratio)
N20 Emissions = (C released) x 92.8% x (44/12) x (N20 to C02 emission ratio)
Where CH4 to CO2 emission ratio is 0.003 and N2O to CO2 emission ratio is 0.0002. See the explanation in Annex
3.13 for more details on the CH4 and N2O to CO2 emission ratios.
7-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Estimates for C emitted from forest fires are the same estimates used to generate estimates of CCh presented earlier
in Box 7-3. Estimates for C emitted include emissions from wildfires in both Alaska and the lower 48 states as well
as emissions from prescribed fires in the lower 48 states only (based on expert judgment that prescribed fires only
occur in the lower 48 states) (Smith 2008a). The IPCC (2006) default combustion factor of 0.45 for "all 'other'
temperate forests" was applied in estimating C emitted from both wildfires and prescribed fires. See the explanation
in Annex 3.13 for more details on the methodology used to estimate C emitted from forest fires.
Table 7-16: Estimated Carbon Released from Forest Fires for U.S. Forests (Tg/yr)
Year C Emitted (Tg/yr)
1990
11.7
2008
2009
2010
2011
2012
40.9
26.9
22.1
65.2
71.3
Uncertainty and Time-Series Consistency
Non-CO2 gases emitted from forest fires depend on several variables, including: forest area for Alaska and the lower
48 states; average C densities for wildfires in Alaska, wildfires in the lower 48 states, and prescribed fires in the
lower 48 states; emission ratios; and combustion factor values (proportion of biomass consumed by fire). To
quantify the uncertainties for emissions from forest fires, a Monte Carlo (Tier 2) uncertainty analysis was performed
using information about the uncertainty surrounding each of these variables. The results of the Tier 2 quantitative
uncertainty analysis are summarized in Table 7-17.
Table 7-17: Tier 2 Quantitative Uncertainty Estimates of Non-COz Emissions from Forest
Fires in Forest Land Remaining Forest Land'(Tg COz Eq. and Percent)
Source
2012 Emission
Gas Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (%)
Lower
Bound
Non-CCh
Non-CCh
Emissions from
Emissions from
Forest Fires
Forest Fires
CH4
N20
15.
12.
,3
5
2
3
.7
.2
Upper
Bound
42.1
30.4
Lower
Bound
-82%
-74%
Upper
Bound
+176%
+144%
a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
Tier 1 and Tier 2 QA/QC activities were conducted consistent with the U.S. QA/QC plan. Source-specific quality
control measures for forest fires included checking input data, documentation, and calculations to ensure data were
properly handled through the inventory process. Errors that were found during this process were corrected as
necessary.
Land Use, Land-Use Change, and Forestry 7-31
-------
Recalculations Discussion
The National Association of State Foresters (NASF) releases data on land under wildland protection every several
years. In 2011, NASF released these data for the year 2008, which affected the ratio of forest land to land under
wildland protection for the years 2007 through 2009. For each of these three years, the updated ratio decreased the
forest area burned estimates for the lower 48 states by around 15 percent. Seethe explanation in Annex 3.13 for
more details on how the forestland to land under wildland protection ratio is used to calculate forest fire emissions.
In previous Inventory reports, the methodology has assumed that the C density of forest areas burned in wild and
prescribed fires does not vary between years. This assumption has been in contrast to the forest C stock estimates,
which are updated annually for all years based on data from the USDA Forest Service. The methodology adopted
for the current and previous Inventory improves the C density factors by incorporating dynamic C density values
based on the annual C pool data provided by the USDA Forest Service for the years 1990 to 2012. As a result of
this update, estimates of total CCh and non-CCh emissions from forest fires increased by 1 percent for 1990 through
2010 as compared to the estimates included in the previous Inventory. However, estimates of total CC>2 and non-
CO2 emissions from forest fires decreased by 2 percent for 2011 as compared to the estimates included in the
previous Inventory. For more information on how C density contributes to estimates of emissions from forest fires,
see Annex 3.13.
Planned Improvements
The default combustion factor of 0.45 from IPCC (2006) was applied in estimating C emitted from both wildfires
and prescribed fires. Additional research into the availability of a combustion factor specific to prescribed fires is
being conducted.
Another area of improvement is to evaluate other methods of obtaining data on forest area burned by replacing ratios
of forest land to land under wildland protection with Monitoring Trends in Burn Severity (MTBS) burn area data.
MTBS data is available from 1984 to 2011. MTBS burn area data could be used to develop the national area burned
and resulting CC>2 and non-CCh emissions. Additional research is required to determine appropriate uncertainty
inputs for national area burned data derived from MTBS data.
Direct N2O Fluxes from Forest Soils (IPCC Source Category 5A1)
Of the synthetic nitrogen (N) fertilizers applied to soils in the United States, no more than one percent is applied to
forest soils. Application rates are similar to those occurring on cropped soils, but in any given year, only a small
proportion of total forested land receives N fertilizer. This is because forests are typically fertilized only twice
during their approximately 40-year growth cycle (once at planting and once approximately 20 years later). Thus,
while the rate of N fertilizer application for the area of forests that receives N fertilizer in any given year is relatively
high, the average annual application is quite low as inferred by dividing all forest land that may undergo N
fertilization at some point during its growing cycle by the amount of N fertilizer added to these forests in a given
year. Direct N2O emissions from forest soils in 2012 were 0.4 Tg CCh Eq. (1.2 Gg). Emissions have increased by
455 percent from 1990 to 2012 as a result of an increase in the area of N fertilized pine plantations in the
southeastern United States and Douglas-fir timberland in western Washington and Oregon. Total forest soil N2O
emissions are summarized in Table 7-18.
7-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 7-18: Direct NzO Fluxes from Soils in Forest LandRemaining Forest Land'(Tg COz Eq.
and Gg NzO)
Year Tg CCh Eq. Gg
1990 0.1
2008
2009
2010
2011
2012
0.4
0.4
0.4
0.4
0.4
1.2
1.2
1.2
1.2
1.2
Note: These estimates include direct N2O
emissions from N fertilizer additions only.
Indirect N2O emissions from fertilizer additions
are reported in the Agriculture chapter. These
estimates include emissions from both Forest
Land Remaining Forest Land and from Land
Converted to Forest Land.
Methodology
The IPCC Tier 1 approach was used to estimate N2O from soils within Forest Land Remaining Forest Land.
According to U.S. Forest Service statistics for 1996 (USDA Forest Service 2001), approximately 75 percent of trees
planted were for timber, and about 60 percent of national total harvested forest area is in the southeastern United
States. Although southeastern pine plantations represent the majority of fertilized forests in the United States, this
Inventory also accounted for N fertilizer application to commercial Douglas-fir stands in western Oregon and
Washington. For the Southeast, estimates of direct N2O emissions from fertilizer applications to forests were based
on the area of pine plantations receiving fertilizer in the southeastern United States and estimated application rates
(Albaugh et al. 2007; Fox et al. 2007). Not accounting for fertilizer applied to non-pine plantations is justified
because fertilization is routine for pine forests but rare for hardwoods (Binkley et al. 1995). For each year, the area
of pine receiving N fertilizer was multiplied by the weighted average of the reported range of N fertilization rates
(121 Ibs. N per acre). Area data for pine plantations receiving fertilizer in the Southeast were not available for 2005,
2006, 2007 and 2008, so data from 2004 were used for these years. For commercial forests in Oregon and
Washington, only fertilizer applied to Douglas-fir was accounted for, because the vast majority (~95 percent) of the
total fertilizer applied to forests in this region is applied to Douglas-fir (Briggs 2007). Estimates of total Douglas-fir
area and the portion of fertilized area were multiplied to obtain annual area estimates of fertilized Douglas-fir stands.
The annual area estimates were multiplied by the typical rate used in this region (200 Ibs. N per acre) to estimate
total N applied (Briggs 2007), and the total N applied to forests was multiplied by the IPCC (2006) default emission
factor of 1 percent to estimate direct N2O emissions. The volatilization and leaching/runoff N fractions for forest
land, calculated according to the IPCC default factors of 10 percent and 30 percent, respectively, were included with
the indirect emissions in the Agricultural Soil Management source category (consistent with reporting guidance that
all indirect emissions are included in the Agricultural Soil Management source category).
Uncertainty and Time-Series Consistency
The amount of N2O emitted from forests depends not only on N inputs and fertilized area, but also on a large
number of variables, including organic C availability, oxygen gas partial pressure, soil moisture content, pH,
temperature, and tree planting/harvesting cycles. The effect of the combined interaction of these variables on N2O
flux is complex and highly uncertain. IPCC (2006) does not incorporate any of these variables into the default
methodology, except variation in estimated fertilizer application rates and estimated areas of forested land receiving
N fertilizer. All forest soils are treated equivalently under this methodology. Furthermore, only synthetic N
fertilizers are captured, so applications of organic N fertilizers are not estimated. However, the total quantity of
Land Use, Land-Use Change, and Forestry 7-33
-------
organic N inputs to soils is included in the Agricultural Soil Management and Settlements Remaining Settlements
sections.
Uncertainties exist in the fertilization rates, annual area of forest lands receiving fertilizer, and the emission factors.
Fertilization rates were assigned a default level224 of uncertainty at ±50 percent, and area receiving fertilizer was
assigned a ±20 percent according to expert knowledge (Binkley 2004). IPCC (2006) provided estimates for the
uncertainty associated with direct N2O emission factor for synthetic N fertilizer application to soils. Quantitative
uncertainty of this source category was estimated through the IPCC-recommended Tier 2 uncertainty estimation
methodology. The uncertainty ranges around the 2005 activity data and emission factor input variables were
directly applied to the 2012 emission estimates. The results of the quantitative uncertainty analysis are summarized
in Table 7-19. N2O fluxes from soils were estimated to be between 0.1 and 1.1 Tg CO2 Eq. at a 95 percent
confidence level. This indicates a range of 59 percent below and 211 percent above the 2012 emission estimate of
0.4 Tg CO2 Eq.
Table 7-19: Quantitative Uncertainty Estimates of NzO Fluxes from Soils in Forest Land
Remaining Forest Land(Tg COz Eq. and Percent)
2012 Emission Uncertainty Range Relative to Emission3
Source Gas Estimate Estimate
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Forest Land Remaining Forest Land: N2O
Fluxes trom Soils
Q4
Q1
_59%
Note: This estimate includes direct N2O emissions from N fertilizer additions to both Forest Land Remaining Forest Land
and Land Converted to Forest Land.
a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Planned Improvements
State-level area data will be obtained for southeastern pine plantations and northwestern Douglas-fir forests to
estimate soil N2O emission by state and provide information about regional variation in emission patterns.
7.3 Land Converted to Forest Land (IPCC
Source Category 5A2)
Land-use change is constantly occurring, and areas under a number of differing land-use types are converted to
forest each year, just as forest land is converted to other uses. While the magnitude of these changes is known (see
Table 7-6), research is ongoing to track C across Forest Land Remaining Forest Land and Land Converted to Forest
Land areas. Until such time that reliable and comprehensive estimates of C across these land-use and land-use
change categories can be produced, it is not possible to separate CO2 or N2O fluxes on Land Converted to Forest
Land from fluxes on Forest Land Remaining Forest Land at this time.
224 Uncertainty is unknown for the fertilization rates so a conservative value of ±50% was used in the analysis.
7-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
7.4 Cropland Remaining Cropland (IPCC Source
Category 5B1)
Mineral and Organic Soil Carbon Stock Changes
Soils contain both organic and inorganic forms of C, but soil organic carbon (SOC) stocks are the main source and
sink for atmospheric CO2 in most soils. Changes in inorganic C stocks are typically minor. In addition, SOC is the
dominant organic C pool in cropland ecosystems, because biomass and dead organic matter have considerably less C
and those pools are relatively ephemeral. IPCC (2006) recommends reporting changes in SOC stocks due to
agricultural land-use and management activities on mineral and organic soils.225
Typical well-drained mineral soils contain from 1 to 6 percent organic C by weight, although mineral soils that are
saturated with water for substantial periods during the year may contain significantly more C (NRCS 1999).
Conversion of mineral soils from their native state to agricultural uses can cause as much as half of the SOC to be
decomposed and the C lost to the atmosphere. The rate and ultimate magnitude of C loss will depend on pre-
conversion conditions, conversion method and subsequent management practices, in addition to the climate and soil
type. In the tropics, 40 to 60 percent of the C loss generally occurs within the first 10 years following conversion; C
stocks continue to decline in subsequent decades but at a much slower rate. In temperate regions, C loss can
continue for several decades, reducing stocks by 20 to 40 percent of native C levels. Eventually, the soil can reach a
new equilibrium that reflects a balance between C inputs (e.g., decayed plant matter, roots, and organic amendments
such as manure and crop residues) and C loss through microbial decomposition of organic matter. However, land
use, management, and other conditions may change before the new equilibrium is reached. The quantity and quality
of organic matter inputs and their rate of decomposition are determined by the combined interaction of climate, soil
properties, and land use. Land use and agricultural practices, such as clearing, drainage, tillage, planting, grazing,
crop residue management, fertilization, and flooding, can modify both organic matter inputs and decomposition, and
thereby result in a net flux of C to or from the soil C pool.
Organic soils, also referred to as histosols, include all soils with more than 12 to 20 percent organic C by weight,
depending on clay content (NRCS 1999, Brady and Weil 1999). The organic layer of these soils can be very deep
(i.e., several meters), forming under inundated conditions in which minimal decomposition of plant residue occurs.
When organic soils are prepared for crop production, they are drained and tilled, leading to aeration of the soil,
which accelerates the rate of decomposition and CO2 emissions. Because of the depth and richness of the organic
layers, C loss from drained organic soils can continue over long periods of time. The rate of CO2 emissions varies
depending on climate and composition (i.e., decomposability) of the organic matter. Also, the use of organic soils
for annual crop production leads to higher C loss rates than drainage of organic soils in grassland or forests, due to
deeper drainage and more intensive management practices in cropland (IPCC/UNEP/OECD/IEA 1997). Carbon
losses are estimated from drained organic soils under both grassland and cropland management in this Inventory.
Cropland Remaining Cropland includes all cropland in an inventory year that had been cropland for the last 20
years according to the USDA National Resources Inventory (NRI) land-use survey (USDA-NRCS 2009).226 The
inventory includes all privately-owned croplands in the conterminous United States and Hawaii, but there is between
1 to 1.5 million hectares of Cropland Remaining Cropland on federal lands between 1990 and 2012 that is not
currently included in the estimation of C stock changes (i.e., less than 1 percent of the total cropland area in the
United States). In addition, there is a relatively small amount of cropland in Alaska, about 28,700 hectares, which is
not included in the inventory. This leads to a discrepancy between the total amount of managed area in Cropland
Remaining Cropland (see Section 7.1) and the cropland area included in the Inventory. Improvements are underway
to include federal croplands in future C inventories, in addition to the cropland in Alaska.
225 CO2 emissions associated with liming are also estimated but are included in a separate section of the report.
226 NRI points were classified according to land-use history records starting in 1982 when the NRI survey began, and
consequently the classifications were based on less than 20 years from 1990 to 2001.
Land Use, Land-Use Change, and Forestry 7-35
-------
The area of Cropland Remaining Cropland changes through time as land is converted to or from cropland
management. CCh emissions and removals227 due to changes in mineral soil C stocks are estimated using a Tier 3
approach for the majority of annual crops. A Tier 2 IPCC method is used for the remaining crops (vegetables,
tobacco, perennial/horticultural crops, and rice) not included in the Tier 3 method. In addition, a Tier 2 method is
used for very gravelly, cobbly, or shaley soils (i.e., classified as soils that have greater than 35 percent of soil
volume comprised of gravel, cobbles, or shale) and for additional changes in mineral soil C stocks that were not
addressed with the Tier 3 approach (i.e., change in C stocks after 2007 due to Conservation Reserve Program
enrollment). Emissions from organic soils are estimated using a Tier 2 IPCC method.
Of the two sub-source categories, land-use and land management of mineral soils was the most important
component of total net C stock change; especially in the early part of the time series (see Table 7-20 and Table
7-21). (Note: Estimates after 2007 are based on NRI data from 2007 and therefore do not fully reflect changes
occurring in the latter part of the time series. In 2012, mineral soils were estimated to remove 48.6 Tg CCh Eq. (13.3
Tg C). This rate of C storage in mineral soils represented about a 36 percent decrease in the rate since the initial
reporting year of 1990. Emissions from organic soils were 22.1 Tg CC>2 Eq. (6.0 Tg C) in 2012, which decreased by
8% compared to the emissions in 1990. In total, United States agricultural soils in Cropland Remaining Cropland
sequestered approximately 26.5 Tg CO2 Eq. (7.2 Tg C) in 2012.
Table 7-20: Net COz Flux from Soil C Stock Changes in Cropland Remaining Cropland (^g COz
Eq.)
Soil Type
Mineral Soils
Organic Soils
Total Net Flux
1990
(75.9) 1
24.0 •
(51.9)
2005
(51.5) 1
22.4 •
(29.1)
2008
(52.0)
22.1
(29.8)
2009
(51.4)
22.1
(29.2)
2010
(49.8)
22.1
(27.6)
2011
(49.7)
22.1
(27.5)
2012
(48.6)
22.1
(26.5)
Note: Totals may not sum due to independent rounding.
Note: Estimates after 2007 are based on NRI data from 2007 and therefore do not fully reflect changes occurring in the latter part
of the time series.
Table 7-21: Net COz Flux from Soil C Stock Changes in Cropland Remaining Cropland (^g C)
Soil Type 1990 2005 2008 2009 2010 2011 2012
Mineral Soils (20.7) I (14.1) I (14.2) (14.0) (13.6) (13.5) (13.3)
Organic Soils 6.5 6.1 6.0 6.0 6.0 6.0 6.0
Total Net Flux (14.2) (7.9) (8.1) (8.0) (7.5) (7.5) (7.2)
Note: Totals may not sum due to independent rounding.
Note: Estimates after 2007 are based on NRI data from 2007 and therefore do not fully reflect changes occurring in the latter part
of the time series.
The net reduction in soil C accumulation over the time series (49 percent lower for 2012, relative to 1990) was
largely due to the declining influence of annual cropland enrolled in the Conservation Reserve Program, which
began in the late 1980s. In addition, over 2 million hectares of land was returned to production from the
Conservation Reserve Program during the last 5 years, leading to a reduction in soil C stocks. However, there were
still positive increases in C stocks from the nearly 12 million hectares of land enrolled in this reserve program, as
well as from intensification of crop production by limiting the use of bare-summer fallow in semi-arid regions,
increased hay production, and adoption of conservation tillage (i.e., reduced- and no-till practices).
The spatial variability in the 2012 annual CCh flux is displayed in Figure 7-5 and Figure 7-6 for C stock changes in
mineral and organic soils, respectively. The highest rates of net C accumulation in mineral soils occurred in the
Midwest, which is the area with the largest amounts of cropland managed with conservation tillage, followed by the
south-central and northwest regions of the United States. Emissions from organic soils were highest in Southeastern
Coastal Region (particularly Florida), upper Midwest and Northeast surrounding the Great Lakes, and the Pacific
227
Note that removals occur through crop and forage uptake of CCh into biomass C that is later incorporated into soil pools.
7-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Coast (particularly California), coinciding with largest concentrations of organic soils in the United States that are
used for agricultural production.
Figure 7-5: Total Net Annual COz Flux for Mineral Soils under Agricultural Management
within States, 2012, Cropland Remaining Cropland
Note Values greater than zero represent emissions,
and values less than zero represent sequestration.
Map accounts for fluxes associated with the Tier 2
and 3 inventory compulations See methodology
for additional details
Tg C02 Eq/yr
n>o
Q-o.1 too
n -o.s to -0.1
H -1 to -0.5
• -2 to -1
•
Land Use, Land-Use Change, and Forestry 7-37
-------
Figure 7-6: Total Net Annual COz Flux for Organic Soils under Agricultural Management
within States, 2012, Cropland Remaining Cropland
Methodology
The following section includes a description of the methodology used to estimate changes in soil C stocks due to: (1)
agricultural land-use and management activities on mineral soils; and (2) agricultural land-use and management
activities on organic soils for Cropland Remaining Cropland.
Soil C stock changes were estimated for Cropland Remaining Cropland (as well as agricultural land falling into the
IPCC categories Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland)
according to land-use histories recorded in the USDA National Resources Inventory (NRI) survey (USDA-NRCS
2009). The NRI is a statistically-based sample of all non-federal land, and includes approximately 529,558 points in
agricultural land for the conterminous United States and Hawaii.228 Each point is associated with an "expansion
factor" that allows scaling of C stock changes from NRI points to the entire country (i.e., each expansion factor
represents the amount of area with the same land-use/management history as the sample point). Land-use and some
management information (e.g., crop type, soil attributes, and irrigation) were originally collected for each NRI point
on a 5-year cycle beginning in 1982. For cropland, data were collected for 4 out of 5 years in the cycle (i.e., 1979-
1982, 1984-1987, 1989-1992, and 1994-1997). In 1998, the NRI program began collecting annual data, and data are
currently available through 2007. NRI points were classified as Cropland Remaining Cropland in a given year
between 1990 and 2007 if the land use had been cropland for 20 years.229 Cropland includes all land used to
produce food and fiber, or forage that is harvested and used as feed (e.g., hay and silage), in addition to cropland that
has been enrolled in the Conservation Reserve Program (i.e., considered reserve cropland).
228
points were classified as agricultural if under grassland or cropland management between 1990 and 2007.
229 NRI points were classified according to land-use history records starting in 1982 when the NRI survey began. Therefore, the
classification prior to 2002 was based on less than 20 years of recorded land-use history for the time series.
7-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Mineral Soil Carbon Stock Changes
An IPCC Tier 3 model-based approach (Ogle et al. 2010) was applied to estimate C stock changes for mineral soils
used to produce a majority of annual crops in the United States in terms of land area, including alfalfa hay, barley,
corn, cotton, dry beans, grass hay, grass-clover hay, oats, onions, peanuts, potatoes, rice, sorghum, soybeans, sugar
beets, sunflowers, tomatoes, and wheat. The model-based approach uses the DAYCENT biogeochemical model
(Parton et al. 1998; Del Grosso et al. 2001, 2011) to estimate soil C stock changes and soil nitrous oxide emissions
from agricultural soil management. Carbon and N dynamics are linked in plant-soil systems through
biogeochemical processes of microbial decomposition and plant production (McGill and Cole 1981). Coupling the
two source categories (i.e., agricultural soil C and N2O) in a single inventory analysis ensures that there is a
consistent treatment of the processes and interactions between C and N cycling in soils.
The remaining crops on mineral soils were estimated using an IPCC Tier 2 method (Ogle et al. 2003), including
some vegetables, tobacco, perennial/horticultural crops, and crops that are rotated with these crops. The Tier 2
method was also used for very gravelly, cobbly, or shaley soils (greater than 35 percent by volume). Mineral SOC
stocks were estimated using a Tier 2 method for these areas because the DAYCENT model, which is used for the
Tier 3 method, has not been fully tested for estimating C stock changes associated with these crops and rotations, as
well as cobbly, gravelly, or shaley soils. An additional stock change calculation was estimated for mineral soils
using Tier 2 emission factors to account for enrollment patterns in the Conservation Reserve Program after 2007,
which was not addressed by the Tier 3 method.
Further elaboration on the methodology and data used to estimate stock changes from mineral soils are described
below and in Annex 3.12.
Tier 3 Approach
Mineral SOC stocks and stock changes were estimated using the DAYCENT biogeochemical model (Parton et al.
1998; Del Grosso et al. 2001, 2011), which simulates cycling of C, N and other nutrients in cropland, grassland,
forest, and savanna ecosystems. The DAYCENT model utilizes the soil C modeling framework developed in the
Century model (Parton et al. 1987, 1988, 1994; Metherell et al. 1993), but has been refined to simulate dynamics at a
daily time-step. Crop production is simulated with NASA-CASA production algorithm (Potter et al. 1993, Potter et
al. 2007) using the MODIS Enhanced Vegetation Index (EVI) products, MOD13Q1 and MYD13Q1, with a pixel
resolution of 250m. A prediction algorithm was developed to estimate EVI (Gurung et al. 2009) for gap-filling
during years over the inventory time series when EVI data were not available (e.g., data from the MODIS sensor
were only available after 2000 following the launch of the Aqua and Terra Satellites). The modeling approach uses
daily weather data as an input, along with information about soil physical properties. Input data on land use and
management are specified at a daily resolution and include land-use type, crop/forage type, and management
activities (e.g., planting, harvesting, fertilization, manure amendments, tillage, irrigation, residue removal, grazing,
and fire). The model simulates net primary productivity and C additions to soil, soil temperature, and water
dynamics, in addition to turnover, stabilization, and mineralization of soil organic matter C and nutrients (N, P, K,
S). This method is more accurate than the Tier 1 and 2 approaches provided by the IPCC because the simulation
model treats changes as continuous over time rather than the simplified discrete changes represented in the default
method (see Box 7-4 for additional information).
Box 7-4: Tier 3 Approach for Soil C Stocks Compared to Tier 1 or 2 Approaches
A Tier 3 model-based approach is used to estimate soil C stock changes on the majority of agricultural land with
mineral soils. This approach entails several fundamental differences compared to the IPCC Tier 1 or 2 methods,
which classify land areas into a number of discrete categories based on highly aggregated information ion about
climate, soil, and management (i.e., only six climate regions, seven soil types and eleven management systems occur
in U.S. agricultural land under the IPCC classification). Input variables to the Tier 3 model, including climate, soils,
and management activities (e.g., fertilization, crop species, tillage, etc.), are represented in considerably more detail
both temporally and spatially, and exhibit multi-dimensional interactions through the more complex model structure
compared with the IPCC Tier 1 or 2 approach. The spatial resolution of the analysis is also finer in the Tier 3
method compared to the lower tier methods as implemented in the United States for previous Inventories (e.g.,
Land Use, Land-Use Change, and Forestry 7-39
-------
almost 400,000 individual NRI point locations in individual fields compared to data aggregated to 181 Major Land
Resource Areas (MLRAs) for Tier 1 and 2 analyses).
The Tier 3 model simulates a continuous time period rather than the equilibrium step change used in the IPCC
methodology (Tier 1 and 2). More specifically, the DAYCENT model (i.e., daily time-step version of the Century
model) simulates soil C dynamics (and CCh emissions and uptake) on a daily time step based on C emissions and
removals resulting from plant production and decomposition processes. The changes in soil C stocks are influenced
by not only changes in land use and management but also weather variability and secondary feedbacks between
management activities, climate, and soils, as they affect primary production and decomposition. This latter
characteristic constitutes one of the greatest differences between the methods, and forms the basis for a more
complete accounting of soil C stock changes in the Tier 3 approach compared with Tier 2 methodology.
Consequently, variable weather patterns and other environmental constraints that interact with land use and
management can affect the time frame over which stock changes occur in response to management decisions.
Historical land-use patterns are simulated with DAYCENT based on the USDA National Resources Inventory (NRI)
survey, in addition to information on irrigation. Additional sources of activity data were used to supplement the
land-use information from NRI. The Conservation Technology Information Center (CTIC 2004) provided annual
data on tillage activity at the county level since 1989, with adjustments for long-term adoption of no-till agriculture
(Towery 2001). Information on fertilizer use and rates by crop type for different regions of the United States were
obtained primarily from the USDA Economic Research Service Cropping Practices Survey (USDA-ERS 1997,
2011) with additional data from other sources, including the National Agricultural Statistics Service (NASS 1992,
1999, 2004). Frequency and rates of manure application to cropland during 1997 were estimated from data
compiled by the USDA Natural Resources Conservation Service (Edmonds et al. 2003), and then adjusted using
county-level estimates of manure available for application in other years. Specifically, county-scale ratios of
manure available for application to soils in other years relative to 1997 were used to adjust the area amended with
manure (see Annex 3.12 for further details). Greater availability of managed manure N relative to 1997 was, thus,
assumed to increase the area amended with manure, while reduced availability of manure N relative to 1997 was
assumed to reduce the amended area. Data on the county-level N available for application were estimated for
managed systems based on the total amount of N excreted in manure minus N losses during storage and transport,
and including the addition of N from bedding materials. Nitrogen losses include direct N2O emissions, volatilization
of ammonia and NOX, runoff and leaching, and poultry manure used as a feed supplement. For unmanaged systems,
it is assumed that no N losses or additions occur prior to the application of manure to the soil. More information on
livestock manure production is available in the Manure Management, Section 6.2, and Annex 3.11.
Daily weather data were used as an input in the model simulations based on gridded data at a 32 km scale from the
North America Regional Reanalysis Product (NARR) (Mesinger et al. 2006). Soil attributes were obtained from the
Soil Survey Geographic Database (SSURGO) (Soil Survey Staff 2011). The carbon dynamics at each NRI point
was simulated 100 times as part of the uncertainty analysis, yielding a total of over 18 million simulation runs for
the analysis. Uncertainty in the carbon stock estimates from DAYCENT associated with parameterization and
model algorithms were adjusted using a structural uncertainty estimator accounting for uncertainty in model
algorithms and parameter values (Ogle et al. 2007, 2010). Carbon stocks and 95 percent confidence intervals were
estimated for each year between 1990 and 2007, but C stock changes from 2008 to 2012 were assumed to be similar
to 2007 because no additional activity data are currently available from the NRI for the latter years.
Tier 2 Approach
In the IPCC Tier 2 method, data on climate, soil types, land-use, and land management activity were used to classify
land area and apply appropriate stock change factors. Major Land Resource Areas (MLRAs) formed the base spatial
unit for conducting the Tier 2 analysis. MLRAs represent a geographic unit with relatively similar soils, climate,
water resources, and land uses (NRCS 1981). MLRAs were classified into climate regions according to the IPCC
categories using the PRISM climate database of Daly et al. (1994), and the factors were assigned based on the land
management systems in the MLRA in addition to the climate and soil types.
Reference C stocks were estimated using the National Soil Survey Characterization Database (NRCS 1997) with
cultivated cropland as the reference condition, rather than native vegetation as used in IPCC (2003, 2006).
Changing the reference condition was necessary because soil measurements under agricultural management are
7-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
much more common and easily identified in the National Soil Survey Characterization Database (NRCS 1997) than
native reference conditions.
U. S.-specific stock change factors were derived from published literature to determine the impact of management
practices on SOC storage, including changes in tillage, cropping rotations and intensification, and land-use change
between cultivated and uncultivated conditions (Ogle et al. 2003, Ogle et al. 2006). U.S. factors associated with
organic matter amendments were not estimated because there were an insufficient number of studies in the United
States to analyze the impacts. Instead, factors from IPCC (2003) were used to estimate the effect of those activities.
Activity data were primarily based on the historical land-use/management patterns recorded in the NRI. Each NRI
point was classified by land use, soil type, climate region (using PRISM data, Daly et al. 1994) and management
condition. Classification of cropland area by tillage practice was based on data from the Conservation Technology
Information Center (CTIC 2004, Towery 2001) as described above. Activity data on wetland restoration of
Conservation Reserve Program land were obtained from Euliss and Gleason (2002). Manure N amendments over
the inventory time period were based on application rates and areas amended with manure N from Edmonds et al.
(2003), in addition to the managed manure production data discussed in the methodology subsection for the Tier 3
analysis.
Combining information from these data sources, SOC stocks for mineral soils were estimated 50,000 times for 1982,
1992, 1997, 2002 and 2007, using a Monte Carlo stochastic simulation approach and probability distribution
functions for U.S.-specific stock change factors, reference C stocks, and land-use activity data (Ogle et al. 2002,
Ogle et al. 2003, Ogle et al. 2006). The annual C flux for 1990 through 1992 was determined by calculating the
average annual change in stocks between 1982 and 1992; annual C flux for 1993 through 1997 was determined by
calculating the average annual change in stocks between 1992 and 1997; annual C flux for 1998 through 2002 was
determined by calculating the average annual change in stocks between 1998 and 2002; and annual C flux from
2003 through 2012 was determined by calculating the average annual change in stocks between 2003 and 2007.
Additional Mineral C Stock Change
Annual C flux estimates for mineral soils between 2008 and 2012 were adjusted to account for additional C stock
changes associated with gains or losses in soil C after 2007 due to changes in Conservation Reserve Program
enrollment. The change in enrollment relative to 2007 was based on data from USDA-FSA (2012) for 2008 through
2012. The differences in mineral soil areas were multiplied by 0.5 metric tons C per hectare per year to estimate the
net effect on soil C stocks. The stock change rate is based on country-specific factors and the IPCC default method
(see Annex 3.11 for further discussion).
Organic Soil Carbon Stock Changes
Annual C emissions from drained organic soils in Cropland Remaining Cropland were estimated using the Tier 2
method provided in IPCC (2003, 2006), with U.S.-specific C loss rates (Ogle et al. 2003) rather than default IPCC
rates. The final estimates included a measure of uncertainty as determined from the Monte Carlo Stochastic
Simulation with 50,000 iterations. Emissions were based on the annual data from 1990 to 2007 for Cropland
Remaining Cropland areas in the 2007 National Resources Inventory (USDA-NRCS 2009). The annual emissions
estimated for 2007 were applied to 2007 through 2012 because no additional data were available beyond 2007.
Uncertainty and Time-Series Consistency
Uncertainty associated with the Cropland Remaining Cropland land-use category was addressed for changes in
agricultural soil C stocks (including both mineral and organic soils). Uncertainty estimates are presented in Table
7-22 for each subsource (mineral soil C stocks and organic soil C stocks) and method that was used in the inventory
analysis (i.e., Tier 2 and Tier 3). Uncertainty for the portions of the Inventory estimated with Tier 2 and 3
approaches was derived using a Monte Carlo approach (see Annex 3.12 for further discussion). Uncertainty
estimates from each approach were combined using the error propagation equation in accordance with IPCC (2006).
The combined uncertainty was calculated by taking the square root of the sum of the squares of the standard
deviations of the uncertain quantities. The combined uncertainty for soil C stocks in Cropland Remaining Cropland
ranged from 165 percent below to 167 percent above the 2012 stock change estimate of -26.5 Tg CO2 Eq.
Land Use, Land-Use Change, and Forestry 7-41
-------
Table 7-22: Tier 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring
within Cropland Remaining Cropland(Tg COz Eq. and Percent)
Source
Mineral Soil C Stocks: Cropland Remaining Cropland,
Tier 3 Inventory Methodology
Mineral Soil C Stocks: Cropland Remaining Cropland,
Tier 2 Inventory Methodology
Mineral Soil C Stocks: Cropland Remaining Cropland
(Change in CRP enrollment relative to 2003)
Organic Soil C Stocks: Cropland Remaining Cropland,
Tier 2 Inventory Methodology
2012 Flux
Estimate
(Tg C02
Eq.)
(50.6)
(2.8)
4 8
22.1
Uncertainty Range Relative to Flux
Estimate3
(Tg C02
Lower
Bound
(93.4)
(5.1)
24
14.0
Eq.)
Upper
Bound
(7.8)
(0.9)
72
32.5
("
Lower
Bound
-85%
-80%
-50%
-37%
/0)
Upper
Bound
85%
68%
50%
47%
Combined Uncertainty for Flux associated with
Agricultural Soil Carbon Stock Change in Cropland
Remaining Cropland
(26.5)
(70.2)
17.7
-165%
167%
a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Uncertainty is also associated with lack of reporting of agricultural biomass and litter C stock changes. Biomass C
stock changes are likely minor in perennial crops, such as orchards and nut plantations, given the small amount of
change in land used to produce these commodities in the United States. In contrast, agroforestry practices, such as
shelterbelts, riparian forests and intercropping with trees, may have led to significant changes in biomass C stocks,
at least in some regions of the United States, but there are currently no datasets to evaluate the trends. Changes in
litter C stocks are also assumed to be negligible in croplands over annual time frames, although there are certainly
significant changes at sub-annual time scales across seasons. However, this trend may change in the future,
particularly if crop residue becomes a viable feedstock for bioenergy production.
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Methodological recalculations in the current Inventory were associated with the following improvements: 1) refining
the temperature algorithm that is used for simulating crop production and carbon inputs to the soil in the DAYCENT
biogeochemical model; 2) increasing the number of experimental sites that are used to evaluate the structural
uncertainty in the DAYCENT model; and 3) recalculation of Tier 2 organic soil C emissions using annual data from
the NRI rather than estimating emissions every 5 years and assuming the emissions remain constant between the
years. The change in SOC stocks increased by an average of 12.1 Tg CCh eq. over the time series as a result of
these improvements to the Inventory. The increase was largely due to refinement of the temperature algorithm and
changes in the C inputs to the soil pool.
QA/QC and Verification
Quality control measures included checking input data, model scripts, and results to ensure data were properly
handled throughout the inventory process. Inventory reporting forms and text were reviewed and revised as needed
to correct transcription errors. As discussed in the uncertainty section, results were compared to field measurements,
and a statistical relationship was developed to assess uncertainties in the model's predictive capability. The
comparisons included over 45 long-term experiments, representing about 800 combinations of management
treatments across all of the sites (Ogle et al. 2007) (See Annex 3.12 for more information).
7-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Planned Improvements
Two major planned improvements are underway. The first is to update the time series of land use and management
data from the USDA National Resources Inventory so that it is extended from 2008 through 2010 for both the Tier 2
and 3 methods. Fertilization and tillage activity data will also be updated as part of this improvement. The remote-
sensing based data on the Enhanced Vegetation Index will be extended through 2010 in order to use the EVI data to
drive crop production in D AYCENT. Overall, this improvement will extend the time series of activity data for the
Tier 2 and 3 analyses through 2010.
The second major planned improvement is to analyze C stock changes on federal lands and Alaska for cropland and
managed grassland, using the Tier 2 method for mineral and organic soils that is described earlier in this section.
This analysis will initially focus on land use change, which typically has a larger impact on soil C stock changes, but
will be further refined over time to incorporate more of the management data.
Other improvements are planned for the DAYCENT biogeochemical model. Specifically, crop parameters
associated with temperature effects on plant production will be further improved in DAYCENT with additional
model calibration. Senescence events following grain filling in crops, such as wheat, will also be further evaluated
and refined as needed.
An improvement is also underway to simulate crop residue burning in the DAYCENT based on the amount of crop
residues burned according to the data that is used in the Field Burning of Agricultural Residues source category
(Section 6.5). This improvement will more accurately represent the C inputs to the soil that are associated with
residue burning.
All of these improvements are expected to be completed for the 1990 through 2013 Inventory. However, the time
line may be extended if there are insufficient resources to fund all or part of these planned improvements.
IPCC (2006) recommends reporting CO2 emissions from lime additions (in the form of crushed limestone (CaCO3)
and dolomite (CaMg(CO3)2) to agricultural soils. Limestone and dolomite are added by land managers to increase
soil pH or reduce acidification. When these compounds come in contact with acid soils, they degrade, thereby
generating CO2. The rate and ultimate magnitude of degradation of applied limestone and dolomite depends on the
soil conditions, soil type, climate regime, and the type of mineral applied. Emissions from liming have fluctuated
over the past twenty-two years, ranging from 3.7 Tg CO2 Eq. to 5.0 Tg CO2 Eq. In 2012, liming of agricultural soils
in the United States resulted in emissions of 3.9 Tg CO2 Eq. (1.1 Tg C), representing about a 16 percent decrease in
emissions since 1990 (see Table 7-23 and Table 7-24). The trend is driven entirely by the amount of lime and
dolomite estimated to have been applied to soils over the time period.
Table 7-23: Emissions from Liming of Agricultural Soils (Tg COz Eq.)
Source
Limestone
Dolomite
Total3
1990
4.1
0.6
4.7
2005
3.9
0.4
4.3
2008
4.4
0.6
5.0
2009
3.4
0.3
3.7
2010
4.3
0.5
4.8
2011
3.4
0.4
3.9
2012
3.5
0.4
3.9
a Also includes emissions from liming on Land Converted to Cropland, Grassland Remaining
Grassland, Land Converted to Grassland, and Settlements Remaining Settlements as it is not
currently possible to apportion the data by land use category.
Note: Lotals may not sum due to independent rounding.
Table 7-24: Emissions from Liming of Agricultural Soils (Tg C)
Source 1990 2005 2008 2009 2010 2011 2012
Limestone 1.1 1.1 1.2 0.9 1.2 0.9 1.0
Dolomite 0.2 0.1 0.2 0.1 0.1 0.1 0.1
Land Use, Land-Use Change, and Forestry 7-43
-------
Total" 0 L2 1.4 1.0 1.3 1.1 1.1
a Also includes emissions from liming on Land Converted to Cropland, Grassland Remaining
Grassland, Land Converted to Grassland, and Settlements Remaining Settlements as it is not
currently possible to apportion the data by land use category.
Note: Totals may not sum due to independent rounding.
Methodology
CO2 emissions from degradation of limestone and dolomite applied to agricultural soils were estimated using a Tier
2 methodology consistent with IPCC (2006). The annual amounts of limestone and dolomite applied (see Table
7-25) were multiplied by CO2 emission factors from West and McBride (2005). These emission factors (0.059
metric ton C/metric ton limestone, 0.064 metric ton C/metric ton dolomite) are lower than the IPCC default emission
factors because they account for the portion of agricultural lime that may leach through the soil and travel by rivers
to the ocean (West and McBride 2005). This analysis of lime dissolution is based on liming occurring in the
Mississippi Paver basin, where the vast majority of all U.S. liming takes place (West 2008). U.S. liming that does
not occur in the Mississippi River basin tends to occur under similar soil and rainfall regimes, and, thus, the
emission factor is appropriate for use across the United States (West 2008). The annual application rates of
limestone and dolomite were derived from estimates and industry statistics provided in the Minerals Yearbook and
Mineral Industry Surveys (Tepordei 1993 through 2006; Willett 2007a, b, 2009 through 2013a; USGS 2008 through
2013). To develop these data, the U.S. Geological Survey (USGS; U.S. Bureau of Mines prior to 1997) obtained
production and use information by surveying crushed stone manufacturers. Because some manufacturers were
reluctant to provide information, the estimates of total crushed limestone and dolomite production and use were
divided into three components: (1) production by end-use, as reported by manufacturers (i.e., "specified"
production); (2) production reported by manufacturers without end-uses specified (i.e., "unspecified" production);
and (3) estimated additional production by manufacturers who did not respond to the survey (i.e., "estimated"
production).
Box 7-5: Comparison of the Tier 2 U.S. Inventory Approach and IPCC (2006) Default Approach
Emissions from agricultural liming were estimated using a Tier 2 methodology based on liming emission factors
specific to the United States that are consistent with IPCC (2006) emission default factors, but are specific to U.S.
soil conditions under which liming occurs. For example, as described previously, most liming in the United States
occurs in the Mississippi Paver basin, or in areas that have similar soil and rainfall regimes as the Mississippi Paver
basin. Under such soil conditions, a significant portion of dissolved agricultural lime is predicted to leach through
the soil and travels by rivers to the ocean, the majority of which is then predicted to precipitate in the ocean as
CaCOs (West and McBride, 2005). Therefore, the U.S. specific emissions factors (0.059 metric ton C/metric ton
limestone and 0.064 metric ton C/metric ton dolomite) are about half of the IPCC (2006) emission factors (0.12
metric ton C/metric ton limestone and 0.13 metric ton C/metric ton dolomite). For comparison, the 2012 U.S.
emissions from liming of agricultural soils are 3.9 Tg CCh Eq. using the U.S.-specific West and McBride (2005)
emission factors and 8.0 Tg CCh Eq. using the IPCC (2006) emission factors.
The "unspecified" and "estimated" amounts of crushed limestone and dolomite applied to agricultural soils were
calculated by multiplying the percentage of total "specified" limestone and dolomite production applied to
agricultural soils by the total amounts of "unspecified" and "estimated" limestone and dolomite production. In other
words, the proportion of total "unspecified" and "estimated" crushed limestone and dolomite that was applied to
agricultural soils (as opposed to other uses of the stone) was assumed to be proportionate to the amount of
"specified" crushed limestone and dolomite that was applied to agricultural soils. In addition, data were not
available for 1990, 1992, and 2012 on the fractions of total crushed stone production that were limestone and
dolomite, and on the fractions of limestone and dolomite production that were applied to soils. To estimate the 1990
and 1992 data, a set of average fractions were calculated using the 1991 and 1993 data. These average fractions
were applied to the quantity of "total crushed stone produced or used" reported for 1990 and 1992 in the 1994
Minerals Yearbook (Tepordei 1996). To estimate 2012 data, 2011 fractions were applied to a 2012 estimate of total
crushed stone presented in the USGS Mineral Industry Surveys: Crushed Stone and Sand and Gravel in the First
7-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Quarter of 2012 (USGS 2012); thus, the 2012 data in Table 7-23 through Table 7-25 are shaded to indicate that they
are based on a combination of data and projections.
The primary source for limestone and dolomite activity data is the Minerals Yearbook, published by the Bureau of
Mines through 1994 and by the USGS from 1995 to the present. In 1994, the "Crushed Stone" chapter in the
Minerals Yearbook began rounding (to the nearest thousand metric tons) quantities for total crushed stone produced
or used. It then reported revised (rounded) quantities for each of the years from 1990 to 1993. In order to minimize
the inconsistencies in the activity data, these revised production numbers have been used in all of the subsequent
calculations. Since limestone and dolomite activity data are also available at the state level, the national-level
estimates reported here were broken out by state, although state-level estimates are not reported here. Also, it is
important to note that all emissions from liming are accounted for under Cropland Remaining Cropland because it is
not currently possible to apportion the data to each agricultural land use category (i.e., Cropland Remaining
Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to Grassland, and
Settlements Remaining Settlements). The majority of liming in the United States occurs on Cropland Remaining
Cropland.
Table 7-25: Applied Minerals (Million Metric Tons)
Mineral 1990 2005 2008 2009 2010 2011 2012~
Limestone3 19.0 18.1 20.5 15.7 20.0 15.9 16.1
Dolomite8 24 1.9 2.6 L2 1.9 1.9 1.9
a Data represent amounts applied to Cropland Remaining Cropland, Land Converted to Cropland, Grassland
Remaining Grassland, Land Converted to Grassland, and Settlements Remaining Settlements as it is not
currently possible to apportion the data by land use category.
Uncertainty and Time-Series Consistency
Uncertainty regarding limestone and dolomite activity data inputs was estimated at ±15 percent and assumed to be
uniformly distributed around the inventory estimate (Tepordei 2003b, Willett 2013b). Analysis of the uncertainty
associated with the emission factors included the following: the fraction of agricultural lime dissolved by nitric acid
versus the fraction that reacts with carbonic acid, and the portion of bicarbonate that leaches through the soil and is
transported to the ocean. Uncertainty regarding the time associated with leaching and transport was not accounted
for, but should not change the uncertainty associated with CC>2 emissions (West 2005). The uncertainties associated
with the fraction of agricultural lime dissolved by nitric acid and the portion of bicarbonate that leaches through the
soil were each modeled as a smoothed triangular distribution between ranges of zero percent to 100 percent. The
uncertainty surrounding these two components largely drives the overall uncertainty estimates reported below.
More information on the uncertainty estimates for Liming of Agricultural Soils is contained within the Uncertainty
Annex.
A Monte Carlo (Tier 2) uncertainty analysis was applied to estimate the uncertainty of CO2 emissions from liming.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 7-26. Carbon dioxide emissions
from Liming of Agricultural Soils in 2012 were estimated to be between 0.15 and 8.12 TgCChEq. at the 95 percent
confidence level. This indicates a range of 96 percent below to 106 percent above the 2012 emission estimate of
3.94TgCO2Eq.
Table 7-26: Tier 2 Quantitative Uncertainty Estimates for COz Emissions from Liming of
Agricultural Soils (Tg COz Eq. and Percent)
2012 Emission
Estimate
Source Gas (Tg CCh Eq.)
Uncertainty Range Relative to Emission Estimate3
(Tg COz Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Liming of Agricultural Soilsb CO2 3.94
0.15 8.12 -96% +106%
aRange of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Land Use, Land-Use Change, and Forestry 7-45
-------
b Also includes emissions from liming on Land Converted to Cropland, Grassland Remaining Grassland, Land Converted
to Grassland, and Settlements Remaining Settlements as it is not currently possible to apportion the data by land use
category.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A source-specific QA/QC plan for Liming was developed and implemented. This effort included a Tier 1 analysis,
as well as portions of a Tier 2 analysis. The Tier 2 procedures focused on comparing the magnitude of emission
factors historically to attempt to identify any outliers or inconsistencies. No problems were found.
Recalculations Discussion
Several adjustments were made in the current Inventory to improve the results. The quantity of applied minerals
reported in the previous Inventory for 2010 has been revised; the updated activity data for 2010 for limestone are
approximately 29 thousand metric tons less and the 2010 data for dolomite are approximately 433 thousand metric
tons greater than the data used for the previous Inventory. Consequently, the reported emissions resulting from
liming in 2010 increased by about 2 percent. In the previous Inventory, to estimate 2011 data, 2010 fractions were
applied to a 2011 estimate of total crushed stone presented in the USGS Mineral Industry Surveys: Crushed Stone
and Sand and Gravel in the First Quarter of 2011 (USGS 2011). Since publication of the previous Inventory, the
Minerals Yearbook has published actual quantities of crushed stone sold or used by producers in the United States in
2011. These values have replaced those used in the previous Inventory to calculate the quantity of minerals applied
to soil and the emissions from liming. The updated activity data for 2011 are approximately 2,732 thousand metric
tons less than the data used in the previous Inventory. As a result, the reported emissions from liming for 2011
decreased by about 13 percent.
CO2
The use of urea (CO(NH2)2) as fertilizer leads to emissions of CO2 that was fixed during the industrial production
process. Urea in the presence of water and urease enzymes is converted into ammonium (NH4+), hydroxyl ion (OH),
and bicarbonate (HCOs")- The bicarbonate then evolves into CCh and water. Emissions from urea fertilization in the
United States totaled 3.4 Tg CO2 Eq. (0.9 Tg C) in 2012 (Table 7-27 and Table 7-28). Emissions from urea
fertilization have grown 42 percent between 1990 and 2012, due to an increase in the use of urea as fertilizer.
Table 7-27: COz Emissions from Urea Fertilization in Cropland Remaining Cropland'(Tg COz
Eq.)
Source 1990 2005 2008 2009 2010 2011 2012~
Urea Fertilization" 2A 3.5 3.6 3.6 3.8 4.0 3.4
a Also includes emissions from urea fertilization on Land Converted to Cropland, Grassland
Remaining Grassland, Land Converted to Grassland, Settlements Remaining Settlements, and
Forest Land Remaining Forest Land because it is not currently possible to apportion the data
by land use category.
Table 7-28: COz Emissions from Urea Fertilization in Cropland Remaining Cropland (Tg C)
Source 1990 2005 2008 2009 2010 2011 2012~
Urea Fertilization" 0.7 1.0 1.0 1.0 1.0 1.1 0.9
7-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
a Also includes emissions from urea fertilization on Land Converted to Cropland, Grassland
Remaining Grassland, Land Converted to Grassland, Settlements Remaining Settlements, and Forest
Land Remaining Forest Land because it is not currently possible to apportion the data by land use
category.
Methodology
Carbon dioxide emissions from the application of urea to agricultural soils were estimated using the IPCC (2006)
Tier 1 methodology. The annual amounts of urea fertilizer applied (see Table 7-29) were derived from state-level
fertilizer sales data provided in Commercial Fertilizers (TVA 1991, 1992, 1993, 1994; AAPFCO 1995 through
2013) and were multiplied by the default IPCC (2006) emission factor of 0.20 metric ton of C per metric ton of urea,
which is equal to the C content of urea on an atomic weight basis. Because fertilizer sales data are reported in
fertilizer years (July through June), a calculation was performed to convert the data to calendar years (January
through December). According to historic monthly fertilizer use data (TVA 1992b), 65 percent of total fertilizer
used in any fertilizer year is applied between January and June of that calendar year, and 3 5 percent of total fertilizer
used in any fertilizer year is applied between July and December of the previous calendar year. Fertilizer sales data
for the 2012 fertilizer year were not available in time for publication. Accordingly, urea application in the 2012
fertilizer year was estimated using a linear, least squares trend of consumption over the previous five years (2007
through 2011). A trend of five years was chosen as opposed to a longer trend because it best represented inter-state
and inter-annual variability in consumption. For states where the trend projected negative urea application, the 2011
urea application was used. This applied only to West Virginia. Since 2013 fertilizer year data were not available,
July through December 2012 fertilizer consumption was estimated by calculating the percent change in urea use
from January through June 2011 to January through June 2012. This percent change was then multiplied by the July
through December 2011 data to estimate July through December 2012 fertilizer use; thus, the 2011 and 2012 data in
Table 7-27 through Table 7-29 are shaded to indicate that they are based on a combination of data and projections.
State-level estimates of CO2 emissions from the application of urea to agricultural soils were summed to estimate
total emissions for the entire United States. Since urea activity data are also available at the state level, the national-
level estimates reported here were broken out by state, although state-level estimates are not reported here. Also, it
is important to note that all emissions from urea fertilization are accounted for under Cropland Remaining Cropland
because it is not currently possible to apportion the data to each agricultural land use category (i.e., Cropland
Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to
Grassland, and Settlements Remaining Settlements). The majority of urea fertilization in the United States occurs on
Cropland Remaining Cropland.
Table 7-29: Applied Urea (Million Metric Tons)
1990 2005 2008 2009 2010 2011 2012~
Urea Fertilizer1 3.3 4.8 4.9 4.8 5.2 5.4 4.7
'These numbers represent amounts applied to all agricultural land, including Land Converted to
Cropland, Grassland Remaining Grassland, Land Converted to Grassland, Settlements
Remaining Settlements, and Forest Land Remaining Forest Land because it is not currently
possible to apportion the data by land use category.
Uncertainty and Time-Series Consistency
Uncertainty estimates are presented in Table 7-30 for Urea Fertilization. A Tier 2 Monte Carlo analysis was
completed. The largest source of uncertainty was the default emission factor, which assumes that 100 percent of the
C in CO(NH2)2 applied to soils is ultimately emitted into the environment as CO2. This factor does not incorporate
the possibility that some of the C may be retained in the soil. The emission estimate is, therefore, likely to be high.
In addition, each urea consumption data point has an associated uncertainty. Urea for non-fertilizer use, such as
aircraft deicing, may be included in consumption totals; it was determined through personal communication with
Fertilizer Regulatory Program Coordinator David L. Terry (2007), however, that this amount is most likely very
Land Use, Land-Use Change, and Forestry 7-47
-------
small. Research into aircraft deicing practices also confirmed that urea is used minimally in the industry; a 1992
survey found a known annual usage of approximately 2,000 tons of urea for deicing; this would constitute 0.06
percent of the 1992 consumption of urea (EPA 2000). Similarly, surveys conducted from 2002 to 2005 indicate that
total urea use for deicing at U.S. airports is estimated to be 3,740 MT peryear, or less than 0.07 percent of the
fertilizer total for 2007 (Itle 2009). Lastly, there is uncertainty surrounding the assumptions behind the calculation
that converts fertilizer years to calendar years. Carbon dioxide emissions from urea fertilization of agricultural soils
in 2012 were estimated to be between 2.0 and 3.5 Tg CCh Eq. at the 95 percent confidence level. This indicates a
range of 43 percent below to 3 percent above the 2012 emission estimate of 3.4 TgCChEq.
Table 7-30: Quantitative Uncertainty Estimates for COz Emissions from Urea Fertilization (Tg
COz Eq. and Percent)
Source
2012 Emission
Estimate
Gas (Tg CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(Tg COz Eq.) (%)
Lower Upper
Bound Bound
Lower Upper
Bound Bound
Urea Fertilization CO2 3.44230 1.97 3.53 -43% +3%
aRange of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Note: These numbers represent amounts applied to all agricultural land, including Land Converted to Cropland,
Grassland Remaining Grassland, Land Converted to Grassland, Settlements Remaining Settlements, and Forest Land
Remaining Forest Land because it is not currently possible to apportion the data by land use category
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A source-specific QA/QC plan for Urea was developed and implemented. This effort included a Tier 1 analysis, as
well as portions of a Tier 2 analysis. The Tier 2 procedures focused on comparing the magnitude of emission factors
historically to attempt to identify any outliers or inconsistencies. No problems were found.
Recalculations Discussion
In the current Inventory, July to December 2009 and July to December 2010 urea application data were updated
based on new activity data for fertilizer years 2010 and 2011, and the 2009 and 2010 emission estimates were
revised accordingly. This resulted in a 0.3 percent decrease and a 3.2 percent increase in emissions for 2009 and
2010, respectively. Similarly, the July to December 2011 urea application data were updated with assumptions for
fertilizer year 2012, and the 2011 emission estimate was revised accordingly. The activity data for applied urea
decreased by about 449,000 metric tons for 2011 and this change resulted in an approximately 9.0 percent decrease
in emissions in 2011 relative to the previous Inventory.
Planned Improvements
The primary planned improvement is to investigate using a Tier 2 or Tier 3 approach, which would utilize country-
specific information to estimate a more precise emission factor. This possibility was investigated for the current
Inventory, but no options were identified for updating to a Tier 2 or Tier 3 approach.
This value of 3.44 Tg CCh is rounded and reported as 3.4 Tg CCh in Table 7-25 and the text discussing Table 7-25. For the
uncertainty calculations, the value of 3.44 Tg CCh was used to allow for more precise uncertainty ranges.
7-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
7.5 Land Converted to Cropland (IPCC Source
Category 5B2)
Land Converted to Cropland includes all cropland in an inventory year that had been another land use at any point
during the previous 20 years according to the USDA National Resources Inventory (NRI) land-use survey (USDA-
NRCS 2009).231 Consequently, these lands are retained in this category for 20 years as recommended in the IPCC
guidelines (IPCC 2006) unless there is another land-use change. The inventory includes all privately-owned
croplands in the conterminous United States and Hawaii, but there are approximately 100,000 hectares of Land
Converted to Cropland on federal lands and a minor amount of cropland in Alaska that is not currently included in
the estimation of C stock changes. Consequently there is a discrepancy between the total amount of managed area
in Land Converted to Cropland (see Section 7.1) and the cropland area included in the inventory. Improvements are
underway to include federal croplands in future C inventories.
Background on agricultural C stock changes is provided in Cropland Remaining Cropland and will only be
summarized here for Land Converted to Cropland. Soils are the largest pool of C in agricultural land, and also have
the greatest potential for storage or release of C, because biomass and dead organic matter C pools are relatively
small and ephemeral compared with soils. The IPCC guidelines (IPCC 2006) recommends reporting changes in
SOC stocks due to (1) agricultural land-use and management activities on mineral soils, and (2) agricultural land-use
and management activities on organic soils.232
Land-use and management of mineral soils in Land Converted to Cropland led to losses of C throughout the time
series (Table 7-31 and Table 7-32). Grassland conversion to cropland was the largest source of C losses, though
losses declined over the time series. The total rate of change in soil C stocks was 16.8 Tg CC>2 Eq. (4.6 Tg C) in
2012. Mineral soils were estimated to lose 12.1 Tg CC>2 Eq. (3.3 Tg C) in 2012, while drainage and cultivation of
organic soils led to an annual loss of 4.8 Tg CO2 Eq. (1.3 Tg C) in 2012.
Table 7-31: Net COz Flux from Soil C Stock Changes in Land Converted to Cropland^ Land
Use Change Category (Tg COz Eq.)
Soil Type 1990 2005 2008 2009 2010 2011 2012
Grassland Converted to Cropland
Mineral 22.3 15.0 11.3 11.3 11.3 11.3 11.3
Organic 2.5 4.3 4.0 4.0 4.0 4.0 4.0
Forest Converted to Cropland
Mineral 1.5 0.3 0.3 0.3 0.3 0.3 0.3
Organic (0.2) 0.3 0.2 0.2 0.2 0.2 0.2
Other Lands Converted Cropland
Mineral 0.3 0.1 0.1 0.1 0.1 0.1 0.1
Organic + I + I + + + + +
Settlements Converted Cropland
Mineral 0.6 0.3 0.3 0.3 0.3 0.3 0.3
Organic (0.0) 0.2 0.2 0.2 0.2 0.2 0.2
Wetlands Converted Cropland
Mineral
Organic
Total Mineral Soil Flux
Total Organic Soil Flux
Total Net Flux
0.2 1
(0.2)
24.8
2.1
26.9
Si
15.8
5.1
20.9
^0.1
0.4
12.1
4.8
16.8
0.1
0.4
12.1
4.8
16.8
0.1
0.4
12.1
4.8
16.8
0.1
0.4
12.1
4.8
16.8
0.1
0.4
12.1
4.8
16.8
Note: Estimates after 2007 are based on NRI data from 2007 and therefore do not fully reflect changes occurring in the latter part
of the time series.
231 NRI points were classified according to land-use history records starting in 1982 when the NRI survey began, and
consequently the classifications were based on less than 20 years from 1990 to 2001.
232 CO2 emissions associated with liming urea fertilization are also estimated but included in 7.4 Cropland Remaining Cropland.
Land Use, Land-Use Change, and Forestry 7-49
-------
+ Does not exceed 0.01 Tg CO2 Eq. or 0.5 Gg.
Table 7-32: Net COz Flux from Soil C Stock Changes in Land Converted to Cropland(Tg C)
Soil Type
Grassland Converted to Cropland
Mineral
Organic
Forest Converted to Cropland
Mineral
Organic
Other Lands Converted Cropland
Mineral
Organic
Settlements Converted Cropland
Mineral
Organic
Wetlands Converted Cropland
Mineral
Organic
Total Mineral Soil Flux
Total Organic Soil Flux
Total Net Flux
1990
6.1 |
0.4 1
(0.1)
0.1
1
0.2 1
(0.0)
(0.1)
6.8
0.6
7.3
2005
0.1 1
0.1 |
0.1
4.3
1.4
5.7
2008
3.1
1.1
0.1
0.1
+
+
0.1
+
+
0.1
3.3
1.3
4.6
2009
3.1
1.1
0.1
0.1
+
+
0.1
+
+
0.1
3.3
1.3
4.6
2010
3.1
1.1
0.1
0.1
+
+
0.1
+
+
0.1
3.3
1.3
4.6
2011
3.1
1.1
0.1
0.1
+
+
0.1
+
+
0.1
3.3
1.3
4.6
2012
3.1
1.1
0.1
0.1
+
+
0.1
+
+
0.1
3.3
1.3
4.6
Note: Estimates after 2007 are based on NRI data from 2007 and therefore do not fully reflect changes occurring in the latter part
of the time series.
+ Does not exceed 0.01 Tg CO2 Eq. or 0.5 Gg.
Parentesis indicate net sequestration.
The spatial variability in the 2012 annual CCh flux is displayed in Figure 7-7 and Figure 7-8 for C stock changes in
mineral and organic soil, respectively. Losses occurred in most regions of the United States. In particular,
conversion of grassland and forestland to cropland led to enhanced decomposition of soil organic matter and a net
loss of carbon from the soil pool. Emissions from organic soils were largest in the Southeastern Coastal Region
(particularly Florida), the upper Midwest and Northeast surrounding the Great Lakes, in addition to the Pacific
Coastal Region, which coincides with areas that have a large concentration of cultivated organic soils in the United
States.
7-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure 7-7: Total Net Annual COz Flux for Mineral Soils under Agricultural Management
within States, 2012, Land Converted to Cropland
Note Values greater than zero represent emissions,
and values less than zero represent sequestration
Map accounts for fluxes associated with trie Tier 2
and 3 inventory computations See methodology
for additional details
Tg CO2 Eq/yr
n>o
Q-0.1 toO
n-0-5 to-0.1
I -1 to -0.5
| -2 to -1
Figure 7-8: Total Net Annual COz Flux for Organic Soils under Agricultural Management
within States, 2012, Land Converted to Cropland
Land Use, Land-Use Change, and Forestry 7-51
-------
The following section includes a brief description of the methodology used to estimate changes in soil C stocks due
to agricultural land-use and management activities on mineral and organic soils for Land Converted to Cropland.
Biomass and litter C stock changes are not explicitly included in this category but losses associated with conversion
of forest to cropland are included in the Forest Land Remaining Forest Land section. Further elaboration on the
methodologies and data used to estimate stock changes for mineral and organic soils are provided in the Cropland
Remaining Cropland section and Annex 3.12.
Soil C stock changes were estimated for Land Converted to Cropland according to land-use histories recorded in the
USDA NRI survey (USDA-NRCS 2009). Land-use and some management information (e.g., crop type, soil
attributes, and irrigation) were originally collected for each NRI point on a 5-year cycle beginning in 1982. In 1998,
the NRI program initiated annual data collection, and the annual data are currently available through 2007. NRI
points were classified as Land Converted to Cropland in a given year between 1990 and 2007 if the land use was
cropland but had been another use during the previous 20 years. Cropland includes all land used to produce food or
fiber, or forage that is harvested and used as feed (e.g., hay and silage).
Mineral Soil Carbon Stock Changes
A Tier 3 model-based approach (Ogle et al. 2010) was applied to estimate C stock changes for soils on Land
Converted to Cropland that are used to produce a majority of crops in the United States in terms of land area,
including alfalfa hay, barley, corn, cotton, dry beans, grass hay, grass-clover hay, oats, onions, peanuts, potatoes,
rice, sorghum, soybeans, sugar beets, sunflowers, tomatoes, and wheat. Soil C stock changes on the remaining soils
were estimated with the IPCC Tier 2 method (Ogle et al. 2003), including land used to produce some vegetables,
tobacco, perennial/horticultural crops and crops rotated with these crops; land on very gravelly, cobbly, or shaley
soils (greater than 35 percent by volume); and land converted from forest or federal ownership.233
Tier 3 Approach
Mineral SOC stocks and stock changes were estimated using the DAYCENT biogeochemical model for the Tier 3
method (Parton et al. 1998; Del Grosso et al. 2001, 2011). The DAYCENT model utilizes the soil C modeling
framework developed in the Century model (Parton et al. 1987, 1988, 1994; Metherell et al. 1993), but has been
refined to simulate dynamics at a daily time-step. National estimates were obtained by using the model to simulate
historical land-use change patterns as recorded in the USDA National Resources Inventory (USDA-NRCS 2009). C
stocks and 95 percent confidence intervals were estimated for each year between 1990 and 2007, but C stock
changes from 2008 to 2012 were assumed to be similar to 2007 because no additional activity data are currently
available from the NRI for the latter years. The methods used for Land Converted to Cropland are the same as those
described in the Tier 3 portion of Cropland Remaining Cropland section for mineral soils (see Cropland Remaining
Cropland Tier 3 methods section).
Tier 2 Approach
For the mineral soils not included in the Tier 3 analysis, SOC stock changes were estimated using a Tier 2 Approach
for Land Converted to Cropland as described in the Tier 2 portion of the Cropland Remaining Cropland section for
mineral soils (see Cropland Remaining Cropland Tier 2 methods section for additional information).
Organic Soil Carbon Stock Changes
Annual C emissions from drained organic soils in Land Converted to Cropland were estimated using the Tier 2
method provided in IPCC (2003, 2006), with U.S.-specific C loss rates (Ogle et al. 2003) as described in the
Cropland Remaining Cropland section for organic soils (see Cropland Remaining Cropland for more information).
233 pgderal iancj js not a iancj USg; but rather an ownership designation that is treated as forest or nominal grassland for purposes
of these calculations. The specific use for federal lands is not identified in the NRI survey (USDA-NRCS 2009).
7-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Uncertainty and Time-Series Consistency
Uncertainty analysis for mineral soil C stock changes using the Tier 3 and Tier 2 approaches were based on the same
method described for Cropland Remaining Cropland. The uncertainty for annual C emission estimates from drained
organic soils in Land Converted to Cropland was estimated using the Tier 2 approach, as described in the Cropland
Remaining Cropland section.
Uncertainty estimates are presented in Table 7-33 for each subsource (i.e., mineral soil C stocks and organic soil C
stocks) and method that was used in the Inventory analysis (i.e., Tier 2 and Tier 3). Uncertainty for the portions of
the Inventory estimated with Tier 2 and 3 approaches was derived using a Monte Carlo approach (see Annex 3.12
for further discussion). Uncertainty estimates from each approach were combined using the error propagation
equation in accordance with IPCC (2006), i.e., by taking the square root of the sum of the squares of the standard
deviations of the uncertain quantities. The combined uncertainty for soil C stocks in Land Converted to Cropland
ranged from -68 percent below to 77 percent above the 2012 stock change estimate of 16.8 Tg CCh Eq.
Table 7-33: Tier 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring
within Land Converted to Cropland(Tg COz Eq. and Percent)
2012 Flux Estimate
Source (Tg CCh Eq.)
Grassland Converted to Cropland
Mineral Soil C Stocks: Tier 3
Mineral Soil C Stocks: Tier 2
Organic Soil C Stocks: Tier 2
Forests Converted to Cropland
Mineral Soil C Stocks: Tier 2
Organic Soil C Stocks: Tier 2
Other Lands Converted to Cropland
Mineral Soil C Stocks: Tier 2
Organic Soil C Stocks: Tier 2
Settlements Converted to Cropland
Mineral Soil C Stocks: Tier 2
Organic Soil C Stocks: Tier 2
Wetlands Converted to Croplands
Mineral Soil C Stocks: Tier 2
Organic Soil C Stocks: Tier 2
Total: Land Converted to Cropland
Mineral Soil C Stocks: Tier 3
Mineral Soil C Stocks: Tier 2
Organic Soil C Stocks: Tier 2
15.3
10.5
0.8
4.0
0.5
0.3
0.2
0.1
0.1
NA
0.5
0.3
0.2
0.4
0.1
0.4
16.8
10.5
1.6
4.8
Uncertainty Range Relative to Flux Estimate3
(Tg C02 Eq.) (%)
Lower
Bound
3.9
(0.4)
0.4
0.7
0.2
0.1
0.0
0.1
0.1
NA
0.3
0.2
0.1
0.2
0.04
0.2
5.4
(0.4)
1.1
1.4
Upper
Bound
28.2
21.4
1.2
10.9
1.1
0.4
0.8
0.2
0.2
NA
0.7
0.5
0.3
0.7
0.1
0.6
29.8
21.4
2.0
11.7
Lower
Bound
-75%
-104%
-49%
-83%
-53%
-49%
-100%
-49%
-49%
NA
-36%
-49%
-46%
-45%
-49%
-53%
-68%
-104%
-28%
-70%
Upper
Bound
84%
104%
54%
172%
123%
54%
258%
54%
54%
NA
41%
54%
63%
57%
54%
68%
77%
104%
31%
145%
Note: Parentheses indicate negative values
NA: Other land by definition does not include organic soil (see Section 7.1—Definitions of Land Use in the United States). Consequently, no
land areas, C stock changes, or uncertainty results are estimated for land use conversions from Other lands to Croplands and Other lands to
Grasslands on organic soils.
a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Uncertainty is also associated with lack of reporting of agricultural biomass and litter C stock changes other than the
loss of forest biomass and litter, which is reported in the Forestland Remaining Forestland section of the report.
Biomass C stock changes are likely minor in perennial crops, such as orchards and nut plantations, given the small
amount of change in land used to produce these commodities in the United States. In contrast, agroforestry
practices, such as shelterbelts, riparian forests and intercropping with trees, may have led to significant changes in
biomass C stocks, at least in some regions of the United States, but there are currently no datasets to evaluate the
trends. Changes in litter C stocks are also assumed to be negligible in croplands over annual time frames, although
there are certainly significant changes at sub-annual time scales across seasons. However, this trend may change in
the future, particularly if crop residue becomes a viable feedstock for bioenergy production.
Land Use, Land-Use Change, and Forestry 7-53
-------
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Methodological recalculations in the current Inventory were associated with the following improvements: 1) refining
the temperature algorithm that is used for simulating crop production and carbon inputs to the soil in the DAYCENT
biogeochemical model; 2) increasing the number of experimental sites that are used to evaluate the structural
uncertainty in the DAYCENT model; and 3) recalculation of Tier 2 organic soil C emissions using annual data from
the NRI rather than estimating emissions every 5 years and assuming the same emissions between the years.
Change in SOC stocks declined by an average of 5.1 Tg CCh eq. over the time series as a result of these
improvements to the Inventory.
QA/QC and Verification
See QA/QC and Verification section under Cropland Remaining Cropland.
Planned Improvements
Soil C stock changes with land use conversion from forest land to cropland are undergoing further evaluation to
ensure consistency in the time series. Different methods are used to estimate soil C stock changes in forest land and
croplands, and while the areas have been reconciled between these land uses, there has been limited evaluation of
the consistency in C stock changes with conversion from forest land to cropland. This planned improvement may
not be fully implemented for two more years, depending on resource availability. Additional planned improvements
are discussed in the Cropland Remaining Cropland.
7.6 Grassland Remaining Grassland (IPCC
Source Category 5C1)
Grassland Remaining Grassland includes all grassland in an inventory year that had been grassland for the previous
20 years234 according to the USDA National Resources Inventory (NRI) land use survey (USDA-NRCS 2009). The
inventory includes all privately-owned grasslands in the conterminous United States and Hawaii, but does not
address changes in C stocks for 75 million hectares of Grassland Remaining Grassland on federal lands or any of
the 36 million hectares of managed grasslands in Alaska, leading to a discrepancy with the total amount of managed
area in Grassland Remaining Grassland (see Section 7.1—Representation of the United States Land Base) and the
grassland area included in the Grassland Remaining Grassland (IPCC Source Category 5C1—Section 7.6).
Background on agricultural C stock changes is provided in the Cropland Remaining Cropland section and will only
be summarized here for Grassland Remaining Grassland. Soils are the largest pool of C in agricultural land, and
also have the greatest potential for storage or release of C, because biomass and dead organic matter C pools are
relatively small and ephemeral compared to the soil C pool. IPCC (2006) recommends reporting changes in SOC
stocks due to (1) agricultural land-use and management activities on mineral soils, and (2) agricultural land-use and
management activities on organic soils.235
234 NRI points were classified according to land-use history records starting in 1982 when the NRI survey began, and
consequently the classifications were based on less than 20 years from 1990 to 2001.
235 CO2 emissions associated with liming and urea fertilization are also estimated but included in 7.4 Cropland Remaining
Cropland.
7-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Land-use and management increased soil C in mineral soils of Grassland Remaining Grassland until 2005 when the
trend was reversed to small decreases in soil C. Organic soils lost relatively small amounts of C in each year 1990
through 2012. Due to the pattern for mineral soils, the overall trend has been a gain in soil C through most of the
time series. However, over the last decade most years have seen small losses, estimated at 6.7 Tg CCh Eq. (1.8 Tg
C) in 2012. There was considerable variation over the time series driven by variability in weather patterns and
associated interaction with land management activity. The change rates on per hectare basis were small, however,
even in the years with larger total changes in stocks. Overall, flux rates increased by 16.3 Tg CO2 Eq. (4.4 Tg C)
when comparing the net change in soil C from 1990 and 2012.
Table 7-34: Net COi Flux from Soil C Stock Changes in Grassland Remaining Grassland(Tg
CO2 Eq.)
Soil Type
Mineral Soils
Organic Soils
Total Net Flux
1990
(14.2)
4.6
(9.6) •
2005
2.sl
3.1
5.6
2008
3.7
3.0
6.8
2009
3.7
3.0
6.8
2010
3.7
3.0
6.7
2011
3.7
3.0
6.7
2012
3.7
3.0
6.7
Note: Totals may not sum due to independent rounding. Estimates after 2007 are based on NRI data from 2007 and therefore do
not fully reflect changes occurring in the latter part of the time series.
Parentesis indicate net sequestration.
Table 7-35: Net COz Flux from Soil C Stock Changes in Grassland Remaining Grassland (Tg
C)
Soil Type 1990 2005 2008 2009 2010 2011 2012
Mineral Soils (19) O.lU LO LO LO LO To"
Organic Soils L3 0.8 0.8 0.8 0.8 0.8 0.8
Total Net Flux (2.6) 1.5 1.8 1.8 1.8 1.8 1.8
Note: Totals may not sum due to independent rounding. Estimates after 2007 are based on NRI data from 2007 and therefore do
not fully reflect changes occurring in the latter part of the time series.
Parentesis indicate net sequestration.
The spatial variability in the 2012 annual CO2 flux is displayed in Figure 7-9 and Figure 7-10 for C stock changes in
mineral and organic soils, respectively. Grassland gained soil organic C in several regions during 2012, including
the Northeast, Southeast, portions of the Midwest, and Pacific Coastal Region; although the gains were relatively
small on a per-hectare basis in most of these regions. Emission rates from drained organic soils were highest from
organic soils were largest in the Southeastern Coastal Region (particularly Florida), upper Midwest, coinciding with
two of the areas with large concentrations of organic soils in the United States that are used for agricultural
production.
Land Use, Land-Use Change, and Forestry 7-55
-------
Figure 7-9: Total Net Annual COz Flux for Mineral Soils under Agricultural Management
within States, 2012, Grass/and Remaining Grass/and
Note Values greater than zero represent emissions,
and values less than zero represent sequestration
Map accounts for fluxes associated with the Tier 2
and 3 inventory computations See methodology
for additional details
Tg CO2 Eq/yr
D>°
Q-0.1 toO
Q -0.5 to -0.1
I -1 to -0.5
| -2 to -1
Figure 7-10: Total Net Annual COz Flux for Organic Soils under Agricultural Management
within States, 2012, Grassland Remaining Grassland
7-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The following section includes a brief description of the methodology used to estimate changes in soil C stocks due
to agricultural land-use and management activities on mineral and organic soils for Grassland Remaining
Grassland. Further elaboration on the methodologies and data used to estimate stock changes from mineral and
organic soils are provided in the Cropland Remaining Cropland section and Annex 3.12.
Soil C stock changes were estimated for Grassland Remaining Grassland according to land-use histories recorded in
the USDA NRI survey (USDA-NRCS 2009). Land-use and some management information (e.g., crop type, soil
attributes, and irrigation) were originally collected for each NRI point on a 5-year cycle beginning in 1982. In 1998,
the NRI program initiated annual data collection, and the annual data are currently available through 2007. NRI
points were classified as Grassland Remaining Grassland in a given year between 1990 and 2007 if the land use had
been grassland for 20 years. Grassland includes pasture and rangeland used for grass forage production, where the
primary use is livestock grazing. Rangelands are typically extensive areas of native grassland that are not
intensively managed, while pastures are often seeded grassland, possibly following tree removal, that may or may
not be improved with practices such as irrigation and interseeding legumes.
Mineral Soil Carbon Stock Changes
An IPCC Tier 3 model-based approach (Ogle et al. 2010) was applied to estimate C stock changes for most mineral
soils in Grassland Remaining Grassland. The C stock changes for the remaining soils were estimated with an IPCC
Tier 2 method (Ogle et al. 2003), including gravelly, cobbly, or shaley soils (greater than 35 percent by volume) and
additional stock changes associated with sewage sludge amendments.
Tier 3 Approach
Mineral SOC stocks and stock changes for Grassland Remaining Grassland were estimated using the DAYCENT
biogeochemical model (Parton et al. 1998; Del Grosso et al. 2001, 2011), as described in Cropland Remaining
Cropland. The DAYCENT model utilizes the soil C modeling framework developed in the Century model (Parton
et al. 1987, 1988, 1994; Metherell et al. 1993), but has been refined to simulate dynamics at a daily time-step.
Historical land-use and management patterns were used in the DAYCENT simulations as recorded in the USDA
National Resources Inventory (NRI) survey, with supplemental information on fertilizer use and rates from the
USDA Economic Research Service Cropping Practices Survey (USDA-ERS 1997, 2011) and National Agricultural
Statistics Service (NASS 1992, 1999, 2004). Frequency and rates of manure application to grassland during 1997
were estimated from data compiled by the USDA Natural Resources Conservation Service (Edmonds, et al. 2003),
and then adjusted using county-level estimates of manure available for application in other years. Specifically,
county-scale ratios of manure available for application to soils in other years relative to 1997 were used to adjust the
area amended with manure (see Cropland Remaining Cropland for further details). Greater availability of managed
manure N relative to 1997 was, thus, assumed to increase the area amended with manure, while reduced availability
of manure N relative to 1997 was assumed to reduce the amended area.
The amount of manure produced by each livestock type was calculated for managed and unmanaged waste
management systems based on methods described in the Manure Management, Section 6.2, and Annex 3.11.
Manure N deposition from grazing animals (i.e., PRP manure) was an input to the DAYCENT model (see Annex
3.11), and included approximately 91 percent of total PRP manure (the remainder is deposited on federal lands,
which are currently not included in this inventory). C stocks and 95 percent confidence intervals were estimated for
each year between 1990 and 2007, but C stock changes from 2008 to 2012 were assumed to be similar to 2007
because no additional activity data are currently available from the NRI for the latter years (See Cropland
Remaining Cropland section for additional discussion on the Tier 3 methodology for mineral soils).
Tier 2 Approach
The Tier 2 approach is based on the same methods described in the Tier 2 portion of Cropland Remaining Cropland
section for mineral soils (see Cropland Remaining Cropland Tier 2 methods section for additional information).
Land Use, Land-Use Change, and Forestry 7-57
-------
Additional Mineral C Stock Change Calculations
Annual C flux estimates for mineral soils between 1990 and 2012 were adjusted to account for additional C stock
changes associated with sewage sludge amendments using a Tier 2 method. Estimates of the amounts of sewage
sludge N applied to agricultural land were derived from national data on sewage sludge generation, disposition, and
N content. Total sewage sludge generation data for 1988, 1996, and 1998, in dry mass units, were obtained from an
EPA report (EPA 1999) and estimates for 2004 were obtained from an independent national biosolids survey
(NEBRA 2007). These values were linearly interpolated to estimate values for the intervening years, and linearly
extrapolated to estimate values for years since 2004. N application rates from Kellogg et al. (2000) were used to
determine the amount of area receiving sludge amendments. Although sewage sludge can be added to land managed
for other land uses, it was assumed that agricultural amendments occur in grassland. Cropland is assumed to rarely
be amended with sewage sludge due to the high metal content and other pollutants in human waste. The soil C
storage rate was estimated at 0.38 metric tons C per hectare per year for sewage sludge amendments to grassland.
The stock change rate is based on country-specific factors and the IPCC default method (see Annex 3.12 for further
discussion).
Organic Soil Carbon Stock Changes
Annual C emissions from drained organic soils in Grassland Remaining Grassland were estimated using the Tier 2
method provided in IPCC (2003, 2006), which utilizes U.S.-specific C loss rates (Ogle et al. 2003) rather than
default IPCC rates, as described in the Cropland Remaining Cropland section for organic soils (see Cropland
Remaining Cropland for more information).
Uncertainty and Time-Series Consistency
Uncertainty estimates are presented in Table 7-36 for each subsource (i.e., mineral soil C stocks and organic soil C
stocks) disaggregated to the level of the inventory methodology employed (i.e., Tier 2 and Tier 3). Uncertainty for
the portions of the Inventory estimated with Tier 2 and 3 approaches was derived using a Monte Carlo approach (see
Annex 3.12 for further discussion). Uncertainty estimates from each approach were combined using the error
propagation equation in accordance with IPCC (2006), i.e., by taking the square root of the sum of the squares of the
standard deviations of the uncertain quantities. The combined uncertainty for soil C stocks in Grassland Remaining
Grassland ranged from 529 percent below to 529 percent above the 2012 stock change estimate of 6.7 Tg CO2 Eq.
The large relative uncertainty is due to the small net flux estimate in 2012.
Table 7-36: Tier 2 Quantitative Uncertainty Estimates for C Stock Changes Occurring Within
Grassland Remaining Grassland(Tg COz Eq. and Percent)
Source
2012 Flux Estimate
(Tg CCh Eq.)
Uncertainty Range Relative to Flux
Estimate3
(Tg C02 Eq.)
Mineral Soil C Stocks Grassland Remaining
Grassland, Tier 3 Methodology
Mineral Soil C Stocks: Grassland Remaining
Grassland, Tier 2 Methodology
Mineral Soil C Stocks: Grassland Remaining
Grassland, Tier 2 Methodology (Change in Soil C
due to Sewage Sludge Amendments)
Organic Soil C Stocks: Grassland Remaining
Grassland, Tier 2 Methodology
4.9
0.1
(1.3)
3 0
Lower
Bound
(30.6)
0.0
(2.0)
1 6
Upper
Bound
40.5
0.2
(0.7)
4 9
Lower
Bound
-718%
-86%
-50%
-46%
Upper
Bound
718%
109%
50%
63%
Combined Uncertainty for Flux Associated with
Agricultural Soil Carbon Stock Change in
Grassland Remaining Grassland
6.7
(28.8)
42.3
-529% 529%
Note: Parentheses indicate negative values.
" Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
7-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Uncertainty is also associated with lack of reporting of agricultural biomass and litter C stock changes, in addition to
non-COa greenhouse gas emissions from burning. Biomass C stock changes may be significant for managed
grasslands with woody encroachment that have not attained enough tree cover to be considered forest lands.
Grassland burning is not as common in the United States as other regions of the world, but fires do occur through
management incorporating prescribed burning, and also natural ignition sources. However, changes in litter C
stocks are assumed to be negligible in grasslands over annual time frames, although there are certainly significant
changes at sub-annual time scales across seasons.
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Methodological recalculations in the current Inventory were associated with the following improvements: 1) refining
the temperature algorithm that is used for simulating grass production and carbon inputs to the soil in the
D AYCENT biogeochemical model; 2) increasing the number of experimental sites that are used to evaluate the
structural uncertainty in the DAYCENT model; 3) recalculation of Tier 2 organic soil C emissions using annual data
from the NRI rather than estimating emissions every 5 years and assuming the same emissions between the years;
and 4) simulation of carbon inputs from PRP manure based on livestock management activity data rather than
automated routines in the DAYCENT model. Changes in SOC stocks declined by an average of 1.74 Tg CCh eq.
over the time series as a result of these improvements to the Inventory.
QA/QC and Verification
Quality control measures included checking input data, model scripts, and results to ensure data were properly
handled through the inventory process. DAYCENT simulations had errors in the PRP manure N application. The
error was associated with the scaling state level estimates of PRP manure N from the manure management section to
counties within the states for the DAYCENT simulations. In the previous Inventory report, DAYCENT was used to
simulate the PRP manure N input with automated routines. However, after adjusting the scaling process for the
current dataset, the estimates were based on the PRP manure N and associated C inputs to soils from managed
manure section of this report. This change provided internal consistency between the manure management data and
the agricultural soil management and LULUCF inventories.
Inventory reporting forms and text were reviewed and revised as needed to correct transcription errors. Modeled
results were compared to measurements from several long-term grazing experiments (See Annex 3.12 for more
information).
Planned Improvements
One of the key planned improvements for the Grassland Remaining Grassland is to develop an inventory of carbon
stock changes for the 75 million hectares of federal grasslands in the western United States. While federal grasslands
probably have minimal changes in land management and C stocks, improvements are underway to include federal
grasslands in future C inventories. Grasslands in Alaska will also be further evaluated in the future. This is a
significant improvement and estimates are expected to be available for the 1990-2013 Inventory. The other key
planned improvement is to estimate non-CCh greenhouse gas emissions from burning of grasslands. See Planned
Improvements section under Cropland Remaining Cropland for information about other improvements.
Land Use, Land-Use Change, and Forestry 7-59
-------
7.7 Land Converted to Grassland (IPCC Source
Category 5C2)
Land Converted to Grassland includes all grassland in an inventory year that had been in another land use at any
point during the previous 20 years236 according to the USDA National Resources Inventory (NRI) land-use survey
(USDA-NRCS 2009). Consequently, lands are retained in this category for 20 years as recommended by IPCC
(2006) unless there is another land use change. The Inventory includes all privately-owned grasslands in the
conterminous United States and Hawaii, but does not address changes in C stocks for 800,000-850,000 hectares
Land Converted to Grassland on federal lands across the time series or any of the grassland area in Alaska, leading
to a discrepancy between the total amount of managed area for Land Converted to Grassland (see Section 7.1—
Representation of the United States Land Base) and the grassland area included in Land Converted to Grassland
(IPCC Source Category 5C2—Section 7.7).
Background on agricultural C stock changes is provided in Cropland Remaining Cropland and will only be
summarized here for Land Converted to Grassland. Soils are the largest pool of C in agricultural land, and also
have the greatest potential for storage or release of C, because biomass and dead organic matter C pools are
relatively small and ephemeral compared with soils. IPCC (2006) recommend reporting changes in SOC stocks due
to (1) agricultural land-use and management activities on mineral soils, and (2) agricultural land-use and
management activities on organic soils.237
Land-use and management of mineral soils in Land Converted to Grassland led to an increase in soil C stocks from
1990 through 2012 (see Table 7-37 and Table 7-38). For example, the stock change rates were estimated to remove
8.1 Tg CO2 Eq. (2.2 Tg C) and 9.6 Tg CO2 Eq. (2.6 Tg C) from mineral soils in 1990 and 2012, respectively.
Drainage of organic soils for grazing management led to losses of 0.8 TgCChEq. (0.2 Tg C) and 1.1 Tg CChEq.
(0.3 Tg C) in 1990 and 2012, respectively.
Table 7-37: Net COz Flux from Soil C Stock Changes for Land Converted to Grassland'(Tg COz
Eq.)
Soil Type 1990 2005 2008 2009 2010 2011 2012"
Cropland Converted to Grassland
Mineral (6.3) (8.3) (8.6) (8.5) (8.5) (8.4) (8.4)
Organic 0.5 1.0 0.9 0.9 0.9 0.9 0.9
Forest Converted to Grassland
Mineral (1.1) (0.4) (0.4) (0.4) (0.4) (0.4) (0.4)
Organic 0.1 0.1 0.1 0.1 0.1 0.1 0.1
Other Lands Converted Grassland
Mineral (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2)
Organic + + + + + + +
Settlements Converted Grassland
Mineral (0.4) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5)
Organic +1 +1 + + + + +
Wetlands Converted Grassland
Mineral
Organic
Total Mineral Soil Flux
Total Organic Soil Flux
Total Net Flux
(0.1)
0.1
(8.1)
0.8
(7.3)
(0.1)
0.1
(9.5)
1.3
(8.3)
(0.1)
0.1
(9.8)
1 1.1
(8.7)
(0.1)
0.1
(9.8)
1.1
(8.7)
(0.1)
0.1
(9.7)
1.1
(8.6)
(0.1)
0.1
(9.7)
1.1
(8.6)
(0.1)
0.1
(9.6)
1.1
(8.5)
236 jvjj^j pOmts were classified according to land-use history records starting in 1982 when the NRI survey began, and
consequently the classifications were based on less than 20 years from 1990 to 2001.
237 CO2 emissions associated with liming are also estimated but included in 7.4 Cropland Remaining Cropland.
7-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
I
Note: Estimates after 2007 are based on NRI data from 2007 and therefore do not fully reflect changes occurring in the latter part
of the time series.
+ Does not exceed 0.01 Tg CO2 Eq. or 0.5 Gg.
Table 7-38: Net COz Flux from Soil C Stock Changes for Land Converted to Grassland'(Tg C)
Soil Type 1990 2005 2008 2009 2010 2011 2012"
Cropland Converted to Grassland
Mineral (1.7) (2.3) (2.3) (2.3) (2.3) (2.3) (2.3)
Organic 0.1 0.3 0.2 0.2 0.2 0.2 0.2
Forest Converted to Grassland
Mineral (0.3) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)
Organic +
Other Lands Converted Grassland
Mineral (0.1) (+) (+) (+) (+) (+) (+)
Organic + + + + + + +
Settlements Converted Grassland
Mineral (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)
Organic
Wetlands Converted Grassland
Mineral
Organic + + + + + + j_
Total Mineral Soil Flux (Z2) (16) (Z7)(Z7)(Z7)(Z6)(2.6)
Total Organic Soil Flux 0.2 0.3 0.3 0.3 0.3 0.3 0.3
Total Net Flux (2.0) (2.3) (2.4) (2.4) (2.4) (2.3) (2.3)
Note: Estimates after 2007 are based on NRI data from 2007 and therefore do not fully reflect changes occurring in the latter part
of the time series.
Parentesis indicate net sequestration.
+ Does not exceed 0.01 Tg CO2 Eq. or 0.5 Gg.
The spatial variability in the 2012 annual CCh flux is displayed in Figure 7-11 and Figure 7-12 for C stock changes
in mineral and organic soils, respectively. Soil C stock increased in most states for Land Converted to Grassland.
The largest gains were in the Southeastern region, Northeast, South-Central, Midwest, and northern Great Plains.
The patterns were driven by conversion of annual cropland into continuous pasture. Emissions from organic soils
were highest in the Pacific Coastal Region, Gulf Coast Region, and the upper Midwest and Northeast surrounding
the Great Lakes, coinciding with the largest concentrations of organic soils in the United States that are used for
agricultural production.
<*,
Land Use, Land-Use Change, and Forestry 7-61
-------
Figure 7-11: Total Net Annual COz Flux for Mineral Soils under Agricultural Management
within States, 2012, Land Converted to Grassland
Note Values greater than zero represent emissions
and values less than zero represent sequestration
Map accounts for fluxes associated with the Tier 2
and 3 inventory computations See methodology
for additional details.
Tg CO2 Eq/yr
D>o
G -0.1 to 0
Q-0.5 to-0.1
• -1 to -0.5
• -2 to -1
Figure 7-12: Total Net Annual COz Flux for Organic Soils under Agricultural Management
within States, 2012, Land Converted to Grassland
7-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
This section includes a brief description of the methodology used to estimate changes in soil C stocks due to
agricultural land-use and management activities on mineral soils for Land Converted to Grassland. Biomass and
litter C stock changes are not explicitly included in this category but losses associated with conversion of forest to
grassland are included in the Forest Land Remaining Forest Land section. Further elaboration on the methodologies
and data used to estimate stock changes from mineral and organic soils are provided in the Cropland Remaining
Cropland section and Annex 3.12.
Soil C stock changes were estimated for Land Converted to Grassland according to land-use histories recorded in
the USDA NRI survey (USDA-NRCS 2009). Land-use and some management information (e.g., crop type, soil
attributes, and irrigation) were originally collected for each NRI point on a 5-year cycle beginning in 1982. In 1998,
the NRI program initiated annual data collection, and the annual data are currently available through 2007. NRI
points were classified as Land Converted to Grassland in a given year between 1990 and 2007 if the land use was
grassland, but had been another use in the previous 20 years. Grassland includes pasture and rangeland used for
grass forage production, where the primary use is livestock grazing. Rangeland typically includes extensive areas of
native grassland that are not intensively managed, while pastures are often seeded grassland, possibly following tree
removal, that may or may not be improved with practices such as irrigation and interseeding legumes.
Mineral Soil Carbon Stock Changes
An IPCC Tier 3 model-based approach (Ogle et al. 2010) was applied to estimate C stock changes for Land
Converted to Grassland on most mineral soils. C stock changes on the remaining soils were estimated with an IPCC
Tier 2 approach (Ogle et al. 2003), including prior cropland used to produce vegetables, tobacco, and
perennial/horticultural crops; land areas with very gravelly, cobbly, or shaley soils (greater than 35 percent by
volume); and land converted from forest.238 A Tier 2 approach was also used to estimate additional changes in
mineral soil C stocks due to sewage sludge amendments. However, all stock changes associated with sewage sludge
amendments are reported in the Grassland Remaining Grassland section.
Tier 3 Approach
Mineral SOC stocks and stock changes were estimated using the DAYCENT biogeochemical model (Parton et al.
1998; Del Grosso et al. 2001, 2011) as described for Grassland Remaining Grassland. The DAYCENT model
utilizes the soil C modeling framework developed in the Century model (Parton et al. 1987, 1988, 1994; Metherell et
al. 1993), but has been refined to simulate dynamics at a daily time-step. Historical land-use and management
patterns were used in the DAYCENT simulations as recorded in the NRI survey, with supplemental information on
fertilizer use and rates from the USDA Economic Research Service Cropping Practices Survey (USDA-ERS 1997,
2011) and the National Agricultural Statistics Service (NASS 1992, 1999, 2004) (See Cropland Remaining Cropland
section for additional discussion on the Tier 3 methodology for mineral soils).
Tier 2 Approach
The Tier 2 approach used for Land Converted to Grassland on mineral soils is the same as described for Cropland
Remaining Cropland (see Cropland Remaining Cropland Tier 2 methods section for additional information).
Organic Soil Carbon Stock Changes
Annual C emissions from drained organic soils in Land Converted to Grassland were estimated using the Tier 2
method provided in IPCC (2003, 2006), with U.S.-specific C loss rates (Ogle et al. 2003) as described in the
Cropland Remaining Cropland section for organic soils (see Cropland Remaining Cropland for more information).
238 federal iand is converted into private land in some cases due to changes in ownership. The specific use for federal lands is
not identified in the NRI survey (USDA-NRCS 2009), and so the land is assumed to be forest or nominal grassland for purposes
of these calculations.
Land Use, Land-Use Change, and Forestry 7-63
-------
Uncertainty and Time-Series Consistency
Uncertainty estimates are presented in Table 7-39 for each subsource (i.e., mineral soil C stocks and organic soil C
stocks), disaggregated to the level of the inventory methodology employed (i.e., Tier 2 and Tier 3). Uncertainty for
the portions of the Inventory estimated with Tier 2 and 3 approaches was derived using a Monte Carlo approach (see
Annex 3.12 for further discussion). Uncertainty estimates from each approach were combined using the error
propagation equation in accordance with IPCC (2006) (i.e., by taking the square root of the sum of the squares of the
standard deviations of the uncertain quantities). The combined uncertainty for soil C stocks in Land Converted to
Grassland ranged from -108 percent below to 108 percent above the 2012 stock change estimate of 8.5 Tg CC>2 Eq.
The large relative uncertainty is due to the small net flux estimate in 2012.
Table 7-39: Tier 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring
within Land Converted to Grassland'(Tg COz Eq. and Percent)
Source
2012 Flux Estimate
(Tg CCh Eq.)
Uncertainty Range Relative to Flux Estimate3
(Tg CCh Eq.) (%)
Cropland Converted to Grassland
Mineral Soil C Stocks: Tier 3
Mineral Soil C Stocks: Tier 2
Organic Soil C Stocks: Tier 2
Forests Converted to Grassland
Mineral Soil C Stocks: Tier 2
Organic Soil C Stocks: Tier 2
Other Lands Converted to Grassland
Mineral Soil C Stocks: Tier 2
Organic Soil C Stocks: Tier 2
Settlements Converted to Grassland
Mineral Soil C Stocks: Tier 2
Organic Soil C Stocks: Tier 2
Wetlands Converted to Grasslands
Mineral Soil C Stocks: Tier 2
Organic Soil C Stocks: Tier 2
Total: Land Converted to Grassland
Mineral Soil C Stocks: Tier 3
Mineral Soil C Stocks: Tier 2
Organic Soil C Stocks: Tier 2
(7.5)
(7.1)
(1.3)
0.9
(0.3)
(0.4)
0.1
(0.2)
(0.2)
NA
(0.5)
(0.5)
0.0
(8.5)
(0.1)
0.1
(8.5)
(7.1)
(2.5)
1.1
Lower
Bound
(16.7)
(16.2)
(1.9)
0.3
(0.6)
(0.6)
0.0
(0.3)
(0.3)
NA
(0.7)
(0.8)
0.0
(17.7)
(0.2)
0.0
(17.7)
(16.2)
(3.2)
0.5
Upper
Bound
1.7
2.0
(0.7)
1.8
(0.1)
(0.2)
0.2
(0.1)
(0.1)
NA
(0.3)
(0.3)
0.1
0.7
(0.1)
0.2
0.7
2.0
(1.9)
2.0
Lower
Bound
-122%
-128%
-45%
-63%
-62%
-48%
-100%
-48%
-48%
NA
-51%
-48%
-86%
-108%
-48%
-58%
-108%
-128%
-27%
-52%
Upper
Bound
123%
128%
45%
98%
72%
44%
231%
44%
44%
NA
47%
44%
160%
108%
44%
81%
108%
128%
26%
81%
Note: Parentheses indicate negative values.
NA: Other land by definition does not include organic soil (see Section 7.1—Definitions of Land Use in the United States). Consequently, no
land areas, C stock changes, or uncertainty results are estimated for land use conversions from Other lands to Croplands and Other lands to
Grasslands on organic soils.
a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Uncertainty is also associated with lack of reporting of agricultural biomass and litter C stock changes, other than
the loss of forest biomass and litter, which is reported in the Forestland Remaining Forestland section of the report.
Biomass C stock changes may be significant for managed grasslands with woody encroachment that have not
attained enough tree cover to be considered forest lands. However, changes in litter C stocks are assumed to be
negligible in grasslands over annual time frames, although there are certainly significant changes at sub-annual time
scales across seasons.
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
7-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Recalculations Discussion
Methodological recalculations in the current Inventory were associated with the following improvements: 1) refining
the temperature algorithm that is used for simulating grass production and carbon inputs to the soil in the
D AYCENT biogeochemical model; 2) increasing the number of experimental sites that are used to evaluate the
structural uncertainty in the D AYCENT model; 3) recalculation of Tier 2 organic soil C emissions using annual data
from the NRI rather than estimating emissions every 5 years and assuming the same emissions between the years;
and 4) simulation of carbon inputs from PRP manure based on livestock management activity data rather than
automated routines in the DAYCENT model. Change in SOC stocks declined by an average of 1.12 Tg CO2 Eq.
over the time series as a result of these improvements to the Inventory.
QA/QC and Verification
See the QA/QC and Verification section under Grassland Remaining Grassland.
Planned Improvements
Soil C stock changes with land use conversion from forest land to grassland are undergoing further evaluation to
ensure consistency in the time series. Different methods are used to estimate soil C stock changes in forest land and
grasslands, and while the areas have been reconciled between these land uses, there has been limited evaluation of
the consistency in C stock changes with conversion from forest land to grassland. This planned improvement may
not be fully implemented for two more years, depending on resource availability. Another key planned
improvement for the Land Converted to Grassland category is to develop an inventory of carbon stock changes for
the 800,000-850,000 hectares of Federal grasslands in the western United States. Grasslands in Alaska will also be \
evaluated. See Planned Improvements sections under Cropland Remaining Cropland and Grassland Remaining
Grassland for additional planned improvements.
Wetlands Remaining Wetlands
Peatlands Remaining Peatlands
Emissions from Managed Peatlands
Managed peatlands are peatlands which have been cleared and drained for the production of peat. The production
cycle of a managed peatland has three phases: land conversion in preparation for peat extraction (e.g., clearing
surface biomass, draining), extraction (which results in the emissions reported under Peatlands Remaining
Peatlands), and abandonment, restoration, or conversion of the land to another use.
CO2 emissions from the removal of biomass and the decay of drained peat constitute the major greenhouse gas flux
from managed peatlands. Managed peatlands may also emit CH4 and N2O. The natural production of CH4 is largely
reduced but not entirely shut down when peatlands are drained in preparation for peat extraction (Strack et al., 2004
as cited in IPCC 2006); however, CH4 emissions are assumed to be insignificant under IPCC Tier 1 methodology
(IPCC 2006). N2O emissions from managed peatlands depend on site fertility. In addition, abandoned and restored
peatlands continue to release greenhouse gas emissions, and at present no methodology is provided by IPCC (2006)
to estimate greenhouse gas emissions or removals from restored peatlands; although methodologies will be provided
for rewetted organic soils (which includes rewetted/restored peatlands) in the 2013 Supplement to the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories: Wetlands (the final publication is scheduled for February
2014). This Inventory estimates both CO2 and N2O emissions from Peatlands Remaining Peatlands in accordance
with Tier 1 IPCC (2006) guidelines.
Land Use, Land-Use Change, and Forestry 7-65
-------
CO2 and N2O Emissions from Peatlands Remaining Peatlands
IPCC (2006) recommends reporting CO2 and N2O emissions from lands undergoing active peat extraction (i.e.,
Peatlands Remaining Peatlands) as part of the estimate for emissions from managed wetlands. Peatlands occur
where plant biomass has sunk to the bottom of water bodies and water-logged areas and exhausted the oxygen
supply below the water surface during the course of decay. Due to these anaerobic conditions, much of the plant
matter does not decompose but instead forms layers of peat over decades and centuries. In the United States, peat is
extracted for horticulture and landscaping growing media, and for a wide variety of industrial, personal care, and
other products. It has not been used for fuel in the United States for many decades. Peat is harvested from two
types of peat deposits in the United States: sphagnum bogs in northern states and wetlands in states further south.
The peat from sphagnum bogs in northern states, which is nutrient poor, is generally corrected for acidity and mixed
with fertilizer. Production from more southerly states is relatively coarse (i.e., fibrous) but nutrient rich.
IPCC (2006) recommends considering both on-site and off-site emissions when estimating CO2 emissions from
Peatlands Remaining Peatlands using the Tier 1 approach. Current methodologies estimate only on-site N2O
emissions, since off-site N2O estimates are complicated by the risk of double-counting emissions from nitrogen
fertilizers added to horticultural peat. On-site emissions from managed peatlands occur as the land is cleared of
vegetation and the underlying peat is exposed to sun and weather. As this occurs, some peat deposit is lost and CO2
is emitted from the oxidation of the peat. Since N2O emissions from saturated ecosystems tend to be low unless
there is an exogenous source of nitrogen, N2O emissions from drained peatlands are dependent on nitrogen
mineralization and therefore on soil fertility. Peatlands located on highly fertile soils contain significant amounts of
organic nitrogen in inactive form. Draining land in preparation for peat extraction allows bacteria to convert the
nitrogen into nitrates which leach to the surface where they are reduced to N2O.
Off-site CO2 emissions from managed peatlands occur from the horticultural and landscaping use of peat. Nutrient-
poor (but fertilizer-enriched) peat tends to be used in bedding plants and in greenhouse and plant nursery production,
whereas nutrient-rich (but relatively coarse) peat is used directly in landscaping, athletic fields, golf courses, and
plant nurseries. Most (nearly 98 percent) of the CO2 emissions from peat occur off-site, as the peat is processed and
sold to firms which, in the United States, use it predominantly for horticultural purposes.
Total emissions from Peatlands Remaining Peatlands were estimated to be 0.834 Tg CO2 Eq. in 2012 (see Table
7-40) comprising 0.830 Tg CO2 Eq. (830 Gg) of CO2 and 0.004 Tg CO2 Eq. (0.012 Gg) of N2O. Total emissions in
2012 were about 10 percent smaller than total emissions in 2011. Peat production reported in Alaska in 2012 was 51
percent higher than in 2011. However, peat production reported in the lower 48 states in 2012 was 14 percent lower
than in 2011, resulting in smaller total 48 states plus Alaska emissions from Peatlands Remaining Peatlands in 2012
compared to 2011.
Total emissions from Peatlands Remaining Peatlands have fluctuated between 0.8 and 1.2 Tg CO2 Eq. across the
time series with a decreasing trend from 1990 until 1993 followed by an increasing trend through 2000. After 2000,
emissions generally decreased until 2006 and then increased until 2009, when the trend reversed. Emissions in 2012
represent a decline from emissions in 2011. CO2 emissions from Peatlands Remaining Peatlands have fluctuated
between 0.8 and 1.2 Tg CO2 across the time series, and these emissions drive the trends in total emissions. N2O
emissions remained close to zero across the time series, with a decreasing trend from 1990 until 1995, followed by
an increasing trend through 2001. N2O emissions decreased between 2001 and 2006, followed by a leveling off
between 2008 and 2010, and a decline in 2011 and again in 2012.
Table 7-40: Emissions from Peatlands Remaining Peatlands (Tg COz Eq.)
Gas
CO2
Off-site
On-site
N2O (On-site)
Total
1990
1.0
1.0 1
+ 1
+
1.0
2005
1.1
1.1 1
+ 1
+
1.1
2008
1.0
1.0
+
+
1.0
2009
1.1
1.1
+
+
1.1
2010
1.0
1.0
+
+
1.0
2011
0.9
0.9
+
+
0.9
2012
0.8
0.8
+
+
0.8
+ Less than 0.05 Tg CO2 Eq.
Note: These numbers are based on U.S. production data in accordance with Tier 1 guidelines, which does not take into account
imports, exports and stockpiles (i.e., apparent consumption). Off-site N2O emissions are not estimated to avoid double-counting
N2O emitted from the fertilizer that the peat is mixed with prior to horticultural use (see IPCC 2006).
7-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table 7-41: Emissions from Peatlands Remaining Peatlands (Gg)
Gas
CO2
Off-site
On-site
N2O (On-site)
1990
1,033
1,008
26
+
• 2005
1,079
1,052
27
| +
2008
992
969
24
+
2009
1,089
1,064
25
+
2010
1,010
986
24
+
2011
919
898
22
+
2012
830
812
18
+
+ Lessthan0.5Gg
Note: These numbers are based on U.S. production data in accordance with Tier 1 guidelines, which does not take into account
imports, exports, and stockpiles (i.e., apparent consumption). Off-site N2O emissions are not estimated to avoid double-counting
N2O emitted from the fertilizer that the peat is mixed with prior to horticultural use (see IPCC 2006).
Methodology
Off-Site CO2 Emissions
CO2 emissions from domestic peat production were estimated using a Tier 1 methodology consistent with IPCC
(2006). Off-site CO2 emissions from Peatlands Remaining Peatlands were calculated by apportioning the annual
weight of peat produced in the United States (Table 7-42) into peat extracted from nutrient-rich deposits and peat
extracted from nutrient-poor deposits using annual percentage-by-weight figures. These nutrient-rich and nutrient-
poor production values were then multiplied by the appropriate default C fraction conversion factor taken from
IPCC (2006) in order to obtain off-site emission estimates. For the lower 48 states, both annual percentages of peat
type by weight and domestic peat production data were sourced from estimates and industry statistics provided in
the Minerals Yearbook wAMineral Commodity Summaries from the U.S. Geological Survey (USGS 1991-2013).
To develop these data, the U.S. Geological Survey (USGS; U.S. Bureau of Mines priorto 1997) obtained production
and use information by surveying domestic peat producers. On average, about 75 percent of the peat operations
respond to the survey. USGS estimated data for non-respondents on the basis of prior-year production levels
(Apodaca2011).
The Alaska estimates rely on reported peat production from Alaska's annual Mineral Industry Reports (Szumigala et
al. 2010). Similar to the U.S. Geological Survey, Alaska's Mineral Industry Report methodology solicits voluntary
reporting of peat production from producers. However, the report does not estimate production for the non-
reporting producers, resulting in larger inter-annual variation in reported peat production from Alaska depending on
the number of producers who report in a given year (Szumigala 2011). In addition, in both the lower 48 states and
Alaska, large variations in peat production can also result from variations in precipitation and the subsequent
changes in moisture conditions, since unusually wet years can hamper peat production (USGS 1991-2013). The
methodology estimates Alaska emissions separately from lower 48 emissions because the state conducts its own
mineral survey and reports peat production by volume, rather than by weight (Table 7-43). However, volume
production data were used to calculate off-site CO2 emissions from Alaska applying the same methodology but with
volume-specific C fraction conversion factors from IPCC (2006).239 At the time of writing, the Alaska's annual
Mineral Industry Reports for 2011 and 2012 were not yet published; therefore Alaska's peat production in 2011 and
2012 (reported in cubic meters) were taken from the 2011 and 2012 USGS Minerals Yearbooks (Harbo 2012 as
cited in USGS 2012, Harbo 2013 as cited in USGS 2013).
The apparent consumption of peat, which includes production plus imports minus exports plus the decrease in
stockpiles, in the United States is over two-and-a-half times the amount of domestic peat production. However,
consistent with the Tier 1 method whereby only domestic peat production is accounted for when estimating off-site
emissions, off-site CO2 emissions from the use of peat not produced within the United States are not included in the
Inventory. The United States has increasingly imported peat from Canada for horticultural purposes; from 2007 to
2012, imports of sphagnum moss (nutrient-poor) peat from Canada represented 97 percent of total U.S. peat imports
(USGS 2013). Most peat produced in the United States is reed-sedge peat, generally from southern states, which is
classified as nutrient rich by IPCC (2006). Higher-tier calculations of CO2 emissions from apparent consumption
239 Peat produced from Alaska was assumed to be nutrient poor; as is the case in Canada, "where deposits of high-quality [but
nutrient poor] sphagnum moss are extensive" (USGS 2008).
Land Use, Land-Use Change, and Forestry 7-67
-------
would involve consideration of the percentages of peat types stockpiled (nutrient rich versus nutrient poor) as well
as the percentages of peat types imported and exported.
Table 7-42: Peat Production of Lower 48 States (thousand Metric Tons)
Type of Deposit
Nutrient-Rich
Nutrient-Poor
Total Production
1990
595.1
55.4
692.0
2005
657.6
27.4
685.0
2008
559.7
1 55.4
615.0
2009
560.3
48.7
609.0
2010
558.9
69.1
628.0
2011
511.2
56.8
568.0
2012
409.9
78.1
488.0
Sources: United States Geological Survey (USGS) (1991-2013)Minerals Yearbook: Peat (1994-2012); United States
Geological Survey (USGS) (1996-2013)Mwera/ Commodity Summaries: Peat (1996-2012).
Table 7-43: Peat Production of Alaska (thousand Cubic Meters)
1990 2005 2008 2009 2010 2011 2012
Total Production 49.7 47.8 64.1 183.9 59.8 61.5 93.1
Sources: Division of Geological & Geophysical Surveys (DGGS), Alaska Department of Natural Resources (1997-2011)
Alaska's Mineral Industry Report (1997-2010); United States Geological Survey (USGS) (2012-2013) Mwerafa Yearbook: Peat
(2011-2012).
On-site CO2 Emissions
IPCC (2006) suggests basing the calculation of on-site emission estimates on the area of peatlands managed for peat
extraction differentiated by the nutrient type of the deposit (rich versus poor). Information on the area of land
managed for peat extraction is currently not available for the United States, but in accordance with IPCC (2006), an
average production rate for the industry was applied to derive an area estimate. In a mature industrialized peat
industry, such as exists in the United States and Canada, the vacuum method can extract up to 100 metric tons per
hectare per year (Cleary et al. 2005 as cited in IPCC 2006).24° The area of land managed for peat extraction in the
United States was estimated using nutrient-rich and nutrient-poor production data and the assumption that 100
metric tons of peat are extracted from a single hectare in a single year. The annual land area estimates were then
multiplied by the appropriate nutrient-rich or nutrient-poor IPCC (2006) default emission factor in order to calculate
on-site CCh emission estimates. Production data are not available by weight for Alaska. In order to calculate on-site
emissions resulting from Peatlands Remaining Peatlands in Alaska, the production data by volume were converted
to weight using annual average bulk peat density values, and then converted to land area estimates using the same
assumption that a single hectare yields 100 metric tons. The IPCC (2006) on-site emissions equation also includes a
term which accounts for emissions resulting from the change in C stocks that occurs during the clearing of
vegetation prior to peat extraction. Area data on land undergoing conversion to peatlands for peat extraction is also
unavailable for the United States. However, USGS records show that the number of active operations in the United
States has been declining since 1990; therefore it seems reasonable to assume that no new areas are being cleared of
vegetation for managed peat extraction. Other changes in C stocks in living biomass on managed peatlands are also
assumed to be zero under the Tier 1 methodology (IPCC 2006).
On-site N2O Emissions
IPCC (2006) suggests basing the calculation of on-site N2O emission estimates on the area of nutrient-rich peatlands
managed for peat extraction. These area data are not available directly for the United States, but the on-site CCh
emissions methodology above details the calculation of area data from production data. In order to estimate N2O
emissions, the area of nutrient rich Peatlands Remaining Peatlands was multiplied by the appropriate default
emission factor taken from IPCC (2006).
240 The vacuum method is one type of extraction that annually "mills" or breaks up the surface of the peat into particles, which
then dry during the summer months. The air-dried peat particles are then collected by vacuum harvesters and transported from
the area to stockpiles (IPCC 2006).
7-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Uncertainty and Time-Series Consistency
The uncertainty associated with peat production data was estimated to be ± 25 percent (Apodaca 2008) and assumed
to be normally distributed. The uncertainty associated with peat production data stems from the fact that the USGS
receives data from the smaller peat producers but estimates production from some larger peat distributors. The peat
type production percentages were assumed to have the same uncertainty values and distribution as the peat
production data (i.e., ± 25 percent with a normal distribution). The uncertainty associated with the Alaskan reported
production data was assumed to be the same as the lower 48 states, or ± 25 percent with a normal distribution. It
should be noted that the Alaska Department of Natural Resources estimates that around half of producers do not
respond to their survey with peat production data; therefore, the production numbers reported are likely to
underestimate Alaska peat production (Szumigala 2008). The uncertainty associated with the average bulk density
values was estimated to be ± 25 percent with a normal distribution (Apodaca 2008). IPCC (2006) gives uncertainty
values for the emissions factors for the area of peat deposits managed for peat extraction based on the range of
underlying data used to determine the emission factors. The uncertainty associated with the emission factors was
assumed to be triangularly distributed. The uncertainty values surrounding the C fractions were based on IPCC
(2006) and the uncertainty was assumed to be uniformly distributed. Based on these values and distributions, a
Monte Carlo (Tier 2) uncertainty analysis was applied to estimate the uncertainty of CO2 and N2O emissions from
Peatlands Remaining Peatlands. The results of the Tier 2 quantitative uncertainty analysis are summarized in Table
7-44. CO2 emissions from Peatlands Remaining Peatlands in 2012 were estimated to be between 0.6 and 1.1 Tg
CO2 Eq. at the 95 percent confidence level. This indicates a range of 26 percent below to 30 percent above the 2012
emission estimate of 0.8 Tg CCh Eq. N2O emissions from Peatlands Remaining Peatlands in 2012 were estimated
to be between 0.001 and 0.005 Tg CC>2 Eq. at the 95 percent confidence level. This indicates a range of 73 percent
below to 38 percent above the 2012 emission estimate of 0.004 Tg CCh Eq.
Table 7-44: Tier-2 Quantitative Uncertainty Estimates for COz Emissions from Peatlands
Remaining Peatlands
Source
Peatlands Remaining
Peatlands
Gas
CCh
N2O
2012 Emission
Estimate
(Tg C02 Eq.)
0.8
+
Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (%)
Lower Upper Lower
Bound Bound Bound
0.6 1.1 -26%
+ + -73%
Upper
Bound
30%
38%
+ Does not exceed 0.01 Tg CCh Eq. or 0.5 Gg.
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A QA/QC analysis was performed for data gathering and input, documentation, and calculation. The QA/QC
analysis did not reveal any inaccuracies or incorrect input values.
Recalculations Discussion
The current Inventory represents the sixth Inventory report in which emissions from Peatlands Remaining Peatlands
are included. The Inventory estimates for 2011 are updated to incorporate information on the volume of peat
production in Alaska from the 2011 Minerals Yearbook: Peat (USGS 2012). In the previous Inventory report, peat
production in Alaska in 2011 was assumed to equal the value reported for 2010 in Alaska's 2010 Mineral Industry
Report. Since Alaska's 2011 Mineral Industry Report is not published as of October 2013, the current Inventory
updated 2011 peat production in Alaska based on data from the 2011 Minerals Yearbook: Peat (USGS 2012).
Updating this 2011 input value resulted in a 0.32 percent decrease compared to the previous Inventory report's 2011
emission estimate.
Land Use, Land-Use Change, and Forestry 7-69
-------
Planned Improvements
In order to further improve estimates of CC>2 and N2O emissions from Peatlands Remaining Peatlands, future efforts
will consider options for obtaining better data on the quantity of peat harvested per hectare and the total area
undergoing peat extraction. Additionally, a review will be conducted of the soon to be published 2013 Supplement
to the 2006IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands, which gives additional national-
level inventory methodological guidance on Wetlands, to identify methodologies that are applicable to the United
States, and to revise the methodologies for estimating emissions from Wetlands Remaining Wetlands accordingly.
7.8 Settlements Remaining Settlements
Changes in Carbon Stocks in Urban Trees (IPCC Source
Category 5E1)
Urban forests constitute a significant portion of the total U.S. tree canopy cover (Dwyer et al. 2000). Urban areas
(cities, towns, and villages) are estimated to cover over 3 percent of the United States (U.S. Census Bureau 2012).
With an average tree canopy cover of 35 percent, urban areas account for approximately 5 percent of total tree cover
in the continental United States (Nowak and Greenfield 2012). Trees in urban areas of the United States were
estimated to account for an average annual net sequestration of 75.2 Tg CCh Eq. (20.5 Tg C) over the period from
1990 through 2012. Net C flux from urban trees in 2012 was estimated to be -88.4 Tg CO2 Eq. (-24.1 Tg C).
Annual estimates of CCh flux (Table 7-45) were developed based on periodic (1990, 2000, and 2010) U.S. Census
data on urbanized area. The estimate of urbanized area is smaller than the area categorized as Settlements in the
Representation of the U.S. Land Base developed for this report, by an average of 48 percent over the 1990 through
2012 time series—i.e., the Census urban area is a subset of the Settlements area.
In 2012, urban area was about 44 percent smaller than the total area defined as Settlements. Census area data are
preferentially used to develop C flux estimates for this source category since these data are more applicable for use
with the available peer-reviewed data on urban tree canopy cover and urban tree C sequestration. Annual
sequestration increased by 46 percent between 1990 and 2012 due to increases in urban land area. Data on C storage
and urban tree coverage were collected since the early 1990s and have been applied to the entire time series in this
report. As a result, the estimates presented in this chapter are not truly representative of changes in C stocks in
urban trees for Settlements areas, but are representative of changes in C stocks in urban trees for Census urban area.
The method used in this report does not attempt to scale these estimates to the Settlements area. Therefore, the
estimates presented in this chapter are likely an underestimate of the true changes in C stocks in urban trees in all
Settlements areas—i.e., the changes in C stocks in urban trees presented in this chapter are a subset of the changes in
C stocks in urban trees in all Settlements areas.
Urban trees often grow faster than forest trees because of the relatively open structure of the urban forest (Nowak
and Crane 2002). However, areas in each case are accounted for differently. Because urban areas contain less tree
coverage than forest areas, the C storage per hectare of land is in fact smaller for urban areas. However, urban tree
reporting occurs on a basis of C sequestered per unit area of tree cover, rather than C sequestered per total land area.
Expressed per unit of tree cover, areas covered by urban trees have a greater C density than do forested areas
(Nowak and Crane 2002). Expressed per unit of land area, however, the situation is the opposite: urban areas have
a smaller C density than forest areas.
Table 7-45: Net C Flux from Urban Trees (Tg CCh Eq. and Tg C)
Year Tg CCh Eq. Tg C
1990 (60.4) (16.5)
2005 (80.5) (22.0)
2008 (83.9) (22.9)
2009 (85.0) (23.2)
7-70 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
2010 (86.1) (23.5)
2011 (87.3) (23.8)
2012 (88.4) (24.1)
Note: Parentheses indicate net
sequestration.
Methodology
Methods for quantifying urban tree biomass, C sequestration, and C emissions from tree mortality and
decomposition were taken directly from Nowak et al. (2013), Nowak and Crane (2002), and Nowak (1994). In
general, the methodology used by Nowak et al. (2013) to estimate net C sequestration in urban trees followed three
steps. First, field data from cities and states were used to generate allometric estimates of biomass from measured
tree dimensions. Second, estimates of annual tree growth and biomass increment were generated from published
literature and adjusted for tree condition, land-use class, and growing season to generate estimates of gross C
sequestration in urban trees for all 50 states and the District of Columbia. Third, estimates of C emissions due to
mortality and decomposition were subtracted from gross C sequestration values to derive estimates of net C
sequestration. Finally, sequestration estimates for all 50 states and the District of Columbia, in units of C
sequestered per unit area of tree cover, were used to estimate urban forest C sequestration in the United States by
using urban area estimates from U.S. Census data and urban tree cover percentage estimates for each state and the
District of Columbia from remote sensing data, an approach consistent with Nowak et al. (2013).
This approach is also consistent with the default IPCC methodology in IPCC (2006), although sufficient data are not
yet available to separately determine interannual gains and losses in C stocks in the living biomass of urban trees.
In order to generate the allometric relationships between tree dimensions and tree biomass for cities and states,
Nowak et al. (2013) and previously published research (Nowak and Crane 2002; and Nowak 1994, 2007c, and 2009)
collected field measurements in a number of U.S. cities between 1989 and 2012. For a sample of trees in each of the
cities in Table 7-46, data including tree measurements of stem diameter, tree height, crown height and crown width,
and information on location, species, and canopy condition were collected. The data for each tree were converted
into C storage by applying allometric equations to estimate aboveground biomass, a root-to-shoot ratio to convert
aboveground biomass estimates to whole tree biomass, moisture content, a C content of 50 percent (dry weight
basis), and an adjustment factor of 0.8 to account for urban trees having less aboveground biomass for a given stem
diameter than predicted by allometric equations based on forest trees (Nowak 1994). C storage estimates for
deciduous trees include only C stored in wood. These calculations were then used to develop an allometric equation
relating tree dimensions to C storage for each species of tree, encompassing a range of diameters.
Tree growth was estimated using annual height growth and diameter growth rates for specific land uses and diameter
classes. Growth calculations were adjusted by a factor to account for tree condition (fair to excellent, poor, critical,
dying, or dead). For each tree, the difference in C storage estimates between year 1 and year (x + 1) represents the
gross amount of C sequestered. These annual gross C sequestration rates for each species (or genus), diameter class,
and land-use condition (e.g., parks, transportation, vacant, golf courses) were then scaled up to city estimates using
tree population information. The area of assessment for each city or state was defined by its political boundaries;
parks and other forested urban areas were thus included in sequestration estimates (Nowak 2011).
Most of the field data used to develop the methodology of Nowak etal. (2013) were analyzed using the U.S. Forest
Service's Urban Forest Effects (UFORE) model. UFORE is a computer model that uses standardized field data
from random plots in each city and local air pollution and meteorological data to quantify urban forest structure,
values of the urban forest, and environmental effects, including total C stored and annual C sequestration. UFORE
was used with field data from a stratified random sample of plots in each city to quantify the characteristics of the
urban forest. (Nowak et al. 2007a).
Where gross C sequestration accounts for all carbon sequestered, net C sequestration takes into account carbon
emissions associated with urban trees. Net C emissions include tree death and removals. Estimates of net C
emissions from urban trees were derived by applying estimates of annual mortality and condition, and assumptions
about whether dead trees were removed from the site to the total C stock estimate for each city. Estimates of annual
mortality rates by diameter class and condition class were derived from a study of street-tree mortality (Nowak
1986). Different decomposition rates were applied to dead trees left standing compared with those removed from
Land Use, Land-Use Change, and Forestry 7-71
-------
the site. For removed trees, different rates were applied to the removed/aboveground biomass in contrast to the
belowground biomass. The estimated annual gross C emission rates for each species (or genus), diameter class, and
condition class were then scaled up to city estimates using tree population information.
The data for all 50 states and the District of Columbia are described in Nowak et al. (2013), which builds upon
previous research, including: Nowak and Crane (2002), Nowak et al. (2007a), and references cited therein. The
allometric equations applied to the field data for each tree were taken from the scientific literature (see Nowak 1994,
Nowak et al. 2002), but if no allometric equation could be found for the particular species, the average result for the
genus was used. The adjustment (0.8) to account for less live tree biomass in urban trees was based on information
in Nowak (1994). Measured tree growth rates for street (Frelich 1992; Fleming 1988; Nowak 1994), park (deVries
1987), and forest (Smith and Shifley 1984) trees were standardized to an average length of growing season (153
frost free days) and adjusted for site competition and tree condition. Standardized growth rates of trees of the same
species or genus were then compared to determine the average difference between standardized street tree growth
and standardized park and forest growth rates. Crown light exposure (CLE) measurements (number of sides and/or
top of tree exposed to sunlight) were used to represent forest, park, and open (street) tree growth conditions. Local
tree base growth rates (BG) were then calculated as the average standardized growth rate for open-grown trees
multiplied by the number of frost free days divided by 153. Growth rates were then adjusted for CLE. The CLE
adjusted growth rate was then adjusted based on tree health and tree condition to determine the final growth rate.
Assumptions for which dead trees would be removed versus left standing were developed specific to each land use
and were based on expert judgment of the authors. Decomposition rates were based on literature estimates (Nowak
etal. 2013).
Estimates of gross and net sequestration rates for each of the 50 states and the District of Columbia (Table 7-46)
were compiled in units of C sequestration per unit area of tree canopy cover. These rates were used in conjunction
with estimates of state urban area and urban tree cover data to calculate each state's annual net C sequestration by
urban trees. This method was described in Nowak et al. (2013) and has been modified to incorporate U.S. Census
data.
Specifically, urban area estimates were based on 1990, 2000, and 2010 U.S. Census data. The 1990 U.S. Census
defined urban land as "urbanized areas," which included land with a population density greater than 1,000 people
per square mile, and adjacent "urban places," which had predefined political boundaries and a population total
greater than 2,500. In 2000, the U.S. Census replaced the "urban places" category with a new category of urban
land called an "urban cluster," which included areas with more than 500 people per square mile. In 2010, the
Census updated its definitions to have "urban areas" encompassing Census tract delineated cities with 50,000 or
more people, and "urban clusters" containing Census tract delineated locations with between 2,500 and 50,000
people. Urban land area increased by approximately 23 percent from 1990 to 2000 and 14 percent from 2000 to
2010; Nowak et al. (2005) estimate that the changes in the definition of urban land are responsible for approximately
20 percent of the total reported increase in urban land area from 1990 to 2000. Under all Census (i.e., 1990, 2000,
and 2010) definitions, the urban category encompasses most cities, towns, and villages (i.e., it includes both urban
and suburban areas). Settlements area, as assessed in the Representation of the U.S. Land Base developed for this
report, encompassed all developed parcels greater than 0.1 hectares in size, including rural transportation corridors,
and as previously mentioned represents a larger area than the Census-derived urban area estimates. However, the
smaller, Census-derived urban area estimates were deemed to be more suitable for estimating national urban tree
cover given the data available in the peer-reviewed literature (i.e., the data set available is consistent with Census
urban rather than Settlements areas), and the recognized overlap in the changes in C stocks between urban forest and
non-urban forest (see Planned Improvements below). US Census urban area data is reported as a series of
continuous blocks of urban area in each state. The blocks or urban area were summed to create each state's urban
area estimate.
Net annual C sequestration estimates were derived for all 50 states and the District of Columbia by multiplying the
gross annual emission estimates by 0.74, the standard ratio for net/gross sequestration set out in Table 3 of Nowak
et. al. (2013) (unless data existed for both gross and net sequestration for the state in Table 2 of Nowak et. al. (2013),
in which case they were divided to get a state-specific ratio). The gross and net annual C sequestration values for
each state were multiplied by each state' s area of tree cover, which was the product of the state' s urban/community
area as defined in the U.S. Census (2012) and the state's urban/community tree cover percentage. The
urban/community tree cover percentage estimates for all 50 states were obtained from Nowak and Greenfield
(2012), which compiled ten years of research including Dwyer et al. (2000), Nowak et al. (2002), Nowak (2007a),
and Nowak (2009). The urban/community tree cover percentage estimate for the District of Columbia was obtained
7-72 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
from Nowak et al. (2013). The urban area estimates were taken from the 2010 U.S. Census (2012). The equation,
used to calculate the summed carbon sequestration amounts, can be written as follows:
Net annual C sequestration = Gross sequestration rate x Net to Gross sequestration ratio x Urban Area x %
Tree Cover
Table 7-46: Annual C Sequestration (Metric Tons C/yr), Tree Cover (Percent), and Annual C
Sequestration per Area of Tree Cover (kg C/m2-yr) for 50 states plus the District of Columbia
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
DC
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Gross Annual Net Annual
Sequestration Sequestration
1,123,944
44,895
369,243
411,363
2,092,278
149,005
766,512
129,813
14,557
3,331,471
2,476,627
241,105
24,658
747,411
396,776
115,796
182,154
237,287
727,949
107,875
586,554
1,294,359
731,314
349,007
480,298
488,287
52,675
49,685
41,797
244,715
1,192,996
68,789
1,090,092
1,989,946
14,372
910,839
358,363
257,480
1,241,922
136,841
1,063,705
20,356
1,030,972
2,712,954
87,623
831,718
33,223
273,239
304,409
1,548,286
110,264
567,219
96,062
11,568
2,465,288
1,832,704
178,417
18,247
553,084
366,882
85,689
141,747
175,592
538,683
79,827
434,050
957,826
541,172
258,265
355,421
361,332
38,980
41,927
30,929
181,089
882,817
50,904
806,668
1,472,560
6,829
674,021
265,189
190,535
919,022
101,262
787,141
17,653
921,810
2,007,586
64,841
Gross Annual Net Annual Net: Gross
Sequestration Sequestration Annual
Tree per Area of per Area of Sequestration
Cover Tree Cover Tree Cover Ratio
55.2
39.8
17.6
42.3
25.1
18.5
67.4
35.0
35.0
35.5
54.1
39.9
10.0
25.4
23.7
19.0
25.0
22.1
34.9
52.3
34.3
65.1
35.0
34.0
47.3
31.5
36.3
15.0
9.6
66.0
53.3
12.0
42.6
51.1
13.0
31.5
31.2
36.6
41.0
51.0
48.9
14.0
43.8
31.4
16.4
0.343
0.168
0.354
0.331
0.389
0.197
0.239
0.335
0.263
0.475
0.353
0.581
0.184
0.283
0.250
0.240
0.283
0.286
0.397
0.221
0.323
0.254
0.220
0.229
0.344
0.285
0.184
0.238
0.207
0.217
0.294
0.263
0.240
0.312
0.223
0.248
0.332
0.242
0.244
0.258
0.338
0.236
0.303
0.368
0.215
0.254
0.124
0.262
0.245
0.288
0.146
0.177
0.248
0.209
0.352
0.261
0.430
0.136
0.209
0.231
0.178
0.220
0.212
0.294
0.164
0.239
0.188
0.163
0.169
0.255
0.211
0.136
0.201
0.153
0.161
0.218
0.195
0.178
0.231
0.106
0.184
0.246
0.179
0.181
0.191
0.250
0.205
0.271
0.272
0.159
0.74
0.74
0.74
0.74
0.74
0.74
0.74
0.74
0.79
0.74
0.74
0.74
0.74
0.74
0.92
0.74
0.78
0.74
0.74
0.74
0.74
0.74
0.74
0.74
0.74
0.74
0.74
0.84
0.74
0.74
0.74
0.74
0.74
0.74
0.48
0.74
0.74
0.74
0.74
0.74
0.74
0.87
0.89
0.74
0.74
Land Use, Land-Use Change, and Forestry 7-73
-------
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
46,111
822,286
560,055
249,592
356,405
18,726
34,122
608,492
414,440
184,698
263,739
13,857
53.0
39.8
34.6
61.0
31.8
19.9
0.213
0.293
0.258
0.241
0.225
0.182
0.158
0.217
0.191
0.178
0.167
0.135
0.74
0.74
0.74
0.74
0.74
0.74
Uncertainty and Time-Series Consistency
Uncertainty associated with changes in C stocks in urban trees includes the uncertainty associated with urban area,
percent urban tree coverage, and estimates of gross and net C sequestration for each of the 50 states and the District
of Columbia. A 10 percent uncertainty was associated with urban area estimates based on expert judgment.
Uncertainty associated with estimates of percent urban tree coverage for each of the 50 states was based on standard
error estimates reported by Nowak and Greenfield (2012). Uncertainty associated with estimate of percent urban
tree coverage for the District of Columbia was based on the standard error estimate reported by Nowak et al. (2013).
Uncertainty associated with estimates of gross and net C sequestration for each of the 50 states and the District of
Columbia was based on standard error estimates for each of the state-level sequestration estimates reported by
Nowak etal. (2013). These estimates are based on field data collected in each of the 50 states and the District of
Columbia, and uncertainty in these estimates increases as they are scaled up to the national level.
Additional uncertainty is associated with the biomass equations, conversion factors, and decomposition assumptions
used to calculate C sequestration and emission estimates (Nowak et al. 2002). These results also exclude changes in
soil C stocks, and there may be some overlap between the urban tree C estimates and the forest tree C estimates.
Due to data limitations, urban soil flux is not quantified as part of this analysis, while reconciliation of urban tree
and forest tree estimates will be addressed through the land-representation effort described in the Planned
Improvements section of this chapter.
A Monte Carlo (Tier 2) uncertainty analysis was applied to estimate the overall uncertainty of the sequestration
estimate. The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 7-47. The net C flux
from changes in C stocks in urban trees in 2012 was estimated to be between -130.2 and -46.5 Tg CCh Eq. at a 95
percent confidence level. This indicates a range of 47 percent more sequestration to 47 percent less sequestration
than the 2012 flux estimate of -88.4 Tg CO2 Eq.
The 2012 uncertainty estimates are greater than those of 2011 due to the revised methodology which has a high
uncertainty dependence (99 percent) on one variable—the standard ratio for net/gross sequestration (or 0.74)
(Nowak et. al. 2013). This variable has a high uncertainty bound which was calculated using the standard errors of
the two variables (average net sequestration and average gross sequestration) that were used in calculating the ratio.
Table 7-47: Tier 2 Quantitative Uncertainty Estimates for Net C Flux from Changes in C
Stocks in Urban Trees (Tg COz Eq. and Percent)
2012 Flux Estimate Uncertainty Range Relative to Flux Estimate3
Source Gas (Tg CCh Eq.) (Tg CCh Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Changes in C Stocks in 47%
Urban Trees v ' v ' v '
Note: Parentheses indicate negative values or net sequestration.
a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Details on the emission trends through time are described in more detail in the Methodology section, above.
QA/QC and Verification
Tier 1 and Tier 2 QA/QC activities were conducted consistent with the U.S. QA/QC plan. Source-specific quality
control measures for urban trees included checking input data, documentation, and calculations to ensure data were
properly handled through the inventory process. Errors that were found during this process were corrected as
7-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
necessary. The net C flux resulting from urban trees was predominately calculated using state and city-specific
estimates of gross and net C sequestration estimates for urban trees and urban tree coverage area published in the
literature. The validity of these data for their use in this section of the inventory was evaluated through
correspondence established with Dr. David J. Nowak, author of the papers. Through this correspondence, the
methods used to collect the urban tree sequestration and area data were further clarified and the use of these data in
the inventory was reviewed and validated (Nowak 2002a, 2007b, 2011, and Nowak et al. 2013).
Recalculations
The 1990 to 2011 net C flux estimates were recalculated relative to the previous Inventory because of a major
change in methodology. Previously, data from 28 cities were used to inform a national estimate of net sequestration
per unit tree cover. The sequestration per unit tree cover was multiplied along with a value of national urban area
and an estimated national tree cover percentage to get urban tree carbon sequestration. The new methodology in the
current inventory uses reported state level estimates of gross sequestration, state level totals for urban area, and state
level urban tree cover percentages. The change in methodology resulted in an average annual net sequestration
increase of 16.4 Tg CCh Eq. (28 percent) in urban trees compared to the previous report across the entire time-series.
Planned Improvements
A consistent representation of the managed land base in the United States is discussed at the beginning of the Land
Use, Land-Use Change, and Forestry chapter, and discusses a planned improvement by the USDA Forest Service to
reconcile the overlap between urban forest and non-urban forest greenhouse gas inventories. Urban forest
inventories are including areas also defined as forest land under the Forest Inventory and Analysis (FIA) program of
the USDA Forest Service, resulting in "double-counting" of these land areas in estimates of C stocks and fluxes for
this report. For example, Nowak et al. (2013) estimates that 13.7 percent of urban land is measured by the forest
inventory plots, and could be responsible for up to 87 Tg C of overlap.
Future research may also enable more complete coverage of changes in the C stock in urban trees for all Settlements
land. To provide estimates for all Settlements, research would need to establish the extent of overlap between
Settlements and Census-defined urban areas, and would have to characterize sequestration on non-urban Settlements
land.
Direct N2O Fluxes from Settlement Soils (IPCC Source Category
5E1)
Of the synthetic N fertilizers applied to soils in the United States, approximately 2.4 percent are currently applied to
lawns, golf courses, and other landscaping occurring within settlement areas. Application rates are lower than those
occurring on cropped soils, and, therefore, account for a smaller proportion of total U.S. soil N2O emissions per unit
area. In addition to synthetic N fertilizers, a portion of surface applied sewage sludge is applied to settlement areas.
In 2012, N2O emissions from settlement soils were 1.5 Tg CO2 Eq. (4.7 Gg). There was an overall increase of 48
percent over the period from 1990 through 2012 due to a general increase in the application of synthetic N fertilizers
to an expanding settlement area. Interannual variability in these emissions is directly attributable to interannual
variability in total synthetic fertilizer consumption and sewage sludge applications in the United States. Emissions
from this source are summarized in Table 7-48.
Table 7-48: Direct NzO Fluxes from Soils in Settlements Remaining Settlements(Tg COz Eq.
and Gg N2O)
Year Tg CCh Eq. Gg N2O
1990 1.0
2008
2009
Land Use, Land-Use Change, and Forestry 7-75
-------
2010 1.5 4.8
2011 1.5 4.9
2012 y 4.7
Note: These estimates include direct
N2O emissions from N fertilizer
additions only. Indirect N2O emissions
from fertilizer additions are reported in
the Agriculture chapter. These
estimates include emissions from both
Settlements Remaining Settlements and
from Land Converted to Settlements.
Methodology
For soils within Settlements Remaining Settlements, the IPCC Tier 1 approach was used to estimate soil N2O
emissions from synthetic N fertilizer and sewage sludge additions. Estimates of direct N2O emissions from soils in
settlements were based on the amount of N in synthetic commercial fertilizers applied to settlement soils, and the
amount of N in sewage sludge applied to non-agricultural land and surface disposal of sewage sludge (see Annex
3.11 for a detailed discussion of the methodology for estimating sewage sludge application).
Nitrogen applications to settlement soils are estimated using data compiled by the USGS (Ruddy et al. 2006). The
USGS estimated on-farm and non-farm fertilizer use is based on sales records at the county level from 1982 through
2001 (Ruddy et al. 2006). Non-farm N fertilizer was assumed to be applied to settlements and forest lands; values
for 2002 through 2008 were based on 2001 values adjusted for annual total N fertilizer sales in the United States
because there is no new activity data on application after 2001. Settlement application was calculated by subtracting
forest application from total non-farm fertilizer use. Sewage sludge applications were derived from national data on
sewage sludge generation, disposition, and N content (see Annex 3.11 for further detail). The total amount of N
resulting from these sources was multiplied by the IPCC default emission factor for applied N (1 percent) to
estimate direct N2O emissions (IPCC 2006). The volatilized and leached/runoff N fractions for settlements,
calculated with the IPCC default volatilization factors (10 or 20 percent, respectively, for synthetic or organic N
fertilizers) and leaching/runoff factor for wet areas (30 percent), were included with indirect emissions, as reported
in the N2O Emissions from Agricultural Soil Management source category of the Agriculture chapter (consistent
with reporting guidance that all indirect emissions are included in the Agricultural Soil Management source
category).
Uncertainty and Time-Series Consistency
The amount of N2O emitted from settlements depends not only on N inputs and fertilized area, but also on a large
number of variables, including organic C availability, oxygen gas partial pressure, soil moisture content, pH,
temperature, and irrigation/watering practices. The effect of the combined interaction of these variables on N2O flux
is complex and highly uncertain. The IPCC default methodology does not explicitly incorporate any of these
variables, except variations in fertilizer N and sewage sludge application rates. All settlement soils are treated
equivalently under this methodology.
Uncertainties exist in both the fertilizer N and sewage sludge application rates in addition to the emission factors.
Uncertainty in fertilizer N application was assigned a default level of ±50 percent.241 Uncertainty in the amounts of
sewage sludge applied to non-agricultural lands and used in surface disposal was derived from variability in several
factors, including: (1) N content of sewage sludge; (2) total sludge applied in 2000; (3) wastewater existing flow in
1996 and 2000; and (4) the sewage sludge disposal practice distributions to non-agricultural land application and
surface disposal. Uncertainty in the emission factors was provided by the IPCC (2006).
Quantitative uncertainty of this source category was estimated through the IPCC-recommended Tier 2 uncertainty
estimation methodology. The uncertainty ranges around the 2005 activity data and emission factor input variables
241 No uncertainty is provided with the USGS fertilizer consumption data (Ruddy et al. 2006) so a conservative ±50% was used
in the analysis.
7-76 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
were directly applied to the 2012 emission estimates. The results of the quantitative uncertainty analysis are
summarized in Table 7-49. N2O emissions from soils in Settlements Remaining Settlements in 2012 were estimated
to be between 0.7 and 3.8 Tg CCh Eq. at a 95 percent confidence level. This indicates a range of 49 percent below
to 163 percent above the 2012 emission estimate of 1.5 Tg CC>2 Eq.
Table 7-49: Quantitative Uncertainty Estimates of NzO Emissions from Soils in Settlements
Remaining Settlements (J$ COz Eq. and Percent)
Source
2012 Emission
Gas Estimate
(Tg COz Eq.)
Uncertainty Range Relative to Emission Estimate3
(Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Settlements Remaining
Settlements: N2O Fluxes from N2O 1.5 0.7 3.8 -49% 163%
Soils
Note: This estimate includes direct N2O emissions from N fertilizer additions to both Settlements Remaining Settlements
and from Land Converted to Settlements.
a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Planned Improvements
A minor improvement is planned to update the uncertainty analysis for direct emissions from settlements to be
consistent with the most recent activity data for this source.
7.9 Land Converted to Settlements (IPCC
Source Category 5E2)
Land-use change is constantly occurring, and land under a number of uses undergoes urbanization in the United
States each year. However, data on the amount of land converted to settlements is currently lacking. Given the lack
of available information relevant to this particular IPCC source category, it is not possible to separate CCh or N2O
fluxes on Land Converted to Settlements from fluxes on Settlements Remaining Settlements at this time.
7.10 Other (IPCC Source Category 5G)
Changes in Yard Trimming and Food Scrap Carbon Stocks in
Landfills
In the United States, yard trimmings (i.e., grass clippings, leaves, and branches) and food scraps account for a
significant portion of the municipal waste stream, and a large fraction of the collected yard trimmings and food
scraps are discarded in landfills. Carbon contained in landfilled yard trimmings and food scraps can be stored for
very long periods.
Carbon storage estimates are associated with particular land uses. For example, harvested wood products are
accounted for under Forest Land Remaining Forest Land because these wood products are considered a component
of the forest ecosystem. The wood products serve as reservoirs to which C resulting from photosynthesis in trees is
transferred, but the removals in this case occur in the forest. Carbon stock changes in yard trimmings and food
scraps are associated with settlements, but removals in this case do not occur within settlements. To address this
complexity, yard trimming and food scrap C storage is reported under the "Other" source category.
Land Use, Land-Use Change, and Forestry 7-77
-------
Both the amount of yard trimmings collected annually and the fraction that is landfilled have declined over the last
decade. In 1990, over 53 million metric tons (wet weight) of yard trimmings and food scraps were generated (i.e.,
put at the curb for collection to be taken to disposal sites or to composting facilities) (EPA 2014; Schneider 2007,
2008). Since then, programs banning or discouraging yard trimmings disposal have led to an increase in backyard
composting and the use of mulching mowers, and a consequent 3 percent decrease in the tonnage of yard trimmings
generated (i.e., collected for composting or disposal). At the same time, an increase in the number of municipal
composting facilities has reduced the proportion of collected yard trimmings that are discarded in landfills—from 72
percent in 1990 to 35 percent in 2012. The net effect of the reduction in generation and the increase in composting
is a 53 percent decrease in the quantity of yard trimmings disposed of in landfills since 1990.
Food scrap generation has grown by 53 percent since 1990, and though the proportion of food scraps discarded in
landfills has decreased slightly from 82 percent in 1990 to 78 percent in 2012, the tonnage disposed of in landfills
has increased considerably (by 47 percent). Overall, the decrease in the landfill disposal rate of yard trimmings has
more than compensated for the increase in food scrap disposal in landfills, and the net result is a decrease in annual
landfill C storage from 24.2 Tg CO2 Eq. (6.6 Tg C) in 1990 to 13.0 Tg CO2 Eq. (3.6 Tg C) in 2012 (Table 7-50 and
Table 7-51).
Table 7-50: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills (Tg
CO2 Eq.)
Carbon Pool 1990 2005 2008 2009 2010 2011 2012
Yard Trimmings (21.0) (7.4) (7.0) (8.5) (9.3) (9.4) (9.3)
Grass (1.8) (0.6) (0.6) (0.8) (0.9) (0.9) (0.9)
Leaves (9.0) (3.4) (3.2) (3.9) (4.2) (4.3) (4.3)
Branches (10.2) I (3.4) (3.1) (3.8) (4.1) (4.2) (4.2)
Food Scraps (3.2) (4.6) (4.2) (4.4) (4.3) (4.1) (3.7)
Total Net Flux (24.2) (12.0) (11.2) (12.9) (13.6) (13.5) (13.0)
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values
Table 7-51: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills (Tg C)
Carbon Pool
Yard Trimmings
Grass
Leaves
Branches
Food Scraps
Total Net Flux
1990
(5.7)
(0.5)
(2.5)
(2.8)
(0.9)
(6.6)
2005
(2.0)
(0.2)
(0.9)
(0.9)
(1.3)
(3.3)
2008
(1.9)
(0.2)
(0.9)
(0.9)
(1.1)
(3.0)
2009
(2.3)
(0.2)
(1.1)
(1.0)
(1.2)
(3.5)
2010
(2.5)
(0.3)
(1.1)
(1.1)
(1.2)
(3.7)
2011
(2.6)
(0.3)
(1.2)
(1.1)
(1.1)
(3.7)
2012
(2.5)
(0.2)
(1.2)
(1.1)
(1.0)
(3.6)
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values
Methodology
When wastes of biogenic origin (such as yard trimmings and food scraps) are landfilled and do not completely
decompose, the C that remains is effectively removed from the global C cycle. Empirical evidence indicates that
yard trimmings and food scraps do not completely decompose in landfills (Barlaz 1998, 2005, 2008; De la Cruz and
Barlaz 2010), and thus the stock of C in landfills can increase, with the net effect being a net atmospheric removal of
C. Estimates of net C flux resulting from landfilled yard trimmings and food scraps were developed by estimating
the change in landfilled C stocks between inventory years, based on methodologies presented for the Land Use,
Land-Use Change, and Forestry sector in IPCC (2003). Carbon stock estimates were calculated by determining the
mass of landfilled C resulting from yard trimmings or food scraps discarded in a given year; adding the accumulated
landfilled C from previous years; and subtracting the mass of C that was landfilled in previous years that
decomposed.
To determine the total landfilled C stocks for a given year, the following were estimated: (1) the composition of the
yard trimmings; (2) the mass of yard trimmings and food scraps discarded in landfills; (3) the C storage factor of the
landfilled yard trimmings and food scraps; and (4) the rate of decomposition of the degradable C. The composition
of yard trimmings was assumed to be 30 percent grass clippings, 40 percent leaves, and 30 percent branches on a
7-78 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
wet weight basis (Oshins and Block 2000). The yard trimmings were subdivided, because each component has its
own unique adjusted C storage factor (i.e., moisture content and C content) and rate of decomposition. The mass of
yard trimmings and food scraps disposed of in landfills was estimated by multiplying the quantity of yard trimmings
and food scraps discarded by the proportion of discards managed in landfills. Data on discards (i.e., the amount
generated minus the amount diverted to centralized composting facilities) for both yard trimmings and food scraps
were taken primarily from Municipal Solid Waste Generation, Recycling, and Disposal in the United States: 2012
Facts and Figures (EPA 2014), which provides data for 1960, 1970, 1980, 1990, 2000, 2005, 2008 and 2010
through 2012. To provide data for some of the missing years, detailed backup data were obtained from Schneider
(2007, 2008). Remaining years in the time series for which data were not provided were estimated using linear
interpolation. The EPA (2014) report does not subdivide the discards (i.e., total generated minus composted) of
individual materials into volumes landfilled and combusted, although it provides a volume of overall waste stream
discards managed in landfills242 and combustors with energy recovery (i.e., ranging from 100 percent and 0 percent,
respectively, in 1960 to 81 percent and 19 percent in 2000); it is assumed that the proportion of each individual
material (food scraps, grass, leaves, branches) that is landfilled is the same as the proportion across the overall waste
stream.
The amount of C disposed of in landfills each year, starting in 1960, was estimated by converting the discarded
landfilled yard trimmings and food scraps from a wet weight to a dry weight basis, and then multiplying by the
initial (i.e., pre-decomposition) C content (as a fraction of dry weight). The dry weight of landfilled material was
calculated using dry weight to wet weight ratios (Tchobanoglous et al. 1993, cited by Barlaz 1998) and the initial C
contents and the C storage factors were determined by Barlaz (1998, 2005, 2008) (Table 7-52).
The amount of C remaining in the landfill for each subsequent year was tracked based on a simple model of C fate.
As demonstrated by Barlaz (1998, 2005, 2008), a portion of the initial C resists decomposition and is essentially
persistent in the landfill environment. Barlaz (1998, 2005, 2008) conducted a series of experiments designed to
measure biodegradation of yard trimmings, food scraps, and other materials, in conditions designed to promote
decomposition (i.e., by providing ample moisture and nutrients). After measuring the initial C content, the materials
were placed in sealed containers along with methanogenic microbes from a landfill. Once decomposition was
complete, the yard trimmings and food scraps were re-analyzed for C content; the C remaining in the solid sample
can be expressed as a proportion of initial C (shown in the row labeled "C Storage Factor, Proportion of Initial C
Stored (%)" in Table 7-52).
The modeling approach applied to simulate U.S. landfill C flows builds on the findings of Barlaz (1998, 2005,
2008). The proportion of C stored is assumed to persist in landfills. The remaining portion is assumed to degrade
over time, resulting in emissions of CH4 and CO2. (The CH4 emissions resulting from decomposition of yard
trimmings and food scraps are accounted for in the Waste chapter.) The degradable portion of the C is assumed to
decay according to first-order kinetics. The decay rates for each of the materials are shown in Table 7-52.
The first-order decay rates, k, for each component were derived from De la Cruz and Barlaz (2010). De la Cruz and
Barlaz (2010) calculate first-order decay rates using laboratory data published in Eleazer et al. (1997), and a
correction factor,/ is found so that the weighted average decay rate for all components is equal to the AP-42 default
decay rate (0.04) for mixed MSW for regions that receive more than 25 inches of rain annually. Because AP-42
values were developed using landfill data from approximately 1990, 1990 waste composition for the United States
from EPA's Characterization of Municipal Solid Waste in the United States: 1990 Update was used to calculate/
This correction factor is then multiplied by the Eleazer et al. (1997) decay rates of each waste component to develop
field-scale first-order decay rates.
De la Cruz and Barlaz (2010) also use other assumed initial decay rates for mixed MSW in place of the AP-42
default value based on different types of environments in which landfills in the United States are found, including
dry conditions (less than 25 inches of rain annually, &=0.02) and bioreactor landfill conditions (moisture is
controlled for rapid decomposition, k=0.12). The Landfills section of the Inventory (which estimates CH4
emissions) estimates the overall MSW decay rate by partitioning the U.S. landfill population into three categories,
242 EPA (2013) reports discards in two categories: "combustion with energy recovery" and "landfill, other disposal," which
includes combustion without energy recovery. For years in which there is data from previous EPA reports on combustion without
energy recovery, EPA assumes these estimates are still applicable. For 2000 to present, EPA assumes that any combustion of
MSW that occurs includes energy recovery, so all discards to "landfill, other disposal" are assumed to go to landfills.
Land Use, Land-Use Change, and Forestry 7-79
-------
based on annual precipitation ranges of: (1) less than 20 inches of rain per year, (2) 20 to 40 inches of rain per year,
and (3) greater than 40 inches of rain per year. These correspond to overall MSW decay rates of 0.020, 0.038, and
0.057 year"1, respectively.
De la Cruz and Barlaz (2010) calculate component-specific decay rates corresponding to the first value (0.020
year1), but not for the other two overall MSW decay rates. To maintain consistency between landfill methodologies
across the Inventory, the correction factors (/) were developed for decay rates of 0.038 and 0.057 year"1 through
linear interpolation. A weighted national average component-specific decay rate was calculated by assuming that
waste generation is proportional to population (the same assumption used in the landfill methane emission estimate),
based on population data from the 2000 U.S. Census. The component-specific decay rates are shown in Table 7-52.
For each of the four materials (grass, leaves, branches, food scraps), the stock of C in landfills for any given year is
calculated according to the following formula:
LFC,,t = E W,,n x (1 -MC,) x ICC, x {[CS, x ICC,] + [(1 - (CS, x ICC,)) x e-«<-«>]}
n
where,
/ = Year for which C stocks are being estimated (year),
/ = Waste type for which C stocks are being estimated (grass, leaves, branches, food scraps),
LFCtf = Stock of C in landfills in year /, for waste / (metric tons),
Wi:n = Mass of waste /' disposed of in landfills in year n (metric tons, wet weight),
n = Year in which the waste was disposed of (year, where 1960 <«
-------
C Storage Factor, Proportion of Initial C
Stored (%) 53 85 77 16
Initial C Content (%) 45 46 49 51
Decay Rate (year1) 0.323 0.185 0.016 0.156
Table 7-53: C Stocks in Yard Trimmings and Food Scraps in Landfills (Tg C)
Carbon Pool
Yard Trimmings
Branches
Leaves
Grass
Food Scraps
Total Carbon Stocks
1990
155.8
14.5
66.7
74.6
21.3
177.2
• 2005
203.
18,
87,
97,
31.
234.
0
.1
.4
.5
,9
,9
•
2008
208.8
18.6
90.0
100.2
35.1
244.0
2009
211.1
18.8
91.1
101.2
36.4
247.5
2010
213
19
92
102
37,
251,
.7
.0
.2
.4
.5
.2
2011
216.2
19.3
93.4
103.5
38.6
254.9
2012
218.8
19.5
94.6
104.6
39.6
258.4
Note: Totals may not sum due to independent rounding.
Uncertainty and Time-Series Consistency
The uncertainty analysis for landfilled yard trimmings and food scraps includes an evaluation of the effects of
uncertainty for the following data and factors: disposal in landfills per year (tons of C), initial C content, moisture
content, decay rate, and proportion of C stored. The C storage landfill estimates are also a function of the
composition of the yard trimmings (i.e., the proportions of grass, leaves and branches in the yard trimmings
mixture). There are respective uncertainties associated with each of these factors.
A Monte Carlo (Tier 2) uncertainty analysis was applied to estimate the overall uncertainty of the sequestration
estimate. The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 7-54. Total yard
trimmings and food scraps CO2 flux in 2012 was estimated to be between -19.8 and -5.2 Tg CO2 Eq. at a 95 percent
confidence level (or 19 of 20 Monte Carlo stochastic simulations). This indicates a range of 52 percent below to 60
percent above the 2012 flux estimate of -13.0 Tg CCh Eq. More information on the uncertainty estimates for Yard
Trimmings and Food Scraps in Landfills is contained within the Uncertainty Annex.
Table 7-54: Tier 2 Quantitative Uncertainty Estimates for COz Flux from Yard Trimmings and
Food Scraps in Landfills (Tg COz Eq. and Percent)
Source
2012 Flux
Estimate
Gas (Tg CO2 Eq.)
Uncertainty Range Relative to Flux Estimate3
(Tg C02 Eq.) (%)
Lower Upper
Bound Bound
Lower Upper
Bound Bound
Yard Trimmings and Food ^ _52%
Scraps
a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Note: Parentheses indicate negative values or net C sequestration.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A QA/QC analysis was performed for data gathering and input, documentation, and calculation. The QA/QC
analysis did not reveal any inaccuracies or incorrect input values.
Land Use, Land-Use Change, and Forestry 7-81
-------
Recalculations Discussion
The current Inventory has been revised relative to the previous report. Input data for 2012 was published in
February 2014 mMunicipal Solid Waste Generation, Recycling, and Disposal in the United States: 2012 Facts and
Figures (EPA 2014), and several of the inputs were updated for previous years. The final C stock and C flux
estimates changed because of the decomposition model (see Methodology for more information regarding the
decomposition model), which calculates the C that remains from yard trimmings and food scraps that were landfilled
in past years.
Planned Improvements
Future work is planned to evaluate the consistency between the estimates of C storage described in this chapter and
the estimates of landfill CH4 emissions described in the Waste chapter. For example, the Waste chapter does not
distinguish landfill CH4 emissions from yard trimmings and food scraps separately from landfill CH4 emissions from
total bulk (i.e., municipal solid) waste, which includes yard trimmings and food scraps.
7-82 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
8. Waste
Waste management and treatment activities are sources of greenhouse gas emissions (see Figure 8-1). Landfills
accounted for approximately 18.1 percent of total U.S. anthropogenic methane (CH4) emissions in 2012, the third
largest contribution of any CH4 source in the United States. Additionally, wastewater treatment and composting of
organic waste accounted for approximately 2.2 percent and less than 1 percent of U.S. CH4 emissions, respectively.
Nitrous oxide (N2O) emissions from the discharge of wastewater treatment effluents into aquatic environments were
estimated, as were N2O emissions from the treatment process itself. N2O emissions from composting were also
estimated. Together, these waste activities account for less than 2 percent of total U.S. N2O emissions. Nitrogen
oxides (NOX), carbon monoxide (CO), and non-CH4 volatile organic compounds (NMVOCs) are emitted by waste
activities, and are addressed separately at the end of this chapter. A summary of greenhouse gas emissions from the
Waste chapter is presented in Table 8-1 and Table 8-2.
Figure 8-1: 2012 Waste Chapter Greenhouse Gas Sources
Landfills
Wastewater Treatment
Composting
Waste as a Portion of all
Emissions
1.9%
25
50 75
Tg CO2 Eq.
100 125
Box 8-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks
In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emission
inventories, the emissions and sinks presented in this report and this chapter, are organized by source and sink
categories and calculated using internationally-accepted methods provided by the Intergovernmental Panel on
Waste 8-1
-------
Climate Change (IPCC).243 Additionally, the calculated emissions and sinks in a given year for the United States
are presented in a common manner in line with the UNFCCC reporting guidelines for the reporting of inventories
under this international agreement.244 The use of consistent methods to calculate emissions and sinks by all nations
providing their inventories to the UNFCCC ensures that these reports are comparable. In this regard, U.S. emissions
and sinks reported in this inventory report are comparable to emissions and sinks reported by other countries.
Emissions and sinks provided in this inventory do not preclude alternative examinations,245 but rather this inventory
presents emissions and sinks in a common format consistent with how countries are to report inventories under the
UNFCCC. The report itself, and this chapter, follows this standardized format, and provides an explanation of the
IPCC methods used to calculate emissions and sinks, and the manner in which those calculations are conducted.
Overall, in 2012, waste activities generated emissions of 124.0 Tg CCh Eq., or just under 2 percent of total U.S.
greenhouse gas emissions.
Table 8-1: Emissions from Waste (Tg COz Eq.)
Gas/Source
CH4
Landfills
Wastewater Treatment
Composting
N20
Domestic Wastewater
Treatment
Composting
Total
1990
161.2
147.8 1
13.21
0.3
3.8
0.4
165.0
2005
127.0
112.1
13.3 1
1.6
6.2
4.5
1.7 H
133.2
2008
129.3
114.3
13.3
1.7
6.6
4.8
1.9
136.0
2009
130.0
115.3
13.1
1.6
6.6
4.8
1.8
136.5
2010
124.5
109.9
13.0
1.5
6.6
4.9
1.7
131.1
2011
121.8
107.4
12.8
1.6
6.7
5.0
1.7
128.5
2012
117.2
102.8
12.8
1.6
6.8
5.0
1.8
124.0
Note: Totals may not sum due to independent rounding.
Table 8-2: Emissions from Waste (Gg)
Gas/Source 1990 2005 2008 2009 2010 2011 2012
CH4 7,678 6,048 6,159 6,190 5,926 5,798 5,580
Landfills 7,036 5,339 5,444 5,492 5,234 5,112 4,897
Wastewater Treatment 626 635 635 623 619 611 608
Composting 151 751 80 75 73 75 76
N20 12l 201 21 21 21 22 22
Domestic Wastewater
Treatment 111 141 15 16 16 16 16
Composting 1 ll_^B 6 6 5 6 6_
Note: Totals may not sum due to independent rounding.
Carbon dioxide, CH4, and N2O emissions from the incineration of waste are accounted for in the Energy sector
rather than in the Waste sector because almost all incineration of municipal solid waste (MSW) in the United States
occurs at waste-to-energy facilities where useful energy is recovered. Similarly, the Energy sector also includes an
estimate of emissions from burning waste tires and hazardous industrial waste, because virtually all of the
combustion occurs in industrial and utility boilers that recover energy. The incineration of waste in the United States
243 See .
244 See.
245 For example, see .
8-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
in 2012 resulted in 12.6 Tg CCh Eq. emissions, more than half of which is attributable to the combustion of plastics.
For more details on emissions from the incineration of waste, see Section 3.3.
Methodological guidance for this chapter was taken from the 2006IPCC Guidelines for National Greenhouse Gas
Inventories. This latest guidance from the IPCC best represents the understanding of emissions profiles from
activities in the waste sector. The use of the most recently published calculation methodologies by the IPCC, as
contained in the 2006 IPCC Guidelines for waste source categories, is fully in line with the IPCC Good Practice
Guidance for methodological choice to improve rigor and accuracy. In addition, the improvements in using the latest
methodological guidance from the IPCC has been recognized by the UNFCCC's Subsidiary Body for Scientific and
Technological Advice in the conclusions of its 30th Session.246 Numerous U.S. inventory experts were involved in
the development of the 2006 IPCC Guidelines, and their expertise has provided this latest guidance from the IPCC
with the most appropriate calculation methods that are then used in this chapter.
Box 8-2: Waste Data from the Greenhouse Gas Reporting Program
On October 30, 2009, the U.S. EPA published a rule for the mandatory reporting of greenhouse gases from large
GHG emissions sources in the United States. Implementation of 40 CFR Part 98 is referred to as EPA's
Greenhouse Gas Reporting Program (GHGRP). 40 CFR part 98 applies to direct greenhouse gas emitters, fossil
fuel suppliers, industrial gas suppliers, and facilities that inject COa underground for sequestration or other
reasons and requires reporting by 41 industrial categories. Reporting is at the facility level, except for certain
suppliers of fossil fuels and industrial greenhouse gases. In general, the threshold for reporting is 25,000 metric
tons or more of CO2 Eq. per year.
EPA's GHGRP dataset and the data presented in this inventory report are complementary and, as indicated in the
respective planned improvements sections for source categories in this chapter, EPA is analyzing how to use
facility-level GHGRP data to improve the national estimates presented in this inventory. Most methodologies
used in EPA's GHGRP are consistent with IPCC, though for EPA's GHGRP, facilities collect detailed
information specific to their operations according to detailed measurement standards. This may differ with the
more aggregated data collected for the inventory to estimate total, national U.S. emissions. It should be noted that
the definitions for source categories in the GHGRP may differ from those used in this inventory in meeting the
UNFCCC reporting guidelines. In line with the UNFCCC reporting guidelines, the inventory report is a
comprehensive accounting of all emissions from source categories identified in the IPCC guidelines. Further
information on the reporting categorizations in EPA's GHGRP and specific data caveats associated with
monitoring methods in EPA's GHGRP has been provided on the EPA's GHGRP website.247
EPA presents the data collected by EPA's GHGRP through a data publication tool248 that allows data to be
viewed in several formats including maps, tables, charts and graphs for individual facilities or groups of facilities.
246 jhggg Subsidiary Body for Scientific and Technological Advice (SBSTA) conclusions state, "The SBSTA acknowledged
that the 2006 IPCC Guidelines contain the most recent scientific methodologies available to estimate emissions by sources and
removals by sinks of greenhouse gases (GHGs) not controlled by the Montreal Protocol, and recognized that Parties have gained
experience with the 2006 IPCC Guidelines. The SBSTA also acknowledged that the information contained in the 2006 IPCC
Guidelines enables Parties to further improve the quality of their GHG inventories." See
247 See
.
248 See .
Waste 8^
-------
8.1 Landfills (IPCC Source Category 6A1)
In the United States, solid waste is managed by landfilling, recovery through recycling or composting, and
combustion through waste-to-energy facilities. Disposing of solid waste in modern, managed landfills is the most
commonly used waste management technique in the United States. More information on how solid waste data are
collected and managed in the United States is provided in Box 8-1 and Box 8-2. The municipal solid waste (MSW)
and industrial waste landfills referred to in this section are all modern landfills that must comply with a variety of
regulations as discussed in Box 8-3. Disposing of waste in illegal dumping sites is not considered to have occurred
in years later than 1980 and these sites are not considered to contribute to net emissions in this section for the
inventory time frame of 1990 to 2012. MSW landfills, or sanitary landfills, are sites where MSW is managed to
prevent or minimize health, safety, and environmental impacts. Waste is deposited in different cells and covered
daily with soil; many have environmental monitoring systems to track performance, collect leachate, and collect
landfill gas. Industrial waste landfills are constructed in a similar way as MSW landfills, but accept waste produced
by industrial activity, such as factories, mills, and mines.
After being placed in a landfill, organic waste (such as paper, food scraps, and yard trimmings) is initially
decomposed by aerobic bacteria. After the oxygen has been depleted, the remaining waste is available for
consumption by anaerobic bacteria, which break down organic matter into substances such as cellulose, amino acids,
and sugars. These substances are further broken down through fermentation into gases and short-chain organic
compounds that form the substrates for the growth of methanogenic bacteria. These methane (CH4) producing
anaerobic bacteria convert the fermentation products into stabilized organic materials and biogas consisting of
approximately 50 percent biogenic carbon dioxide (CO2) and 50 percent CH4, by volume. Landfill biogas also
contains trace amounts of non-methane organic compounds (NMOC) and volatile organic compounds (VOC) that
either result from decomposition by-products or volatilization of biodegradable wastes (EPA 2008).
Methane and CO2 are the primary constituents of landfill gas generation and emissions. However, the 2006
Intergovernmental Panel on Climate Change (IPCC) Guidelines set an international convention to not report
biogenic CO2 released due to landfill decomposition in the Waste sector (IPCC 2006). Carbon dioxide emissions
from landfills are estimated and reported for under the Land Use/Land Use Change and Forestry (LULUCF) sector
(see Box 8-4). Additionally, emissions of NMOC and VOC are not estimated because they are considered to be
emitted in trace amounts. Nitrous oxide (N2O) emissions from the disposal and application of sewage sludge on
landfills are also not explicitly modeled as part of greenhouse gas emissions from landfills. N2O emissions from
sewage sludge applied to landfills as a daily cover or for disposal are expected to be relatively small because the
microbial environment in an anaerobic landfill is not very conducive to the nitrification and denitrification processes
that result in N2O emissions. Furthermore, the 2006 IPCC Guidelines (IPCC 2006) did not include a methodology
for estimating N2O emissions from solid waste disposal sites "because they are not significant." Therefore, only CH4
generation and emissions are estimated for landfills under the Waste sector.
Methane generation and emissions from landfills are a function of several factors, including: (1) the total amount of
waste-in-place, which is the total waste landfilled annually over the operational lifetime of a landfill; (2) the
characteristics of the landfill receiving waste (e.g., composition of waste-in-place, size, climate, cover material); (3)
the amount of CH4 that is recovered and either flared or used for energy purposes; and (4) the amount of CH4
oxidized as the landfill gas passes through the cover material into the atmosphere. Each landfill has unique
characteristics, but all managed landfills practice similar operating practices, including the application of a daily and
intermediate cover material over the waste being disposed of in the landfill to prevent odor and reduce risks to
public health. Based on recent literature, the specific type of cover material used can affect the rate of oxidation of
landfill gas (RTI2011). The most commonly used cover materials are soil, clay, and sand. Some states also permit
the use of green waste, tarps, waste derived materials, sewage sludge or biosolids, and contaminated soil as a daily
cover. Methane production typically begins one or two years after waste is disposed of in a landfill and will continue
for 10 to 60 years or longer as the degradable waste decomposes over time.
In 2012, landfill CH4 emissions were approximately 102.8 Tg CO2 Eq. (4,897 Gg), representing the third largest
source of CH4 emissions in the United States, behind natural gas systems and enteric fermentation. Emissions from
MSW landfills, which received about 69 percent of the total solid waste generated in the United States, accounted
for about 95 percent of total landfill emissions, while industrial landfills accounted for the remainder.
Approximately 1,900 to 2,000 operational MSW landfills exist in the United States, with the largest landfills
8-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
receiving most of the waste and generating the majority of the CH4 emitted (EPA 2010; BioCycle 2010; WBJ 2010).
Conversely, there are approximately 3,200 MSW landfills in the United States that have been closed since 1980 (for
which a closure data is known, WBJ 2010). While the number of active MSW landfills has decreased significantly
over the past 20 years, from approximately 6,326 in 1990 to approximately 2,000 in 2010, the average landfill size
has increased (EPA 2010; BioCycle 2010; WBJ 2010). The exact number of active and closed dedicated industrial
waste landfills is not known at this time, but the Waste Business Journal total for landfills accepting industrial and
construction and demolition debris for 2010 is 1,305 (WBJ 2010). Conversely, only 176 facilities with industrial
waste landfills reported under subpart TT (Industrial Waste Landfills) of EPA's GHGRP in 2011 and 2012,
indicating that there may be several hundreds of industrial waste landfills that are not required to report under EPA's
GHGRP, or that the actual number of industrial waste landfills in the United States is relatively low compared to
MSW landfills.
The estimated annual quantity of waste placed in MSW landfills increased 26 percent from approximately 205 Tg in
1990 to 284 Tg in 2012 (see Annex 3.14). The annual amount of waste generated and subsequently disposed in
MSW landfills varies annually and depends on several factors (e.g., the economy, consumer patterns, recycling and
composting programs, inclusion in a garbage collection service). The total amount of MSW generated is expected to
increase as the U.S. population continues to grow. The percentage of waste landfilled, however, may decline due to
increased recycling and composting practices.
Net CH4 emissions have fluctuated from year to year, but a slowly decreasing trend has been observed over the past
decade despite increased waste disposal amounts. For example, from 1990 to 2012, net CH4 emissions from landfills
decreased by approximately 30 percent (see Table 8-3 and Table 8-4). This decreasing trend can be attributed to a 21
percent reduction in the amount of decomposable materials (i.e., paper and paperboard, food scraps, and yard
trimmings) discarded in MSW landfills over the time series (EPA 2010) and an increase in the amount of landfill gas
collected and combusted (i.e., used for energy or flared) at MSW landfills, resulting in lower net CH4 emissions
from MSW landfills.249 For instance, in 1990, approximately 954 Gg of CH4 were recovered and combusted from
landfills, while in 2012, approximately 8,648 Gg of CH4 were combusted, representing an average annual increase
in the quantity of CH4 recovered and combusted at MSW landfills from 1990 to 2012 of 11 percent (see Annex
3.14). Landfill gas collection and control is not accounted for at industrial waste landfills in the solid waste
emissions inventory (see the Methodology discussion for more information).
The quantity of recovered CH4 that is either flared or used for energy purposes at MSW landfills has continually
increased as a result of 1996 federal regulations that require large MSW landfills to collect and combust landfill gas
(see 40 CFR Part 60, Subpart Cc 2005 and 40 CFR Part 60, Subpart WWW 2005). Voluntary programs that
encourage CH4 recovery and beneficial reuse, such as EPA's Landfill Methane Outreach Program (LMOP) and
federal and state incentives that promote renewable energy (e.g., tax credits, low interest loans, and Renewable
Portfolio Standards), have also contributed to increased interest in landfill gas collection and control. In 2012, an
estimated 67 new landfill gas-to-energy (LFGTE) projects and 3 new flares began operation (EPA 2012). While the
amount of landfill gas collected and combusted continues to increase every year, the rate of increase in collection
and combustion no longer exceeds the rate of additional CH4 generation from the amount of organic MSW landfilled
as the U.S. population grows.
249 Due to a lack of data specific to industrial waste landfills, landfill gas recovery is only estimated for MSW landfills.
Waste
-------
Table 8-3: CH4 Emissions from Landfills (Tg COz Eq.)
Activity
MSW Landfills
Industrial
Landfills
Recovered
Gas-to-Energy
Flared
Oxidized
Total
1990
172.6
11.6
(13.3)
(6.7)
(16.4)
147.8
2005
240.8
15.4
(55.9)
(75.7)
(12.5)
112.1
2008
260.0
15.7
(67.2)
(81.5)
(12.7)
114.3
2009
265.1
15.8
(74.2)
(78.6)
(12.8)
115.3
2010
270.1
15.9
(82.5)
(81.4)
(12.2)
109.9
2011
275.1
15.9
(88.0)
(83.7)
(11.9)
107.4
2012
280.0
15.9
(96.8)
(84.8)
(11.4)
102.8
Table 8-4: ChU Emissions from Landfills (Gg)
Activity
MSW Landfills
Industrial
Landfills
Recovered
Gas-to-Energy
Flared
Oxidized
Total
1990
8,219
553
(634)
(321)
(782)
7,036
2005
11,466
732
(2,660)
(3,606)
(593)
5,339
2008
12,380
748
(3,198)
(3,880)
(605)
5,444
2009
12,623
753
(3,532)
(3,743)
(610)
5,492
2010
12,863
756
(3,927)
(3,876)
(582)
5,234
2011
13,099
758
(4,190)
(3,986)
(568)
5,112
2012
13,331
758
(4,608)
(4,040)
(544)
4,897
Methodology
CH4 emissions from landfills were estimated as the CH4 produced from MSW landfills, plus the CH4 produced by
industrial waste landfills, minus the CH4 recovered and combusted from MSW landfills, minus the CH4 oxidized
before being released into the atmosphere:
CH4,Solid Waste = [CH4.MSW + CH4,Ind — R] — Ox
where,
CH4)Soiid waste = CH4 emissions from solid waste
CH4)Msw = CH4 generation from MSW landfills,
CH4jnd = CH4 generation from industrial landfills,
R = CH4 recovered and combusted (only for MSW landfills), and
Ox = CH4 oxidized from MSW and industrial waste landfills before release to the atmosphere.
The methodology for estimating CH4 emissions from landfills is based on the first order decay model described by
the IPCC (IPCC 2006). Methane generation is based on nationwide waste disposal data; it is not landfill-specific.
The amount of CH4 recovered, however, is landfill-specific, but only for MSW landfills due to a lack of data
specific to industrial waste landfills. Values for the CH4 generation potential (L0) and decay rate constant (k) used in
the first order decay model were obtained from an analysis of CH4 recovery rates for a database of 52 landfills and
from published studies of other landfills (RTI2004; EPA 1998; SWANA 1998; Peer, Thorneloe, and Epperson
1993). The decay rate constant was found to increase with average annual rainfall; consequently, values of k were
developed for 3 ranges of rainfall, or climate types (wet, arid, and temperate). The annual quantity of waste placed in
landfills was apportioned to the 3 ranges of rainfall based on the percent of the U. S. population in each of the 3
ranges. Historical census data were used to account for the shift in population to more arid areas over time. An
overview of the data sources and methodology used to calculate CH4 generation and recovery is provided below,
while a more detailed description of the methodology used to estimate CH4 emissions from landfills can be found in
Annex 3.14.
8-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
States and local municipalities across the United States do not consistently track and report quantities of collected
waste or their end-of-life disposal methods to a centralized system. Therefore, national MSW landfill waste
generation and disposal data are obtained from the BioCycle State of Garbage surveys, published approximately
every two years, with the most recent publication date of 2010. The State of Garbage (SOG) survey is the only
continually updated nationwide survey of waste disposed in landfills in the United States and is the primary data
source with which to estimate CH4 emissions from MSW landfills. The SOG surveys use the principles of mass
balance where all MSW generated is equal to the amount of MSW landfilled, combusted in waste-to-energy plants,
composted, and/or recycled (BioCycle 2010). This approach assumes that all waste management methods are
tracked and reported to state agencies. Survey respondents are asked to provide a breakdown of MSW generated and
managed by landfilling, recycling, composting, and combustion (in waste-to-energy facilities) in actual tonnages as
opposed to reporting a percent generated under each waste disposal option. The data reported through the survey are
adjusted to exclude non-MSW materials (e.g., industrial and agricultural wastes, construction and demolition debris,
automobile scrap, and sludge from wastewater treatment plants) that may be included in survey responses. All state
disposal data are adjusted for imports and exports where imported waste is included in a particular state's total while
exported waste is not. Methodological changes have occurred over the time that the SOG survey has been published,
and this has affected the fluctuating trends observed in the data (RTI2013).
The SOG survey is voluntary and not all states provide data for each survey year. Where no waste generation data
are provided by a state in the SOG survey, the amount generated is estimated using one of the following methods:
the waste per capita from a previous SOG survey is multiplied by that particular state's population, or the average
nationwide waste per capita rate is multiplied by that particular state's population. The quantities of waste generated
across all states are summed and that value is then used as the nationwide quantity of waste generated in a given
reporting year.
State-specific landfill waste generation data and a national average disposal factor for 1989 through 2008 were
obtained from the SOG survey for every two years (i.e., 2002, 2004, 2006, and 2008 as published in BioCycle 2006,
2008, and 2010). State-specific landfill waste generation data for the years in-between the SOG surveys (e.g., 2001,
2003, 2005, 2007, 2009, 2010, 2011, and 2012) were extrapolated based on the SOG data and the U.S. Census
population data. The most recent SOG survey was published in 2010 for the 2008 year; therefore, the annual
quantities of waste generated for the years through 2012 were determined based on the 2010 data and population
growth. Waste generation data will be updated as new reports are published. Because the SOG survey does not
account for waste generated in U.S. territories, waste generation for the territories was estimated using population
data obtained from the U.S. Census Bureau (2009, 2013) and national per capita solid waste generation from the
SOG survey (2010).
Estimates of the quantity of waste landfilled from 1989 to the current inventory year are determined by applying a
waste disposal factor to the total amount of waste generated (i.e., the SOG data). A waste disposal factor is
determined for each year an SOG survey is published and equals the ratio of the total amount of waste landfilled to
the total amount of waste generated. The waste disposal factor is interpolated for the years in-between the SOG
surveys, as is done for the amount of waste generated for a given survey year.
Estimates of the annual quantity of waste landfilled for 1960 through 1988 were obtained from EPA's
Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress (EPA 1993) and an
extensive landfill survey by the EPA's Office of Solid Waste in 1986 (EPA 1988). Although waste placed in
landfills in the 1940s and 1950s contributes very little to current CH4 generation, estimates for those years were
included in the first order decay model for completeness in accounting for CH4 generation rates and are based on the
population in those years and the per capita rate for land disposal for the 1960s. For calculations in this inventory,
wastes landfilled prior to 1980 were broken into two groups: wastes disposed in landfills (Methane Conversion
Factor, MCF, of 1) and those disposed in dumps (MCF of 0.6). All calculations after 1980 assume waste is disposed
in managed, modern landfills. Please see Annex 3.14 for more details.
Methane recovery is currently only accounted for at MSW landfills. Data collected through EPA's GHGRP for
industrial waste landfills (subpart TT) show that only 2 of the 176 facilities, or 1 percent of facilities, reporting in the
2012 reporting year have active gas collection systems. EPA's GHGRP is not a national database and no
comprehensive data regarding gas collection systems have been published for industrial waste landfills.
Assumptions regarding a percentage of landfill gas collection systems, or a total annual amount of landfill gas
collected for the non-reporting industrial waste landfills, have not been made for the inventory methodology.
Waste 8-7
-------
The estimated landfill gas recovered per year at MSW landfills was based on a combination of three databases: the
flare vendor database (contains updated sales data collected from vendors of flaring equipment), a database of
landfill gas-to-energy (LFGTE) projects compiled by LMOP (EPA 2012), and a database developed by the Energy
Information Administration (EIA) for the voluntary reporting of greenhouse gases (EIA 2007). Based on the
information provided by the EIA and flare vendor databases, the CH4 combusted by flares in operation from 1990 to
the current inventory year was estimated. Information provided by the EIA and LMOP databases were used to
estimate methane combusted in LFGTE projects over the time series. The three databases were carefully compared
to identify landfills that were in two or all three of the databases to avoid double or triple counting CH4 reductions.
The flare vendor database estimates CH4 combusted by flares using the midpoint of a flare's reported capacity while
the EIA database uses landfill-specific measured gas flow. As the EIA database only includes data through 2006, the
amount of CH4 recovered from 2007 to the current inventory year for projects included in the EIA database were
assumed to be the same as in 2006. This quantity likely underestimates flaring because these databases do not have
information on all flares in operation. The EIA database is no longer being updated and it is expected that data
obtained from the EPA's GHGRP will serve as a supplemental data source for facility-reported recovery data in
future inventories. Additionally, the EIA and LMOP databases provided data on landfill gas flow and energy
generation for landfills with LFGTE projects. If a landfill in the EIA database was also in the LMOP and/or the flare
vendor database, the emissions avoided were based on the EIA data because landfill owners or operators reported
the amount recovered based on measurements of gas flow and concentration, and the reporting accounted for
changes over time. If both flare data and LMOP recovery data were available for any of the remaining landfills (i.e.,
not in the EIA database), then the emissions recovery was based on the LMOP data, which provides reported
landfill-specific data on gas flow for direct use projects and project capacity (i.e., megawatts) for electricity projects.
The flare data, on the other hand, only provide a range of landfill gas flow for a given flare size. Given that each
LFGTE project is likely to also have a flare, double counting reductions from flares and LFGTE projects in the
LMOP database was avoided by subtracting emission reductions associated with LFGTE projects for which a flare
had not been identified from the emission reductions associated with flares (referred to as the flare correction
factor). A further explanation of the methodology used to estimate the landfill gas recovered can be found in
Annex 3.14.
A destruction efficiency of 99 percent was applied to CH4 recovered to estimate CH4 emissions avoided due to the
combusting of CH4 in destruction devices, i.e., flares. The destruction efficiency value was selected based on the
range of efficiencies (86 to 99+ percent) recommended for flares in EPA's AP-42 Compilation of Air Pollutant
Emission Factors, Draft Chapter 2.4, Table 2.4-3 (EPA 2008). A typical value of 97.7 percent was presented for the
non-methane components (i.e., volatile organic compounds and non-methane organic compounds) in test results
(EPA 2008). An arithmetic average of 98.3 percent and a median value of 99 percent are derived from the test
results presented in EPA (2008). Thus, a value of 99 percent for the destruction efficiency of flares has been used in
Inventory methodology. Other data sources supporting a 99 percent destruction efficiency include those used to
establish new source performance standards (NSPS) for landfills and in recommendations for shutdown flares used
in the LMOP.
Emissions from industrial waste landfills were estimated from industrial production data (ERG 2013), waste
disposal factors, and the first order decay model. As over 99 percent of the organic waste placed in industrial waste
landfills originated from the food processing (meat, vegetables, fruits) and pulp and paper industries, estimates of
industrial landfill emissions focused on these two sectors (EPA 1993). There are currently no data sources that track
and report the amount and type of waste disposed of in industrial waste landfills in the United States. Therefore, the
amount of waste landfilled is assumed to be a fraction of production that is held constant over the time series as
explained in Annex 3.14. The composition of waste disposed of in industrial waste landfills is expected to be more
consistent in terms of composition and quantity than that disposed of in MSW landfills.
The amount of CH4 oxidized by the landfill cover at both municipal and industrial waste landfills was assumed to be
10 percent of the CH4 generated that is not recovered (IPCC 2006, Mancinelli and McKay 1985, Czepiel et al.
1996). To calculate net CH4 emissions, both CH4 recovered and CH4 oxidized were subtracted from CH4 generated
at municipal and industrial waste landfills.
8-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Uncertainty and Time-Series Consistency
Several types of uncertainty are associated with the estimates of CH4 emissions from MSW and industrial waste
landfills. The primary uncertainty concerns the characterization of landfills. Information is not available on two
fundamental factors affecting CH4 production: the amount and composition of waste placed in every MSW and
industrial waste landfill for each year of its operation. The SOG survey is the only nationwide data source that
compiles the amount of MSW disposed at the state-level. The surveys do not include information on waste
composition and there are no comprehensive data sets that compile quantities of waste disposed or waste
composition by landfill. Some MSW landfills have conducted detailed waste composition studies, but landfills in the
United States are not required to perform these types of studies. The approach used here assumes that the CH4
generation potential and the rate of decay that produces CH4, as determined from several studies of CH4 recovery at
MSW landfills, are representative of conditions at U.S. landfills. When this top-down approach is applied at the
nationwide level, the uncertainties are assumed to be less than when applying this approach to individual landfills
and then aggregating the results to the national level. In other words, this approach may over- and under-estimate
CH4 generation at some landfills if used at the facility-level, but the end result is expected to balance out because it
is being applied nationwide. There is also a high degree of uncertainty and variability associated with the first order
decay model, particularly when a homogeneous waste composition and hypothetical decomposition rates are applied
to heterogeneous landfills (IPCC 2006).
Additionally, there is a lack of landfill-specific information regarding the number and type of industrial waste
landfills in the United States. The approach used here assumes that the majority (99 percent) of industrial waste
disposed of in industrial waste landfills consists of waste from the pulp and paper and food and beverage industries.
However, because waste generation and disposal data are not available in an existing data source for all U.S.
industrial waste landfills, we apply a straight disposal factor over the entire time series to the amount of waste
generated to determine the amounts disposed.
Aside from the uncertainty in estimating CH4 generation potential, uncertainty exists in the estimates of the landfill
gas oxidized. A constant oxidation factor of 10 percent as recommended by the Intergovernmental Panel on Climate
Change (IPCC) for managed landfills is used for both MSW and industrial waste landfills regardless of climate, the
type of cover material, and/or presence of a gas collection system. The number of field studies measuring the rate of
oxidation has increased substantially since the IPCC 2006 Guidelines were published and, as discussed in the
Potential Improvements section, efforts are being made to review the literature and revise this value based on recent,
peer-reviewed studies.
Another significant source of uncertainty lies with the estimates of CH4 that are recovered by flaring and gas-to-
energy projects at MSW landfills. Three separate databases containing recovery information are used to determine
the total amount of CH4 recovered and there are uncertainties associated with each. The LMOP database and the
flare vendor databases are updated annually, while the EIA database has not been updated since 2005 and will
essentially be replaced by GHGRP data for a portion of landfills (i.e., those meeting the GHGRP thresholds). To
avoid double counting and to use the most relevant estimate of CH4 recovery for a given landfill, a hierarchical
approach is used among the three databases. The EIA data are given precedence because CH4 recovery was directly
reported by landfills, the LMOP data are given second priority because CH4 recovery is estimated from facility-
reported LFGTE system characteristics, and the flare data are given third priority because this database contains
minimal information about the flare and no site-specific operating characteristics (Bronstein et al., 2012). The IPCC
default value of 10 percent for uncertainty in recovery estimates was used in the uncertainty analysis when metering
of landfill gas was in place (for about 64 percent of the CH4 estimated to be recovered). This 10 percent uncertainty
factor applies to 2 of the 3 databases (EIA and LMOP). For flaring without metered recovery data (approximately 34
percent of the CH4 estimated to be recovered), a much higher uncertainty of approximately 50 percent was used
(e.g., when recovery was estimated as 50 percent of the flare's design capacity). The compounding uncertainties
associated with the 3 databases leads to the large upper and lower bounds for MSW landfills presented in Table 8-5.
The results of the IPCC Good Practice Guidance Tier 2 quantitative uncertainty analysis are summarized in Table
8-5. In 2012, landfill CH4 emissions were estimated to be between 45.0 and 151.3 Tg CO2 Eq., which indicates a
range of 47 percent below to 56 percent above the 2012 emission estimate of 102.9 TgCO2Eq.
Waste 8-9
-------
Table 8-5: Tier 2 Quantitative Uncertainty Estimates for Cm Emissions from Landfills (Tg COz
Eg. and Percent)
Source
Landfills
MSW
Industrial
Gas
CH4
CH4
CH4
2012 Emission
Estimate
(Tg C02 Eq.)
102.9
88.5
14.4
Uncertainty Range Relative to Emission Estimate3
(Tg
Lower
Bound
45.0
30.9
10.5
CO2 Eq.)
Upper
Bound
151.3
137.5
17.4
(°x
Lower
Bound
-56%
-65%
-27%
'»)
Upper
Bound
+47%
+55%
+21%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above. Methodological recalculations were applied to the entire time-series to ensure time-series consistency from
1990 through 2012. Details on the emission trends through time are described in more detail in the Methodology
section, above.
QA/QC and Verification
A QA/QC analysis was performed for data gathering and input, documentation, and calculation. QA/QC checks are
not performed on the published data used to populate the Inventory data set, including the SOG survey data and the
published LMOP database. A primary focus of the QA/QC checks was to ensure that CH4 recovery estimates were
not double-counted and that all LFGTE projects and flares were included in the respective project databases. Both
manual and electronic checks were made to ensure that emission avoidance from each landfill was calculated in only
one of the three databases. The primary calculation spreadsheet is tailored from the IPCC waste model and has been
verified previously using the original, peer-reviewed IPCC waste model. All model input values were verified by
secondary QA/QC review. A data linking error was identified during the QA/QC review of the summary data
spreadsheet. The industrial waste generation data for 2012 was found to be linking to the 2011 industrial waste
generation data. This error results in an increase in net methane emissions of 2.2 Gg (0.05 Tg CO2 Eq.) for 2012.
This will be corrected in the 1990-2013 Inventory report, and improved initial QA/QC procedures will be
implemented to avoid any similar errors.
Recalculations Discussion
When conducted, methodological recalculations are applied to the entire time-series to ensure time-series
consistency from 1990 through the current inventory year. Methodological changes were made to the amount of
MSW landfill waste generation data for states that did not report an annual amount of waste generated in the SOG
surveys for the 2004, 2006, and 2008 data. This change impacted the data for 2003 through 2012. This recalculation
was warranted after reviewing the waste generation and disposal trends over the time series, particularly for years
after 2004 where a noticeable decrease in the amount of waste generated was calculated. The methodology used by
the SOG survey changed (BioCycle 2006) to include only MSW in the values reported in the survey (i.e., other
wastes that may be disposed of in an MSW landfill were excluded). This change resulted in the decrease in total
waste generation between years before and after 2006. As states got more accustomed to the revised survey
questions, they were presumed to be better able to report the MSW portions. Further investigation is warranted for
the years after 2006 to better account for the non-MSW portion of waste that is disposed of in MSW landfills.
For states that did not report an amount of waste generated in the surveys, the recalculations made to the 1990
through 2012 inventory used the most recent SOG state-specific waste per capita data from one of the previous SOG
surveys. These recalculations resulted in a 3.0 million metric ton decrease in the estimate amount of MSW generated
in 2003 and an 8.0 million metric ton decrease in the estimated amount of MSW generated in 2004, reducing landfill
methane emissions by 0.05 to 0.4 Tg CO2 Eq. from 2004 through 2007. An 8.4 million metric ton increase in the
MSW generation estimate for 2006 and a 39.2 million metric ton increase in the MSW generation estimate for 2008
increased emissions by under 0.7 to 4.3 Tg COae from 2008 through 2011. The large change in the 2008 data results
from the fact that 13 states did not report 2008 data for the 2010 SOG survey. One of these states is California.
8-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Previously, the 2008 nationwide waste per capita rate (1.33 tons peryear) was used to estimate the amount of waste
generated in California for 2008. This change resulted in using the California-specific waste generation rate from a
previous survey (for the year 2004) of 2.17 tons per year, which was more reflective of waste generation in that state
than the nationwide waste generation rate.
Improvements being examined include incorporating data from the EPA's GHGRP and recent peer-reviewed
literature, modifying the default oxidation factor applied to MSW and industrial waste landfills, and either
modifying the bulk waste degradable organic carbon (DOC) value or estimating emissions using a waste-specific
approach in the first order decay model.
Beginning in 2011, all MSW landfills that accepted waste on or after January 1, 1980 and generate CH4 in amounts
equivalent to 25,000 metric tons or more of carbon dioxide equivalent (CCh Eq.) were required to calculate and
report their greenhouse gas emissions to EPA through its GHGRP. The MSW landfill source category of EPA's
GHGRP consists of the landfill, landfill gas collection systems, and landfill gas destruction devices, including flares.
Potential improvements to the inventory methodology may be made using the GHGRP data, specifically for inputs
to the first order decay equation. The approach used in the inventory to estimate CH4 generation assumes a bulk
waste-specific DOC value that may not accurately capture the changing waste composition over the time series (e.g.,
the reduction of organics entering the landfill environment due to increased composting, see Box 8-4). Using data
obtained from EPA's GHGRP and any publicly available landfill-specific waste characterization studies in the
United States, the methodology may be modified to incorporate a waste composition approach, or revisions may be
made to the bulk waste DOC value currently used. Additionally, GHGRP data could be analyzed and a weighted
average for the CH4 correction factor (MCF), fraction of CH4 (F) in the landfill gas, the destruction efficiency of
flares, and the decay rate constant (k) could replace the values currently used in the inventory.
The most significant contribution of GHGRP data to the emission estimates is expected to be the amount of
recovered landfill gas and other information related to the gas collection system (Bronstein et al. 2012). Information
for landfills with gas collection systems reporting under EPA's GHGRP will be incorporated into the inventory data
set and the measured CH4 recovery data will be used for the reporting landfills in lieu of the EIA, LMOP, and flare
vendor data. GHGRP data undergo an extensive series of verification steps, are more reliable and accurate than the
data currently used, and will reduce uncertainties surrounding CH4 recovery when applied to the landfills in the
inventory data set (Bronstein et al. 2012).
In addition to MSW landfills, industrial waste landfills at facilities emitting CH4 in amounts equivalent to 25,000
metric tons or more of CO2 Eq. were required to report their GHG emissions beginning in September 2012 through
EPA's GHGRP. Similar data for industrial waste landfills as is required for the MSW landfills will be reported. Any
additions or improvements to the inventory using reported GHGRP data will be made for the industrial waste
landfill portion of the inventory. One possible improvement is the addition of industrial sectors other than pulp and
paper, and food and beverage (e.g., metal foundries, petroleum refineries, and chemical manufacturing facilities). Of
particular interest in the GHGRP data set for industrial waste landfills will be the presence of gas collection systems
since recovery is not currently associated with industrial waste landfills in the inventory methodology. It is unlikely
that data reported through EPA's GHGRP for industrial waste landfills will yield improved estimates for k and L0
for the industrial sectors. However, EPA is considering an update to the L0 and k values for the pulp and paper
sector and will work with stakeholders to gather data and other feedback on potential changes to these values.
The addition of this higher tier data will improve the emission calculations to provide a more accurate representation
of greenhouse gas emissions from MSW and industrial waste landfills. It is expected that these potential
improvements can occur as early as the 1990 to 2013 inventory year since EPA's GHGRP equation inputs for both
MSW and industrial waste landfills will have been reported and verified by that time.250 Facility-level reporting data
250 Due to the large numbers of entities reporting under the GHGRP and the large number of data reporting elements, EPA
concluded that case-by-case determinations would not result in a timely release of non-confidential data. EPA determined
through a series of rulemaking actions which categories of data elements to protect as confidential business information (CBI).
Any data submitted under the Reporting Program that is classified as CBI will be protected under the provisions of 40 CFR part
Waste 8-11
-------
from EPA's GHGRP are not available for all inventory years as reported in this inventory; therefore, particular
attention will be made to ensure time series consistency while incorporating data from EPA's GHGRP that would be
useful to improve the emissions estimates for MSW landfills. In implementing improvements and integration of data
from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in national inventories will
be relied upon.251
As a first step toward revising the oxidation factor used in the inventory, a literature review was conducted in 2011
(RTI 2011). A standard CH4 oxidation factor of 10 percent has been used for both industrial and MSW landfills
since the inventory began and is currently recommended as the default for well-managed landfills in the latest IPCC
guidelines (2006). Recent comments on the inventory methodology indicated that a default oxidation factor of 10
percent may be less than oxidation rates achieved at well-managed landfills with gas collection and control. The
impact of different landfill cover types on the rate of oxidation warrants further investigation as well.
Currently, one oxidation factor (10 percent) is applied to the total amount of waste generated nationwide. Changing
the oxidation factor and calculating the amount of CH4 oxidized from landfills with gas collection and control
requires the estimation of waste disposed in these types of landfills. The inventory methodology uses waste
generation data from the SOG surveys, which report the total amount of waste generated and disposed nationwide
by state. In 2010, the State of Garbage survey requested data on the presence of landfill gas collection systems for
the first time. Twenty-eight states reported that 260 out of 1,414 (18 percent) operational landfills recovered landfill
gas (BioCycle 2010). However, the survey did not include closed landfills with gas collection and control systems.
In the future, the amount of states collecting and reporting this information is expected to increase. GHGRP data for
MSW landfills could be used to fill in the gaps related to the amount of waste disposed in landfills with gas
collection systems. Although EPA's GHGRP does not capture every landfill in the United States, larger landfills are
expected to meet the reporting thresholds and will be reporting waste disposal information by year beginning in
March 2013. After incorporating GHGRP data, it may be possible to calculate the amount of waste disposed of at
landfills with and without gas collection systems in the United States, which will allow the inventory waste model to
apply different oxidation factors depending on the presence of a gas collection system.
Box 8-3: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks
Municipal solid waste generated in the United States can be managed through landfilling, recycling, composting,
and combustion with energy recovery. There are two main sources for nationwide solid waste management data in
the United States,
• The BioCycle and Earth Engineering Center of Columbia University's State of Garbage (SOG) in America
surveys and
• The EPA's Municipal Solid Waste in The United States: Facts and Figures reports.
The SOG surveys collect state-reported data on the amount of waste generated and the waste managed via different
management options: landfilling, recycling, composting, and combustion. The survey asks for actual tonnages
instead of percentages in each waste category (e.g., residential, commercial, industrial, construction and demolition,
organics, tires) for each waste management option. If such a breakdown is not available, the survey asks for total
tons landfilled. The data are adjusted for imports and exports so that the principles of mass balance are adhered to,
whereby the amount of waste managed does not exceed the amount of waste generated. The SOG reports present
survey data aggregated to the state level.
The EPA Facts and Figures reports use a materials flow methodology, which relies heavily on a mass balance
approach. Data are gathered from industry associations, key businesses, similar industry sources, and government
agencies (e.g., the Department of Commerce and the U.S. Census Bureau) and are used to estimate tons of materials
and products generated, recycled, or discarded nationwide. The amount of MSW generated is estimated by adjusting
2, Subpart B. According to Clean Air Act section 114(c), "emission data" cannot be classified as CBI. EPA deferred the reporting
requirements for inputs to emission equations until 2013 for some data and 2015 for others to allow EPA to fully evaluate issues
regarding the release of these data. Reporting of all inputs for MSW landfills and the majority of inputs for industrial waste
landfills were deferred from reporting until 2013.
251 See: .
8-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
the imports and exports of produced materials. MSW that is not recycled, composted, or combusted is assumed to be
landfilled. The data presented in the report are nationwide totals.
The State of Garbage surveys are the preferred data source for estimating waste generation and disposal amounts in
the inventory because they are considered a more objective, numbers-based analysis of solid waste management in
the United States. However, the EPA Facts and Figures reports are useful when investigating waste management
trends at the nationwide level and for typical waste composition data, which the State of Garbage surveys do not
request.
In this Inventory, emissions from solid waste management are presented separately by waste management option,
except for recycling of waste materials. Emissions from recycling are attributed to the stationary combustion of
fossil fuels that may be used to power on-site recycling machinery, and are presented in the stationary combustion
chapter in the Energy sector, although the emissions estimates are not called out separately. Emissions from solid
waste disposal in landfills and the composting of solid waste materials are presented in the Landfills and
Composting chapters in the Waste sector of this report. In the United States, almost all incineration of MSW occurs
at waste-to-energy facilities or industrial facilities where useful energy is recovered, and thus emissions from waste
incineration are accounted for in the Incineration chapter of the Energy sector of this report.
Box 8-4: Overview of the Waste Sector
As shown in Figure 8-2 and Figure 8-3, landfilling of MSW is currently and has been the most common waste
management practice. A large portion of materials in the waste stream are recovered for recycling and composting,
which is becoming an increasingly prevalent trend throughout the country. Materials that are composted would have
normally been disposed of in a landfill.
Figure 8-2: Management of Municipal Solid Waste in the United States, 2010 (BioCycle 2010)
Composted
6%
MSWtoWTE
7%
Waste 8-13
-------
Figure 8-3: MSW Management Trends from 1990 to 2010 (EPA 2011)
160
140
120
100
80
60
40
20
Landfilling
Recycling
Combustion with
Energy Recovery
(green)
Composting (red)
o*-trNm^ru-iuDr-.oocji
OICTlClO^O^O^aiO^G^C^
O O O
Table 8-6 presents a typical composition of waste disposed of at a typical MSW landfill in the United States over
time. It is important to note that the actual composition of waste entering each landfill will vary from that presented
in Table 8-6. Understanding how the waste composition changes over time, specifically for the degradable waste
types, is important for estimating greenhouse gas emissions. For certain degradable waste types (i.e., paper and
paperboard), the amounts discarded have decreased over time due to an increase in recovery (see Table 8-6 and
Figure 8-4). Landfill ban legislation affecting yard trimmings resulted in an increase of composting from 1990 to
2008. Table 8-6 and Figure 8-4 do not reflect the impact of backyard composting on yard trimming generation and
recovery estimates. The recovery of food trimmings has been consistently low. Increased recovery of degradable
materials reduces the CH4 generation potential and CH4 emissions from landfills.
Table 8-6: Materials Discarded in the Municipal Waste Stream by Waste Type, Percent
Waste Type
1990
2005
2007
2008
2009
2010
Paper and Paperboard 30.0% 24.5% I 21.7% 19.7% 14.8% 15.3%
Glass 6.0% 5.7% 5.5% 5.3% 5.0% 4.8%
Metals 7.2% 7.7% 7.9% 8.0% 8.0% 8.3%
Plastics 9.6% 15.7% I 16.4% 16.0% 15.8% 16.3%
Rubber and Leather 3.1% 3.5% 3.6% 3.7% 3.7% 3.8%
Lextiles 2.9% 5.5% 5.9% 6.2% 6.3% 6.4%
Wood 6.9% 7.4% 7.5% 7.6% 7.7% 7.8%
Other3 1.4% 1.8% 1.9% 1.9% 1.9% 1.9%
FoodScrapsb 13.6% I 17.9% I 18.2% 18.6% 19.1% 19.3%
Yard Lrimmingsc 17.6% I 7.0% 6.7% 6.6% 7.6% 8.1%
Miscellaneous
Inorganic Wastes 1.7% B 2.1% B 2.1% 2.2% 2.2% 2.2%
a Includes electrolytes in batteries and fluff pulp, feces, and urine in disposable diapers. Details may
not add to totals due to rounding. Source: EPA 2011.
b Data for food scraps were estimated using sampling studies in various parts of the country in
combination with demographic data on population, grocery store sales, restaurant sales, number of
employees, and number of prisoners, students, and patients in institutions. Source: EPA 2010.
0 Data for yard trimmings were estimated using sampling studies, population data, and published
sources documenting legislation affecting yard trimmings disposal in landfills. Source: EPA 2010.
8-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure 8-4: Percent of Recovered Degradable Materials from 1990 to 2010, percent (EPA
2011)
70% -
60% -
50% -
40% -
30% -
20% -
10% -
0%
Paper and
Parjerboard (bluel
Yard Trimmings
(green)
Food Scraps (red)
o
01
01
8
8
8
00
o
o
01
o
o
o
rH
o
Box 8-5: Description of a Modern, Managed Landfill
Modern, managed landfills are well-engineered facilities that are located, designed, operated, and monitored to
ensure compliance with federal, state, and tribal regulations. Municipal solid waste (MSW) landfills must be
designed to protect the environment from contaminants which may be present in the solid waste stream.
Requirements for affected MSW landfills may include:
• Siting requirements to protect sensitive areas (e.g., airports, floodplains, wetlands, fault areas, seismic
impact zones, and unstable areas)
• Design requirements for new landfills to ensure that Maximum Contaminant Levels (MCLs) will not be
exceeded in the uppermost aquifer (e.g., composite liners and leachate collection systems)
• Leachate collection and removal systems
• Operating practices (e.g., daily and intermediate cover, receipt of regulated hazardous wastes, use of
landfill cover material, access options to prevent illegal dumping, use of a collection system to prevent
stormwater run-on/run-off, record-keeping)
Air monitoring requirements (explosive gases)
Groundwater monitoring requirements
Closure and post-closure care requirements (e.g., final cover construction), and
Corrective action provisions.
Specific federal regulations that affected MSW landfills must comply with include the 40 CFR Part 258 (Subtitle D
of RCRA), or equivalent state regulations and the New Source Performance Standards (NSPS) 40 CFR Part 60
Subpart WWW. Additionally, state and tribal requirements may exist.252
252 por more information regarding federal MSW landfill regulations, see
.
Waste 8-15
-------
Box 8-6: Biogenic Wastes in Landfills
Regarding the depositing of wastes of biogenic origin in landfills (i.e., all degradable waste), empirical evidence
shows that some of these wastes degrade very slowly in landfills, and the C they contain is effectively sequestered in
landfills over aperiod of time (Barlaz 1998, 2006). Estimates of C removals from landfilling of forest products, yard
trimmings, and food scraps are further described in the Land Use, Land-Use Change, and Forestry chapter, based on
methods presented in IPCC (2003) and IPCC (2006).
8.2 Wastewater Treatment (IPCC Source
Category 6B)
Wastewater treatment processes can produce anthropogenic CH4 and N2O emissions. Wastewater from domestic253
and industrial sources is treated to remove soluble organic matter, suspended solids, pathogenic organisms, and
chemical contaminants. Treatment may either occur on site, most commonly through septic systems or package
plants, or off site at centralized treatment systems. Centralized wastewater treatment systems may include a variety
of processes, ranging from lagooning to advanced tertiary treatment technology for removing nutrients. In the
United States, approximately 20 percent of domestic wastewater is treated in septic systems or other on-site systems,
while the rest is collected and treated centrally (U.S. Census Bureau 2011).
Soluble organic matter is generally removed using biological processes in which microorganisms consume the
organic matter for maintenance and growth. The resulting biomass (sludge) is removed from the effluent prior to
discharge to the receiving stream. Microorganisms can biodegrade soluble organic material in wastewater under
aerobic or anaerobic conditions, where the latter condition produces CH4. During collection and treatment,
wastewater may be accidentally or deliberately managed under anaerobic conditions. In addition, the sludge may be
further biodegraded under aerobic or anaerobic conditions. The generation of N2O may also result from the
treatment of domestic wastewater during both nitrification and denitrification of the N present, usually in the form of
urea, ammonia, and proteins. These compounds are converted to nitrate (NO3) through the aerobic process of
nitrification. Denitrification occurs under anoxic conditions (without free oxygen), and involves the biological
conversion of nitrate into dinitrogen gas (N2). N2O can be an intermediate product of both processes, but has
typically been associated with denitrification. Recent research suggests that higher emissions of N2O may in fact
originate from nitrification (Ahn et al. 2010).
The principal factor in determining the CH4 generation potential of wastewater is the amount of degradable organic
material in the wastewater. Common parameters used to measure the organic component of the wastewater are the
Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD). Under the same conditions,
wastewater with higher COD (or BOD) concentrations will generally yield more CH4 than wastewater with lower
COD (or BOD) concentrations. BOD represents the amount of oxygen that would be required to completely
consume the organic matter contained in the wastewater through aerobic decomposition processes, while COD
measures the total material available for chemical oxidation (both biodegradable and non-biodegradable). Because
BOD is an aerobic parameter, it is preferable to use COD to estimate CH4 production. The principal factor in
determining the N2O generation potential of wastewater is the amount of N in the wastewater. The variability of N
in the influent to the treatment system, as well as the operating conditions of the treatment system itself, also impact
the N2O generation potential.
In 2012, CH4 emissions from domestic wastewater treatment were 7.8 Tg CO2 Eq. (373 Gg CH4). Emissions
remained fairly steady from 1990 through 1997, but have decreased since that time due to decreasing percentages of
wastewater being treated in anaerobic systems, including reduced use of on-site septic systems and central anaerobic
253 Throughout the inventory, emissions from domestic wastewater also include any commercial and industrial wastewater
collected and co-treated with domestic wastewater.
8-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
treatment systems (EPA 1992, 1996, 2000, and 2004, U.S. Census 2011). In 2012, CH4 emissions from industrial
wastewater treatment were estimated to be 4.9 Tg CO2 Eq. (234 Gg CH4). Industrial emission sources have
generally increased across the time series through 1999 and then fluctuated up and down with production changes
associated with the treatment of wastewater from the pulp and paper manufacturing, meat and poultry processing,
fruit and vegetable processing, starch-based ethanol production, and petroleum refining industries. Table 8-7 and
Table 8-8 provide CH4 and N2O emission estimates from domestic and industrial wastewater treatment.
With respect to N2O, the United States identifies two distinct sources for N2O emissions from domestic wastewater:
emissions from centralized wastewater treatment processes, and emissions from effluent from centralized treatment
systems that has been discharged into aquatic environments. The 2012 emissions of N2O from centralized
wastewater treatment processes and from effluent were estimated to be 0.3 Tg CO2 Eq. (1 Gg N2O) and 4.7 Tg CO2
Eq. (15.2 Gg N2O), respectively. Total N2O emissions from domestic wastewater were estimated to be 5.0 Tg CO2
Eq. (16.2 Gg N2O). N2O emissions from wastewater treatment processes gradually increased across the time series
as a result of increasing U.S. population and protein consumption.
Table 8-7: ChU and NzO Emissions from Domestic and Industrial Wastewater Treatment (Tg
COz Eq.)
Activity
CH4
Domestic
Industrial*
N2O
Domestic
Total
1990
13.2
8.8
4.3
3.5
3.5
16.7
2005
13.3
8.4
4.9
4.5
4.5
17.8
2008
13.3
8.2
5.1
4.8
4.8
18.1
2009
13.1
8.2
4.9
4.8
4.8
17.9
2010
13.0
8.1
4.9
4.9
4.9
17.9
2011
12.8
7.9
4.9
5.0
5.0
17.8
2012
12.8
7.8
4.9
5.0
5.0
17.8
a Industrial activity includes the pulp and paper manufacturing, meat and poultry processing, fruit and vegetable
processing, starch-based ethanol production, and petroleum refining industries.
Note: Totals may not sum due to independent rounding.
Table 8-8: CH4 and NzO Emissions from Domestic and Industrial Wastewater Treatment (Gg)
Activity
CH4
Domestic
Industrial*
N20
Domestic
1990
626
421
206 1
11 1
11 I
2005
635
401 1
234 1
14
14 |
2008
635
393
242
15
15
2009
623
392
231
16
16
2010
619
384
235
16
16
2011
611
375
235
16
16
2012
608
373
234
16
16
* Industrial activity includes the pulp and paper manufacturing, meat and poultry processing, fruit and vegetable
processing, starch-based ethanol production, and petroleum refining industries.
Note: Totals may not sum due to independent rounding.
Methodology
Domestic Wastewater CH4 Emission Estimates
Domestic wastewater CH4 emissions originate from both septic systems and from centralized treatment systems,
such as publicly owned treatment works (POTWs). Within these centralized systems, CH4 emissions can arise from
aerobic systems that are not well managed or that are designed to have periods of anaerobic activity (e.g.,
constructed wetlands), anaerobic systems (anaerobic lagoons and facultative lagoons), and from anaerobic digesters
when the captured biogas is not completely combusted. CH4 emissions from septic systems were estimated by
multiplying the United States population by the percent of wastewater treated in septic systems (about 20 percent)
and an emission factor (10.7 g CH4/capita/day), and then converting the result to Gg/year. Methane emissions from
POTWs were estimated by multiplying the total BOD5 produced in the United States by the percent of wastewater
treated centrally (about 80 percent), the relative percentage of wastewater treated by aerobic and anaerobic systems,
the relative percentage of wastewater facilities with primary treatment, the percentage of BOD5 treated after primary
Waste 8-17
-------
treatment (67.5 percent), the maximum CH4-producing capacity of domestic wastewater (0.6), and the relative
MCFs for well-managed aerobic (zero), not well managed aerobic (0.3), and anaerobic (0.8) systems with all aerobic
systems assumed to be well-managed. Methane emissions from anaerobic digesters were estimated by multiplying
the amount of biogas generated by wastewater sludge treated in anaerobic digesters by the proportion of CH4 in
digester biogas (0.65), the density of CH4 (662 g CH4/m3 CH4), and the destruction efficiency associated with
burning the biogas in an energy/thermal device (0.99). The methodological equations are:
Emissions from Septic Systems = A
= USpop x (% onsite) x (EFSEpTic) x 1/10A9 x Days
Emissions from Centrally Treated Aerobic Systems = B
= [(% collected) x (total BOD5 produced) x (% aerobic) x (% aerobic w/out primary) + (% collected) x (total BOD5
produced) x (% aerobic) x (% aerobic w/primary) x (l-% BOD removed in prim, treat.)] x (% operations not well
managed) x (B0) x (MCF-aerobic_not_well_man)
Emissions from Centrally Treated Anaerobic Systems = C
= [(% collected) x (total BOD5 produced) x (% anaerobic) x (% anaerobic w/out primary) + (% collected) x (total
BOD 5 produced) x (% anaerobic) x (% anaerobic w/primary) x (1-%BOD removed in prim, treat.)] x (B0) x (MCF-
anaerobic)
Emissions from Anaerobic Digesters = D
= [(POTW_flow_AD) x (digester gas)/ (per capita flow)] x conversion to m3
ofCH4) x(l-DE) x l/lOA9
(FRAC_CH4) x (365.25) x (density
Total CH4 Emissions (Gg) = A + B + C + D
where,
USpop
% onsite
% collected
% aerobic
% anaerobic
% aerobic w/out primary
% aerobic w/primary
% BOD removed in prim, treat.
% operations not well managed
% anaerobic w/out primary
% anaerobic w/primary
EFsEPTIC
Days
Total BOD5 produced
Bo
1/10A6
MCF-aerobic_not_well_man.
MCF-anaerobic
DE
POTW_flow_AD
digester gas
per capita flow
conversion to m3
= U.S. population
= Flow to septic systems / total flow
= Flow to POTWs / total flow
= Flow to aerobic systems / total flow to POTWs
= Flow to anaerobic systems / total flow to POTWs
= Percent of aerobic systems that do not employ primary treatment
= Percent of aerobic systems that employ primary treatment
= 32.5%
= Percent of aerobic systems that are not well managed and in which
some anaerobic degradation occurs
= Percent of anaerobic systems that do not employ primary treatment
= Percent of anaerobic systems that employ primary treatment
= Methane emission factor (10.7 g CH4/capita/day) - septic systems
= days per year (365.25)
= kg BOD/capita/day x U.S. population x 365.25 days/yr
= Maximum CH4-producing capacity for domestic wastewater (0.60 kg
CH4/kgBOD)
= Conversion factor, kg to Gg
= CH4 correction factor for aerobic systems that are not well managed
(0.3)
= CH4 correction factor for anaerobic systems (0.8)
= CH4 destruction efficiency from flaring or burning in engine (0.99 for
enclosed flares)
= Wastewater influent flow to POTWs that have anaerobic digesters
(MOD)
= Cubic feet of digester gas produced per person per day (1.0
ft3/person/day) (Metcalf and Eddy 2003)
= Wastewater flow to POTW per person per day (100 gal/person/day)
= Conversion factor, ft3 to m3 (0.0283)
8-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
FRAC_CH4 = Proportion CH4 in biogas (0.65)
density of CH4 = 662 (g CHVm3 CH4)
1/10A9 = Conversion factor, g to Gg
U.S. population data were taken from the U.S. Census Bureau International Database (U.S. Census 2013) and
include the populations of the United States, American Samoa, Guam, Northern Mariana Islands, Puerto Rico, and
the Virgin Islands. Table 8-9 presents U.S. population and total BOD5 produced for 1990 through 2012, while Table
8-10 presents domestic wastewater CH4 emissions for both septic and centralized systems in 2012. The proportions
of domestic wastewater treated onsite versus at centralized treatment plants were based on data from the 1989, 1991,
1993, 1995, 1997, 1999, 2001, 2003, 2005, 2007, 2009, 2011 American Housing Surveys conducted by the U.S.
Census Bureau (U.S. Census 2011), with data for intervening years obtained by linear interpolation and data for
2012 forecasted using 1990-2011 data. The percent of wastewater flow to aerobic and anaerobic systems, the
percent of aerobic and anaerobic systems that do and do not employ primary treatment, and the wastewater flow to
POTWs that have anaerobic digesters were obtained from the 1992, 1996, 2000, and 2004 Clean Watershed Needs
Survey (EPA 1992, 1996, 2000, and 2004). Data for intervening years were obtained by linear interpolation and the
years 2004 through 2012 were forecasted from the rest of the time series. The BOD5 production rate (0.09
kg/capita/day) and the percent BOD5 removed by primary treatment for domestic wastewater were obtained from
Metcalf and Eddy (2003). The CH4 emission factor (0.6 kg CH4/kg BOD5) and the MCF used for centralized
treatment systems were taken from IPCC (2006), while the CH4 emission factor (10.7 g CH4/capita/day) used for
septic systems were taken from Leverenz et al. (2010). The CH4 destruction efficiency for methane recovered from
sludge digestion operations, 99 percent, was selected based on the range of efficiencies (98 to 100 percent)
recommended for flares in AP-42 Compilation of Air Pollutant Emission Factors, Chapter 2.4 (EPA 1998),
efficiencies used to establish new source performance standards (NSPS) for landfills, and in recommendations for
closed flares used by the Landfill Methane Outreach Program (LMOP). The cubic feet of digester gas produced per
person per day (1.0 ft3/person/day) and the proportion of CH4 in biogas (0.65) come from Metcalf and Eddy (2003).
The wastewater flow to a POTW (100 gal/person/day) was taken from the Great Lakes-Upper Mississippi River
Board of State and Provincial Public Health and Environmental Managers, "Recommended Standards for
Wastewater Facilities (Ten-State Standards)" (2004).
Table 8-9: U.S. Population (Millions) and Domestic Wastewater BODs Produced (Gg)
Year Population BODs
1990 253 8,333
2008 308 10,132
2009 311 10,220
2010 313 10,303
2011 316 10,377
2012 318 10,450
Source: U.S. Census Bureau (2013);
Metcalf & Eddy 2003).
Table 8-10: Domestic Wastewater CH4 Emissions from Septic and Centralized Systems
(2012)
CH4 emissions (Tg CCh Eq.)
Septic Systems
Centralized Systems
(including anaerobic
sludge digestion)
Total
5.1
2.8
7.8
% of Domestic Wastewater CH4
66.2%
33.8%
100%
Note: Totals may not sum due to independent rounding.
Waste 8-19
-------
Industrial Wastewater CH4 Emission Estimates
Methane emission estimates from industrial wastewater were developed according to the methodology described in
IPCC (2006). Industry categories that are likely to produce significant CH4 emissions from wastewater treatment
were identified and included in the inventory. The main criteria used to identify these industries are whether they
generate high volumes of wastewater, whether there is a high organic wastewater load, and whether the wastewater
is treated using methods that result in CH4 emissions. The top five industries that meet these criteria are pulp and
paper manufacturing; meat and poultry processing; vegetables, fruits, and juices processing; starch-based ethanol
production; and petroleum refining. Wastewater treatment emissions for these sectors for 2012 are displayed in
Table 8-11 below. Table 8-12 contains production data for these industries.
Table 8-11: Industrial Wastewater CH4 Emissions by Sector (2012)
CH4 emissions (Tg CCh Eq.) % of Industrial Wastewater CH4
Meat & Poultry
Pulp & Paper
Fruit & Vegetables
Petroleum Refineries
3.7
0.9
0.1
0.1
74%
19%
2%
2%
Ethanol Refineries
0.1
2%
Total
4.9
100%
Note: Totals may not sum due to independent rounding.
Table 8-12: U.S. Pulp and Paper, Meat, Poultry/ Vegetables, Fruits and Juices, Ethanol, and
Petroleum Refining Production (Tg)
Year
Meat
Pulp and (Live Weight
Paper3 Killed)
Poultry
(Live Weight
Killed)
Vegetables,
Fruits and
Juices
Ethanol
Petroleum
Refining
1990
128.9
27.3
14.6
38.7
702.4
2008
2009
2010
2011
2012
133.1
120.4
128.6
128.3
132.3
34.4
33.8
33.7
33.8
33.8
26.6
25.2
25.9
26.2
26.1
45.1
46.5
43.2
44.3
44.8
27.8
32.7
39.7
41.7
39.7
836.8
822.4
848.6
858.8
852.8
aPulp and paper production is the sum of woodpulp production plus paper and paperboard production.
Methane emissions from these categories were estimated by multiplying the annual product output by the average
outflow, the organics loading (in COD) in the outflow, the maximum CH4 producing potential of industrial
wastewater (B0), and the percentage of organic loading assumed to degrade anaerobically in a given treatment
system (MCF). Ratios of BOD:COD in various industrial wastewaters were obtained from EPA (1997a) and used to
estimate COD loadings. The B0 value used for all industries is the IPCC default value of 0.25 kg CH4/kg COD
(IPCC 2006).
For each industry, the percent of plants in the industry that treat wastewater on site, the percent of plants that have a
primary treatment step prior to biological treatment, and the percent of plants that treat wastewater anaerobically
were defined. The percent of wastewater treated anaerobically onsite (TA) was estimated for both primary treatment
(%TAP) and secondary treatment (%TAS). For plants that have primary treatment in place, an estimate of COD that
is removed prior to wastewater treatment in the anaerobic treatment units was incorporated.
The methodological equations are:
CH4 (industrial wastewater) = [P x W x COD x %TAP x B0 x MCF] + [P x W x COD x %TAS x B0 x MCF]
8-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
where,
%TAP = [%Plants0 x %WWa,p x %CODP]
%TAS = [%Plantsa x %WWa,s x %CODS] + [%Plantst x %WWa,t x %CODS]
CH4 (industrial wastewater)= Total CH4 emissions from industrial wastewater (kg/year)
P
W
COD
%TAP
%TAS
%Plants0
%WWa,P
%CODP
%Plantsa
%Plantet
%WWa,s
%wwat
%CODS
Bo
MCF
= Industry output (metric tons/year)
= Wastewater generated (m3/metric ton of product)
= Organics loading in wastewater (kg/m3)
= Percent of wastewater treated anaerobically on site in primary treatment
= Percent of wastewater treated anaerobically on site in secondary treatment
= Percent of plants with onsite treatment
= Percent of wastewater treated anaerobically in primary treatment
= Percent of COD entering primary treatment
= Percent of plants with anaerobic secondary treatment
= Percent of plants with other secondary treatment
= Percent of wastewater treated anaerobically in anaerobic secondary treatment
= percent of wastewater treated anaerobically in other secondary treatment
= percent of COD entering secondary treatment
= Maximum CH4 producing potential of industrial wastewater (default value of
0.25 kg CH4/kg COD)
= CH4 correction factor, indicating the extent to which the organic content
(measured as COD) degrades anaerobically
Alternate methodological equations for calculating %TA were used for secondary treatment in the pulp and paper
industry to account for aerobic systems with anaerobic portions. These equations are:
%TAa = [%Plantsa*%WWas*%CODs]+[%Plantst*%WWat*CODs]
%TAat = [%Plantsat*%WWas*%CODs]
where,
%TAa
%TAat
%Plantsa
%Plantsa,t
%WWa,s
%wwat
%CODS
= Percent of wastewater treated anaerobically on site in secondary treatment
= Percent of wastewater treated in aerobic systems with anaerobic portions on
site in secondary treatment
= Percent of plants with anaerobic secondary treatment
= Percent of plants with partially anaerobic secondary treatment
= Percent of wastewater treated anaerobically in anaerobic secondary treatment
= Percent of wastewater treated anaerobically in other secondary treatment
= Percent of COD entering secondary treatment
As described below, the values presented in Table 8-13 were used in the emission calculations and are described in
detail in Aguiar and Bartram (2008), Bicknell et al. (2013), and Aguiar et al. (2013).
Table 8-13: Variables Used to Calculate Percent Wastewater Treated Anaerobically by
Industry (%)
Variable
%TAP
%TAS
%TAa
%TAa,t
%Plants0
%Plantsa
%Plantsa,t
%Plantst
Pulp
and
Paper
0
0
2.2
11.8
0
5
28
35
Meat
Processing
0
33
0
0
100
33
0
67
Poultry
Processing
0
25
0
0
100
25
0
75
Industry
Fruit/
Vegetable
Processing
0
4.2
0
0
11
5.5
0
5.5
Ethanol
Production
-Wet Mill
0
33.3
0
0
100
33.3
0
66.7
Ethanol
Production
- Dry Mill
0
75
0
0
100
75
0
25
Petroleum
Refining
0
23.6
0
0
100
23.6
0
0
Waste 8-21
-------
%WWa,p
%WWa,S
%WWa,t
%CODP
%CODS
0
100
0
100
42
0
100
0
100
100
0
100
0
100
100
0
100
0
100
77
0
100
0
100
100
0
100
0
100
100
0
100
0
100
100
Sources: Aguiar and Bartram (2008) Planned Revisions of the Industrial Wastewater Inventory Emission Estimates for the
1990-2007 Inventory. August 10, 2008; Bicknell et al. (2013) Revisions to Pulp and Paper Wastewater Inventory. October
2013; and Aguiar et al. (2013) Revisions to the Petroleum Wastewater Inventory. October 2013.
Pulp and Paper. Wastewater treatment for the pulp and paper industry typically includes neutralization, screening,
sedimentation, and flotation/hydrocycloning to remove solids (World Bank 1999, Nemerow and Dasgupta 1991).
Secondary treatment (storage, settling, and biological treatment) mainly consists of lagooning. In determining the
percent that degrades anaerobically, both primary and secondary treatment were considered. In the United States,
primary treatment is focused on solids removal, equalization, neutralization, and color reduction (EPA 1993). The
vast majority of pulp and paper mills with on-site treatment systems use mechanical clarifiers to remove suspended
solids from the wastewater. About 10 percent of pulp and paper mills with treatment systems use settling ponds for
primary treatment and these are more likely to be located at mills that do not perform secondary treatment (EPA
1993). However, because the vast majority of primary treatment operations at U.S. pulp and paper mills use
mechanical clarifiers, and less than 10 percent of pulp and paper wastewater is managed in primary settling ponds
that are not expected to have anaerobic conditions, negligible emissions are assumed to occur during primary
treatment.
Approximately 42 percent of the BOD passes on to secondary treatment, which consists of activated sludge, aerated
stabilization basins, or non-aerated stabilization basins. Based on EPA's OAQPS Pulp and Paper Sector Survey, 5.3
percent of pulp and paper mills reported using anaerobic secondary treatment for wastewater and/or pulp
condensates (Bicknell et al. 2011). Twenty-eight percent (28%) of mills also reported the use of quiescent settling
ponds. Using engineering judgment, these systems were determined to be aerobic with possible anaerobic portions.
For the truly anaerobic systems, an MCF of 0.8 is used, as these are typically deep stabilization basins. For the
partially anaerobic systems, an MCF of 0.2 is used, which is the IPCC suggested MCF for shallow lagoons.
A time series of CH4 emissions for 1990 through 2001 was developed based on production figures reported in the
Lockwood-Post Directory (Lockwood-Post 2002). Data from the Food and Agricultural Organization of the United
Nations (FAO) database FAOSTAT were used for 2002 through 2012 (FAO 2013). The overall wastewater outflow
varies based on a time series outlined in Bicknell et al. (2013) to reflect historical and current industry wastewater
flow, and the average BOD concentrations in raw wastewater was estimated to be 0.4 gram BOD/liter (EPA 1997b,
EPA 1993, World Bank 1999). The COD:BOD ratio used to convert the organic loading to COD for pulp and paper
mills was 2 (EPA 1997a).
Meat and Poultry Processing. The meat and poultry processing industry makes extensive use of anaerobic lagoons
in sequence with screening, fat traps, and dissolved air flotation when treating wastewater on site. About 33 percent
of meat processing operations (EPA 2002) and 25 percent of poultry processing operations (U.S. Poultry 2006)
perform on-site treatment in anaerobic lagoons. The IPCC default B0 of 0.25 kg CH4/kg COD and default MCF of
0.8 for anaerobic lagoons were used to estimate the CH4 produced from these on-site treatment systems. Production
data, in carcass weight and live weight killed for the meat and poultry industry, were obtained from the USD A
Agricultural Statistics Database and the Agricultural Statistics Annual Reports (USDA 2013). Data collected by
EPA's Office of Water provided estimates for wastewater flows into anaerobic lagoons: 5.3 and 12.5 m3/metric ton
for meat and poultry production (live weight killed), respectively (EPA 2002). The loadings are 2.8 and 1.5 g
BOD/liter for meat and poultry, respectively. The COD:BOD ratio used to convert the organic loading to COD for
both meat and poultry facilities was 3 (EPA 1997a).
Vegetables, Fruits, and Juices Processing. Treatment of wastewater from fruits, vegetables, and juices processing
includes screening, coagulation/settling, and biological treatment (lagooning). The flows are frequently seasonal,
and robust treatment systems are preferred for on-site treatment. Effluent is suitable for discharge to the sewer.
This industry is likely to use lagoons intended for aerobic operation, but the large seasonal loadings may develop
limited anaerobic zones. In addition, some anaerobic lagoons may also be used (Nemerow and Dasgupta 1991).
Consequently, 4.2 percent of these wastewater organics are assumed to degrade anaerobically. The IPCC default B0
of 0.25 kg CH4/kg COD and default MCF of 0.8 for anaerobic treatment were used to estimate the CH4 produced
from these on-site treatment systems. The USDA National Agricultural Statistics Service (USDA 2013) provided
8-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
production data for potatoes, other vegetables, citrus fruit, non-citrus fruit, and grapes processed for wine. Outflow
and BOD data, presented in Table 8-14, were obtained from EPA (1974) for potato, citrus fruit, and apple
processing, and from EPA (1975) for all other sectors. The COD:BOD ratio used to convert the organic loading to
COD for all fruit, vegetable, and juice facilities was 1.5 (EPA 1997a).
Table 8-14: Wastewater Flow (m3/ton) and BOD Production (g/L) for U.S. Vegetables, Fruits,
and Juices Production
Commodity Wastewater Outflow (m3/ton) BOD (g/L)
Vegetables
Potatoes
Other Vegetables
Fruit
Apples
Citrus
Non-citrus
Grapes (for wine)
10.27
8.67
3.66
10.11
12.42
2.78
1.765
0.791
1.371
0.317
1.204
1.831
Ethanol Production. Ethanol, or ethyl alcohol, is produced primarily for use as a fuel component, but is also used in
industrial applications and in the manufacture of beverage alcohol. Ethanol can be produced from the fermentation
of sugar-based feedstocks (e.g., molasses and beets), starch- or grain-based feedstocks (e.g., corn, sorghum, and
beverage waste), and cellulosic biomass feedstocks (e.g., agricultural wastes, wood, and bagasse). Ethanol can also
be produced synthetically from ethylene or hydrogen and carbon monoxide. However, synthetic ethanol comprises
only about 2 percent of ethanol production, and although the Department of Energy predicts cellulosic ethanol to
greatly increase in the coming years, currently it is only in an experimental stage in the United States. Currently,
ethanol is mostly made from sugar and starch crops, but with advances in technology, cellulosic biomass is
increasingly used as ethanol feedstock (US DOE 2013).
Ethanol is produced from corn (or other starch-based feedstocks) primarily by two methods: wet milling and dry
milling. Historically, the majority of ethanol was produced by the wet milling process, but now the majority is
produced by the dry milling process. The wastewater generated at ethanol production facilities is handled in a
variety of ways. Dry milling facilities often combine the resulting evaporator condensate with other process
wastewaters, such as equipment wash water, scrubber water, and boiler blowdown and anaerobically treat this
wastewater using various types of digesters. Wet milling facilities often treat their steepwater condensate in
anaerobic systems followed by aerobic polishing systems. Wet milling facilities may treat the stillage (or processed
stillage) from the ethanol fermentation/distillation process separately or together with steepwater and/or wash water.
CH4 generated in anaerobic digesters is commonly collected and either flared or used as fuel in the ethanol
production process (ERG 2006).
Available information was compiled from the industry on wastewater generation rates, which ranged from 1.25
gallons per gallon ethanol produced (for dry milling) to 10 gallons per gallon ethanol produced (for wet milling)
(Ruocco 2006a,b; Merrick 1998; Donovan 1996; and NRBP 2001). COD concentrations were also found to be
about 3 g/L (Ruocco 2006a; Merrick 1998; White and Johnson 2003). The amount of wastewater treated
anaerobically was estimated, along with how much of the CH4 is recovered through the use of biomethanators (ERG
2006). Methane emissions were then estimated as follows:
Methane = [Production x Flow x COD x 3.785 x ([%Plants0 x %WWa,P x %CODP] + [%Plantsa x %WWa,s x %CODS] +
[%Plantst x %WWa,t x %CODS]) x B0 x MCF x % Not Recovered] + [Production x Flow x 3.785 x COD x ([%Plants0 x
%WWa,p x %CODP] + [%PlantSa x %WWa,s x %CODS] + [%Plantst x %WWa,t x %CODS]) x B0 x MCF x (% Recovered) x (1-
DE)] x 1/1QA9
where,
Production = gallons ethanol produced (wet milling or dry milling)
Flow = gallons wastewater generated per gallon ethanol produced (1.25 dry milling, 10 wet milling)
COD = COD concentration in influent (3 g/1)
3.785 = conversion, gallons to liters
Waste 8-23
-------
%Plants0 = percent of plants with onsite treatment (100%)
%WWa,p = percent of wastewater treated anaerobically in primary treatment (0%)
%CODP = percent of COD entering primary treatment (100%)
%PlantSa = percent of plants with anaerobic secondary treatment (33.3% wet, 75% dry)
%Plantst = percent of plants with other secondary treatment (66.7% wet, 25% dry)
%WWa,s = percent of wastewater treated anaerobically in anaerobic secondary treatment (100%)
%WWa,t = percent of wastewater treated anaerobically in other secondary treatment (0%)
%CODS = percent of COD entering secondary treatment (100%)
Bo = maximum methane producing capacity (0.25 g CHVg COD)
MCF = methane conversion factor (0.8 for anaerobic systems)
% Recovered = percent of wastewater treated in system with emission recovery
% Not Recovered = 1 - percent of wastewater treated in system with emission recovery
DE = destruction efficiency of recovery system (99%)
1/10A9 = conversion factor, g to Gg
A time series of CH4 emissions for 1990 through 2012 was developed based on production data from the Renewable
Fuels Association (RFA 2013).
Petroleum Refining. Petroleum refining wastewater treatment operations have the potential to produce CH4
emissions from anaerobic wastewater treatment. EPA's Office of Air and Radiation performed an Information
Collection Request (ICR) for petroleum refineries in 2011.254 Of the responding facilities, 23.6 percent reported
using non-aerated surface impoundments or other biological treatment units, both of which have the potential to lead
to anaerobic conditions (Aguiar et al. 2013). In addition, the wastewater generation rate was determined to be 26.4
gallons per barrel of finished product (Aguiar etal. 2013). An average COD value in the wastewater was estimated
at 0.45 kg/m3 (Benyahia et al. 2006).
The equation used to calculate CH4 generation at petroleum refining wastewater treatment systems is presented
below:
Methane = Flow x COD x TA x B0 x MCF
where,
Flow = Annual flow treated through anaerobic treatment system (m3/year)
COD = COD loading in wastewater entering anaerobic treatment system (kg/m3)
TA = Percent of wastewater treated anaerobically on site
BO = maximum methane producing potential of industrial wastewater (default value of 0.25
kg CH4 /kg COD)
MCF = methane conversion factor (0.3)
A time series of CH4 emissions for 1990 through 2012 was developed based on production data from the Energy
Information Association (EIA 2013).
Domestic Wastewater N2O Emission Estimates
N2O emissions from domestic wastewater (wastewater treatment) were estimated using the IPCC (2006)
methodology, including calculations that take into account N removal with sewage sludge, non-consumption and
industrial/commercial wastewater N, and emissions from advanced centralized wastewater treatment plants:
• In the United States, a certain amount of N is removed with sewage sludge, which is applied to land, incinerated,
or landfilled (NSLUDGE). The N disposal into aquatic environments is reduced to account for the sewage sludge
application.
• The IPCC methodology uses annual, per capita protein consumption (kg protein/person-year). For this
inventory, the amount of protein available to be consumed is estimated based on per capita annual food
availability data and its protein content, and then adjusts that data using a factor to account for the fraction of
protein actually consumed.
254 Av
Available online at
-------
• Small amounts of gaseous nitrogen oxides are formed as byproducts in the conversion of nitrate to N gas in
anoxic biological treatment systems. Approximately 7 g N2O is generated per capita per year if wastewater
treatment includes intentional nitrification and denitrification (Scheehle and Doom 2001). Analysis of the 2004
CWNS shows that plants with denitrification as one of their unit operations serve a population of 2.4 million
people. Based on an emission factor of 7 g per capita per year, approximately 21.2 metric tons of additional N2O
may have been emitted via denitrification in 2004. Similar analyses were completed for each year in the
inventory using data from CWNS on the amount of wastewater in centralized systems treated in denitrification
units. Plants without intentional nitrification/denitrification are assumed to generate 3.2 g N2O per capita per
year.
N2O emissions from domestic wastewater were estimated using the following methodology:
N2OlOTAL = N2OpLANT + N2OEFFLUENT
N2OpLANT = N2C>NIT/DEMT + N2OwOUT MT/DEMT
N2ONIT/DEMT= [(USpQPND) X EF2 X FlND-COM] X 1/10A9
N2OwOUT MT/DEMT = {[(USpOP X WWTP) - USpOPMj] x FlND-COM X EFl} X 1/10A9
N2OEFFLUENT = {[(((USpop x WWTP) - (0.9 x USpopND)) x Protein x FNPR x FNON-CON x FTND-COM) - NSLUDGE] x EF3 x
44/28} x 1/10A6
where,
N2OTOTAL = Annual emissions of N2O (Gg)
N2OpLANT = N2O emissions from centralized wastewater treatment plants (Gg)
N2OMT/DENiT = N2O emissions from centralized wastewater treatment plants with
nitrification/denitrification (Gg)
N2Owour NIT/DENIT = N2O emissions from centralized wastewater treatment plants without
nitrification/denitrification (Gg)
N2OEFFLUENT = N2O emissions from wastewater effluent discharged to aquatic environments (Gg)
USpop = U.S. population
USpopND = U. S. population that is served by biological denitrification (from CWNS)
WWTP = Fraction of population using WWTP (as opposed to septic systems)
EFi = Emission factor (3.2 g N2O/person-year) - plant with no intentional denitrification
EF2 = Emission factor (7 g N2O/person-year) - plant with intentional denitrification
Protein = Annual per capita protein consumption (kg/person/year)
FNPR = Fraction of N in protein, default = 0.16 (kg N/kg protein)
FNON-CON = Factor for non-consumed protein added to wastewater (1.4)
FTND-COM = Factor for industrial and commercial co-discharged protein into the sewer system
(1.25)
NSLUDGE = N removed with sludge, kg N/yr
EF3 = Emission factor (0.005 kg N2O -N/kg sewage-N produced) - from effluent
0.9 = Amount of nitrogen removed by denitrification systems (EPA 2008)
44/28 = Molecular weight ratio of N2O to N2
U.S. population data were taken from the U.S. Census Bureau International Database (U.S. Census 2013) and
include the populations of the United States, American Samoa, Guam, Northern Mariana Islands, Puerto Rico, and
the Virgin Islands. The fraction of the U.S. population using wastewater treatment plants is based on data from the
1989, 1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005, 2007, 2009, and 2011 American Housing Survey (U.S.
Census 2011). Data for intervening years were obtained by linear interpolation and data from 2012 were forecasted
using 1990-2011 data. The emission factor (EFi) used to estimate emissions from wastewater treatment for plants
without intentional denitrification was taken from IPCC (2006), while the emission factor (EF2) used to estimate
emissions from wastewater treatment for plants with intentional denitrification was taken from Scheehle and Doom
(2001). Data on annual per capita protein intake were provided by the U.S. Department of Agriculture Economic
Research Service (USDA 2012). Protein consumption data for 2007 through 2012 were extrapolated from data for
1990 through 2006. An emission factor to estimate emissions from effluent (EF3) has not been specifically
estimated for the United States, thus the default IPCC value (0.005 kg N2O-N/kg sewage-N produced) was applied.
The fraction of N in protein (0.16 kg N/kg protein) was also obtained from IPCC (2006). The factor for non-
Waste 8-25
-------
consumed protein and the factor for industrial and commercial co-discharged protein were obtained from IPCC
(2006). Sludge generation was obtained from EPA (1999) for 1988, 1996, and 1998 and from Beecher et al. (2007)
for 2004. Intervening years were interpolated, and estimates for 2005 through 2012 were forecasted from the rest of
the time series. An estimate for the N removed as sludge (NSLUDGE) was obtained by determining the amount of
sludge disposed by incineration, by land application (agriculture or other), through surface disposal, in landfills, or
through ocean dumping. In 2012, 280 Gg N was removed with sludge. Table 8-15 presents the data for U.S.
population, population served by biological denitrification, population served by wastewater treatment plants,
available protein, protein consumed, and nitrogen removed with sludge.
Table 8-15: U.S. Population (Millions), Population Served by Biological Denitrification
(Millions), Fraction of Population Served by Wastewater Treatment (%), Available Protein
(kg/person-year), Protein Consumed (kg/person-year), and Nitrogen Removed with Sludge
(Gg-N/year)
Year Population Population^ WWTP Population Available Protein Protein Consumed N Removed
1990
253
75.6
38.4
29.3
215.6
260.3
2007
2008
2009
2010
2011
2012
305
308
311
313
316
318
2.8
2.9
2.9
3.0
3.0
3.0
79.4
79.4
79.3
80.0
80.6
80.4
40.7
40.8
40.9
41.0
41.1
41.2
31.2
31.3
31.4
31.5
31.6
31.6
265.9
268.7
271.4
274.2
277.0
279.8
Uncertainty and Time-Series Consistency
The overall uncertainty associated with both the 2012 CH4 and N2O emission estimates from wastewater treatment
and discharge was calculated using the IPCC Good Practice Guidance Tier 2 methodology (2000). Uncertainty
associated with the parameters used to estimate CH4 emissions include that of numerous input variables used to
model emissions from domestic wastewater, and wastewater from pulp and paper manufacture, meat and poultry
processing, fruits and vegetable processing, ethanol production, and petroleum refining. Uncertainty associated with
the parameters used to estimate N2O emissions include that of sewage sludge disposal, total U.S. population,
average protein consumed per person, fraction of N in protein, non-consumption nitrogen factor, emission factors
per capita and per mass of sewage-N, and for the percentage of total population using centralized wastewater
treatment plants.
The results of this Tier 2 quantitative uncertainty analysis are summarized in Table 8-16. Methane emissions from
wastewater treatment were estimated to be between 9.3 and 15.4 Tg CO2 Eq. at the 95 percent confidence level (or
in 19 out of 20 Monte Carlo Stochastic Simulations). This indicates a range of approximately 27 percent below to
21 percent above the 2012 emissions estimate of 12.8 Tg CO2 Eq. N2O emissions from wastewater treatment were
estimated to be between 1.2 and 10.1 Tg CO2 Eq., which indicates a range of approximately 75 percent below to 100
percent above the 2012 emissions estimate of 5.03 Tg CO2 Eq.
Table 8-16: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Wastewater
Treatment (Tg COz Eq. and Percent)
Source
Wastewater Treatment
Domestic
Industrial
Wastewater Treatment
2012 Emission
Gas Estimate
(Tg C02 Eq.)
CH4
CH4
CH4
N2O
12.8
7.8
4.9
5.03
Uncertainty Range Relative to Emission
Estimate3
(Tg C02 Eq.) (%)
Lower
Bound
9.3
5.8
2.4
1.2
Upper
Bound
15.4
10.1
6.9
10.1
Lower
Bound
-27%
-26%
-51%
-75%
Upper
Bound
+21%
+29%
+41%
+100%
8-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent
confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above. Methodological recalculations were applied to the entire time-series to ensure time-series consistency from
1990 through 2012. Details on the emission trends through time are described in more detail in the Methodology
section, above.
QA/QC and Verification
A QA/QC analysis was performed on activity data, documentation, and emission calculations. This effort included a
Tier 1 analysis, including the following checks:
• Checked for transcription errors in data input;
• Ensured references were specified for all activity data used in the calculations;
• Checked a sample of each emission calculation used for the source category;
• Checked that parameter and emission units were correctly recorded and that appropriate conversion factors
were used;
• Checked for temporal consistency in time series input data for each portion of the source category;
• Confirmed that estimates were calculated and reported for all portions of the source category and for all years;
• Investigated data gaps that affected emissions estimates trends; and
• Compared estimates to previous estimates to identify significant changes.
All transcription errors identified were corrected. The QA/QC analysis did not reveal any systemic inaccuracies or
incorrect input values.
Recalculations Discussion
Production data were updated to reflect revised USD A NASS datasets. In addition, a new source of data was
identified for pulp and paper production and incorporated this inventory year. These data were used to revise
production values of wood pulp and paper and paperboard for 2002 through 2012. In addition, the most recent
USDA ERS data were used to update protein values from 1990 through 2006. The updated ERS data also resulted in
small changes in forecasted values from 2007.
Using the information summarized in Bicknell et al. (2013) and Aguiar et al. (2013), both pulp and paper and
petroleum refining estimates were updated to be consistent with the most current and representative data available
for these industries. Primarily due to these new data, overall industry emissions from industrial wastewater treatment
decreased by 40% from the 1990-2011 Inventory.
In addition, an improved forecasting methodology for domestic wastewater resulted in small changes to both nitrous
oxide and methane emissions beginning in 2005.
Planned Improvements
The methodology to estimate CH4 emissions from domestic wastewater treatment currently utilizes estimates for the
percentage of centrally treated wastewater that is treated by aerobic systems and anaerobic systems. These data
come from the 1992, 1996, 2000, and 2004 CWNS. The question of whether activity data for wastewater treatment
systems are sufficient across the time series to further differentiate aerobic systems with the potential to generate
small amounts of CH4 (aerobic lagoons) versus other types of aerobic systems, and to differentiate between
anaerobic systems to allow for the use of different MCFs for different types of anaerobic treatment systems,
continues to be explored. The CWNS data for 2008 were evaluated for incorporation into the inventory, but due to
significant changes in format, this dataset is not sufficiently detailed for inventory calculations. However, additional
information and other data continue to be evaluated to update future years of the inventory, including anaerobic
digester data compiled by the North East Biosolids and Residuals Association (NEBRA) in collaboration with
several other entities. These data, available at www.biogasdata.org, are still preliminary, and not yet complete for
Waste 8^7
-------
inclusion in the inventory. EPA will continue to monitor the status of these data as a potential source of digester,
sludge, and biogas data from POTWs.
Data collected under the EPA's GHGRP will be investigated for use in improving the emission estimates for the
industrial wastewater category. Particular attention will be made to ensure time series consistency, as the facility-
level reporting data from EPA's GHGRP are not available for all inventory years as reported in this inventory. In
implementing improvements and integration of data from EPA's GHGRP, the latest guidance from the IPCC on the
use of facility-level data in national inventories will be relied upon.255 For all industries, EPA will continue to
review new research on industrial wastewater characteristics, utilization of treatment systems, and associated
greenhouse gas emissions as it becomes available. Before the incorporation of any new data, EPA will ensure it is
representative of industry conditions.
Wastewater inventory submissions from other countries will be reviewed for additional data and methodologies that
could be used to inform the US wastewater inventory calculations. Items to be investigated include emission factors,
specific methodologies, and additional industries that could be used to improve or supplement the wastewater
treatment emissions calculations. In addition to this investigation, EPA will investigate reports from the Global
Water Research Coalition to inform potential updates to the inventory based on international research.
Currently, for domestic wastewater, it is assumed that all aerobic wastewater treatment systems are well managed
and produce no CH4 and that all anaerobic systems have an MCF of 0.8. Efforts to obtain better data reflecting
emissions from various types of municipal treatment systems are currently being pursued by researchers, including
the Water Environment Research Federation (WERF). This research includes data on emissions from partially
anaerobic treatment systems. In addition, information on flare efficiencies are being reviewed for potential updates
to the inventory.
With respect to estimating N2O emissions, the default emission factors for indirect N2O from wastewater effluent
and direct N2O from centralized wastewater treatment facilities have a high uncertainty. Research is being
conducted by WERF to measure N2O emissions from municipal treatment systems and is periodically reviewed for
its utility for the inventory. In addition, a literature review has been conducted focused on N2O emissions from
wastewater treatment to determine the state of such research and identify data to develop a country-specific N2O
emission factor or alternate emission factor or method. Such data will continue to be reviewed as they are available
to determine if a country-specific N2O emission factor can or should be developed, or if alternate emission factors
should be used. EPA will also follow up with the authors of any relevant studies, including those from WERF, to
determine if there is additional information available on potential methodological revisions.
Previously, new measurement data from WERF were used to develop U.S.-specific emission factors for CH4
emissions from septic systems and incorporated it into the inventory emissions calculation. Due to the high
uncertainty of the measurements forN2O from septic systems, estimates of N2O emissions were not included.
Appropriate emission factors for septic system N2O emissions will continue to be investigated as the data collected
by WERF indicate that septic soil systems are a source of N2O emissions.
In addition, the estimate of N entering municipal treatment systems is under review. The factor that accounts for
non-sewage N in wastewater (bath, laundry, kitchen, industrial components) also has a high uncertainty. Obtaining
data on the changes in average influent N concentrations to centralized treatment systems over the time series would
improve the estimate of total N entering the system, which would reduce or eliminate the need for other factors for
non-consumed protein or industrial flow. The dataset previously provided by the National Association of Clean
Water Agencies (NACWA) was reviewed to determine if it was representative of the larger population of
centralized treatment plants for potential inclusion into the inventory. However, this limited dataset was not
representative of the number of systems by state or the service populations served in the United States, and therefore
could not be incorporated into the inventory methodology. Additional data sources will continue to be researched
with the goal of improving the uncertainty of the estimate of N entering municipal treatment systems.
The value used for N content of sludge continues to be investigated. This value is driving the N2O emissions for
wastewater treatment and is static over the time series. To date, new data have not been identified that would be able
to establish a time series for this value. The amount of sludge produced and sludge disposal practices will also be
255
See: .
8-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
investigated. In addition, based on UNFCCC review comments, improving the transparency of the fate of sludge
produced in wastewater treatment will also be investigated.
A review of other industrial wastewater treatment sources for those industries believed to discharge significant loads
of BOD and COD has been ongoing. Food processing industries have the highest potential for CH4 generation due
to the waste characteristics generated, and the greater likelihood to treat the wastes anaerobically. However, in all
cases there is dated information available on U.S. treatment operations for these industries. Previously, organic
chemicals, the seafood processing industry, and coffee processing were investigated to estimate their potential to
generate CH4. Due to the insignificant amount of CH4 estimated to be emitted and the lack of reliable, up-to-date
activity data, these industries were not selected for inclusion in the inventory. Preliminary analyses of the beer and
malt and dairy products industries have been performed. These industries will continue to be investigated for
incorporation. Other industries will be reviewed as necessary for inclusion in future years of the inventory using
EPA's Permit Compliance System and Toxics Release inventory.
8.3 Waste Incineration (IPCC Source Category
6C)
As stated earlier in this chapter, CO2, N2O, and CH4 emissions from the incineration of waste are accounted for in
the Energy sector rather than in the Waste sector because almost all incineration of municipal solid waste (MSW) in
the United States occurs at waste-to-energy facilities where useful energy is recovered. Similarly, the Energy sector
also includes an estimate of emissions from burning waste tires and hazardous industrial waste, because virtually all
of the combustion occurs in industrial and utility boilers that recover energy. The incineration of waste in the United
States in 2012 resulted in 12.6 Tg CO2 Eq. emissions, over half of which is attributable to the combustion of
plastics. For more details on emissions from the incineration of waste, see Section 3.3 of the Energy chapter.
Additional sources of emissions from waste incineration include non-hazardous industrial waste incineration and
medical waste incineration. As described in Annex 5 of this report, data are not readily available for these sources
and emissions estimates are not provided. Further investigations will be made, including assessing the applicability
of state-level data collected for EPA's National Emission Inventory (NEI).256
8.4 Composting (IPCC Source Category 6D)
Composting of organic waste, such as food waste, garden (yard) and park waste, and sludge, is common in the
United States. Advantages of composting include reduced volume in the waste material, stabilization of the waste,
and destruction of pathogens in the waste material. The end products of composting, depending on its quality, can
be recycled as fertilizer and soil amendment, or be disposed in a landfill.
Composting is an aerobic process and a large fraction of the degradable organic carbon in the waste material is
converted into carbon dioxide (CO2). Methane (CH4) is formed in anaerobic sections of the compost, but it is
oxidized to a large extent in the aerobic sections of the compost. Anaerobic sections are created in composting piles
when there is excessive moisture or inadequate aeration (or mixing) of the compost pile. The estimated CH4
released into the atmosphere ranges from less than 1 percent to a few percent of the initial C content in the material
(IPCC 2006). Depending on how well the compost pile is managed, nitrous oxide (N2O) emissions can be produced.
The formation of N2O depends on the initial nitrogen content of the material and is mostly due to nitrogen oxide
(NOx) denitrification during the later composting stages. Emissions vary and range from less than 0.5 percent to 5
percent of the initial nitrogen content of the material (IPCC 2006). Animal manures are typically expected to
generate more N2O than, for example, yard waste, however data are limited.
256
See .
Waste 8-29
-------
From 1990 to 2012, the amount of material composted in the United States has increased from 3,810 Ggto 18,919
Gg, an increase of approximately 397 percent. From 2000 to 2012, the amount of material composted in the United
States has increased by approximately 27 percent. Emissions of CH4 and N2O from composting have increased by
the same percentage. In 2012, CH4 emissions from composting (see Table 8-17 and Table 8-18) were 1.6 Tg CO2
Eq. (75.7 Gg), and N2O emissions from composting were 1.8 Tg CO2 Eq. (5.7 Gg). The wastes composted
primarily include yard trimmings (grass, leaves, and tree and brush trimmings) and food scraps from residences and
commercial establishments (such as grocery stores, restaurants, and school and factory cafeterias). The composted
waste quantities reported here do not include backyard composting. The growth in composting since the 1990s is
attributable to primarily two factors: (1) steady growth in population and residential housing, and (2) the enactment
of legislation by state and local governments that discouraged the disposal of yard trimmings in landfills. Most bans
on disposal of yard trimmings initiated in the early 1990s (U.S. Composting Council 2010). By 2010, 25 states,
representing about 50 percent of the nation's population, have enacted such legislation (BioCycle, 2010). Despite
these factors, the total amount of waste composted exhibited a downward trend between 2008 and 2009 and then
started recovering every year after that, but it is still not at the same level it was in 2008 (see Table 8-17). The
percent change between 2008 and 2012 is approximately 6 percent. The same trend is observed in the total waste
generated and is consistent with trends in the United States economy, e.g., the beginning of the recession in 2008.
Table 8-17: ChU and NzO Emissions from Composting (Tg COz Eq.)
Activity
CH4
N20
Total
1990
0.3
0.4
0.7
2005
1.6
1.7 •
3.3
2008
1.7
1.9
3.5
2009
1.6
1.8
3.3
2010
1.5
1.7
3.2
2011
1.6
1.7
3.3
2012
1.6
1.8
3.3
Note: Totals may not sum due to independent rounding.
Table 8-18: ChU and NzO Emissions from Composting (Gg)
Activity
CH4
N20
1990 •
15.2
1.1
2005
74.6
5.6
2008
80.2
6.0
2009
75.3
5.6
2010
73.2
5.5
2011
75.1
5.6
2012
75.7
5.7
Note: Totals may not sum due to independent rounding.
Methodology
Methane and N2O emissions from composting depend on factors such as the type of waste composted, the amount
and type of supporting material (such as wood chips and peat) used, temperature, moisture content and aeration
during the process.
The emissions shown in Table 8-17 and Table 8-18 were estimated using the IPCC default (Tier 1) methodology
(IPCC 2006), which is the product of an emission factor and the mass of organic waste composted (note: no CH4
recovery is expected to occur at composting operations):
Et=Mx EFi
where,
Ei = CH4 or N2O emissions from composting, Gg CH4 or N2O,
M = mass of organic waste composted in Gg,
EFi = emission factor for composting, 4 g CH4/kg of waste treated (wet basis) and 0.3 g
N2O/kg of waste treated (wet basis) (IPCC 2006), and
i = designates either CH4 or N2O.
Estimates of the quantity of waste composted (M) are presented in Table 8-19. Estimates of the quantity composted
for 1990, 2005 and 2007 through 2010 were taken from Municipal Solid Waste in the United States: 2010 Facts and
Figures (EPA 2011); estimates of the quantity composted for 2006 were taken from EPA's Municipal Solid Waste
In The United States: 2006 Facts and Figures (EPA 2007); estimates of the quantity composted for 2011 were
taken from EPA's Municipal Solid Waste In The United States: 2011 Facts and Figures (EPA 2013); estimates of
8-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
the quantity composted for 2012 were calculated using the 2011 quantity composted and a ratio of the U.S.
population in 2011 and 2012 (U.S. Census Bureau 2013).
Table 8-19: U.S. Waste Composted (Gg)
Activity 1990 2005 2008 2009 2010 2011 2012
Waste
Composted 3,810 18,643 20,049 18,824 18,298 18,779 18,919
Source: EPA 2007, EPA 2011 and EPA 2013.
Uncertainty and Time-Series Consistency
The estimated uncertainty from the 2006 IPCC Guidelines is ±50 percent for the Tier 1 methodology. Emissions
from composting in 2012 were estimated to be between 1.7 and 5.0 Tg CCh Eq., which indicates a range of 50
percent below to 50 percent above the actual 2012 emission estimate of 3.3 Tg CCh Eq. (see Table 8-20).
Table 8-20 : Tier 1 Quantitative Uncertainty Estimates for Emissions from Composting (Tg
COz Eq. and Percent)
2012 Emission
Source Gas Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate
(Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Composting CH4,N2O 3.3
1.7 5.0 -50% +50%
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2012. Details on the emission trends through time are described in more detail in the Methodology section,
above. Methodological recalculations were applied to the entire time-series to ensure time-series consistency from
1990 through 2012. Details on the emission trends through time are described in more detail in the Methodology
section, above.
QA/QC and Verification
A QA/QC analysis was performed for data gathering and input, documentation, and calculation. A primary focus of
the QA/QC checks was to ensure that the amount of waste composted annually was correct according to the latest
EPAMunicipal Solid Waste In The United States: Facts and Figures report (EPA 2013).
Recalculations Discussion
The estimated amount of waste composted in 2011 was updated relative to the previous Inventory based on new data
contained in EPA's Municipal Solid Waste In The United States: 2011 Facts and Figures (EPA 2013). The amounts
of CH4 and N2O emissions estimates presented in Table 8-17 and Table 8-18 were revised accordingly. No
methodological changes were made.
Planned Improvements
In the future, additional efforts will be made to improve the estimates of CH4 and N2O emissions from composting.
For example, a literature search may be conducted to determine if emission factors specific to various composting
systems and composted materials are available. Further cooperation with estimating emissions in cooperation with
the LULUCF Other section will be made.
Waste 8-31
-------
8.5 Waste Sources of Indirect Greenhouse
Gases
In addition to the main greenhouse gases addressed above, waste generating and handling processes are also sources
of indirect greenhouse gas emissions. Total emissions of NOX, CO, and NMVOCs from waste sources for the years
1990 through 2012 are provided in Table 8-21.
Table 8-21: Emissions of NOX, CO, and NMVOC from Waste (Gg)
Gas/Source 1990 2005 2008 2009 2010 2011 2012
NOx +~| 2~|2 I I I f~
Landfills + I 2 I 2 1 1 1 1
Wastewater Treatment + I + I + + + + +
Miscellaneous3 + I + I + + + + +
CO ll 7l 6 55 55
Landfills I I el 5 5 5 44
Wastewater Treatment + I + I + + + + +
Miscellaneous3 + I + I + + + + +
NMVOCs 673 114 54 49 44 38 38
Wastewater Treatment 57 49 23 21 19 17 17
Miscellaneous3 557 43 20 18 17 15 15
Landfills 58_B__22_B__LO 9 8 7 7
a Miscellaneous includes TSDFs (Treatment, Storage, and Disposal Facilities under the Resource
Conservation and Recovery Act [42 U.S.C. § 6924, SWDA § 3004]) and other waste categories.
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.5 Gg.
Methodology
Emission estimates for 1990 through 2012 were obtained from data published on the National Emission Inventory
(NET) Air Pollutant Emission Trends web site (EPA 2013), and disaggregated based on EPA (2003). Emission
estimates for 2012 for non-EGU and non-mobile sources are held constant from 2011 in EPA (2013). Emission
estimates of these gases were provided by sector, using a "top down" estimating procedure—emissions were
calculated either for individual sources or for many sources combined, using basic activity data (e.g., the amount of
raw material processed) as an indicator of emissions. National activity data were collected for individual source
categories from various agencies. Depending on the source category, these basic activity data may include data on
production, fuel deliveries, raw material processed, etc.
Uncertainty and Time-Series Consistency
No quantitative estimates of uncertainty were calculated for this source category. Methodological recalculations
were applied to the entire time-series to ensure time-series consistency from 1990 through 2012. Details on the
emission trends through time are described in more detail in the Methodology section, above. Methodological
recalculations were applied to the entire time-series to ensure time-series consistency from 1990 through
2012. Details on the emission trends through time are described in more detail in the Methodology section, above.
8-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
9. Other
The United States does not report any greenhouse gas emissions under the Intergovernmental Panel on Climate
Change (IPCC) "Other" sector.
Other 9-1
-------
10.Recalculations and Improvements
Each year, emission and sink estimates are recalculated and revised for all years in the Inventory of U. S. Greenhouse
Gas Emissions and Sinks, as attempts are made to improve both the analyses themselves, through the use of better
methods or data, and the overall usefulness of the report. In this effort, the United States follows both the IPCC
Good Practice Guidance (IPCC 2000) and the 2006 IPCC Guidelines (IPCC 2006), which states, "Both
methodological changes and refinements over time are an essential part of improving inventory quality. It is good
practice to change or refine methods" when: available data have changed; the previously used method is not
consistent with the IPCC guidelines for that category; a category has become key; the previously used method is
insufficient to reflect mitigation activities in a transparent manner; the capacity for inventory preparation has
increased; new inventory methods become available; and for correction of errors."
The results of all methodological changes and historical data updates made in this year's report are presented in this
section; detailed descriptions of each recalculation are contained within each source's description found in this
report, if applicable. Table 10-1 summarizes the quantitative effect of these changes on U.S. greenhouse gas
emissions and sinks and Table 10-2 summarizes the quantitative effect on annual net CO2 fluxes, both relative to the
previously published U.S. Inventory (i.e., the 1990 through 2011 report). These tables present the magnitude of
these changes in units of teragrams of carbon dioxide equivalent (Tg CO2 Eq.).
The Recalculations Discussion section of each source's section presents the details of each recalculation. In general,
when methodological changes have been implemented, the entire time series (i.e., 1990 through 2011) has been
recalculated to reflect the change, per IPCC (2006). Changes in historical data are generally the result of changes in
statistical data supplied by other agencies.
The following ten emission sources and sinks, which are listed in absolute descending order of annual change in
emissions or sequestration between 1990 and 2011, underwent some of the most significant methodological and
historical data changes. A brief summary of the recalculations and/or improvements undertaken is provided for each
of the ten sources.
• Agricultural Soil Management fTV^Oj. Methodological recalculations in the current Inventory were
associated with the following improvements: 1) Driving the DAYCENT simulations with input data for the
excretion of C and N onto Pasture/Range/Paddock based on national livestock population data instead
being internally generated by the DAYCENT model (note that revised total PRP N additions increased
from 6.9 to 7.2 Tg N on average); 2) expanding the number of experimental study sites used to quantify
model uncertainty for direct N2O emissions and bias correction; 3) refining the temperature algorithm that
is used for simulating crop production and carbon inputs to the soil in the DAYCENT biogeochemical
model; and (4) recalculation of Tier 2 organic soil N2O emissions using annual data from the NRI rather
than estimating emissions for every 5 years and holding emissions constant between the years. These
changes resulted in an increase in emissions of approximately 23 per cent on average relative to the
previous Inventory and a decrease in the upper bound of the 95 percent confidence interval for direct N2O
emissions from 40 to 29 percent. The differences are mainly due to the refinement of temperature
algorithm in the model and expansion of the number of field studies used to develop the statistical function
for estimating uncertainty in the model structure and parameters. In particular, additional studies showed
very high N2O emissions during some years that were not captured by DAYCENT. This resulted in a
relatively large adjustment in a portion of the DAYCENT simulated N2O emissions to capture the high N2O
emission rates.
10-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
• Land Use, Land-Use Change, and Forestry (CO2). Changes were driven by modification to the flux
estimates for Settlements Remaining Settlements and Cropland Remaining Cropland. These changes were
influenced by the following:
o A major change was made in the urban trees methodology, which previously relied on a national
estimate of net sequestration per unit tree cover, a national estimate of urban area, and national
estimate of tree cover percentage. The new methodology uses reported state level estimates of
gross sequestration, state level totals for urban area, and state level urban tree cover percentages.
o Changes were made to mineral and organic soil carbon stocks methodology temperature algorithm
that is used for simulating crop production and carbon inputs to the soil in the D AYCENT
biogeochemical model; increasing the number of experimental sites that are used to evaluate the
structural uncertainty in the DAYCENT model; and recalculation of Tier 2 organic soil C
emissions.
o The quantity of applied minerals reported for agricultural liming in the previous Inventory for
2010 has been revised; the updated activity data for 2010 for limestone are approximately 29
thousand metric tons less and the 2010 data for dolomite are approximately 433 thousand metric
tons greater than the data used for the previous Inventory. And updated published 2011 data from
the Minerals Yearbook have replaced those used in the previous Inventory to calculate the quantity
of minerals applied to soil and the emissions from liming.
o In the current Inventory, July to December 2009 and July to December 2010 urea application data
were updated based on new activity data for fertilizer years 2010 and 2011, and the 2009 and 2010
emission estimates were revised accordingly. Similarly, the July to December 2011 urea
application data were updated with assumptions for fertilizer year 2012, and the 2011 emission
estimate was revised accordingly.
• Natural Gas Systems (CH4). EPA received information and data related to the emission estimates through
the Inventory preparation process and previous Inventories' formal public notice periods. EPA carefully
evaluated all relevant information provided, and updates were made to estimates for completions with
hydraulic fracturing and workovers with hydraulic fracturing (refracturing), Natural GasSTAR reductions,
and well counts and completion and workover counts. Emission estimates will continue to be refined to
reflect the most robust data and information available. The recalculations in the current Inventory relative
to the previous report primarily impacted CH4 emission estimates in the production sector, which for the
year 2011, decreased from 53.4 Tg CC>2 Eq. in the previous Inventory to 42.6 Tg CC>2 Eq. in the current
Inventory.
• Substitution of Ozone Depleting Substances (HFCs). A review of the Mobile Vehicle Air Conditioning
(MVAC) light-duty vehicle (LDV) and light-duty truck (LOT) end-uses led to revisions in the assumed
transition scenarios, stock and growth rate assumptions, and equipment lifetime. Updated annual sales and
registration data was used to update the installed base, annual growth rate, and lifetime for the MVAC end-
uses. In addition, although HFC-134a has been the dominant refrigerant in MVACs since the 1990s, an
additional transition to HFO-1234yf was added to the Vintaging Model beginning with vehicles
manufactured in 2012 to reflect a recent shift in new vehicles to HFO-1234yf. Overall, these changes to the
Vintaging Model increased GHG emissions on average by 7 percent across the time series relative to the
previous report.
• Enteric Fermentation (CH4). Recalculations were made relative to the previous Inventory due to changes in
activity data, including the following:
o In the previous Inventory, aggregation in the 1992 feedlot cattle was linked incorrectly. This
correction resulted in a decrease in emissions for that year of 0.2 percent.
o The USD A published minor revisions in several categories that affected historical emissions
estimated for cattle in 2011, including dairy cow milk production for several states and cattle
populations for January 1, 2012. These changes had an insignificant impact on the overall results.
o Calves 4-6 months were added to emissions estimates for the first time in the current Inventory.
The inclusion of calves has increased emissions from beef cattle by approximately 3 percent per
year. In addition, for the first time calf populations for enteric fermentation were differentiated
into dairy and beef calves. During this process, total calf populations were updated slightly, so that
the enteric fermentation calf populations differ an average of 0.9 percent per year from manure
management calf populations.
Recalculations 10-3
-------
o Horse population data was obtained for 1987 and 1992 from USDA census data, resulting in a
change in population estimates for 1990 through 1996. This resulted in an average decrease of 6.3
percent for those years relative to the previous report.
o Populations of American bison and mules and asses were revised to extrapolate data beyond the
2007 census based on a linear trend rather than following trends in bison slaughter and holding
values constant. These changes resulted in average decrease of 3.2 percent and increase of 31.4
percent, respectively, for those years. Additionally, the name of this population group was revised
from mules, burros, and donkeys to mules and asses to be consistent with the IPCC CRF tables.
• Wastewater Treatment (CH4 and N2O). In the current Inventory, production data were updated to reflect
revised USDA National Agricultural Statistics Service (NASS) datasets, relative to the previous report. In
addition, a new source of data was identified for pulp and paper production and incorporated into the
current Inventory. These data were used to revise production values of wood pulp and paper and
paperboard for 2002 through 2012. In addition, the most recent USDA Economic Research Service (ERS)
data were used to update protein values from 1990 through 2006. The updated ERS data also resulted in
small changes in forecasted values from 2007. Using the information summarized in Bicknell, et al. (2013)
and Aguiar, et al. (2013), both pulp and paper and petroleum refining estimates were updated to be
consistent with the most current and representative data available for these industries. Primarily due to
these new data, overall industry emissions from industrial wastewater treatment decreased by 40 percent
from the 1990-2011 Inventory report. In addition, an improved forecasting methodology for domestic
wastewater resulted in small changes to both N2O and CH4 emissions beginning in 2005.
• Carbon Emitted from Non-Energy Uses of Fossil Fuels (CO 2). Relative to the previous Inventory,
emissions from non-energy uses of fossil fuels decreased by an average of 3.2 TgCO2Eq. (2.3 percent)
across the entire time series. Changes ranged from an increase of about 3 Tg CO2 Eq. in 1990 to a decrease
of about 13 Tg CO2 Eq. in 2009. The main catalyst for these recalculations was changes to historic fossil
fuel consumption input data acquired from the Energy Information Administration (El A). The El A
annually revises its fossil fuel consumption estimates, which may affect previously-reported Inventory
emissions from non-energy uses of fossil fuels. Since the methodology for calculating emissions from non-
energy uses of fossil fuels remained the same relative to the previous Inventory, changes to consumption
input data is the primary cause of the recalculations. Overall, the net effect of these changes was a slight
decrease in emission estimates across the entire time series. In addition, EPA's National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data released updated data in December 2013, which
included new data through 2011 and revised data for previous years. Additionally, EPA's MSWFacts and
Figures was released in February 2014, which included data for 2012 and revised data for prior years.
• Coal Mining (CH4). For the current Inventory, updated mine maps were received for the Jim Walter
Resources Blue Creek #4 and #7 mines (JWR 2010) that showed changes in the planned locations of areas
to be mined through. The updated mine plans provided a more accurate depiction of the dates and locations
at which the pre-drainage wells were mined through. As a result, the mined-through dates were adjusted for
some wells relative to the previous Inventory, and underground emissions avoided values changed slightly
for 2011. Prior to the current Inventory, vented degasification emissions from underground coal mines were
typically estimated based on drainage efficiencies reported by either the mining company or Mine Safety
and Health Administration (MSHA). However, beginning in 2011, underground coal mines began
reporting CH4 emissions from degasification systems to EPA under its GHGRP, which requires
degasification quantities to be measured weekly, thus offering a more accurate account than previous
methods. As a result, data reported to EPA's GHGRP in 2012 were used to estimate vented degasification
volumes for those mines. In 2012, GHGRP-reported vented degasification emission totals were
approximately 30 percent lower when compared to the previous estimation method; however, the
difference only represents approximately 1.5 percent of the overall coal mining emission inventory. In
2012, the surface mining emission factor was revised downward from 200 percent to 150 percent of the
average in situ CH4 content of the mined coal seam. In previous Inventory reports, a 200 percent factor
was used as a conservative measure due to a lack of U.S. data. Based on surface mine emissions studies
conducted used in Canada and Australia (King 1994, Saghatfi 2013), this emission factor was adjusted to
more closely align with those studies where actual measurements have been taken of similar coals. While
the gas content of the coal accounts for CH4 liberated from the mined coal, this emission factor accounts for
10-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
additional CH4 released from the over- and under-lying strata surrounding the mined coal seam. The
change was made across the entire time series.
Fossil Fuel Combustion (CO2). The Energy Information Administration (EIA 2014) updated energy
consumption statistics across the time series relative to the previous Inventory. One such revision is the
inclusion of past residential coal estimates into commercial coal statistics for the years 2008 to 2011. These
revisions primarily impacted the previous emission estimates from 2008 to 2011; however, additional
revisions to industrial and transportation petroleum consumption as well as industrial natural gas and coal
consumption impacted emission estimates across the time series. Overall, these changes resulted in an
average annual increase of 1.3 Tg CO2 Eq. (less than 0.1 percent) in CO2 emissions from fossil fuel
combustion for the period 1990 through 2011, relative to the previous report.
Rice Cultivation (CH4). An updated literature review of rice emission factor estimates was conducted for
the current Inventory, resulting in an updated set of regional rice emission factors. In the previous
Inventory, two U.S. average emission factors were applied to rice area harvested—one for the primary crop
(210 kg CH4/hectare-season) and one for the ratoon crop (780 kg CH4/hectare-season). The updated
emission factors, based on the recent literature, replace the primary crop emission factor with two
California-specific emission factors based on flooding practices and an updated non-California primary
crop emission factor of 237 kg CH4/hectare-season. The new emission factors were applied across the full
time series, as they represent the same assumptions about rice cultivation practices. The change in emission
factors resulted, on average, in an 8.3 percent increase in emissions from 1990 to 2011.
Table 10-1: Revisions to U.S. Greenhouse Gas Emissions (Tg COz Eq.)
Gas/Source
CO2
Fossil Fuel Combustion
Electricity Generation
Transportation
Industrial
Residential
Commercial
U.S. Territories
Non-Energy Use of Fuels
Natural Gas Systems
Cement Production
Lime Production
Other Process Uses of Carbonates
Glass Production
Soda Ash Production and Consumption
Carbon Dioxide Consumption
Incineration of Waste
Titanium Dioxide Production
Aluminum Production
Iron and Steel Production & Metallurgical Coke
Production
Ferroalloy Production
Ammonia Production
Urea Consumption for Non-Agricultural Purposes
Phosphoric Acid Production
Petrochemical Production
Silicon Carbide Production and Consumption
Lead Production
1990
(0.1)
(3.5)
+
+
(3.5)
+
NC
3.4
(0.1)
NC
NC
(0.1)
NC
NC
NC
NC
NC
NC
NC
NC
0.1
NC
NC
NC
~
2005
2.9
4.2l
+
+•
4.2l
+
1(1.7)1
0.1
0.7|
(0.3)H
NcB
NcB
(0.1)
NcB
+B
Ncl
Ncl
NcB
Ncl
Ncl
Ncl
O.ll
Ncl
Ncl
NC
2008
(7.9)
2.8
+
0.5
2.1
(0.7)
1.0
NC
(11.5)
0.1
0.6
(0.4)
NC
NC
(0.1)
NC
+
NC
NC
NC
NC
0.5
NC
+
NC
NC
NC
2009
(11.8)
3.3
+
(1.5)
4.9
(0.7)
0.6
NC
(15.9)
0.4
(0.2)
NC
NC
(0.1)
NC
+
NC
NC
NC
NC
0.6
+
+
NC
NC
NC
2010
(14.1)
(3.2)
+
1.2
(4.7)
0.2
0.1
NC
(12.0)
0.3
(0.3)
NC
NC
(0.1)
0.1
+
NC
NC
NC
NC
0.5
0.4
+
NC
NC
NC
2011
(20.7)
(6.1)
+
2.9
(4.5)
(3.8)
(0.6)
(0.1)
(13.2)
2.7
0.4
(0.3)
0.2
NC
(0.1)
+
0.1
(0.2)
NC
(4.3)
NC
0.6
(0.3)
+
NC
NC
NC
Average
Annual
Change
(1.5)
1.3
+
0.1
1.3
(0.2)
(3.2)
0.2
0.6
(0.3)
+
NC
(0.1)
+
+
NC
(0.2)
NC
0.1
+
0.1
NC
NC
NC
Recalculations 10-5
-------
Zinc Production
Cropland Remaining Cropland
Wetlands Remaining Wetlands
Petroleum Systems
Land Use, Land-Use Change, and Forestry (Sink)"
Biomass - Wood"
International Bunker Fuels"
Biomass - Ethanol"
CH4
Stationary Combustion
Mobile Combustion
Coal Mining
Abandoned Underground Coal Mines
Natural Gas Systems
Petroleum Systems
Petrochemical Production
Silicon Carbide Production and Consumption
Iron and Steel Production & Metallurgical Coke
Production
Ferroalloy Production
Enteric Fermentation
Manure Management
Rice Cultivation
Field Burning of Agricultural Residues
Forest Land Remaining Forest Land
Landfills
Wastewater Treatment
Composting
Incineration of Waste
International Bunker Fuels"
N2O
Stationary Combustion
Mobile Combustion
Adipic Acid Production
Nitric Acid Production
Manure Management
Agricultural Soil Management
Field Burning of Agricultural Residues
Wastewater Treatment
N2O from Product Uses
Incineration of Waste
Settlements Remaining Settlements
Forest Land Remaining Forest Land
Composting
Wetlands Remaining Wetlands
International Bunker Fuels"
HFCs
Substitution of Ozone Depleting Substances
HCFC-22 Production
Semiconductor Manufacture
PFCs
Aluminum Production
Semiconductor Manufacture
SF6
Electrical Transmission and Distribution
Semiconductor Manufacture
NC
NC
NC
(36.6)1
+
NC
NC
(4.2)
+
NC
(3.0)
NC
(4.8)
0.6
NC
NC
NC
NC
o.eB
0.1
+
+
(2.8)
NC
NC
NC
54.3
+
NC
NC
NC
+1
54.2
+
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
(32.9)
+
NC
NC
(7.9)
_,_
+
(3.3)
NC
(7.0)
(0.4)
NC
NC
NC
"
0.7
+
0.1
(0.4)
(3.1)
NC
NC
NC
59.7
+
+
NC
NC
NC
59.8
+
(0.2)
NC
NC
NC
0.1
NC
NC
NC
4.8
4.8
NC
+
(0.6)
NC
1(0.6)
(0.3)
(0.1)
(0.2)
NC
NC
NC
NC
(78.4)
1.9
NC
NC
(12.8)
0.1
+
(3.6)
NC
(11.8)
(1.2)
NC
NC
NC
NC
5.6
0.6
+
0.1
0.7
(3.3)
NC
NC
NC
73.6
+
+
NC
NC
NC
73.6
+
(0.1)
NC
NC
NC
0.1
NC
NC
NC
18.6
18.7
NC
(0.1)
(1.5)
NC
(1.5)
(0.7)
(0.2)
(0.4)
NC
NC
NC
(79.0)
4.5
NC
+
(7.3)
0.4
+
(3.2)
NC
(7.8)
(1.4)
NC
NC
NC
NC
5.5
0.6
+
0.1
2.0
(3.4)
NC
NC
NC
73.5
0.1
+
NC
NC
NC
73.6
+
(0.2)
NC
NC
+
0.1
NC
NC
NC
23.2
23.3
NC
(0.1)
(1.2)
NC
(1.2)
(0.2)
(0.5)
(0.3)
0.2
NC
(79.2)
(0.5)
NC
+
(7.2)
0.1
+
(3.2)
NC
(8.9)
(1.3)
NC
NC
NC
NC
5.6
0.7
+
0.1
3.2
(3.4)
NC
NC
NC
65.4
+
+
NC
+
NC
65.6
+
(0.2)
NC
NC
+
+
NC
NC
NC
22.7
22.9
NC
(0.2)
(2.2)
NC
(2.2)
(0.3)
(0.5)
(0.6)
(0.3)
(75.3)
2.4
0.3
0.1
(8.9)
0.1
+
(3.4)
(11.5)
(1.0)
NC
NC
NC
NC
5.6
0.5
0.1
(0.2)
4.3
(3.3)
+
NC
+
60.3
(0.4)
+
NC
0.3
+
60.6
+
(0.2)
NC
NC
+
(0.2)
+
NC
+
19.6
19.8
NC
(0.2)
(1.0)
NC
(1.0)
1.4
0.2
(0.2)
(30.7)
0.5
+
+
(8.0)
+
+
(3.1)
(8.2)
(0.1)
NC
NC
NC
NC
5.5
0.6
+
+
0.4
(3.2)
+
NC
+
56.4
+
+
NC
+
+
56.4
+
(0.1)
NC
NC
+
+
+
NC
+
6.2
6.2
NC
+
(0.4)
NC
(0.4)
(0.1)
(0.1)
(0.1)
10-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Magnesium Production and Processing NC + + 0-7
0.9 1.5 0.1
Net Change in Total Emissions" 50.0 58.5 69.3 76.2 64.4 50.7
Percent Change 0.8% 0.8% 1.0% 1.2% 0.9% 0.8%
+ Absolute value does not exceed +5 Tg CCh Eq. or +5 percent.
Parentheses indicate negative values
NC (No Change)
a Not included in emissions total.
b Excludes net CCh flux from Land Use, Land-Use Change, and Forestry, and emissions from International
Bunker Fuels.
Note: Totals may not sum due to independent rounding.
Table 10-2: Revisions to Annual Net COz Fluxes from Land Use, Land-Use Change, and
Forestry (Tg COz Eq.)
Component: Net CCh Flux From
Land Use, Land-Use Change,
and Forestry
Forest Land Remaining Forest Land
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Settlements Remaining Settlements
Other
Net Change in Total Flux
Percent Change
1990
(7.8)
(17.8)1
5.8l
(4.3)
0.3
(12.9)1
NC
(36.6)
-4.6%
2005
(22.2)
(8.8)
7.3
6.6
2.0
(0.4)
(32.9)
-3.3%
2008
(37.7)
(24.7)
2.3
(0.4)
0.3
(17.9)
(0.3)
(78.4)
-8.7%
2009
(38.1)
(24.7)
2.3
(0.5)
0.2
(18.1)
(0.3)
(79.0)
-8.9%
2010
(38.1)
(24.7)
2.3
(0.6)
0.2
(18.2)
(0.2)
(79.2)
-8.9%
Average
Annual
2011 Change
(33.6)
(24.7)
2.3
(0.7)
0.2
(18.4)
(0.4)
(75.3)
-8.3%
(9.3)
(11.5)
5.3
1.9
1.2
(16.0)
(0.1)
NC (No Change)
Note: Numbers in parentheses indicate a decrease in estimated net flux of CCh to the atmosphere, or
an increase in net sequestration.
Note: Totals may not sum due to independent rounding.
+ Absolute value does not exceed 0.05 Tg CCh Eq. or 0.05 percent
Recalculations 10-7
-------
-------
11.References
Executive Summary
BEA (2013) 2013 Comprehensive Revision of the National Income and Product Accounts: Current-dollar and
"real" GDP, 1929-2032. Bureau of Economic Analysis (BEA), U.S. Department of Commerce, Washington, DC.
Last Modified December 2013. Available online at < http://www.bea.gov/national/index.htnrfgdp >.
EIA (2014) Electricity Generation. Monthly Energy Review, February 2014., Energy Information Administration,
U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2013/11).
EIA (2012) International Energy Annual 2012. Energy Information Administration (EIA), U.S. Department of
Energy. Washington, DC. Updated October 2012. Available online at
.
EPA (2013) "1970 - 2013 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available
online at .
IPCC (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovern-mental Panel on Climate Change [Stacker, T.F., D. Qin, G.-K. Plattner, M.
Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 1535 pp.
IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen,
M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United
Kingdom 996 pp.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, J. Penman, et al. (eds.). Available
online at . August 13, 2004.
IPCC (2001) Climate Change 2001: The Scientific Basis. Intergovernmental Panel on Climate Change, J.T.
Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, C.A. Johnson, and K. Maskell (eds.).
Cambridge University Press. Cambridge, United Kingdom.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.,
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change. Montreal. May
2000. IPCC-XWDoc. 10 (1.IV.2000).
References 11-1
-------
IPCC (1996) Climate Change 1995: The Science of Climate Change. Intergovernmental Panel on Climate Change,
J.T. Houghton, L.G. Meira Filho, B.A. Callander, N. Harris, A. Kattenberg, and K. Maskell. (eds.). Cambridge
University Press. Cambridge, United Kingdom.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
NOAA/ESRL (2013) "Trends in Atmospheric Carbon Dioxide." Available online at
. 10 December 2013.
UNFCCC (2003) National Communications: Greenhouse Gas Inventories from Parties included in Annex I to the
Convention, UNFCCC Guidelines on Reporting and Review. Conference of the Parties, Eighth Session, New Delhi.
(FCCC/CP/2002/8). March 28, 2003.
U.S. Census Bureau (2013) U.S. Census Bureau International Database (IDE). December 2013. Available online at
.
CDIAC (2013) "Recent Greenhouse Gas Concentrations." TJ. Biasing; DOI: 10.3334/CDIAC/atg.032. Available
online at . 9 December 2013.
EPA (2009) Technical Support Document for the Endangerment and Cause or Contribute Findings for Greenhouse
Gases Under Section 202(a) of the Clean Air Act. U.S. Environmental Protection Agency. December 2009.
IPCC (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovern-mental Panel on Climate Change [Stacker, T.F., D. Qin, G.-K. Plattner, M.
Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 1535 pp.
IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen,
M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United
Kingdom 996 pp.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, J. Penman, et al. (eds.). Available
online at . August 13, 2004.
IPCC (2001) Climate Change 2001: The Scientific Basis. Intergovernmental Panel on Climate Change, J.T.
Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, C.A. Johnson, and K. Maskell (eds.).
Cambridge University Press. Cambridge, United Kingdom.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change. Montreal. May
2000. IPCC-XWDoc. 10 (1.IV.2000).
IPCC (1999) Aviation and the Global Atmosphere. Intergovernmental Panel on Climate Change. J.E. Penner, et al.
(eds.). Cambridge University Press. Cambridge, United Kingdom.
IPCC (1996) Climate Change 1995: The Science of Climate Change. Intergovernmental Panel on Climate Change,
J.T. Houghton, L.G. Meira Filho, B.A. Callander, N. Harris, A. Kattenberg, and K. Maskell (eds.). Cambridge
University Press. Cambridge, United Kingdom.
11-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
IPCC/TEAP (2005) Special Report: Safeguarding the Ozone Layer and the Global Climate System, Chapter 4:
Refrigeration. 2005. Available at http://www.auto-
ts.com/hcfc/technology%20option/Refrigeration/transport%20refrigeration.pdf
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
Jacobson, M.Z. (2001) "Strong Radiative Heating Due to the Mixing State of Black Carbon in Atmospheric
Aerosols." Nature, 409:695-697.
NOAA/ESRL (2013) "Trends in Atmospheric Carbon Dioxide." Available online at
. 9 December 2013.
UNEP/WMO (1999) Information Unit on Climate Change. Framework Convention on Climate Change. Available
online at .
UNFCCC (2006) Updated UNFCCC Reporting Guidelines on Annual Inventories Following Incorporation of the
Provisions of Decision 14/CP. 11. United Nations Framework Convention on Climate Change, Nairobi.
(FCCC/SBSTA/2006/9). August 16, 2006.
Trends in Greenhouse Gas Emissions
BEA (2013) 2009 Comprehensive Revision of the National Income and Product Accounts: Current-dollar and
"real" GDP, 1929-2009. Bureau of Economic Analysis (BEA), U.S. Department of Commerce, Washington, DC. .
Available online at < http://www.bea.gov/national/index.htnrfgdp >.
Duffield, J. (2006) Personal communication. Jim Duffield, Office of Energy Policy and New Uses, USDA and
Lauren Flinn, ICF International. December 2006.
EIA (2014) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, February 2014, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2013/10).
EPA (2013). "1970 - 2013 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards, November
2013. Available online at .
IPCC (2001) Climate Change 2001: The Scientific Basis. Intergovernmental Panel on Climate Change, J.T.
Houghton, Y. Ding, DJ. Griggs, M. Noguer, P.J. van der Linden, X. Dai, C.A. Johnson, and K. Maskell (eds.).
Cambridge University Press. Cambridge, United Kingdom.
U.S. Census Bureau (2013) U.S. Census Bureau International Database (IDE). Available online at
.
Energy
EIA (2010) Indicators: CO2 Emissions. International Energy Statistics 2010. Energy Information Administration
.
Carbon Dioxide Emissions from Fossil Fuel Combustion
AAR (2008 through 2013) Railroad Facts. Policy and Economics Department, Association of American Railroads,
Washington, DC. Obtained from Clyde Crimmel at AAR.
AISI (2004 through 2013a) Annual Statistical Report, American Iron and Steel Institute, Washington, DC.
References 11-3
-------
APTA (2007 through 2013) Public Transportation Fact Book. American Public Transportation Association,
Washington, DC. Available online at .
APTA (2006) Commuter Rail National Totals. American Public Transportation Association, Washington, DC.
Available online at .
BEA (2013) 2011 Comprehensive Revision of the National Income and Product Accounts: Current-dollar and
"real" GDP, 1929-2012. Bureau of Economic Analysis (BEA), U.S. Department of Commerce, Washington, DC.
November, 2013. Available online at < http://www.bea.gov/national/index.htrrrfgdp >.
BEA (1991 through 2013) Unpublished BE-36 survey data. Bureau of Economic Analysis, U.S. Department of
Commerce. Washington, DC.
Benson, D. (2002 through 2004) Unpublished data. Upper Great Plains Transportation Institute, North Dakota State
University and American Short Line & Regional Railroad Association.
Coffeyville Resources Nitrogen Fertilizers (2013) Nitrogen Fertilizer Operations. Available online at
.
Dakota Gasification Company (2006) CO2 Pipeline Route and Designation Information. Bismarck, ND. Available
online at.
DESC (2013) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense
Energy Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, DC.
DHS (2008) Email Communication. ElissaKay, Department of Homeland Security and Joe Aamidor, ICF
International. January 11, 2008.
DOC (1991 through 2013) Unpublished Report of Bunker Fuel Oil Laden on Vessels Cleared for Foreign Countries.
Form-563. Foreign Trade Division, Bureau of the Census, U.S. Department of Commerce. Washington, DC.
DOE (1993 through 2013) Transportation Energy Data Book. Office of Transportation Technologies, Center for
Transportation Analysis, Energy Division, Oak Ridge National Laboratory. ORNL-6978.
DOE (2012) 2010 Worldwide Gasification Database. National Energy Technology Laboratory and Gasification
Technologies Council. Available online at
. Accessed on 15 March
2012
DOT (1991 through 2013) Airline Fuel Cost and Consumption. U.S. Department of Transportation, Bureau of
Transportation Statistics, Washington, DC. DAI-10. http://www.transtats.bts.gov/fuel.asp.
Eastman Gasification Services Company (2011) Project Data on Eastman Chemical Company's Chemicals-from-
Coal Complex in Kingsport, TN. Available online at
.
EIA (2014) Monthly Energy Review, February 2014, Energy Information Administration, U.S. Department of
Energy, Washington, DC. DOE/EIA-0035(2013/12).
EIA (1991 through 2013) Fuel Oil and Kerosene Sales. Energy Information Administration, U.S. Department of
Energy. Washington, DC. Available at: http://www.eia.gov/petroleum/fueloilkerosene/
EIA (2013a) Natural Gas Annual 2012. Energy Information Administration, U.S. Department of Energy.
Washington, DC. DOE/EIA-0131(06).
EIA (2013b) Quarterly Coal Report: April - June 2013. Energy Information Administration, U.S. Department of
Energy. Washington, DC. DOE/EIA-0121.
EIA (2013c) U.S. Energy-Related Carbon Dioxide Emissions, 2012. Energy Information Administration, U.S.
Department of Energy. Washington, DC. October 2013. Available online at
.
EIA (2012). International Energy Statistics 2012. Energy Information Administration, U.S. Department of Energy.
Washington, DC. < http://tonto.eia.doe.gov/cfapps/ipdbproject/IEDIndex3.cfm>.
11-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
EIA (2009a) Emissions of Greenhouse Gases in the United States 2008, Draft Report. Office of Integrated Analysis
and Forecasting, Energy Information Administration, U.S. Department of Energy. Washington, DC. DOE-EIA-
0573(2009).
EIA (2009b) Manufacturing Consumption of Energy 2006. Energy Information Administration, U.S. Department of
Energy. Washington, DC. Released July, 2009.
EIA (2008) Historical Natural Gas Annual, 1930- 2008. Energy Information Administration, U.S. Department of
Energy. Washington, DC.
EIA (2007) Personal Communication. Joel Lou, Energy Information Administration, and Aaron Beaudette, ICF
International. Residual and Distillate Fuel Oil Consumption for Vessel Bunkering (Both International and Domestic)
for American Samoa, U.S. Pacific Islands, and Wake Island. October 24, 2007.
EIA (2002) Alternative Fuels Data Tables. Energy Information Administration, U.S. Department of Energy.
Washington, DC. Available online at .
EIA (2001) U.S. Coal, Domestic and International Issues. Energy Information Administration, U.S. Department of
Energy. Washington, DC. March 2001.
EPA(2013a) Acid Rain Program Dataset 1996-2012. Office of Air and Radiation, Office of Atmospheric Programs,
U.S. Environmental Protection Agency, Washington, D.C.
EPA (2013b; NONROAD 2008a Model. Office of Transportation and Air Quality, U.S. Environmental Protection
Agency. Available online at .
EPA (2013cj. Motor Vehicle Emissions Simulator (Moves) 2010b. Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Available online at < http://www.epa.gov/otaq/models/moves/index.htm>.
EPA (20 lOa) Carbon Content Coefficients Developed for EPA's Mandatory Reporting Rule. Office of Air and
Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
Erickson, T. (2003) Plains CC>2 Reduction (PCOR) Partnership. Presented at the Regional Carbon Sequestration
Partnership Meeting Pittsburgh, Pennsylvania, Energy and Environmental Research Center, University of North
Dakota. November 3, 2003. Available online at .
FAA (2014) Personal Communication between FAA and Leif Hockstad for aviation emissions estimates from the
Aviation Environmental Design Tool (AEDT). January 2014.
Fitzpatrick, E. (2002) The Weyburn Project: A Model for International Collaboration. Available online at
.
FHWA (1996 through 2014) Highway Statistics. Federal Highway Administration, U.S. Department of
Transportation, Washington, DC. Report FHWA-PL-96-023-annual. Available online at
.
FRB (2013) Industrial Production and Capacity Utilization. Federal Reserve Statistical Release, G. 17, Federal
Reserve Board. Available online at . March 30, 2012.
Gaffney, J. (2007) Email Communication. John Gaffney, American Public Transportation Association and Joe
Aamidor, ICF International. December 17, 2007.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
Jacobs, G. (2010) Personal communication. Gwendolyn Jacobs, Energy Information Administration and Rubaab
Bhangu, ICF International. U.S. Territories Fossil Fuel Consumption, 1990-2009. Unpublished. U.S. Energy
Information Administration. Washington, DC.
References 11-5
-------
Marland, G. and A. Pippin (1990) "United States Emissions of Carbon Dioxide to the Earth's Atmosphere by
Economic Activity." Energy Systems and Policy, 14(4):323.
SAIC/EIA (2001) Monte Carlo Simulations of Uncertainty in U.S. Greenhouse Gas Emission Estimates. Final
Report. Prepared by Science Applications International Corporation (S AIC) for Office of Integrated Analysis and
Forecasting, Energy Information Administration, U.S. Department of Energy. Washington, DC. June 22, 2001.
U.S. Bureau of the Census (2011) Current Industrial Reports Fertilizer Materials and Related Products: 2010
Summary. Available online at .
USAA(2012a) U.S. Primary Aluminum Production 2011. U.S. Aluminum Association, Washington, DC. January,
2012.
USAA (2012b) U.S. Primary Aluminum Production 2012. U.S. Aluminum Association, Washington, DC.
October, 2012.
USAF (1998) Fuel Logistics Planning. U.S. Air Force: AFPAM23-221. May 1, 1998.
USGS (1994 through 2011) Minerals Yearbook: Lead Annual Report. U.S. Geological Survey, Reston, VA.
USGS (1991 through 2011) Minerals Yearbook: Manufactured Abrasives Annual Report. U.S. Geological Survey,
Reston, VA.
USGS (2011) 2010Mineral Yearbook; Aluminum [AdvancedRelease]. U.S. Geological Survey, Reston, VA.
USGS (1991 through 2010a)Minerals Yearbook: Silicon Annual Report. U.S. Geological Survey, Reston, VA.
USGS (1991 through 20lOb) Mineral Yearbook: Titanium Annual Report. U.S. Geological Survey, Reston, VA.
USGS (2010) 2009 Mineral Commodity Summaries: Aluminum. U.S. Geological Survey, Reston, VA.
USGS (2009) 2008 Mineral Yearbook: Aluminum. U.S. Geological Survey, Reston, VA.
USGS (2007) 2006Mineral Yearbook: Aluminum. U.S. Geological Survey, Reston, VA.
USGS (1995, 1998, 2000 through 2002) Mineral Yearbook: Aluminum Annual Report. U.S. Geological Survey,
Reston, VA.
Whorton, D. (2006 through 2012) Personal communication, Class II and III Rail energy consumption, American
Short Line and Regional Railroad Association.
Stationary Combustion (excluding CO2)
EIA (2014a) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, February 2014, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2013/02).
EIA (2014b) Electricity in the United States. Electricty Explained. Energy Information Administration, U.S.
Department of Energy, Washington, DC. Available online at
EPA (2012) "2009 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards.
EPA (2010; NONROAD 2009a Model. Office of Transportation and Air Quality, U.S. Environmental Protection
Agency. Available online at .
EPA (2003) E-mail correspondance. Air pollutant data. Office of Air Pollution to the Office of Air Quality Planning
and Standards, U.S. Environmental Protection Agency (EPA). December 22, 2003.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
11-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. ,
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change. Montreal. May
2000. IPCC-XWDoc. 10 (1.IV.2000).
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
Jacobs, G. (2010) Personal communication. Gwendolyn Jacobs, Energy Information Administration and Rubaab
Bhangu, ICF International. U.S. Territories Fossil Fuel Consumption, 1990-2009. Unpublished. U.S. Energy
Information Administration. Washington, DC.
SAIC/EIA (2001) Monte Carlo Simulations of Uncertainty in U.S. Greenhouse Gas Emission Estimates. Final
Report. Prepared by Science Applications International Corporation (SAIC) for Office of Integrated Analysis and
Forecasting, Energy Information Administration, U.S. Department of Energy. Washington, DC. June 22, 2001.
Mobile Combustion (excluding CO2)
AAR (2008 through 2013) Railroad Facts. Policy and Economics Department, Association of American Railroads,
Washington, DC. Obtained from Clyde Crimmel at AAR.
ANL (2006) Argonne National Laboratory (2006) GREET model Version 1.7. June 2006.
APTA (2007 through 2013) Public Transportation Fact Book. American Public Transportation Association,
Washington, DC. Available online at .
APTA (2006) Commuter Rail National Totals. American Public Transportation Association, Washington, DC.
Available online at .
Benson, D. (2002 through 2004) Personal communication. Unpublished data developed by the Upper Great Plains
Transportation Institute, North Dakota State University and American Short Line & Regional Railroad Association.
BEA (1991 through 2013) Unpublished BE-36 survey data. Bureau of Economic Analysis, U.S. Department of
Commerce. Washington, DC.
Browning, L. (2009) Personal communication with Lou Browning, "Suggested New Emission Factors for Marine
Vessels.", ICF International.
Browning, L. (2005) Personal communication with Lou Browning, Emission control technologies for diesel
highway vehicles specialist, ICF International.
Browning, L. (2003) "VMT Projections for Alternative Fueled and Advanced Technology Vehicles through 2025."
13th CRC On-Road Vehicle Emissions Workshop. April 2003.
DHS (2008) Email Communication. ElissaKay, Department of Homeland Security and Joe Aamidor, ICF
International. January 11, 2008.
DESC (2013) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense
Energy Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, DC.
DOC (1991 through 2013) Unpublished Report of Bunker Fuel Oil Laden on Vessels Cleared for Foreign Countries.
Form-563. Foreign Trade Division, Bureau of the Census, U.S. Department of Commerce. Washington, DC.
DOE (1993 through 2013) Transportation Energy Data Book. Office of Transportation Technologies, Center for
Transportation Analysis, Energy Division, Oak Ridge National Laboratory. ORNL-6978.
DOT (1991 through 2013) Airline Fuel Cost and Consumption. U.S. Department of Transportation, Bureau of
Transportation Statistics, Washington, DC. DAI-10. http://www.transtats.bts.gov/fuel.asp.
EIA (2014) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, February 2014, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2012/09).
EIA (1991 through 2013) Fuel Oil and Kerosene Sales. Energy Information Administration, U.S. Department of
Energy. Washington, DC. Available at: http://www.eia.gov/petroleum/fueloilkerosene/
References 11-7
-------
EIA (2007 through 2011) Natural Gas Annual. Energy Information Administration, U.S. Department of Energy,
Washington, DC. DOE/EIA-0131(11).
EIA (2011) Annual Energy Review 2010. Energy Information Administration, U.S. Department of Energy,
Washington, DC. DOE/EIA-0384(2011). October 19, 2011.
EIA (2008) "Table 3.1: World Petroleum Supply and Disposition." International Energy Annual. Energy
Information Administration, U.S. Department of Energy. Washington, DC. Available online at
.
EIA (2007) Personal Communication. Joel Lou, Energy Information Administration and Aaron Beaudette, ICF
International. Residual and Distillate Fuel Oil Consumption for Vessel Bunkering (Both International and Domestic)
for American Samoa, U.S. Pacific Islands, and Wake Island. October 24, 2007.
EIA (2002) Alternative Fuels Data Tables. Energy Information Administration, U.S. Department of Energy,
Washington, DC. Available online at .
EPA (2013ay). Motor Vehicle Emissions Simulator (Moves) 2010b. Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Available online at < http://www.epa.gov/otaq/models/moves/index.htm>.
EPA (2013b; NONROAD 2008a Model. Office of Transportation and Air Quality, U.S. Environmental Protection
Agency. Available online at .
EPA (2011) Motor Vehicle Emission Simulator (MOVES). Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Available online at .
EPA (2008) "1970 - 2007 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available
online at .
EPA (2007a) Annual Certification Test Results Report. Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Available online at .
EPA (2007b) Confidential Engine Family Sales Data Submitted To EPA By Manufacturers. Office of
Transportation and Air Quality, U.S. Environmental Protection Agency.
EPA (2000) Mobile6 Vehicle Emission Modeling Software. Office of Mobile Sources, U.S. Environmental
Protection Agency, Ann Arbor, Michigan.
EPA (1999a) Emission Facts: The History of Reducing Tailpipe Emissions. Office of Mobile Sources. May 1999.
EPA 420-F-99-017. Available online at .
EPA (1999b) Regulatory Announcement: EPA's Program for Cleaner Vehicles and Cleaner Gasoline. Office of
Mobile Sources. December 1999. EPA420-F-99-051. Available online at .
EPA (1998) Emissions of Nitrous Oxide from Highway Mobile Sources: Comments on the Draft Inventory of U.S.
Greenhouse Gas Emissions and Sinks, 1990-1996. Office of Mobile Sources, Assessment and Modeling Division,
U.S. Environmental Protection Agency. August 1998. EPA420-R-98-009.
EPA (1997) Mobile Source Emission Factor Model (MOBILESa). Office of Mobile Sources, U.S. Environmental
Protection Agency, Ann Arbor, Michigan.
EPA (1994a) Automobile Emissions: An Overview. Office of Mobile Sources. August 1994. EPA 400-F-92-007.
Available online at .
EPA (1994b) Milestones in Auto Emissions Control. Office of Mobile Sources. August 1994. EPA 400-F-92-014.
Available online at .
EPA (1993) Automobiles and Carbon Monoxide. Office of Mobile Sources. January 1993. EPA 400-F-92-005.
Available online at .
Esser, C. (2003 through 2004) Personal Communication with Charles Esser, Residual and Distillate Fuel Oil
Consumption for Vessel Bunkering (Both International and Domestic) for American Samoa, U.S. Pacific Islands,
and Wake Island.
11-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
FAA (2014) Personal Communication between FAA and Leif Hockstad for aviation emissions estimates from the
Aviation Environmental Design Tool (AEDT). January 2014.
FHWA (1996 through 2014) Highway Statistics. Federal Highway Administration, U.S. Department of
Transportation, Washington, DC. Report FHWA-PL-96-023-annual. Available online at
.
Gaffney, J. (2007) Email Communication. John Gaffney, American Public Transportation Association and Joe
Aamidor, ICF International. December 17, 2007.
ICF (2006a) Revised Gasoline Vehicle EFsfor LEV and Tier 2 Emission Levels. Memorandum from ICF
International to John Davies, Office of Transportation and Air Quality, U.S. Environmental Protection Agency.
November 2006.
ICF (2006b) Revisions to Alternative Fuel Vehicle (AFV) Emission Factors for the U.S. Greenhouse Gas Inventory.
Memorandum from ICF International to John Davies, Office of Transportation and Air Quality, U.S. Environmental
Protection Agency. November 2006.
ICF (2004) Update of Methane and Nitrous Oxide Emission Factors for On-Highway Vehicles. Final Report to U.S.
Environmental Protection Agency. February 2004.
IPCC/UNEP/OECD/IEA (1997) Revised 1996IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency, Paris, France.
Lipman, T. and M. Delucchi (2002) "Emissions of Nitrous Oxide and Methane from Conventional and Alternative
Fuel Motor Vehicles." Climate Change, 53:477-516.
Santoni, G., B. Lee, E. Wood, S. Herndon, R. Miake-Lye, S Wofsy, J. McManus, D. Nelson, M. Zahniser. Aircraft
emissions of methane and nitrous oxide during the alternative aviation fuel experiment. Environ Sci Technol. 2011
Aug 15; 45(16):7075-82.
U.S. Census Bureau (2000) Vehicle Inventory and Use Survey. U.S. Census Bureau, Washington, DC. Database CD-
EC97-VIUS.
Unnasch, S., L. Browning, and E. Kassoy (2001) Refinement of Selected Fuel-Cycle Emissions Analyses, Final
Report to ARE.
Whorton, D. (2006 through 2012) Personal communication, Class II and III Rail energy consumption, American
Short Line and Regional Railroad Association.
Carbon Emitted from Non-Energy Uses of Fossil Fuels
ACC (2013) "U.S. Resin Production & Sales: 2012 vs. 2011," American Chemistry Council. Available online at:
ACC (2012) "Guide to the Business of Chemistry, 2012" American Chemistry Council.
ACC (2003-2011) "PIPS Year-End Resin Statistics for 2010: Production, Sales and Captive Use." Available online
at .
Bank of Canada (2013) Financial Markets Department Year Average of Exchange Rates. Available online at <
http://www.bankofcanada.ca/stats/assets/pdf/nraa-2012.pdf >
Bank of Canada (2012) Financial Markets Department Year Average of Exchange Rates. Available online at <
http://www.bankofcanada.ca/stats/assets/pdf/nraa-2011 .pdf>
EIA (2014) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, February 2014, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2013/2).
EIA (2013) EIA Manufacturing Consumption of Energy (MECS) 2010, U.S. Department of Energy, Energy
Information Administration, Washington, DC.
References 11-9
-------
EIA (2010) EIA Manufacturing Consumption of Energy (MECS) 2006, U.S. Department of Energy, Energy
Information Administration, Washington, DC.
EIA (2005) EIA Manufacturing Consumption of Energy (MECS) 2002, U.S. Department of Energy, Energy
Information Administration, Washington, DC.
EIA (2001) EIA Manufacturing Consumption of Energy (MECS) 1998, U.S. Department of Energy, Energy
Information Administration, Washington, DC.
EIA (1997) EIA Manufacturing Consumption of Energy (MECS) 1994, U.S. Department of Energy, Energy
Information Administration, Washington, DC.
EIA (1994) EIA Manufacturing Consumption of Energy (MECS) 1991, U.S. Department of Energy, Energy
Information Administration, Washington, DC.
EPA (2014) Municipal Solid Waste in the United States: 2012 Facts and Figures. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency, Washington, DC. Available online at
.
EPA (2013a) "1970 - 2013 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available
online at .
EPA (2013b) Municipal Solid Waste in the United States: 2011 Facts and Figures. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency, Washington, DC. Available online at
.
EPA (2013c). Resource Conservation and Recovery Act (RCRA)Info, Biennial Report, GM Form (Section 2- Onsite
Management) and WR Form.
EPA (2011) EPA's Pesticides Industry Sales and Usage, 2006 and 2007 Market Estimates. Available online at
.
EPA (2009) Biennial Reporting System (BRS) Database. U.S. Environmental Protection Agency, Envirofacts
Warehouse. Washington, DC. Available online at . Data for 2001-2007 are
current as of Sept. 9, 2009.
EPA (2004) EPA's Pesticides Industry Sales and Usage, 2000 and 2001 Market Estimates. Available online at
.
EPA (2002) EPA's Pesticides Industry Sales and Usage, 1998 and 1999 Market Estimates, table 3.6. Available
online at . Accessed July 2003.
EPA (2001) AP 42, Volume I, Fifth Edition. Chapter 11: Mineral Products Industry. Available online at
.
EPA (2000a) Biennial Reporting System (BRS). U.S. Environmental Protection Agency, Envirofacts Warehouse.
Washington, DC. Available online at .
EPA (2000b) Toxics Release Inventory, 1998. U.S. Environmental Protection Agency, Office of Environmental
Information, Office of Information Analysis and Access, Washington, DC. Available online at
.
EPA (1999) EPA's Pesticides Industry Sales and Usage, 1996-1997 Market Estimates. Available online at
.
EPA (1998) EPA's Pesticides Industry Sales and Usage, 1994-1995 Market Estimates. Available online at
.
FEE (2013) Fiber Economics Bureau, as cited in C&EN (2013) Lackluster Year for Chemical OUtput: Production
stayed flat or dipped in most world regions in 2012. Chemical &Engineering News, American Chemical Society, 1
July. Available online at http://www.cen-online.org.
11-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
FEE (2012) Fiber Economics Bureau, as cited in C&EN (2012) Too Quiet After the Storm: After a rebound in
2010,chemical production hardly grew in 2011. Chemical &Engineering News, American Chemcical Society, 2
July. Available online at http://www.cen-online.org
FEE (2011) Fiber Economics Bureau, as cited in C&EN (2011) Output Ramps up in all Regions. Chemical
Engineering News, American Chemical Society, 4 July. Available online at .
FEE (2010) Fiber Economics Bureau, as cited in C&EN (2010) Output Declines in U.S., Europe. Chemical&
Engineering News, American Chemical Society, 6 July. Available online at .
FEE (2009) Fiber Economics Bureau, as cited in C&EN (2009) Chemical Output Slipped In Most Regions Chemical
& Engineering News, American Chemical Society, 6 July. Available online at .
FEE (2007) Fiber Economics Bureau, as cited in C&EN (2007) Gains in Chemical Output Continue. Chemical &
Engineering News, American Chemical Society. July 2, 2007. Available online at .
FEE (2005) Fiber Economics Bureau, as cited in C&EN (2005) Production: Growth in Most Regions Chemical &
Engineering News, American Chemical Society, 11 July. Available online at .
FEE (2003) Fiber Economics Bureau, as cited in C&EN (2003) Production Inches Up in Most Countries, Chemical
& Engineering News, American Chemical Society, 7 July. Available online at .
FEE (2001) Fiber Economics Bureau, as cited in ACS (2001) Production: slow gains in output of chemicals and
products lagged behind U.S. economy as a whole Chemical & Engineering News, American Chemical Society, 25
June. Available online at .
Financial Planning Association (2006) Canada/US Cross-Border Tools: US/Canada Exchange Rates. Available
online at . Accessed August 16, 2006.
Gosselin, Smith, and Hodge (1984) "Clinical Toxicology of Commercial Products." Fifth Edition, Williams &
Wilkins, Baltimore.
Huurman, J. W.F. (2006) Recalculation of Dutch Stationary Greenhouse Gas Emissions Based on Sectoral Energy
Statistics 1990-2002. Statistics Netherlands, Voorburg, The Netherlands.
IISRP (2003) "IISRP Forecasts Moderate Growth in North America to 2007" International Institute of Synthetic
Rubber Producers, Inc. New Release. Available online at .
IISRP (2000) "Synthetic Rubber Use Growth to Continue Through 2004, Says IISRP and RMA" International
Institute of Synthetic Rubber Producers press release.
INEGI (2006) Produccion bruta total de las unidades economicas manufactureras por Subsector, Rama, Subrama y
Clase de actividad. Available online at
. Accessed August
15.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, eds.; Institute for Global
Environmental Strategies (IGES). Hayama, Kanagawa, Japan.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
Marland, G., and R.M. Rotty (1984) "Carbon dioxide emissions from fossil fuels: A procedure for estimation and
results for 1950-1982", Tellus 36b:232-261.
NPRA (2002) North American Wax - A Report Card. Available online at
.
RMA (2011) U.S. Scrap Tire Management Summary: 2005-2009. Rubber Manufacturers Association, Washington,
DC. October 2011, updated September 2013.
References 11-11
-------
RMA (2009) "Scrap Tire Markets: Facts and Figures - Scrap Tire Characteristics." Available online at:
http://www.rma.org/scrap_tires/scrap_tire_markets/scrap_tire_characteristics/ Accessed 17 September
2009. Schneider, S. (2007) E-mail between Shelly Schneider of Franklin Associates (a division of ERG) and Sarah
Shapiro of ICF International, January 10, 2007.
U.S. Bureau of the Census (2009) Soap and Other Detergent Manufacturing: 2007. Available online at
.
U.S. Bureau of the Census (2004) Soap and Other Detergent Manufacturing: 2002, Issued December 2004, EC02-
311-325611 (RV). Available online at .
U.S. Bureau of the Census (1999) Soap and Other Detergent Manufacturing: 1997, Available online at
.
U.S. International Trade Commission (1990-2013) "Interactive Tariff and Trade DataWeb: Quick Query." Available
online at . Accessed November 2013.
ArSova, Ljupka, Rob van Haaren, Nora Goldstein, Scott M. Kaufman, and Nickolas J. Themelis (2008) "16th
Annual BioCycle Nationwide Survey: The State of Garbage in America" Biocycle, JG Press, Emmaus, PA.
December.
Bahor, B (2009) Covanta Energy's public review comments re: Draft Inventory of U.S. Greenhouse Gas Emissions
and Sinks: 1990-2007. Submitted via email on April 9, 2009 to Leif Hockstad, U.S. EPA.
De Soete, G.G. (1993) "Nitrous Oxide from Combustion and Industry: Chemistry, Emissions and Control." In A. R.
Van Amstel, (ed) Proc. of the International Workshop Methane and Nitrous Oxide: Methods in National Emission
Inventories and Options for Control, Amersfoort, ML. February 3-5, 1993.
Energy Recovery Council (2009) "2007 Directory of Waste-to-Energy Plants in the United States." Accessed
September 29, 2009.
EPA (2007, 2008, 2011, 2013) Municipal Solid Waste in the United States: Facts and Figures. Office of Solid Waste
and Emergency Response, U.S. Environmental Protection Agency. Washington, DC. Available online at
.
EPA (2006) Solid Waste Management and Greenhouse Gases: A Life-Cycle Assessment of Emissions and Sinks.
Office of Solid Waste and Emergency Response, U.S. Environmental Protection Agency. Washington, DC.
EPA (2000) Characterization of Municipal Solid Waste in the United States: Source Data on the 1999 Update.
Office of Solid Waste, U.S. Environmental Protection Agency. Washington, DC. EPA530-F-00-024.
Goldstein, N. and C. Madtes (2001) "13th Annual BioCycle Nationwide Survey: The State of Garbage in America."
BioCycles, JG Press, Emmaus, PA. December 2001.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Kaufman, et al. (2004) "14th Annual BioCycle Nationwide Survey: The State of Garbage in America 2004"
Biocycle, JG Press, Emmaus, PA. January, 2004.
RMA (2012a) "Rubber FAQs." Rubber Manufacturers Association. Available online at
.
RMA (2012b) "Scrap Tire Markets: Facts and Figures - Scrap Tire Characteristics." Available online at
. Accessed 18 January 2012.
RMA (2011) "U.S. Scrap Tire Management Summary 2005-2009." Rubber Manufacturers Association. October
2011. Available online at: .
11-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Schneider, S. (2007) E-mail between Shelly Schneider of Franklin Associates (a division of ERG) and Sarah
Shapiro of ICF International, January 10, 2007.
Simmons, et al. (2006) "15th Nationwide Survey of Municipal Solid Waste Management in the United States: The
State of Garbage in America" BioCycle, JG Press, Emmaus, PA. April 2006.
vanHaaren, Rob, Thermelis, N., and Goldstein, N. (2010) "The State of Garbage in America." BioCycle, October
2010. Volume 51, Number 10, pg. 16-23.
AAPG (1984) Coalbed Methane Resources of the United States. AAPG Studies in Geology Series #17.
CAR (2013) Project Database. Climate Action Reserve. Retrieved from http://www.climateactionreserve.org/
Consol (2013) Ruby Canyon Summary 2012. CONSOL Energy Inc. excel spreadsheet
Creedy, D.P. (1993) Chemosphere. Vol. 26, pp. 419-440.
EIA (2013) Annual Coal Report 1991-2012 (Formerly called Coal Industry Annual). Table 1. Energy Information
Administration, U.S. Department of Energy, Washington, DC.
EPA (1996) Evaluation and Analysis of Gas Content and Coal Properties of Major Coal Bearing Regions of the
United States. U.S. Environmental Protection Agency. EPA/600/R-96-065.
EPA (2003) "Methane Emissions Estimates & Methodology for Abandoned Coal Mines in the U.S.," Draft Final
Report, Washington DC, June 2003
EPA (2013) Greenhouse Gas Reporting Program: Underground Coal Mines. Retrieved from
http://www.epa.gov/ghgreporting/ghgdata/reported/coalmines.html
EPA (2005) Surface Mines Emissions Assessment. U.S. Environmental Protection Agency Draft Report.
Geological Survey of Alabama State Oil and Gas Board (GSA) (2013) Well Records Database. Retrieved from
http://www.gsa.state.al.us/ogb/database.aspx
IEA (2013) Key World Energy Statistics. Coal Production, International Energy Agency
IPCC (2011) Use of Models and Facility-Level Data in Greenhouse Gas Inventories (Report of IPCC Expert
Meeting on Use of Models and Measurements in Greenhouse Gas Inventories 9-11 August 2010, Sydney, Australia)
eds: Eggleston H.S., Srivastava N., Tanabe K., Baasansuren J., Fukuda M., Pub. IGES, Japan 2011.
JWR (2010) No. 4 & 7 Mines General Area Maps. Walter Energy: Jim Walter Resources
JWR (2013) Wells Intercepted 2012. Jim Walter Resources excel spreadsheet
King, Brian (1994) Management of Methane Emissions from Coal Mines: Environmental, Engineering, Economic
and Institutional Implication of Options, Neil and Gunter Ltd., Halifax, March 1994.
MSHA (2013) Data Transparency atMSHA. Mine Safety and Health Administration. Retrieved from
http://www.msha.gov/
Mutmansky, Jan M. and Yanbei Wang (2000) "Analysis of Potential Errors in Determination of Coal Mine Annual
Methane Emissions." Mineral Resources Engineering, 9(4). December 2000.
Saghafi, Abouna (2013) Estimation of fugitive emissions from open cut coal mining and measurable gas content,
13th Coal Operators' Conference, University of Wollongong, The Australian Institute of Mining and Metallurgy &
Mine Managers Association of Australia, 2013, 306-313.
USBM (1986) Results of the Direct Method Determination of the Gas Contents of U.S. Coal Basins. Circular 9067,
U.S. Bureau of Mines.
West Virginia Geological & Economic Survey (WVGES) (2013) Oil & Gas Production Data. Retrieved from
http://www.wvgs.wvnet.edu/www/datastat/datastat.htm
References 11-13
-------
Abandoned Underground Coal Mines
EPA (2003) Methane Emissions Estimates & Methodology for Abandoned Coal Mines in the U.S. Draft Final
Report. Washington, DC. June 2003.
Mutmansky, Jan M., and Yanbei Wang (2000) Analysis of Potential Errors in Determination of Coal Mine Annual
Methane Emissions. Department of Energy and Geo-Environmental Engineering, Pennsylvania State University.
University Park, PA.
U.S. Department of Labor, Mine Health & Safety Administration (2012) Data Retrieval System. Available online at
.
Natural Gas Systems
AGA (1991 through 1998) Gas Facts. American Gas Association. Washington, DC.
Alabama (2013) Alabama State Oil and Gas Board. Available online at .
Allen et al. (2013) Measurements of methane emissions at natural gas production sites in the United States, doi:
10.1073/pnas.l304880110 PNAS September 16, 2013. Available online at
http://www.pnas.org/content/early/2013/09/10/1304880110.abstract.
API/ANGA (2012) Characterizing Pivotal Sources of Methane Emissions from Natural Gas Production - Summary
and Analysis ofAPIandANGA Survey Responses. Final Report. American Petroleum Institute and America's
Natural Gas Alliance. September 21.
BOEMRE (201 la) Gulf of Mexico Region Offshore Information. Bureau of Ocean Energy Management, Regulation
and Enforcement, U.S. Department of Interior.
BOEMRE (201 Ib) Pacific OCS Region Offshore Information. Bureau of Ocean Energy Management, Regulation
and Enforcement, U.S. Department of Interior.
BOEMRE (201 Ic) GOM and Pacific OCS Platform Activity. Bureau of Ocean Energy Management, Regulation and
Enforcement, U.S. Department of Interior.
BOEMRE (201 Id) Pacific OCS Region. Bureau of Ocean Energy Management, Regulation and Enforcement, U.S.
Department of Interior.
Drillinglnfo (2014) February 2014 Download. DI Desktop® Drillinglnfo, Inc.
EIA (2012a) Formation crosswalk. Energy Information Administration, U.S. Department of Energy, Washington,
DC. Provided July 7.
EIA (2012b) Lease Condensate Production, 1989-2011, Natural Gas Navigator. Energy Information Administration,
U.S. Department of Energy, Washington, DC. Available online at
.
EIA (2013a) "Table 1— Summary of natural gas supply and disposition in the United States, 2008-2013." Natural
Gas Monthly, Energy Information Administration, U.S. Department of Energy, Washington, DC. Available online
at .
EIA (2013b) "Table 2—Natural Gas Consumption in the United States, 2008-2013." Natural Gas Monthly, Energy
Information Administration, U.S. Department of Energy, Washington, DC. Available online at
.
EIA(2013c) "Table 7 - Marketed production of natural gas in selected states and the Federal Gulf of Mexico, 2008-
2013." Natural Gas Monthly, Energy Information Administration, U.S. Department of Energy, Washington, DC.
Available online at .
EIA (2014a) U.S. Natural Gas Imports by Country. Energy Information Administration, U.S. Department of Energy,
Washington, DC. Available online at .
11-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
EIA (2014b) Natural Gas Gross Withdrawals and Production. Energy Information Administration, U.S. Department
of Energy, Washington, DC. Available online at .
EIA (2005) "Table 5—U.S. Crude Oil, Natural Gas, and Natural Gas Liquids Reserves, 1977-2003." Energy
Information Administration, Department of Energy, Washington, DC.
EIA (2004) USLNGMarkets and Uses. Energy Information Administration, U.S. Department of Energy,
Washington, DC. June 2004.
EIA (2001) "Documentation of the Oil and Gas Supply Module (OGSM)." Energy Information Administration, U.S.
Department of Energy, Washington, DC.
EPA (2012) Greenhouse Gas Reporting Program- Subpart W -Petroleum and Natural Gas Systems. Environmental
Protection Agency.
EPA (2013a) Oil and Natural Gas Sector: Standards of Performance for Crude Oil and Natural Gas Production,
Transmission, and Distribution. Background Supplemental Technical Support Document for the Final New Source
Performance Standards. Environmental Protection Agency. September 2013.
EPA (2013b) Oil and Natural Gas Sector: New Source Performance Standards and National Emission Standards
for Hazardous Air Pollutants Reviews. Environmental Protection Agency, 40 CFR Parts 60 and 63, [EPA-HQ-OAR-
2010-0505; FRL-9665-1], RIN 2060-AP76.
EPA (2013c) Natural GasSTAR Reductions 1990-2012. Natural GasSTAR Program. September 2013.
EPA (2013d) Updating GHG Inventory Estimate for Hydraulically Fractured Gas Well Completions and
Workovers. Available online at: http://www.epa.gov/climatechange/Downloads/ghgemissions/memo-update-
emissions-for-hydraulically-workovers.pdf.
EPA(2013e) Greenhouse Gas Reporting Program-Subpart W-Petroleum and Natural Gas Systems.
Environmental Protection Agency. September 2013.
EPA (1999) Estimates of Methane Emissions from the U.S. Oil Industry (Draft Report). Prepared by ICF-Kaiser,
Office of Air and Radiation, U.S. Environmental Protection Agency. October 1999.
EPA/GRI (1996) Methane Emissions from the Natural Gas Industry. Prepared by Harrison, M, T. Shires, J.
Wessels, and R. Cowgill, eds., Radian International LLC for National Risk Management Research Laboratory, Air
Pollution Prevention and Control Division, Research Triangle Park, NC. EPA-600/R-96-080a.
FERC (2011) North American LNG Terminals . Federal Energy Regulatory Commission, Washington, D.C.
GTI (2001) Gas Resource Database: Unconventional Natural Gas and Gas Composition Databases. Second Edition.
GRI-01/0136.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, H.S. Eggleston, L. Buenida, K. Miwa, T Ngara, and K. Tanabe, eds.; Institute for Global
Environmental Strategies (IGES). Hayama, Kanagawa, Japan.
Jackson et al., (2014) Natural Gas Pipeline Leaks Across Washington, D.C., 48 Environ. Science Technology
2051-2058, January 16, 2014, available at http://pubs.acs.org/doi/abs/10.1021/es404474x (last visited March 24,
2014).
Karion et al., (2013) Methane emissions estimate from airborne measurements over a western United States natural
gas field, 40 GEOPHYS. RES. LETT. 4393 (2013).
McGeehan et al., (2014) Beneath Cities, a Decaying Tangle of Gas Pipes, N.Y. Times, March 24, 2014, available
a/http://www.nytimes.com/2014/03/24/nyregion/beneath-cities-a-decaying-tangle-of-gas-pipes.html?hp&_r=0.
Miller etal. (2013) Anthropogenic emissions of methane in the United States. November 25, 2013, doi:
10.1073/pnas.l314392110. Available online at http://www.pnas.org/content/early/2013/ll/20/1314392110.abstract.
OGJ (1997-2013) "Worldwide Gas Processing." Oil & Gas Journal, PennWell Corporation, Tulsa, OK. Available
online at .
References 11-15
-------
Payne, B & Ackley, R., (2013a) "Extended Report and Preliminary Investigation of Ground-Level Ambient
Methane Levels in Manhattan, New York, NY" (11 March 2013).
Payne, B. & Ackley, R., (2013b) "Report on a Survey of Ground-Level Ambient Methane Levels in the Vicinity of
Wyalusing, Bradford County, PA," (Nov. 2013).
Payne, B. & Ackley, R., (2012) "Report to the Clean Air Council on 8 June, 2012 Field Inspection and Methane
Sampling Survey of Parts of Leroy, Granville and Franklin Townships, Bradford County, PA," (2012).
Peischl, J. et al., (2013) "Quantifying Sources of Methane Using Light Alkenes in the Los Angeles Basin, CA," J.
Geophys. Res. Atmos. 118, 4974-4990, doi: 10.1002/jgrd.50413
Petron, Gabrielle, et al. (2012) Hydrocarbon Emissions Characterization in the Colorado Front Range: A Pilot
Study, Journal of Geophysical Research doi: 10.1029/2011JDO16360.
Phillips, N.G., et al.,(2012) "Mapping Urban Pipeline Leaks: Methane Levels Across Boston," Environmental
Pollution, http://10.1016/j.envpoL2012.11.3.
PHMSA (2013a) Transmission Annuals Data. Pipeline and Hazardous Materials Safety Administration, U.S.
Department of Transportation, Washington, DC. Available online at < http://phmsa.dot.gov/pipeline/library/data-
stats >.
PHMSA (2013b) Gas Distribution Annual Data. Pipeline and Hazardous Materials Safety Administration, U.S.
Department of Transportation, Washington, DC. Available online at < http://phmsa.dot.gov/pipeline/library/data-
stats >.
Wyoming (2013) Wyoming Oil and Gas Conservation Commission. Available online at
.
API (2009) Compendium of Greenhouse gas Emissions Methodologies for the Oil and Gas Industry. American
Petroleum Institute. Austin, TX, August 2009.
BOEM (2011a) OCS Platform Activity. Bureau of Ocean Energy Management, U.S. Department of Interior.
Available online at
.
BOEM (201 Ic) Pacific OCS Region. Bureau of Ocean Energy Management, U.S. Department of Interior. Available
online at.
BOEMRE (2004) Gulfwide Emission Inventory Study for the Regional Haze and Ozone Modeling Effort. Bureau of
Ocean Energy Management, Regulation, and Enforcement (formerly Minerals Management Service), U.S.
Department of Interior. OCS Study MMS 2004-072. Available online at
EIA (1990 through 2013) Refinery Capacity Report. Energy Information Administration, U.S. Department of
Energy. Washington, DC. Available online at < http://www.eia.gov/petroleum/refinerycapacity/ >.
EIA (1995 through 2013a) Annual Energy Review. Energy Information Administration, U.S. Department of Energy.
Washington, DC. Available online at < http://www.eia.gov/totalenergy/data/annual/index.cfm >.
EIA (1995 through 2013b) Monthly Energy Review. Energy Information Administration, U.S. Department of
Energy. Washington, DC. Available online at < http://www.eia.gov/totalenergy/data/monthly/index.cfm >.
EIA (1995 through 2013c) Petroleum Supply Annual. Volume 1. U.S Department of Energy Washington, DC.
Available online at: < http://www.eia.gov/petroleum/supply/annual/volumel/>.
11-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
EPA (2005) Incorporating the Mineral Management Service Gulfwide Offshore Activities Data System (GOADS)
2000 data into the methane emissions inventories. Prepared by ICF International. U.S. Environmental Protection
Agency. 2005.
EPA (1999) Estimates of Methane Emissions from the U.S. Oil Industry (Draft Report). Prepared by ICF
International. Office of Air and Radiation, U.S. Environmental Protection Agency. October 1999.
EPA (1999a) Methane Emissions from the U.S. Petroleum Industry. Prepared by Radian International. U.S.
Environmental Protection Agency. February 1999.
EPA/GRI (1996a) Methane Emissions from the Natural Gas Industry, V7: Blow and Purge Activities. Prepared by
Radian. U.S. Environmental Protection Agency. April 1996.
EPA/GRI (1996b) Methane Emissions from the Natural Gas Industry, VI1: Compressor Driver Exhaust. Prepared
by Radian. U.S. Environmental Protection Agency. April 1996.
EPA/GRI (1996c) Methane Emissions from the Natural Gas Industry, VI2: Pneumatic Devices. Prepared by Radian.
U.S. Environmental Protection Agency. April 1996.
EPA/GRI (1996d) Methane Emissions from the Natural Gas Industry, VI3: Chemical Injection Pumps. Prepared by
Radian. U.S. Environmental Protection Agency. April 1996.
HPDI (2011) Production and Permit Data, October 2009.
IOGCC (2011) Marginal Wells: fuel for economic growth 2010 Report. Interstate Oil & Gas Compact Commission.
Available online at .
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, H.S. Eggleston, L. Buenida, K. Miwa, T Ngara, and K. Tanabe, eds.; Institute for Global
Environmental Strategies (IGES). Hayama, Kanagawa, Japan.
OGJ (2013a) Oil and Gas Journal 1990-2013. Pipeline Economics Issue, September 2013.
OGJ (2013b) Oil and Gas Journal 1990-2013. Worldwide Refining Issue, January 2013.
United States Army Corps of Engineers (1995-2010) Waterborne Commerce of the United States, Part 5: National
Summaries. U.S. Army Corps of Engineers. Washington, DC.
Energy Sources of Indirect Greenhouse Gases
EPA (2013) "1970 - 2013 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available
online at .
EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and
the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency. Research Triangle Park, NC. October 1997.
International Bunker Fuels
Anderson, B.E., et al., Alternative Aviation Fuel Experiment (AAFEX), NASA Technical Memorandum, in press,
2011.
ASTM (1989) Military Specification for Turbine Fuels, Aviation, Kerosene Types, NATO F-34 (JP-8) and NATO F-
35. February 10, 1989. Available online at.
Chevron (2000) Aviation Fuels Technical Review (FTR-3). Chevron Products Company, Chapter 2. Available
online at.
DESC (2013) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense
Energy Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, DC.
References 11-17
-------
DHS (2008) Personal Communication with Elissa Kay, Residual and Distillate Fuel Oil Consumption (International
Bunker Fuels). Department of Homeland Security, Bunker Report. January 11, 2008.
DOC (2011) Unpublished Report of Bunker Fuel Oil Laden on Vessels Cleared for Foreign Countries. Form-563.
Foreign Trade Division, Bureau of the Census, U.S. Department of Commerce. Washington, DC.
DOT (1991 through 2012) Fuel Cost and Consumption. Federal Aviation Administration, Bureau of Transportation
Statistics, U.S. Department of Transportation. Washington, DC. DAI-10.
EIA (2014) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, February 2014, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2013/10).
FAA (2013) Personal Communication between FAA and Leif Hockstad for aviation emissions estimates from the
Aviation Environmental Design Tool (AEDT). January 2013.
FAA (2006) System for assessing Aviation's Global Emission (SAGE) Model. Federal Aviation Administration's
Office of Aviation Policy, Planning, and Transportation Topics, 2006.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
USAF (1998) Fuel Logistics Planning. U.S. Air Force pamphlet AFPAM23-221, May 1, 1998.
Wood Biomass and Ethanol Consumption
EIA (2014) Monthly Energy Review, February 2014. Energy Information Administration, U.S. Department of
Energy. Washington, DC. DOE/EIA-0035(2013/10). October 28, 2013.
EPA (2013) Acid Rain Program Dataset 1996-2012. Office of Air and Radiation, Office of Atmospheric Programs,
U.S. Environmental Protection Agency, Washington, D.C.
EPA (2010) Carbon Content Coefficients Developed for EPA's Mandatory Reporting Rule. Office of Air and
Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
Industrial Processes
Cement Production
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
U.S. Bureau of Mines (1990 through 1993) Minerals Yearbook: Cement Annual Report. U.S. Department of the
Interior, Washington, DC.
USGS (1995 through 2013)Minerals Yearbook- Cement. U.S. Geological Survey, Reston, VA.
Van Oss (2013a) 1990-2012 Clinker Production Data Provided by Hendrik van Oss (USGS) via email on November
8,2013.
11-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
VanOss (2013b) Personal communication. Hendrik van Oss, Commodity Specialist of the U.S. Geological Survey
and Gopi Manne, Eastern Research Group, Inc. October 28, 2013.
Lime Production
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories ,
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change. Montreal. May
2000. IPCC-XWDoc. 10 (1.IV.2000).
Lutter (2009) Personal communication. Karen Lutter, California Air Resources Board and Daisy Wang, ERG.
October 18, 2012; October 24, 2012.
Males, E. (2003) Memorandum from Eric Males, National Lime Association to Mr. William N. Irving & Mr. Leif
Hockstad, Environmental Protection Agency. March 6, 2003.
Miller (2013) Personal communication, Michael Miller, U.S. Geological Survey and Gopi Manne, Eastern Research
Group, Inc. October 25, 2013.
Miller (2012) Personal communication, Michael Miller, U.S. Geological Survey and Daisy Wang, Eastern Research
Group, Inc. November 5, 2012.
Miner, R. and B. Upton (2002) Methods for estimating greenhouse gas emissions from lime kilns at kraft pulp mills.
Energy. Vol. 27 (2002), p. 729-738.
Prillaman (2008 through 2012) Personal communication. Hunter Prillaman, National Lime Association and Daisy
Wang, Eastern Research Group, Inc. October 24, 2012.
Seeger (2013) Memorandum from Arline M. Seeger, National Lime Association to Mr. Leif Hockstad,
Environmental Protection Agency. March 15, 2013.
USGS (1992 through 2013) Minerals Yearbook: Lime. U.S. Geological Survey, Reston, VA.
Other Process Uses of Carbonates
U.S. Bureau of Mines (1991 and 1993a) Minerals Yearbook: Crushed Stone Annual Report. U.S. Department of the
Interior. Washington, DC.
U.S. Bureau of Mines (1990 through 1993b) Minerals Yearbook: Magnesium and Magnesium Compounds Annual
Report. U.S. Department of the Interior. Washington, DC.
USGS (2Q13a).Magnesium Metal Mineral Commodity Summary for 2013. U.S. Geological Survey, Reston, VA.
USGS (2013b). Minerals Yearbook: Crushed Stone [Advance Release]. U.S. Geological Survey, Reston, VA.
USGS (1995 through 2Q13a) Minerals Yearbook: Crushed Stone Annual Report. U.S. Geological Survey, Reston,
VA.
USGS (1995 through 2Ql2b) Minerals Yearbook: Magnesium Annual Report. U.S. Geological Survey, Reston, VA.
Willett (2013) Personal communication, Jason Christopher Willett, U.S. Geological Survey and Gopi Manne,
Eastern Research Group, Inc., October 29, 2013.
Soda Ash Production and Consumption
Kostick, D. S. (2012) Personal communication. Dennis S. Kostick of U.S. Department of the Interior - U.S.
Geological Survey, Soda Ash Commodity Specialist with Gopi Manne and Bryan Lange of Eastern Research Group,
Inc. October 2012.
References 11-19
-------
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
USGS (1994 through 2013) Minerals Yearbook: Soda Ash Annual Report. U.S. Geological Survey, Reston, VA.
USGS (2012) Mineral Industry Survey: Soda Ash in July 2012. U.S. Geological Survey, Reston, VA.
USGS (1995a) Trona Resources in the Green River Basin, Southwest Wyoming. U.S. Department of the Interior,
U.S. Geological Survey. Open-File Report 95-476. Wiig, Stephen, Grundy, W.D., Dyni, JohnR.
Glass Production
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
OIT (2002) Glass Industry of the Future: Energy and Environmental Profile of the U.S. Glass Industry. Office of
Industrial Technologies, U.S. Department of Energy. Washington, DC.
U.S. Bureau of Mines (1991 and 1993a) Minerals Yearbook: Crushed Stone Annual Report. U.S. Department of the
Interior. Washington, DC.
U.S. EPA (2009) Technical Support Document for the Glass Manufacturing Sector: Proposed Rule for Mandatory
Reporting of Greenhouse Gases. U.S. Environmental Protection Agency, Washington, DC.
USGS (1995 through 2012) Minerals Yearbook: Crushed Stone Annual Report. U.S. Geological Survey, Reston,
VA.
USGS (1995 through 2013) Minerals Yearbook: Soda Ash Annual Report. U.S. Geological Survey, Reston, VA.
Willett (2013) Personal communication, Jason Christopher Willett, U.S. Geological Survey and Gopi Manne,
Eastern Research Group, Inc. October 29, 2013.
Ammonia Production and Urea Consumption
American Chemistry Council (2013) U.S. Chemical Industry Statistical Handbook.
American Chemistry Council (2012) Business of Chemistry (Annual Data) - Chemicals and Plastic Resins
Production.
Bark (2004) CoffeyvilleNitrogen Plant Available online at <
http://www.gasification.org/uploads/downloads/Conferences/2003/07BARK.pdf>. December 15, 2004.
Coffeyville Resources Nitrogen Fertilizers (2012) Nitrogen Fertilizer Operations. Available online at
.
Coffeyville Resources Nitrogen Fertilizers (2011) Nitrogen Fertilizer Operations. Available online at
.
Coffeyville Resources Nitrogen Fertilizers (2010) Nitrogen Fertilizer Operations. Available online at
.
Coffeyville Resources Nitrogen Fertilizers (2009) Nitrogen Fertilizer Operations. Available online at
.
Coffeyville Resources Nitrogen Fertilizers, LLC (2005 through 2007a) Business Data. Available online at
.
Coffeyville Resources Nitrogen Fertilizers (2007b) Nitrogen Fertilizer Operations. Available online at
.
CVR (2012) CVR Energy, Inc. 2012 Annual Report. Available online at .
11-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
CVR (2008) CVR Energy, Inc. 2008 Annual Report. Available online at .
EFMA (2000a) Best Available Techniques for Pollution Prevention and Control in the European Fertilizer Industry.
Booklet No. 1 of 8: Production of Ammonium. Available online at
EFMA (2000b) Best Available Techniques for Pollution Prevention and Control in the European Fertilizer Industry.
Booklet No. 5 of 8: Production of Urea and Urea Ammonium Nitrate. Available online at
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
U.S. Bureau of the Census (2011) Current Industrial Reports Fertilizer Materials and Related Products: 2010
Summary. Available online at .
U.S. Bureau of the Census (2010) Current Industrial Reports Fertilizer Materials and Related Products: 2009
Summary. Available online at .
U.S. Bureau of the Census (2009) Current Industrial Reports Fertilizer Materials and Related Products: 2008
Summary. Available online at .
U.S. Bureau of the Census (2008) Current Industrial Reports Fertilizer Materials and Related Products: 2007
Summary. Available online at .
U.S. Census Bureau (2007) Current Industrial Reports Fertilizer Materials and Related Products: 2006 Summary.
Available online at < http://www.census.gOv/industry/l/mq325b065.pdf>.
U.S. Census Bureau (2006) Current Industrial Reports Fertilizer Materials and Related Products: 2005 Summary.
Available online at .
U.S. Census Bureau (2002, 2004, 2005) Current Industrial Reports Fertilizer Materials and Related Products:
Fourth Quarter Report Summary. Available online at .
U.S. Census Bureau (1998 through 2002b, 2003) Current Industrial Reports Fertilizer Materials andRelated
Products: Annual Reports Summary. Available online at .
U.S. Census Bureau (2002a) Current Industrial Reports Fertilizer Materials and Related Products: First Quarter
2002. June 2002. Available online at .
U.S. Census Bureau (2002b) Current Industrial Reports Fertilizer Materials and Related Products: Third Quarter
2001. January 2002. Available online at .
U.S. Census Bureau (2001 a) Current Industrial Reports Fertilizer Materials and Related Products: Second Quarter
2001. September 2001. Available online at .
U.S. Census Bureau (1991 through 1994) Current Industrial Reports Fertilizer Materials Annual Report. Report No.
MQ28B. U.S. Census Bureau, Washington, DC.
USGS (1994 through 2009) Minerals Yearbook: Nitrogen. Available online at
.
USGS (2012) 2011 Minerals Yearbook: Nitrogen [Advance Release]. December 2012. Available online at
.
Urea Consumption for Non-Agricultural Purposes
EFMA (2000) Best Available Techniques for Pollution Prevention and Control in the European Fertilizer Industry.
Booklet No. 5 of 8: Production of Urea and Urea Ammonium Nitrate.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
References 11-21
-------
TFI (2002) U.S. Nitrogen Imports/Exports Table. The Fertilizer Institute. Available online at
. August 2002.
U.S. Bureau of the Census (2011) Current Industrial Reports Fertilizer Materials and Related Products: 2010
Summary. Available online at .
U.S. Bureau of the Census (2010) Current Industrial Reports Fertilizer Materials and Related Products: 2009
Summary. Available online at .
U.S. Bureau of the Census (2009) Current Industrial Reports Fertilizer Materials and Related Products: 2008
Summary. Available online at .
U.S. Bureau of the Census (2008) Current Industrial Reports Fertilizer Materials and Related Products: 2007
Summary. Available online at .
U.S. Census Bureau (2007) Current Industrial Reports Fertilizer Materials and Related Products: 2006 Summary.
Available online at < http://www.census.gOv/industry/l/mq325b065.pdf>.
U.S. Census Bureau (2006) Current Industrial Reports Fertilizer Materials and Related Products: 2005 Summary.
Available online at .
U.S. Census Bureau (2002, 2004, 2005) Current Industrial Reports Fertilizer Materials and Related Products:
Fourth Quarter Report Summary. Available online at .
U.S. Census Bureau (1998 through 2002b, 2003) Current Industrial Reports Fertilizer Materials and Related
Products: Annual Reports Summary. Available online at .
U.S. Census Bureau (2002a) Current Industrial Reports Fertilizer Materials and Related Products: First Quarter
2002. June 2002. Available online at .
U.S. Census Bureau (2002b) Current Industrial Reports Fertilizer Materials and Related Products: Third Quarter
2001. January 2002. Available online at .
U.S. Census Bureau (2001 a) Current Industrial Reports Fertilizer Materials and Related Products: Second Quarter
2001. September 2001. Available online at .
U.S. Department of Agriculture (2012) Economic Research Service Data Sets, Data Sets, U.S. Fertilizer
Imports/Exports: Standard Tables. Available online at < http://www.ers.usda.gov/data-products/fertilizer-
importsexports/standard-tables.aspx>.
U.S. ITC (2002) United States International Trade Commission Interactive Tariff and Trade DataWeb, Version
2.5.0. Available online at . August 2002.
USGS (1994 through 2009) Minerals Yearbook: Nitrogen. Available online at
.
Climate Action Reserve (CAR) (2013), Project Report, https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=lll,
accessed on January 18, 2013.
Desai (2012) Personal communication. Mausami Desai, U.S. Environmental Protection Agency, January 25, 2012.
EPA (2013a) Personal communication, Mausami Desai, U.S. Environmental Protection Agency, January 23, 2013.
Includes file "NitricAcidProduction_1990-2011 (EPA).xls."
EPA(2013b) U.S. Greenhouse Gas Reporting Program. Data downloaded from EPA's website on October 28,
2013.
EPA (2010, 2013c) Draft Nitric Acid Database. U.S. Environmental Protection Agency, Office of Air and
Radiation. September, 2010.
EPA (2012) Memorandum from Mausami Desai, U.S. EPA to Mr. Bill Herz, The Fertilizer Institute. November 26,
2012.
11-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency. Research Triangle Park, NC. October 1997.
IFDC (2012) North America Fertilizer Capacity. September, 2012. Provided by The Fertilizer Institute (TFI) to
Mausami Desai, EPA, December 10, 2012.
U.S. Census Bureau (2012) Current Industrial Reports. Fertilizers and Related Chemicals - Second Quarter 2011.
"Table 1: Summary of Production of Principle Fertilizers and Related Chemicals: 2011 to 2009." September, 2011.
MQ325B (ll)-2. Available online at
.
U.S. Census Bureau (2011) Current Industrial Reports. Fertilizers and Related Chemicals: 2010. "Table 1:
Summary of Production of Principle Fertilizers and Related Chemicals: 2009 and 2010." June, 2011. MQ325B(10)-
5. Available online at < http://www.census.gov/manufacturing/cir/historical_data/mq325b/index.html>.
U.S. Census Bureau (2010a) Current Industrial Reports. Fertilizers and Related Chemicals: 2009. "Table 1:
Summary of Production of Principle Fertilizers and Related Chemicals: 2009 and 2008." June, 2010. MQ325B(08)-
5. Available online at < http://www.census.gov/manufacturing/cir/historical_data/mq325b/index.html>.
U.S. Census Bureau (2010b) Personal communication between Hilda Ward (of U.S. Census Bureau) and Caroline
Cochran (of ICF International). October 26, 2010 and November 5, 2010.
U.S. Census Bureau (2009) Current Industrial Reports. Fertilizers and Related Chemicals: 2008. "Table 1:
Shipments and Production of Principal Fertilizers and Related Chemicals: 2004 to 2008." June, 2009. MQ325B(08)-
5. Available online at.
U.S. Census Bureau (2008) Current Industrial Reports. Fertilizers and Related Chemicals: 2007. "Table 1:
Shipments and Production of Principal Fertilizers and Related Chemicals: 2003 to 2007." June, 2008. MQ325B(07)-
5. Available online at .
USGS (2012) 2011 Minerals Yearbook: Nitrogen [Advance Release]. December, 2012. U.S. Geological Survey,
Reston, VA.
ACC (2012) "Business of Chemistry (Annual Data).xls." American Chemistry Council Guide to the Business of
Chemistry. August 2012.
C&EN (1995) "Production of Top 50 Chemicals Increased Substantially in 1994." Chemical & Engineering News,
73(15): 17. April 10, 1995.
C&EN (1994) "Top 50 Chemicals Production Rose Modestly Last Year." Chemical & Engineering News,
72(15): 13. April 11, 1994.
C&EN (1993) "Top 50 Chemicals Production Recovered Last Year." Chemical & Engineer ing News, 71(15): 11.
April 12, 1993.
C&EN (1992) "Production of Top 50 Chemicals Stagnates in 1991." Chemical & Engineering News, 70(15): 17.
April 13, 1992.
CMR (2001) "Chemical Profile: Adipic Acid." Chemical Market Reporter. July 16, 2001.
CMR (1998) "Chemical Profile: Adipic Acid." Chemical Market Reporter. June 15, 1998.
CW (2005) "Product Focus: Adipic Acid." Chemical Week. May 4, 2005.
CW (1999) "Product Focus: Adipic Acid/Adiponitrile." Chemical Week, p. 31. March 10, 1999.
Desai (2012) Personal communication. Mausami Desai, U.S. Environmental Protection Agency and Toby Mandel,
ICF International, January 25, 2012.
Desai (2010) Personal communication. Mausami Desai, U.S. Environmental Protection Agency, and Caroline
Cochran, ICF International. November 8, 2010.
References 11-23
-------
EPA (2013) Greenhouse Gas Reporting Program data, Office of Air and Radiation, Office of Atmospheric
Programs, U.S. Environmental Protection Agency, Washington, D.C. available at
http://ghgdata.epa.gov/ghgp/main.do ICIS (2007) "Adipic Acid." ICIS Chemical Business Americas. July 9, 2007.
EPA (2012) Analysis of Greenhouse Gas Reporting Program data - Subpart E (Adipic Acid), Office of Air and
Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Reimer, R.A., Slaten, C.S., Seapan, M, Koch, T.A. and Triner, V.G. (1999) "Implementation of Technologies for
Abatement of N2O Emissions Associated with Adipic Acid Manufacture." Proceedings of the 2nd Symposium on
Non-CO2 Greenhouse Gases (NCGG-2), Noordwijkerhout, The Netherlands, 8-10 Sept. 1999, Ed. J. van Ham et al.,
Kluwer Academic Publishers, Dordrecht, pp. 347-358.
SEI (2010) Industrial N2O Projects Under the COM: Adipic Acid-A Case for Carbon Leakage? Stockholm
Environment Institute Working Paper WP-US-1006. October 9, 2010.
Thiemens, M.H., and W.C. Trogler (1991) "Nylon production; an unknown source of atmospheric nitrous oxide."
Science 251:932-934.
VA DEQ (2010) Personal communication. Stanley Faggert, Virgina Department of Environmental Quality and
Joseph Herr, ICF International. March 12, 2010.
VA DEQ (2009) Personal communication. Stanley Faggert, Virgina Department of Environmental Quality and
Joseph Herr, ICF International. October 26, 2009.
VA DEQ (2006) Virginia Title V Operating Permit. Honeywell International Inc. Hopewell Plant. Virginia
Department of Environmental Quality. Permit No. PRO50232. Effective January 1, 2007.
Silicon Carbide Production
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, eds.; Institute for Global
Environmental Strategies (IGES). Hayama, Kanagawa, Japan.
U.S. Census Bureau (2005 through 2013) U.SInternational Trade Commission (USITC) Trade DataWeb. Available
online at .
USGS (2012a, 2013a) Minerals Commodity Summary: Abrasives (Manufactured) 2012. U.S. Geological Survey,
Reston, VA. Available online at < http://minerals.usgs.gov/minerals/pubs/commodity/abrasives/>.
USGS (199 la through 20 I3b) Minerals Yearbook: Manufactured Abrasives Annual Report. U.S. Geological
Survey, Reston, VA. Available online at .
USGS (1991b through 2011b, 2012c, and 20l3c) Minerals Yearbook: Silicon Annual Report. U.S. Geological
Survey, Reston, VA. Available online at < http://minerals.usgs.gov/minerals/pubs/commodity/silicon/>.
Petrochemical Production
ACC (2013) U.S. Chemical Industry Statistical Handbook. American Chemistry Council, Arlington, VA.
ACC (2002, 2003, 2005 through 2012) Guide to the Business of Chemistry. American Chemistry Council,
Arlington, VA.
EIA (2004) Annual Energy Review 2003. Energy Information Administration, U.S. Department of Energy.
Washington, DC. DOE/EIA-0384(2003). September 2004.
EIA (2003) Emissions of Greenhouse Gases in the United States 2002. Office of Integrated Analysis and
Forecasting, Energy Information Administration, U.S. Department of Energy. Washington, DC. DOE-EIA-
0573(2002). February 2003.
11-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
EPA (2008) Technical Support Document for the Petrochemical Production Sector: Proposed Rule for Mandatory
Reporting of Greenhouse Gases. U.S. Environmental Protection Agency. September 2008.
EPA (2000) Economic Impact Analysis for the Proposed Carbon Black Manufacturing NESHAP, U.S.
Environmental Protection Agency. Research Triangle Park, NC. EPA-452/D-00-003. May 2000.
European IPPC Bureau (2004) Draft Reference Document on Best Available Techniques in the Large Volume
Inorganic Chemicals—Solid and Others Industry, Table 4.21. European Commission, 224. August 2004.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. Intergovernmental Panel on Climate
Change, United Nations Environment Programme, Organization for Economic Co-Operation and Development,
International Energy Agency. Paris, France.
Jordan, J. (2013) Personal communication, Jim Jordan of Jordan Associates on behalf of the Methanol Institute and
Donna Lazzari, ERG. October 31, 2013
Jordan, J. (2012) Personal communication, Jim Jordan of Jordan Associates on behalf of the Methanol Institute and
Donna Lazzari, ERG. October 8, 2012
Jordan, J. (201 la) Personal communication, Jim Jordan of Jordan Associates on behalf of the Methanol Institute and
PierLaFarge, ICF International. October 19, 2011
Jordan, J. (20 lib) Personal communication, Jim Jordan of Jordan Associates on behalf of the Methanol Institute and
PierLaFarge, ICF International. October 18, 2011
Johnson, G. L. (2013) Personal communication. Greg Johnson of Liskow& Lewis, on behalf of the International
Carbon Black Association (ICBA) and Donna Lazzari, ERG. November 6, 2012
Johnson, G. L. (2012) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
Carbon Black Association (ICBA) and Donna Lazzari, ERG. October 31, 2012
Johnson, G. L. (2011) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
Carbon Black Association (ICBA) and Pier LaFarge, ICF International. October 2011.
Johnson, G. L. (2010) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
Carbon Black Association (ICBA) and Caroline Cochran, ICF International. September 2010.
Johnson, G. L. (2009) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
Carbon Black Association (ICBA) and Jean Y. Kim, ICF International. October 2009.
Johnson, G. L. (2008) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
Carbon Black Association (ICBA) and Jean Y. Kim, ICF International. November 2008.
Johnson, G. L. (2007) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
Carbon Black Association (ICBA) and Tristan Kessler, ICF International. November 2007.
Johnson, G. L. (2006) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
Carbon Black Association (ICBA) and Erin Fraser, ICF International. October 2006.
Johnson, G. L. (2005) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
Carbon Black Association (ICBA) and Erin Fraser, ICF International. October 2005.
Johnson, G. L. (2003) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
Carbon Black Association (ICBA) and Caren Mintz, ICF International November 2003.
Othmer, K. (1992) Carbon (Carbon Black), Vol. 4, 1045.
The Innovation Group (2004) Carbon Black Plant Capacity. Available online at .
U.S. Census Bureau (2007) 2006 Economic Census: Manufacturing—Industry Series: Carbon Black Manufacturing.
Department of Commerce. Washington, DC. EC0731I3. June 2009.
U.S. Census Bureau (2004) 2002 Economic Census: Manufacturing—Industry Series: Carbon Black Manufacturing.
Department of Commerce. Washington, DC. EC02-311-325182. September 2004.
References 11-25
-------
U.S. Census Bureau (1999) 1997 Economic Census: Manufacturing—Industry Series: Carbon Black Manufacturing.
Department of Commerce. Washington, DC. EC97M-3251F. August 1999.
Titanium Dioxide Production
Gambogi, J. (2002) Telephone communication. Joseph Gambogi, Commodity Specialist, U.S. Geological Survey
and Philip Groth, ICF International. November 2002.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
USGS (USGS 1991 through2013a)M/nera& Yearbook: Titanium 2010 Annual Report. U.S. Geological Survey,
Reston, VA.
USGS (2013b) Mineral Commodity Summary: Titanium and Titanium Dioxide 2013. U.S. Geological Survey,
Reston, VA.
Carbon Dioxide Consumption
Allis, R. et al. (2000) Natural CO2 Reservoirs on the Colorado Plateau and Southern Rocky Mountains: Candidates
for CO2 Sequestration. Utah Geological Survey and Utah Energy and Geoscience Institute. Salt Lake City, Utah.
ARI (1990 through 2011) CO2 Use in Enhanced Oil Recovery. Deliverable to ICF International under Task Order
102, July 15, 2011.
ARI (2007) CO2-EOR: An Enabling Bridge for the Oil Transition. Presented at "Modeling the Oil Transition—a
DOE/EPA Workshop on the Economic and Environmental Implications of Global Energy Transitions."
Washington, DC. April 20-21, 2007.
ARI (2006) CO2-EOR: An Enabling Bridge for the Oil Transition. Presented at "Modeling the Oil Transition—a
DOE/EPA Workshop on the Economic and Environmental Implications of Global Energy Transitions."
Washington, DC. April 20-21, 2006.
Broadhead (2003) Personal communication. Ron Broadhead, Principal Senior Petroleum Geologist and Adjunct
faculty, Earth and Environmental Sciences Department, New Mexico Bureau of Geology and Mineral Resources,
and Robin Pestrusak, ICF International. September 5, 2003.
COGCC (2013) Monthly CO2 Produced by County. Available online at
. Accessed October
2013.
Denbury Resources Inc. (2002 through 2013) Annual Report: 2012 Form 10-K. Available online at <
http://www.denbury.com/investor-relations/SEC-Filings/SEC-Filings-Details/default.aspx?FilingId=9123234 >.
Accessed October 2013.
New Mexico Bureau of Geology and Mineral Resources (2006) Natural Accumulations of Carbon Dioxide in New
Mexico and Adjacent Parts of Colorado and Arizona: Commercial Accumulation of CO2. Available online at
.
Phosphoric Acid Production
EFMA (2000) "Production of Phosphoric Acid." Best Available Techniques for Pollution Prevention and Control in
the European Fertilizer Industry. Booklet 4 of 8. European Fertilizer Manufacturers Association. Available online at
.
FIPR (2003a) "Analyses of Some Phosphate Rocks." Facsimile Gary Albarelli, the Florida Institute of Phosphate
Research, Bartow, Florida, to Robert Lanza, ICF International. July 29, 2003.
11-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
FIPR (2003b) Florida Institute of Phosphate Research. Personal communication. Mr. Michael Lloyd, Laboratory
Manager, FIPR, Bartow, Florida, to Mr. Robert Lanza, ICF International. August 2003.
NCDENR (2013) North Carolina Department of Environment and Natural Resources, Title V Air Permit Review
for PCS Phosphate Company, Inc. - Aurora. Available online at
http://www.ncair.org/permits/permit_reviews/PCS_rev_08282012.pdf. Accessed on January 25, 2013.
USGS (1994 through 2013) Minerals Yearbook. Phosphate Rock Annual Report. U.S. Geological Survey, Reston,
VA. USGS (2012b) Personal communication between Stephen Jasinski (USGS) and Mausami Desai (EPA) on
October 12, 2012.
USGS (2013a) Mineral Commodity Summaries: Phosphate Rock. January 2013. U.S. Geological Survey, Reston,
VA. Available online at http://minerals.usgs.gov/minerals/pubs/commodity/phosphate_rock/mcs-2013-phosp.pdf.
Iron and Steel Production and Metallurgical Coke Production
AISI (2004 through 2013a) Annual Statistical Report, American Iron and Steel Institute, Washington, DC.
AISI (2013b) Personal communication, Mausami Desai, U.S. EPA, and the American Iron and Steel Institute,
October 2013.
AISI (2008b) Personal communication, Mausami Desai, U.S. EPA, and the American Iron and Steel Institute,
October 2008.
DOE (2000) Energy and Environmental Profile of the U.S. Iron and Steel Industry. Office of Industrial
Technologies, U.S. Department of Energy. August 2000. DOE/EE-0229.EIA
EIA (1998 through 2013) Quarterly Coal Report: October-December, Energy Information Administration, U.S.
Department of Energy. Washington, DC. DOE/EIA-0121.
EIA (2Ql2a) Annual Energy Review 2011, Energy Information Administration, U.S. Department of Energy.
Washington, DC. DOE/EIA-0384(2011).
EIA (2012b) Natural Gas Annual 2011, Energy Information Administration, U.S. Department of Energy.
Washington, DC. DOE/EIA-0131(11).
EIA (2012c) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, September 2012,
Energy Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2012/09).
EIA (1992) Coal and lignite production. EIA State Energy Data Report 1992, Energy Information Administration,
U.S. Department of Energy, Washington, DC.
EPA (2010) Carbon Content Coefficients Developed for EPA's Mandatory Reporting Rule. Office of Air and
Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
Fenton (2013) Personal communication. Michael Fenton, Commodity Specialist, U.S. Geological Survey and Marty
Wolf, Eastern Research Group. October 25, 2013.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC/UNEP/OECD/IEA (1995) "Volume 3: Greenhouse Gas Inventory Reference Manual. Table 2-2".IPCC
Guidelines for National Greenhouse Gas Inventories. Intergovernmental Panel on Climate Change, United Nations
Environment Programme, Organization for Economic Co-Operation and Development, International Energy
Agency. IPCC WG1 Technical Support Unit, United Kingdom.
USGS (1991 through 2012) USGS Minerals Yearbook-Iron and Steel Scrap.. U.S. Geological Survey, Reston,
VA.
References 11-27
-------
Ferroalloy Production
Corathers, L. (2012) Personal communication. Lisa Corathers, Commodity Specialist, U.S. Geological Survey and
Paul Stewart, ICF International. March 09, 2012.
Corathers, L. (2011) Personal communication. Lisa Corathers, Commodity Specialist, U.S. Geological Survey and
Paul Stewart, ICF International. March 11, 2011.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Onder, H., and E.A. Bagdoyan (1993) Everything You've Always Wanted to Know about Petroleum Coke. Allis
Mineral Systems.
Tuck, C. (2013) Personal communication. Christopher Tuck, Commodity Specialist, U.S. Geological Survey and
Marty Wolf, Eastern Research Group. October 30, 2013.
USGS (1996 through 2012) Minerals Yearbook: Silicon Annual Report. U.S. Geological Survey, Reston, VA.
Aluminum Production
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change. Montreal. May
2000. IPCC-XVI/Doc. 10 (1.IV.2000).
U.S. EPA Greenhouse Gas Reporting Program (GHGRP) Envirofacts. Subpart F: Aluminum Production. Available
online at
USAA(2013a) U.S. Primary Aluminum Production 2012. U.S. Aluminum Association, Washington, DC. January,
2013.
USAA (2013b) U.S. Primary Aluminum Production 2013. U.S. Aluminum Association, Washington, DC.
December, 2013.
USAA (2012) U.S. Primary Aluminum Production 2011. U.S. Aluminum Association, Washington, DC. January,
2012.
USAA (2011) U.S. Primary Aluminum Production 2010. U.S. Aluminum Association, Washington, DC.
USAA (2010) U.S. Primary Aluminum Production 2009. U.S. Aluminum Association, Washington, DC.
USAA (2008, 2009) U.S. Primary Aluminum Production. U.S. Aluminum Association, Washington, DC.
USAA (2004, 2005, 2006) Primary Aluminum Statistics. U.S. Aluminum Association, Washington, DC.
USGS (2013a) 2012Mineral Yearbook: Aluminum [AdvancedRelease]. U.S. Geological Survey, Reston, VA.
USGS (2013b) 2012 Mineral Commodity Summaries: Aluminum. U.S. Geological Survey, Reston, VA.
USGS (2007) 2006Mineral Yearbook: Aluminum. U.S. Geological Survey, Reston, VA.
USGS (1995, 1998, 2000, 2001, 2002) Minerals Yearbook: Aluminum Annual Report. U.S. Geological Survey,
Reston, VA.
Magnesium Production and Processing
Bartos S., C. Laush, J. Scharfenberg, and R. Kantamaneni (2007) "Reducing greenhouse gas emissions from
magnesium die casting," Journal of Cleaner Production, 15: 979-987, March.
11-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Gjestland, H. and D. Magers (1996) "Practical Usage of Sulphur [Sulfur] Hexafluoride for Melt Protection in the
Magnesium Die Casting Industry," #13,1996 Annual Conference Proceedings, International Magnesium
Association. Ube City, Japan.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
RAND (2002) RAND Environmental Science and Policy Center, "Production and Distribution of SFe by End-Use
Applications" Katie D. Smythe. International Conference on SFe and the Environment: Emission Reduction
Strategies. San Diego, CA. November 21-22, 2002.
U.S. EPA Greenhouse Gas Reporting Program (GHGRP) Envirofacts. Subpart T: Magnesium Production and
Processing. Available online at
USGS (2002, 2003, 2005 through 2008, 201 Ib, and 2012) Minerals Yearbook: Magnesium Annual Report. U.S.
Geological Survey, Reston, VA. Available online at
.
USGS (2010a) Mineral Commodity Summaries: Magnesium Metal. U.S. Geological Survey, Reston, VA. Available
online at < http://minerals.usgs.gov/minerals/pubs/commodity/magnesium/mcs-2010-mgmet.pdf>.
Horsehead Corp. (2013) Form 10-k, Annual Report for the Fiscal Year Ended December 31, 2012. Available at:
.
Submitted March 18, 2013.
Horsehead Corp. (2012) Form 10-k, Annual Report for the Fiscal Year Ended December, 31, 2011. Available at:
. Submitted March 9,
2012.
Horsehead Corp. (2011) 10-k Annual Report for the Fiscal Year Ended December, 31 2010. Available at:
. Submitted March 16, 2011.
Horsehead Corp. (2010a) 10-k Annual Report for the Fiscal Year Ended December, 31 2009. Available at:
. Submitted March 16, 2010.
Horsehead Corp. (2010b) Horsehead Holding Corp. Provides Update on Operations at its Monaco, PA Plant. July
28, 2010. Available at: .
Horsehead Corp (2008) 10-k Annual Report for the Fiscal Year Ended December, 31 2007. Available at:
. Submitted March 31, 2008.
Horsehead Corp (2007) Registration Statement (General Form) S-l. Available at . Submitted April 13, 2007.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
PIZO (2012) Available at . Accessed October 10, 2012.
Rowland (2012) Personal communication. Art Rowland, Plant Manager, Steel Dust Recycling LLC and Gopi
Marine, Eastern Research Group, Inc.; October 5, 2012.
Sjardin (2003) CO2 Emission Factors for Non-Energy Use in the Non-Ferrous Metal, Ferroalloys and Inorganics
Industry. Copernicus Institute. Utrecht, the Netherlands.
Steel Dust Recycling LLC (2013) Available at < http://steeldust.com/home.htm>. Accessed October 29, 2013.
USGS (2014) 2014 Mineral Commodity Summary: Zinc. U.S. Geological Survey, Reston, VA.
USGS (1995 through 2012) Minerals Yearbook: Zinc Annual Report. U.S. Geological Survey, Reston, VA.
References 11-29
-------
Viklund-White C. (2000) "The Use of LCA for the Environmental Evaluation of the Recycling of Galvanized
Steel." ISIJ International. Volume 40 No. 3: 292-299.
Lead Production
Dutrizac, J.E., V. Ramachandran, and J.A. Gonzalez (2000) Lead-Zinc 2000. The Minerals, Metals, and Materials
Society.
Guberman, D. (2013) Personal communication. David E. Guberman, Commodity Specialist, U.S. Geological Survey
and Marty Wolf, Eastern Research Group, Inc.; November 5, 2013.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Morris, D., F.R. Steward, and P. Evans (1983) Energy Efficiency of a Lead Smelter. Energy 8(5):337-349.
Sjardin, M. (2003) CO 2 Emission Factors for Non-Energy Use in the Non-Ferrous Metal, Ferroalloys and
Inorganics Industry. Copernicus Institute. Utrecht, the Netherlands.
Ullman (1997) Ullman 's Encyclopedia of Industrial Chemistry: Fifth Edition. Volume A5. John Wiley and Sons.
USGS (2013) 2013 Mineral Commodity Summary, Lead. U.S. Geological Survey, Reston, VA.
USGS (1995 through 2013a) Minerals Yearbook: Lead Annual Report. U.S. Geological Survey, Reston, VA.
HCFC-22 Production
ARAP (2010) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. September 10, 2010.
ARAP (2009) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. September 21, 2009.
ARAP (2008) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. October 17, 2008.
ARAP (2007) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. October 2, 2007.
ARAP (2006) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Sally Rand of the U.S. Environmental Protection Agency. July 11, 2006.
ARAP (2005) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. August 9, 2005.
ARAP (2004) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. June 3, 2004.
ARAP (2003) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Sally Rand of the U.S. Environmental Protection Agency. August 18, 2003.
ARAP (2002) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. August 7, 2002.
ARAP (2001) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. August 6, 2001.
ARAP (2000) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Sally Rand of the U.S. Environmental Protection Agency. August 13, 2000.
ARAP (1999) Facsimile from Dave Stirpe, Executive Director, Alliance for Responsible Atmospheric Policy to
Deborah Ottinger Schaefer of the U.S. Environmental Protection Agency. September 23, 1999.
11-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
ARAP (1997) Letter from Dave Stirpe, Director, Alliance for Responsible Atmospheric Policy to Elizabeth Dutrow
of the U.S. Environmental Protection Agency. December 23, 1997.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
RTI (2008) "Verification of Emission Estimates of HFC-23 from the Production of HCFC-22 Emissions from 1990
through 2006." Report prepared by RTI International for the Climate Change Division. March, 2008.
RTI (1997) "Verification of Emission Estimates of HFC-23 from the Production of HCFC-22: Emissions from
1990 through 1996." Report prepared by Research Triangle Institute for the Cadmus Group. November 25, 1997;
revised February 16, 1998.
Substitution of Ozone Depleting Substances
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Semiconductor Manufacture
Burton, C.S., and R. Beizaie (2001) "EPA's PFC Emissions Model (PEVM) v. 2.14: Description and
Documentation" prepared for Office of Global Programs, U. S. Environmental Protection Agency, Washington, DC.
November 2001.
Citigroup Smith Barney (2005) Global Supply/Demand Model for Semiconductors. March 2005.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, eds. Institute for Global
Environmental Strategies (IGES). Hayama, Kanagawa, Japan.
ITRS (2007, 2008, 2011) International Technology Roadmap for Semiconductors: 2006 Update, January 2007;
International Technology Roadmap for Semiconductors: 2007 Edition, January 2008; International Technoology
Roadmap for Semiconductors: 2011, January 2012; Update, Available online at
. Theses and earlier editions and updates are available at
. Information about the number of interconnect layers for years 1990-2010 is contained in
Burton and Beizaie, 2001. PEVM is updated using new editions and updates of the ITRS, which are published
annually.
SEMI - Semiconductor Equipment and Materials Industry (2013) World Fab Forecast, May 2013 Edition.
SEMI - Semiconductor Equipment and Materials Industry (2012) World Fab Forecast, August 2012 Edition.
Semiconductor Industry Association (SIA) (2011) SICAS Capacity and Utilization Rates Q4 2011. Available online
at < http://www.semiconductors.org/industry _statistics/semiconductor_capacity_utilization_sicas_reports/>.
Semiconductor Industry Association (SIA) (2009) STATS: SICAS Capacity and Utilization Rates Q1-Q4 2008, Ql-
Q4 2009, Q1-Q4 2010. Available online at.
U.S. EPA (2006) Uses and Emissions of Liquid PFC Heat Transfer Fluids from the Electronics Sector. U.S.
Environmental Protection Agency, Washington, DC. EPA-430-R-06-901.
U.S. EPA Greenhouse Gas Reporting Program (GHGRP) Envirofacts. Subpart I: Electronics Manufacture.
Available online at
VLSI Research, Inc. (2012) Worldwide Silicon Demand. August 2012.
References 11-31
-------
Electrical Transmission and Distribution
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Levin et al. (2010) "The Global SFe Source Inferred from Long-term High Precision Atmospheric Measurements
and its Comparison with Emission Inventories."Atmospheric Chemistry and Physics, 10: 2655-2662.
O'Connell, P., F. Heil, J. Henriot, G. Mauthe, H. Morrison, L. Neimeyer, M. Pittroff, R. Probst, J.P. Tailebois
(2002) SF6 in the Electric Industry, Status 2000, CIGRE. February 2002.
RAND (2004) "Trends in SF6 Sales and End-Use Applications: 1961-2003," Katie D. Smythe. International
Conference on SFe and the Environment: Emission Reduction Strategies. RAND Environmental Science and Policy
Center, Scottsdale, AZ. December 1-3, 2004.
UDI (2013). 2013 UDI Directory of Electric Power Producers an d Distributors,121st Edition,Platts.
UDI (2010) 2010 UDI Directory of Electric Power Producers and Distributors, 118th Edition, Platts.
UDI (2007) 2007 UDI Directory of Electric Power Producers and Distributors, 115th Edition, Platts.
UDI (2004) 2004 UDI Directory of Electric Power Producers and Distributors, 112th Edition, Platts.
UDI (2001) 2007 UDI Directory of Electric Power Producers and Distributors, 109th Edition, Platts.
Industrial Sources of Indirect Greenhouse Gases
EPA (2013) "1970 - 2013 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available
online at .
EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and
the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency. Research Triangle Park, NC. October 1997.
Solvent and Other Product Use
Nitrous Oxide from Product Uses
Airgas (2012) Airgas, INC. Form 10-K. Annual Report Pursuant to Section 13 or 15 (d) of the SEC Act of 1934.
Fiscal year ended March, 31, 2012. Available online at
.
CGA (2003) "CGA Nitrous Oxide Abuse Hotline: CGA/NWSA Nitrous Oxide Fact Sheet." Compressed Gas
Association. November 3, 2003.
CGA (2002) "CGA/NWSA Nitrous Oxide Fact Sheet." Compressed Gas Association. March 25, 2002.
FTC (2001) Federal Trade Commission: Analysis of Agreement Containing Consent Order
To Aid Public Comment. FTC File No. 001-0040. October, 2001. Available online at
.
Heydorn, B. (1997) "Nitrous Oxide—North America." Chemical Economics Handbook, SRI Consulting. May 1997.
11-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories; Volume 3, Chapter 8.4 N2O From
Product Uses. The National Greenhouse Gas Inventories Programme, The Intergovernmental Panel on Climate
Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Ottinger (2014) Personal communication. Deborah Ottinger (CCD, U.S. EPA) and Mausami Desai (U.S. EPA).
Email received on January 29, 2014.
Tupman, M. (2003) Personal communication .Martin Tupman, Airgas Nitrous Oxide and Daniel Lieberman, ICF
International. August 8, 2003.
Indirect Greenhouse Gas Emissions from Solvent Use Solvent
Use
EPA (2013) "1970 - 2013 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available
online at .
EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and
the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency. Research Triangle Park, NC. October 1997.
Agriculture
Enteric Fermentation
Archibeque, S. (2011) Personal Communication. Shawn Archibeque, Colorado State University, Fort Collins,
Colorado and staff at ICF International.
Crutzen, P.J., I. Aselmann, and W. Seiler (1986) Methane Production by Domestic Animals, Wild Ruminants,
Other Herbivores, Fauna, and Humans. Tellus, 38B:271-284.
Donovan, K. (1999) Personal Communication. Kacey Donovan, University of California at Davis and staff at ICF
International.
Doren, P.E., J. F. Baker, C. R. Long and T. C. Cartwright (1989) Estimating Parameters of Growth Curves of Bulls,
J Animal Science 67:1432-1445.
Enns, M. (2008) Personal Communication. Dr. Mark Enns, Colorado State University and staff at ICF International.
Galyean and Gleghorn (2001) Summary of the 2000 Texas Tech University Consulting Nutritionist Survey. Texas
Tech University. Available online at. June
2009.
Holstein Association (2010) History of the Holstein Breed (website). Available online at
. Accessed September 2010.
ICF (2006) Cattle Enteric Fermentation Model: Model Documentation. Prepared by ICF International for the
Environmental Protection Agency. June 2006.
ICF (2003) Uncertainty Analysis of 2001 Inventory Estimates of Methane Emissions from Livestock Enteric
Fermentation in the U.S. Memorandum from ICF International to the Environmental Protection Agency. May 2003.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
References 11-33
-------
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change. Montreal. May
2000. IPCC-XWDoc. 10 (1.IV.2000).
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
Johnson, D. (2002) Personal Communication. Don Johnson, Colorado State University, Fort Collins, and ICF
International.
Johnson, D. (1999) Personal Communication. Don Johnson, Colorado State University, Fort Collins, and David
Conneely, ICF International.
Johnson, K. (2010) Personal Communication. Kris Johnson, Washington State University, Pullman, and ICF
International.
Kebreab E., K. A. Johnson, S. L. Archibeque, D. Pape, and T. Wirth (2008) Model for estimating enteric methane
emissions from United States dairy and feedlot cattle. J. Anim. Sci. 86: 2738-2748.
Lippke, H., T. D. Forbes, and W. C. Ellis. (2000) Effect of supplements on growth and forage intake by stacker
steers grazing wheat pasture. J. Anim. Sci. 78:1625-1635
National Bison Association (2011) Handling & Carcass Info (on website). Available online at:
. Accessed August 16, 2011.
National Bison Association (1999) Total Bison Population—1999. Report provided during personal email
communication with Dave Carter, Executive Director, National Bison Association July 19, 2011.
NRC (1999) 1996 BeefNRC: Appendix Table 22. National Research Council.
NRC (1984) Nutrient requirements for beef cattle (6th Ed.). National Academy Press, Washington, DC.
Pinchak, W.E., D. R. Tolleson, M. McCloy, L. J. Hunt, R. J. Gill, R. J. Ansley, and S. J. Bevers (2004) Morbidity
effects on productivity and profitability of stacker cattle grazing in the southern plains. J. Anim. Sci. 82:2773-2779.
Platter, W. J., J. D. Tatum, K. E. Belk, J. A. Scanga, and G. C. Smith (2003) Effects of repetitive use of hormonal
implants on beef carcass quality, tenderness, and consumer ratings of beef palatability. J. Anim. Sci. 81:984-996.
Preston, R.L. 2010. What's The Feed Composition Value of That Cattle Feed? Beef Magazine, March 1, 2010.
Available at: http:^eefmagazine.com/nutrition/feed-composition-tables/feed-composition-value-cattle~0301
Skogerboe, T. L., L. Thompson, J. M. Cunningham, A. C. Brake, V. K. Karle (2000) The effectiveness of a single
dose of doramectin pour-on in the control of gastrointestinal nematodes in yearling stacker cattle. Vet. Parasitology
87:173-181.
Soliva, C.R.. (2006). Report to the attention of IPCC about the data set and calculation method used to estimate
methane formation from enteric fermentation of agricultural livestock population and manure management in Swiss
agriculture. On behalf of the Federal Office for the Environment (FOEN), Berne, Switzerland,
USD A (2013) Quick Stats: Agricultural Statistics Database. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. Available online at . Accessed July
26,2013.
USD A (2007) Census of Agriculture: 2007 Census Report. United States Department of Agriculture. Available
online at: .
USD A (2002) Census of Agriculture: 2002 Census Report. United States Department of Agriculture. Available
online at: .
USDA (1997) Census of Agriculture: 1997 Census Report. United States Department of Agriculture. Available
online at: . Accessed July 18, 2011.
11-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
USDA (1996) Beef Cow/Calf Health and Productivity Audit (CHAPA): Forage Analyses from Cow/Calf Herds in 18
States. National Agriculture Statistics Service, U.S. Department of Agriculture. Washington, DC. Available online
at . March 1996.
USDA (1992) Census of Agriculture: 1992 Census Report. United States Department of Agriculture. Available
online at: . Accessed July 18, 2011.
USD A: APHIS :VS (2010) Beef 2007-08, Part V: Reference of Beef Cow-calfManagement Practices in the United
States, 2007-08. USDA-APHIS-VS, CEAH. Fort Collins, CO.
USD A: APHIS :VS (2002) Reference of 2002 Dairy Management Practices. USDA-APHIS-VS, CEAH. Fort
Collins, CO. Available online at .
USDA:APHIS:VS (1998) Beef '97, PartsI-IV. USDA-APHIS-VS, CEAH. Fort Collins, CO. Available online at <
http://www.aphis.usda.gov/animal_health/nahms/beefcowcah7index.shtml#beef97>
USDA:APHIS:VS (1996) Reference of 1996 Dairy Management Practices. USDA-APHIS-VS, CEAH. Fort
Collins, CO. Available online at .
USDA:APHIS:VS (1994) Beef Cow/Calf Health and Productivity Audit. USDA-APHIS-VS, CEAH. Fort Collins,
CO. Available online at .
USDA:APHIS:VS (1993) Beef Cow/Calf Health and Productivity Audit. USDA-APHIS-VS, CEAH. Fort Collins,
CO. August 1993. Available online at .
Vasconcelos and Galyean (2007) Nutritional recommendations of feedlot consulting nutritionists: The 2007 Texas
Tech University Study. J. Anim. Sci. 85:2772-2781.
Anderson, S. (2000) Personal Communication. Steve Anderson, Agricultural Statistician, National Agriculture
Statistics Service, U.S. Department of Agriculture and Lee-Ann Tracy, ERG. Washington, DC. May 31, 2000.
ASAE (1998) ASAE Standards 1998, 45* Edition. American Society of Agricultural Engineers. St. Joseph, MI.
Bryant, M.P., V.H. Varel, R.A. Frobish, and H.R. Isaacson (1976) In H.G. Schlegel (ed.); Seminar on Microbial
Energy Conversion. E. Goltz KG. Gottingen, Germany.
Bush, E. (1998) Personal communication with Eric Bush, Centers for Epidemiology and Animal Health, U.S.
Department of Agriculture regarding National Animal Health Monitoring System's (NAHMS) Swine '95 Study.
Deal, P. (2000) Personal Communication. Peter B. Deal, Rangeland Management Specialist, Florida Natural
Resource Conservation Service and Lee-Ann Tracy, ERG. June 21, 2000.
EPA (2012) AgSTAR Anaerobic Digester Database. Available online at:
.
EPA (2008) Climate Leaders Greenhouse Gas Inventory Protocol Offset Project Methodology for Project Type
Managing Manure with Biogas Recovery Systems. Available online at
.
EPA (2006) AgSTAR Digest. Office of Air and Radiation, U.S. Environmental Protection Agency. Washington, DC.
Winter 2006. Available online at . Retrieved July 2006.
EPA (2005) National Emission Inventory—Ammonia Emissions from Animal Agricultural Operations, Revised
Draft Report. U.S. Environmental Protection Agency. Washington, DC. April 22, 2005. Available online at
. Retrieved
August 2007.
EPA (2003) AgSTAR Digest. Office of Air and Radiation, U.S. Environmental Protection Agency. Washington, DC.
Winter 2003. Available online at . Retrieved July 2006.
References 11-35
-------
EPA (2002a) Development Document for the Final Revisions to the National Pollutant Discharge Elimination
System (NPDES) Regulation and the Effluent Guidelines for Concentrated Animal Feeding Operations (CAFOS).
U.S. Environmental Protection Agency. EPA-821-R-03-001. December 2002.
EPA (2002b) Cost Methodology for the Final Revisions to the National Pollutant Discharge Elimination System
Regulation and the Effluent Guidelines for Concentrated Animal Feeding Operations. U.S. Environmental
Protection Agency. EPA-821-R-03-004. December 2002.
EPA (2000) AgSTAR Digest. Office of Air and Radiation, U.S. Environmental Protection Agency. Washington, DC.
Spring 2000. Available online at: http://www.epa.gov/agstar/news-events/digest/2000digest.pdf.
EPA (1992) Global Methane Emissions from Livestock and Poultry Manure, Office of Air and Radiation, U.S.
Environmental Protection Agency. February 1992.
ERG (2010a) "Typical Animal Mass Values for Inventory Swine Categories." Memorandum to EPA from ERG.
July 19,2010.
ERG (2010b) Telecon with William Boyd of USD A NRCS and Cortney Me of ERG Concerning Updated VS and
NexRates. Augusts, 2010.
ERG (2010c) "Updating Current Inventory Manure Characteristics new USD A Agricultural Waste Management
Field Handbook Values." Memorandum to EPA from ERG. August 13, 2010.
ERG (2008) "Methodology for Improving Methane Emissions Estimates and Emission Reductions from Anaerobic
Digestion System for the 1990-2007 Greenhouse Gas Inventory for Manure Management." Memorandum to EPA
from ERG. August 18, 2008.
ERG (2003a) "Methodology for Estimating Uncertainty for Manure Management Greenhouse Gas Inventory."
Contract No. GS-10F-0036, Task Order 005. Memorandum to EPA from ERG, Lexington, MA. September 26,
2003.
ERG (2003b) "Changes to Beef Calves and Beef Cows Typical Animal Mass in the Manure Management
Greenhouse Gas Inventory." Memorandum to EPA from ERG, October 7, 2003.
ERG (2001) Summary of development ofMDP Factor for methane conversion factor calculations. ERG, Lexington,
MA. September 2001.
ERG (2000a) Calculations: Percent Distribution of Manure for Waste Management Systems. ERG, Lexington, MA.
August 2000.
ERG (2000b) Discussion of Methodology for Estimating Animal Waste Characteristics (Summary of B0 Literature
Review). ERG, Lexington, MA. June 2000.
Garrett, W.N. and D.E. Johnson (1983) "Nutritional energetics of ruminants." Journal of Animal Science,
57(suppl.2):478-497.
Groffman, P.M., R. Brumme, K. Butterbach-Bahl, K.E. Dobbie, A.R. Mosier, D. Ojima, H. Papen, W.J. Parton,
K. A. Smith, and C. Wagner-Riddle (2000) "Evaluating annual nitrous oxide fluxes at the ecosystem scale." Global
Biogeochemcial Cycles, 14(4): 1061-1070.
Hashimoto, A.G. (1984) "Methane from Swine Manure: Effect of Temperature and Influent Substrate Composition
on Kinetic Parameter (k)."^4gr/cuItural Wastes, 9:299-308.
Hashimoto, A.G., V.H. Varel, and Y.R. Chen (1981) "Ultimate Methane Yield from Beef Cattle Manure; Effect of
Temperature, Ration Constituents, Antibiotics and Manure Age." Agricultural Wastes, 3:241-256.
Hill, D.T. (1984) "Methane Productivity of the Major Animal Types." Transactions oftheASAE, 27(2):530-540.
Hill, D.T. (1982) "Design of Digestion Systems for Maximum Methane Production." Transactions of the ASAE,
25(1):226-230.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
11-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Johnson, D. (2000) Personal Communication. Dan Johnson, State Water Management Engineer, California Natural
Resource Conservation Service and Lee-Ann Tracy, ERG. June 23, 2000.
Lange, J. (2000) Personal Communication. JohnLange, Agricultural Statistician, U.S. Department of Agriculture,
National Agriculture Statistics Service and Lee-Ann Tracy, ERG. Washington, DC. May 8, 2000.
Meagher, M. (1986). Bison bison. Mammalian Species. 266: 1-8.
Miller, P. (2000) Personal Communication. Paul Miller, Iowa Natural Resource Conservation Service and Lee-Ann
Tracy, ERG. June 12, 2000.
Milton, B. (2000) Personal Communication. Bob Milton, Chief of Livestock Branch, U.S. Department of
Agriculture, National Agriculture Statistics Service and Lee-Ann Tracy, ERG. May 1, 2000.
Moffroid, K and D. Pape. (2013) 1990-2012 Volatile Solids and Nitrogen Excretion Rates. Dataset to EPA from
ICF International. August 2013.
Morris, G.R. (1976) Anaerobic Fermentation of Animal Wastes: A Kinetic and Empirical Design Fermentation.
M.S. Thesis. Cornell University.
National Bison Association (1999) Total Bison Population—1999. Report provided during personal email
communication with Dave Carter, Executive Director, National Bison Association July 19, 2011.
NOAA (2012) National Climate Data Center (NCDC). Available online at
(for all states except Alaska and Hawaii) and . (for Alaska and
Hawaii). September 2012.
Ott, S.L. (2000) Dairy '96 Study. Stephen L. Ott, Animal and Plant Health Inspection Service, U.S. Department of
Agriculture. June 19, 2000.
Poe, G., N. Bills, B. Bellows, P. Crosscombe, R. Koelsch, M. Kreher, and P. Wright (1999) Staff Paper
Documenting the Status of Dairy Manure Management in New York: Current Practices and Willingness to
Participate in Voluntary Programs. Department of Agricultural, Resource, and Managerial Economics; Cornell
University, Ithaca, New York, September.
Safley, L.M., Jr. (2000) Personal Communication. Deb Bartram, ERG and L.M. Safley, President, Agri-Waste
Technology. June and October 2000.
Safley, L.M., Jr. and P.W. Westerman (1990) "Psychrophilic anaerobic digestion of animal manure: proposed design
methodology." Biological Wastes, 34:133-148.
Stettler, D. (2000) Personal Communication. Don Stettler, Environmental Engineer, National Climate Center,
Oregon Natural Resource Conservation Service and Lee-Ann Tracy, ERG. June 27, 2000.
Sweeten, J. (2000) Personal Communication. John Sweeten, Texas A&M University and Indra Mitra, ERG. June
2000.
UEP (1999) Voluntary Survey Results—Estimated Percentage Participation/Activity, Caged Layer Environmental
Management Practices, Industry data submissions for EPA profile development, United Egg Producers and National
Chicken Council. Received from John Thorne, Capitolink. June 2000.
USDA (2013a) Quick Stats: Agricultural Statistics Database. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. Available online at .
USDA (2013b) Chicken and Eggs 2012 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, DC. February 2013. Available online at
.
USDA (2013c) Poultry - Production and Value 2012 Summary. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. April 2013. Available online at
.
USDA (2012a) Chicken and Eggs 2011 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, DC. February 2012. Available online at
.
References 11-37
-------
USD A (2012b) Poultry - Production and Value 2011 Summary. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. April 2012. Available online at
.
USD A (201 la) Chicken and Eggs 2010 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, DC. February 2011. Available online at
.
USD A (20 lib) Poultry - Production and Value 2010 Summary. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. April 2011. Available online at
.
USD A (2010a) Chicken and Eggs 2009 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, DC. February 2010. Available online at
.
USD A (2010b) Poultry - Production and Value 2009 Summary. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. April 2010. Available online at
.
USDA(2009a) 1987, 1992, 1997, 2002, and 2007 Census of Agriculture. National Agriculture Statistics Service,
U.S. Department of Agriculture. Washington, DC. Available online at .
December 2009.
USDA (2009b) Chicken and Eggs 2008 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, DC. February 2009. Available online at
.
USDA (2009c) Poultry - Production and Value 2008 Summary. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. April 2009. Available online at
.
USDA (2009d) Chicken and Eggs - Final Estimates 2003-2007. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. March 2009. Available online at
.
USDA (2009e) Poultry Production and Value—Final Estimates 2003-2007. National Agriculture Statistics Service,
U.S. Department of Agriculture. Washington, DC. May 2009. Available online at
.
USDA (2008) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651.
Natural Resources Conservation Service, U.S. Department of Agriculture.
USDA (2004a) Chicken and Eggs—Final Estimates 1998-2003. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. April 2004. Available online at
.
USDA (2004b) Poultry Production and Value—Final Estimates 1998-2002. National Agriculture Statistics Service,
U.S. Department of Agriculture. Washington, DC. April 2004. Available online at
.
USDA (1999) Poultry Production and Value—Final Estimates 1994-97. National Agriculture Statistics Service,
U.S. Department of Agriculture. Washington, DC. March 1999. Available online at
.
USDA (1998) Chicken and Eggs—Final Estimates 1994-97. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. December 1998. Available online at
.
USDA (1996) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651.
Natural Resources Conservation Service, U.S. Department of Agriculture. July 1996.
11-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
USDA (1995a) Poultry Production and Value—Final Estimates 1988-1993. National Agriculture Statistics Service,
U.S. Department of Agriculture. Washington, DC. March 1995. Available online at
.
USDA (1995b) Chicken and Eggs—Final Estimates 1988-1993. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. December 1995. Available online at
.
USDA (1994) Sheep and Goats—Final Estimates 1989-1993. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. January 31, 1994. Available online at
.
USDA, APHIS (2003) Sheep 2001,Part LReference of Sheep Management in the United States, 2001 and Part
IV: Baseline Reference of 2001 Sheep Fee dlot Health and Management. USDA-APHIS-VS. Fort Collins, CO.
#N356.0702. http://www.aphis.usda.gov/animal_health/nahms/sheep/index.shtml#sheep2001
USDA, APHIS (2000) Layers '99—Part II: References of 1999 Table Egg Layer Management in the U.S. USDA-
APHIS-VS. Fort Collins, CO.
http://www.aphis.usda.gov/animal_health/nahms/poultry/downloads/layers99/Lay ers99_dr_PartII.pdf
USDA, APHIS (1996) Swine '95: Grower/Finisher Part II: Reference of 1995 U.S. Grower/Finisher Health &
Management Practices. USDA-APHIS-VS. Fort Collins, CO.
http://www.aphis.usda.gov/animal_health/nahms/swine/downloads/swine95/Swine95_dr_PartII.pdf
Wright, P. (2000) Personal Communication. Lee-Ann Tracy, ERG and Peter Wright, Cornell University, College of
Agriculture and Life Sciences. June 23, 2000.
Anderson, M. (2008 through 2013) Email correspondence. Monte Anderson, Oklahoma Farm Service Agency and
ICF International.
Baldwin, K., E. Dohlman, N. Childs and L. Forman (2010). Consolidation and Structural Change in the U.S. Rice
Sector. Economic Research Service: U.S. Department of Agriculture, Washington D.C. Available online at
. September 2013.
Baicich, Paul (2013). The Birds and Rice Connection. Bird Watcher's Digest. Available online at
.
Beighley, D. (2011 through 2012) Email correspondence. Donn Beighley, Southeast Missouri State University,
Department of Agriculture and ICF International.
Bollich, P. (2000) Personal Communication. Pat Bollich, Professor with Louisiana State University Agriculture
Center and Payton Deeks, ICF International.
Bossio, D. A., W. Horwath, R.G. Mutters, and C. van Kessel (1999) "Methane pool and flux dynamics in a rice field
following straw incorporation." Soil Biology and Biochemistry, 31:1313-1322.
Buehring, N. (2009 through 2011) Email correspondence. Nathan Buehring, Assistant Professor and Extension Rice
Specialist, Mississippi State University Delta Branch Exp. Station and ICF International.
Byrd, G. T., F. M. Fisher, & R. L. Sass. (2000). Relationships between methane production and emission to lacunal
methane concentrations in rice. Global biogeochemical cycles, 14(1), 73-83.
California Air Resources Board (2003). 2003 Progress Report on the Phase-down of Rice Straw Burning in the
Sacremento Valley Air Basin. Available online at .
Cantens, G. (2004 through 2005) Personal Communication. Janet Lewis, Assistant to Gaston Cantens, Vice
President of Corporate Relations, Florida Crystals Company and ICF International.
Deren, C. (2002) Personal Communication and Dr. Chris Deren, Everglades Research and Education Centre at the
University of Florida and Caren Mintz, ICF International. August 15, 2002.
References 11-39
-------
Environmental Defense Fund, Inc (2011). Creating and Quantifying Carbon Credits from Voluntary Practices on
Rice. Available online at .
Fife, L. (2011) Email correspondence. Les Fife, Sacramento Valley AgBurn Coordinator and ICF International.
Fitzgerald, G.J., K. M. Scow & J. E. Hill (2000) "Fallow Season Straw and Rice Management Effects on Methane
Emissions in California Rice." Global bigeochemical cycles, 14 (3), 767-776.
Gonzalez, R. (2007 through 2013) Email correspondence. Rene Gonzalez, Plant Manager, Sem-Chi Rice Company
and ICF International.
Guethle, D. (1999 through 2010) Personal Communication. David Guethle, Agronomy Specialist, Missouri
Cooperative Extension Service and ICF International.
Hardke, J. (2013) Email correspondence. Dr. Jarrod Hardke, Rice Extension Agronomist at the University of
Arkansas Rice Research and Extension Center and Cassandra Snow, ICF International. July 15, 2013.
Holzapfel-Pschorn, A., R. Conrad, and W. Seiler (1985) "Production, Oxidation, and Emissions of Methane in Rice
Paddies." FEMSMicrobiology Ecology, 31:343-351.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change, Montreal. May
2000. IPCC-XWDoc. 10 (1.IV.2000).
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency, Paris, France.
Kirstein, A. (2003 through 2004, 2006) Personal Communication. Arthur Kirstein, Coordinator, Agricultural
Economic Development Program, Palm Beach County Cooperative Extension Service, FL and ICF International.
Klosterboer, A. (1997, 1999 through 2003) Personal Communication. Arlen Klosterboer, retired Extension
Agronomist, Texas A&M University and ICF International. July 7, 2003.
Kongchum, Manoch (2005). Effect of Plant Residue and Water Management Practices on Soil Redox Chemistry,
Methane Emission, and Rice Productivity. LSU PhD Thesis.
Lancero, J. (2006 through 2010) Email correspondence. Jeff Lancero, California Air Resources Board and ICF
International.
Lee, D. (2003 through 2007) Email correspondence. Danny Lee, OK Farm Service Agency and ICF International.
Lindau, C.W. and P.K. Bollich (1993) "Methane Emissions from Louisiana First and Ratoon Crop Rice." Soil
Science, 156:42-48.
Lindau, C.W., P.K Bollich, and R.D. DeLaune (1995) "Effect of Rice Variety on Methane Emission from Louisiana
Rice." Agriculture, Ecosystems and Environment, 54:109-114.
Linscombe, S. (1999, 2001 through 2013) Email correspondence. Steve Linscombe, Professor with the Rice
Research Station at Louisiana State University Agriculture Center and ICF International.
May hew, W. (1997) Personal Communication. Walter Mayhew, University of Arkansas, Little Rock and Holly
Simpkins, ICF Incorporated. November 24, 1997.
McMillan, A., M. L.Goulden, and S.C. Tyler. (2007). Stoichiometry of CH4 and CO2 flux in a California rice
paddy. Journal of Geophysical Research: Biogeosciences (2005-2012), 112(G1).
Mutters, C. (2001 through 2005) Personal Communication. Mr. Cass Mutters, Rice Farm Advisor for Butte, Glen,
and Tehama Counties University of California, Cooperative Extension Service and ICF International.
11-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Rogers, C.W., K. R. Brye, RJ. Norman, T. Gasnier, D. Frizzell, and J. Branson. (2011). Methane Emissions from a
Silt-Loam Soil Under Direct-Seeded, Delayed-Flood Rice Management.B.R. Wells Rice Research Studies 2011.
306-315.
Sacramento Valley Basinwide Air Pollution Control Council (2005, 2007) Report on the Conditional Rice Straw
Burning Permit Program.. Available online at .
Saichuk, J. (1997) Personal Communication. John Saichuk, Louisiana State University and Holly Simpkins, ICF
Incorporated. November 24, 1997.
Sass, Ronald L (2001). CH4 Emissions from Rice Agriculture. Good Practice Guidance and Uncertainty
Management in National Greenhouse Gas Inventories. 399-417. Available at .
Sass, R. L., P.M. Fisher, and J. A. Andrews. (2002b). Spatial variability in methane emissions from a Texas rice
field with some general implications. Global biogeochemical cycles, 16(1), 15-1.
Sass, R.L., F.M Fisher, P. A. Harcombe, and F.T. Turner (1991a) "Mitigation of Methane Emissions from Rice
Fields: Possible Adverse Effects of Incorporated Rice Straw." Global Biogeochemical Cycles, 5:275-287.
Sass, R.L., F.M. Fisher, F.T. Turner, and M.F. Jund (1991b) "Methane Emissions from Rice Fields as Influenced by
Solar Radiation, Temperature, and Straw Incorporation." Global Biogeochemical Cycles, 5:335-350.
Sass, R.L., F.M. Fisher, P.A. Harcombe, and F.T. Turner (1990) "Methane Production and Emissions in a Texas
Rice Field." Global Biogeochemical Cycles, 4:47-68.
Sass, R. L., J.A. Andrews, A. Ding and F.M. Fisher Jr. (2002a). Spatial and temporal variability in methane
emissions from rice paddies: Implications for assessing regional methane budgets. Nutrient Cycling in
Agroecosystems, 64(1-2), 3-7.
Schueneman, T. (1997, 1999 through 2001) Personal Communication. Tom Schueneman, Agricultural Extension
Agent, Palm Beach County, FL and ICF International.
Slaton, N. (1999 through 2001) Personal Communication. Nathan Slaton, Extension Agronomist—Rice, University
of Arkansas Division of Agriculture Cooperative Extension Service and ICF International.
Stansel, J. (2004 through 2005) Email correspondence. Dr. Jim Stansel, Resident Director and Professor Emeritus,
Texas A&M University Agricultural Research and Extension Center and ICF International.
Stevens, G. (1997) Personal Communication. Gene Stevens, Extension Specialist, Missouri Commercial Agriculture
Program, Delta Research Center and Holly Simpkins, ICF Incorporated. December 17, 1997.
Street, J. (1997, 1999 through 2003) Personal Communication. Joe Street, Rice Specialist, Mississippi State
University, Delta Research Center and ICF International.
Texas Agricultural Experiment Station (2007 through 2013) Texas Rice Acreage by Variety. Agricultural Research
and Extension Center, Texas Agricultural Experiment Station, Texas A&M University System. Available online at
.
Texas Agricultural Experiment Station (2006) 2005 - Texas Rice Crop Statistics Report. Agricultural Research and
Extension Center, Texas Agricultural Experiment Station, Texas A&M University System, p. 8. Available online at
.
USDA (2005 through 2013) Crop Production Summary. National Agricultural Statistics Service, Agricultural
Statistics Board, U.S. Department of Agriculture, Washington, DC. Available online at
.
USDA (2012) Summary of USDA-ARS Research on the Interrelationship of Genetic and Cultural Management
Factors That Impact Grain Arsenic Accumulation in Rice. News and Events. Agricultural Research Service, U.S.
Department of Agriculture, Washington, DC. Available online at
. September 2013.
References 11-41
-------
USDA (2003) Field Crops, Final Estimates 1997-2002. Statistical Bulletin No. 982. National Agricultural
Statistics Service, Agricultural Statistics Board, U.S. Department of Agriculture, Washington, DC. Available online
at . September 2005.
USDA (1998) Field Crops Final Estimates 1992-1997. Statistical Bulletin Number 947 a. National Agricultural
Statistics Service, Agricultural Statistics Board, U.S. Department of Agriculture, Washington, DC. Available online
at . July 2001.
USDA (1994) Field Crops Final Estimates 1987-1992. Statistical Bulletin Number 896. National Agricultural
Statistics Service, Agricultural Statistics Board, U.S. Department of Agriculture, Washington, DC. Available online
at . July 2001.
Vayssieres, M. (2013). Email correspondance. Marc Vayssieres, Ph.D. Cal/EPA Air Resources Board Air Quality
Planning & Science Division and Rachel Steele, ICF International. January 2014.
Walker, T. (2005, 2007 through 2008) Email correspondence. Tim Walker, Assistant Research Professor,
Mississippi State University Delta Branch Exp. Station and ICF International.
Wang, Jim J, S.K. Dodla, S. Viator, M. Kongchum, S. Harrison, S. D. Mudi, S. Liu, Z. Tian (2013). Agriculture
Field Management Practices and Greenhouse Gas Emissions from Louisiana Soils. Louisiana Agriculture, Spring
2013: 8-9. Available online at .
Wilson, C. (2002 through 2007, 2009 through 2012) Personal Communication. Dr. Chuck Wilson, Rice Specialist at
the University of Arkansas Cooperative Extension Service and ICF International.
Yao, H., J. Jingyan, Z. Lianggang, R. L. Sass, and F. M. Fisher. (2001). Comparison of field measurements of CH4
emission from rice cultivation in Nanjing, China and in Texas, USA. Advances in Atmospheric Sciences, 18(6),
1121-1130.
Young, M. (2013) Rice and Ducks. Ducks Unlimited, Memphis, TN. Available online at
.
Agricultural Soil Management
AAPFCO (1995 through 2000a, 2002 through 2007) Commercial Fertilizers. Association of American Plant Food
Control Officials. University of Kentucky. Lexington, KY.
AAPFCO (2008 through 201 Ib) Commercial Fertilizers. Association of American Plant Food Control Officials.
University of Missouri. Columbia, MO.
Cibrowski, P. (1996) Personal Communication. Peter Cibrowski, Minnesota Pollution Control Agency and Heike
Mainhardt, ICF Incorporated. July 29, 1996.
CTIC (2004) 2004 Crop Residue Management Survey. Conservation Technology Information Center. Available
online at .
Del Grosso, S.J., A.R. Mosier, W.J. Parton, and D.S. Ojima (2005) "DAYCENT Model Analysis of Past and
Contemporary Soil N2O and Net Greenhouse Gas Flux for Major Crops in the USA." Soil Tillage and Research, 83:
9-24. doi: 10.1016/j.still.2005.02.007.
Del Grosso, S.J., S.M. Ogle, W.J. Parton, and F.J. Breidt (2010) "Estimating Uncertainty in N2O Emissions from
US Cropland Soils." Global Biogeochemical Cycles, 24, GB1009, doi:10.1029/2009GB003544.
Del Grosso, S.J., W.J. Parton, C.A. Keough, and M. Reyes-Fox. (2011) Special features of the DayCent modeling
package and additional procedures for parameterization, calibration, validation, and applications, in Methods of
Introducing System Models into Agricultural Research, L.R. Ahuja and Liwang Ma, editors, p. 155-176, American
Society of Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, WI. USA.
Del Grosso, S.J., W.J. Parton, A.R. Mosier, M.D. Hartman, J. Brenner, D.S. Ojima, and D.S. Schimel (2001)
"Simulated Interaction of Carbon Dynamics and Nitrogen Trace Gas Fluxes Using the DAYCENT Model." In
Schaffer, M., L. Ma, S. Hansen, (eds.); Modeling Carbon and Nitrogen Dynamics for Soil Management. CRC Press.
Boca Raton, Florida. 303-332.
11-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Del Grosso, S.J., T. Wirth, S.M. Ogle, W.J. Parton (2008) Estimating agricultural nitrous oxide emissions. EOS 89,
529-530.
Delgado, J.A., S.J. Del Grosso, and S.M. Ogle (2009) "15N isotopic crop residue cycling studies and modeling
suggest that IPCC methodologies to assess residue contributions to N2O-N emissions should be reevaluated."
Nutrient Cycling in Agroecosystems, DOI 10.1007/sl0705-009-9300-9.
Edmonds, L., N. Gollehon, R.L. Kellogg, B. Kintzer, L. Knight, C. Lander, J. Lemunyon, D. Meyer, D.C. Moffitt,
and J. Schaeffer (2003) "Costs Associated with Development and Implementation of Comprehensive Nutrient
Management Plans." Part 1. Nutrient Management, Land Treatment, Manure and Wastewater Handling and
Storage, and Recordkeeping. Natural Resource Conservation Service, U.S. Department of Agriculture.
EPA (2003) Clean Watersheds Needs Survey 2000—Report to Congress, U.S. Environmental Protection Agency.
Washington, DC. Available online at .
EPA (1999) Biosolids Generation, Use and Disposal in the United States. Office of Solid Waste, U.S.
Environmental Protection Agency. Available online at .
EPA (1993) Federal Register. Part II. Standards for the Use and Disposal of Sewage Sludge; Final Rules. U.S.
Environmental Protection Agency, 40 CFR Parts 257, 403, and 503.
Gurung, R.B., F.J. Breidt, A. Dutin, and S.M. Ogle (2009) Predicting Enhanced Vegetation Index (EVI) for
ecosystem modeling applications. Remote Sensing of Environment 113:2186-2193.
ILENR (1993) Illinois Inventory of Greenhouse Gas Emissions and Sinks: 1990. Office of Research and Planning,
Illinois Department of Energy and Natural Resources. Springfield, IL.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
McFarland, M.J. (2001) Biosolids Engineering, New York: McGraw-Hill, p. 2.12.
McGill, W.B., and C.V. Cole (1981) Comparative aspects of cycling of organic C, N, S andP through soil organic
matter. Geoderma 26:267-286.
Mesinger, F., G. DiMego, E. Kalnay, K. Mitchell, P. C. Shafran, W. Ebisuzaki, D. Jovic, J. Woollen, E. Rogers, E.
H. Berbery, M. B. Ek, Y. Fan, R. Grumbine, W. Higgins, H. Li, Y. Lin, G. Manikin, D. Parrish, and W. Shi (2006)
North American regional reanalysis. Bulletin of the American Meteorological Society 87:343-360.
NASS (2004) Agricultural Chemical Usage: 2003 Field Crops Summary. Report AgChl(04)a, National Agricultural
Statistics Service, U.S. Department of Agriculture. Available online at
.
NASS (1999) Agricultural Chemical Usage: 1998 Field Crops Summary. Report AgChl(99). National Agricultural
Statistics Service, U.S. Department of Agriculture. Available online at
.
NASS (1992) Agricultural Chemical Usage: 1991 Field Crops Summary. Report AgChl(92). National Agricultural
Statistics Service, U.S. Department of Agriculture. Available online at
.
NEBRA (2007) A National Biosolids Regulation, Quality, End Use & Disposal Survey. North East Biosolids and
Residuals Association, July 21, 2007
NFA (1946) "Charting the Fertilizer Course: Results of NFA's Third Practice Survey." National Fertilizer
Association. The Fertilizer Review. Vol. XXI, 2:7-13.
Noller, J. (1996) Personal Communication. John Noller, Missouri Department of Natural Resources and Heike
Mainhardt, ICF Incorporated. July 30, 1996.
Nusser, S.M., J.J. Goebel (1997) The national resources inventory: a long term monitoring programme.
Environmental and Ecological Statistics, 4, 181-204.
References 11-43
-------
Oregon Department of Energy (1995) Report on Reducing Oregon's Greenhouse Gas Emissions: Appendix D
Inventory and Technical Discussion. Oregon Department of Energy. Salem, OR.
Parton, W.J., M.D. Hartman, D.S. Ojima, andD.S. Schimel (1998) "DAYCENT: Its Land Surface Submodel:
Description and Testing". Glob. Planet. Chang. 19: 35-48.
Potter, C., S. Klooster, A. Huete, and V. Genovese (2007) Terrestrial carbon sinks for the United States predicted
from MODIS satellite data and ecosystem modeling. Earth Interactions 11, Article No. 13, DOI 10.1175/EI228.1.
Potter, C. S., J.T. Randerson, C.B. Fields, P.A. Matson, P.M. Vitousek, H.A. Mooney, and S.A. Klooster. (1993)
"Terrestrial ecosystem production: a process model based on global satellite and surface data." Global
Biogeochemical Cycles 7:811-841.
Ruddy B.C., D.L. Lorenz, and O.K. Mueller (2006) County-level estimates of nutrient inputs to the land surface of
the conterminous United States, 1982-2001. Scientific Investigations Report 2006-5012. US Department of the
Interior.
Scheer, C., SJ. Del Grosso, WJ. Parton, D.W. Rowlings, P.R. Grace (2013) Modeling Nitrous Oxide Emissions
from Irrigated Agriculture: Testing DAYCENT with High Frequency Measurements, Ecological Applications, in
press, http://dx.doi.org/10.1890/13-0570.!
Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. (2005) State
Soil Geographic (STATSGO) Database for State. Available online at
.
Towery, D. (2001) Personal Communication. Dan Towery regarding adjustments to the CTIC (1998) tillage data to
reflect long-term trends, Conservation Technology Information Center, West Lafayette, IN, and Marlen Eve,
National Resource Ecology Laboratory, Fort Collins, CO. February 2001.
TVA (1991 through 1992a, 1993 through 1994) Commercial Fertilizers. Tennessee Valley Authority, Muscle
Shoals, AL.
USDA-ERS (2011) Agricultural Resource Management Survey (ARMS) Farm Financial and Crop Production
Practices: Tailored Reports. Online at: http://ers.usda.gov/Data/ARMS/CropOverview.htm.
USDA-ERS (1997) Cropping Practices Survey Data—1995. Economic Research Service, United States Department
of Agriculture. Available online at .
USDA-NRCS (2009) Summary Report: 2007 National Resources Inventory, Natural Resources Conservation
Service, Washington, DC, and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa,
http://www.nrcs.usda.gov/technical/NRI/2007/2007_NRI_Summary.pdf.
Vogelman, J.E., S.M. Howard, L. Yang, C. R. Larson, B. K. Wylie, and J. N. Van Driel (2001) "Completion of the
1990's National Land Cover Data Set for the conterminous United States." Photogrammetric Engineering and
Remote Sensing, 67:650-662.
Wisconsin Department of Natural Resources (1993) Wisconsin Greenhouse Gas Emissions: Estimates for 1990.
Bureau of Air Management, Wisconsin Department of Natural Resources, Madison, WI.
Field Burning of Agricultural Residues
Anderson, M. (2008 through 2013) Email correspondence. Monte Anderson, Oklahoma Farm Service Agency and
ICF International. July 12, 2010.
Ashman (2008) Email communication. Janet Ashman, Hawaii Agriculture Research Center and Victoria Thompson,
ICF International. Ms. Ashman cited an August 2004 report prepared for the U.S. Dept. of Energy by the Hawaiian
Commercial & Sugar Co., "Closed-Loop Biomass Co-Firing~Pilot-Scale and Full-Scale Test Results."
Barnard, G., and L. Kristoferson (1985) Agricultural Residues as Fuel in the Third World. Earthscan Energy
Information Programme and the Beijer Institute of the Royal Swedish Academy of Sciences. London, England.
Cantens, G. (2004 through 2005) Personal Communication. Janet Lewis, Assistant to Gaston Cantens, Vice
President of Corporate Relations, Florida Crystals Company and ICF International.
11-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Cibrowski, P. (1996) Personal Communication. Peter Cibrowski, Minnesota Pollution Control Agency and Heike
Mainhardt, ICF Incorporated. July 29, 1996.
Deren, C. (2002) Personal communication. Dr. Chris Deren, Everglades Research and Education Centre at the
University of Florida and Caren Mintz, ICF International. August 15, 2002.
EPA (1994) International Anthropogenic Methane Emissions: Estimates for 1990, Report to Congress. EPA 230-R-
93-010. Office of Policy Planning and Evaluation, U.S. Environmental Protection Agency, Washington, DC.
EPA (1992) Prescribed Burning Background Document and Technical Information Document for Prescribed
Burning Best Available Control Measures. Office of Air Quality Planning and Standards, U.S. Environmental
Protection Agency. Research Triangle Park, NC. EPA-450/2-92-003.
Gonzalez, R. (2007 through 2013) Email correspondence. Rene Gonzalez, Plant Manager, Sem-Chi Rice Company
and ICF International.
Guethle, D. (2007-2010) Email correspondence. David Guethle, Agronomy Specialist, Missouri Cooperative
Extension Service and Sarah Menassian, ICF International. July 20, 2009.
Huang, Y., W. Zhang, W. Sun, and X. Zheng (2007) "Net Primary Production of Chinese Croplands from 1950 to
1999." Ecological Applications, 17(3):692-701.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories Programme, Montreal, IPCC-
XVI/Doc. 10 (1.IV.2000). May 2000.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency, Paris, France.
Jenkins, B.M., S.Q. Turn, and R.B. Williams (1992) "Atmospheric emissions from agricultural burning in
California: determination of burn fractions, distribution factors, and crop specific contributions." Agriculture,
Ecosystems and Environment 38:313-330.
Kinoshita, C.M. (1988) "Composition and processing of burned and unburned cane in Hawaii." Intl. Sugar Jnl.
90:1070,34-37.
Kirstein, A. (2003 through 2004) Personal Communication. Arthur Kirstein, Coordinator, Agricultural Economic
Development Program, Palm Beach County Cooperative Extension Service, Florida and ICF International.
Lachnicht, S.L., P.P. Hendrix, R.L. Potter, D.C. Coleman, andD.A. Crossley Jr. (2004) "Winter decomposition of
transgenic cotton residue in conventional-till and no-till systems." Applied Soil Ecology, 27:135-142.
Lee, D. (2003 through 2007) Email correspondence. Danny Lee, OK Farm Service Agency and ICF International.
McCarty, J.L. (2010) Agricultural Residue Burning in the Contiguous United States by Crop Type and State.
Geographic Information Systems (GIS) Data provided to the EPA Climate Change Division by George Pouliot,
Atmospheric Modeling and Analysis Division, EPA. Dr. McCarty's research was supported by the NRI Air Quality
Program of the Cooperative State Research, Education, and Extension Service, USD A, under Agreement No.
20063511216669 and the NASA Earth System Science Fellowship.
McCarty, J.L. (2009) Seasonal and Inter annual Variability of Emissions from Crop Residue Burning in the
Contiguous United States. Dissertation. University of Maryland, College Park.
Schueneman, T. (1999-2001) Personal Communication. Tom Schueneman, Agricultural Extension Agent, Palm
Beach County, FL and ICF International. July 30, 2001.
Schueneman, T.J. and C.W. Deren (2002) "An Overview of the Florida Rice Industry." SS-AGR-77, Agronomy
Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of
Florida. Revised November 2002.
References 11-45
-------
Strehler, A., and W. Stiitzle (1987) "Biomass Residues." In Hall, D.O. and Overend, R.P. (eds.) Biomass. John
Wiley and Sons, Ltd. Chichester, UK.
Turn, S.Q., B.M. Jenkins, J.C. Chow, L.C. Pritchett, D. Campbell, T. Cahill, and S.A. Whalen (1997) "Elemental
characterization of paniculate matter emitted from biomass burning: Wind tunnel derived source profiles for
herbaceous and wood fuels." Journal of Geophysical Research 102(D3):3683-3699.
USDA (2013) Quick Stats: U.S. & All States Data; Crops; Production and Area Harvested; 1990 - 2012. National
Agricultural Statistics Service, U.S. Department of Agriculture. Washington, DC. U.S. Department of Agriculture,
National Agricultural Statistics Service. Washington, D.C., Available online at .
Walker, T. (2008) Email correspondence. Tim Walker, Assistant Research Professor, Mississippi State University
Delta Branch Exp. Station and Sarah Menassian, ICF International. July 25, 2008.
Land Use, Land-Use Change, and Forestry
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. The Intergovernmental
Panel on Climate Change, National Greenhouse Gas Inventories Programme, J. Penman, et al., eds. August 13,
2004. Available online at .
Representation of the U.S. Land Base
Alaska Department of Natural Resources (2006) Alaska Infrastructure 1:63,360. Available online at
Alaska Interagency Fire Management Council (1998) Alaska Interagency Wildland Fire Management Plan.
Available online at
ESRI (2008) ESRI Data & Maps. Redlands, CA: Environmental Systems Research Institute. [CD-ROM]
Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and J. Wickham. (2011)
Completion of the 2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol.
77(9):858-864.
Homer, C., J. Dewitz, J. Fry, M. Coan, N. Hossain, C. Larson, N. Herold, A. McKerrow, J.N. VanDriel and J.
Wickham. 2007. Completion of the 2001 National Land Cover Database for the Conterminous United States,
Photogrammetric Engineering and Remote Sensing, Vol. 73, No. 4, pp 337-341.
IPCC (2010) Revisiting the use of managed land as a proxy for estimating national anthropogenic emissions and
removals. Eds Eggleston HS, Srivastava N, Tanabe K, Baasansuren J, Institute for Global Environmental Studies,
Intergovernmental Panel on Climate Change, Hayama, Kanagawa, Japan.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
NOAA Coastal Change Analysis Program (C-CAP) Regional Land Cover Database. Data collected 1995-present.
Charleston, SC: National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center. Data accessed
at www.csc.noaa.gov/landcover.
Nusser, S.M. and J.J. Goebel (1997) "The national resources inventory: a long-term multi-resource monitoring
programme." Environmental and Ecological Statistics 4:181-204.
Smith, W.B., P.O. Miles, C.H. Perry, and S.A. Pugh (2009) Forest Resources of the United States, 2007. Gen.
Tech. Rep. WO-78. U.S. Department of Agriculture Forest Service, Washington, DC.
U.S. Census Bureau (2010) Topologically Integrated Geographic Encoding and Referencing (TIGER) system
shapefiles. U.S. Census Bureau, Washington, DC. Available online at .
11-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
U.S. Department of Agriculture (2011) County Data - Livestock, 1990-2011. US Department of Agriculture,
National Agriculture Statistics Service, Washington, DC
U.S. Department of Interior (2005) Federal Lands of the United States. National Atlas of the United States, U.S.
Department of the Interior, Washington DC. Available online at
.
Forest Land Remaining Forest Land: Changes in Forest Carbon
Stocks
AF&PA. (2006a and earlier) Statistical roundup. (Monthly). Washington, DC: American Forest & Paper
Association.
AF&PA. (2006b and earlier) Statistics of paper, paperboard and wood pulp. Washington, DC: American Forest &
Paper Association.
Amichev, B. Y. and J. M. Galbraith (2004) "A Revised Methodology for Estimation of Forest Soil Carbon from
Spatial Soils and Forest Inventory Data Sets." Environmental Management 33(Suppl. 1):S74-S86.
Barlaz, M.A. (1998) "Carbon storage during biodegradation of municipal solid waste components in laboratory-
scale landfills." GlobalBiogeochemical Cycles 12 (2):373-380.
Bechtold, W.A.; Patterson, P.L. (2005) The enhanced forest inventory and analysis program—national sampling
design and estimation procedures. Gen. Tech. Rep. SRS-80. Asheville, NC: US Department of Agriculture Forest
Service, Southern Research Station. 85 p.
Birdsey, R. (1996) "Carbon Storage for Major Forest Types and Regions in the Conterminous United States." In
R.N. Sampson and D. Hair, (eds); Forest and Global Change, Volume 2: Forest Management Opportunities for
Mitigating Carbon Emissions. American Forests. Washington, DC, 1-26 and 261-379 (appendices 262 and 263).
Birdsey, R., and L. S. Heath (2001) "Forest Inventory Data, Models, and Assumptions for Monitoring Carbon Flux."
In 5*0/7 Carbon Sequestration and the Greenhouse Effect. Soil Science Society of America. Madison, WI, 125-135.
Birdsey, R.A., andL.S. Heath (1995) "Carbon Changes in U.S. Forests." In Pro ductivity of America's Forests an d
Climate Change. Gen. Tech. Rep. RM-271. Rocky Mountain Forest and Range Experiment Station, Forest Service,
U.S. Department of Agriculture. Fort Collins, CO, 56-70.
Birdsey, R. A., and G. M. Lewis (2003) "Current and Historical Trends in Use, Management, and Disturbance of
U.S. Forestlands." In J. M. Kimble, L. S. Heath, R. A. Birdsey, and R. Lai, editors. The Potential of U.S. Forest
Soils to Sequester Carbon and Mitigate the Greenhouse Effect. CRC Press, New York, 15-34.
Birdsey R, Pregitzer K, Lucier A (2006) Forest carbon management in the United States: 1600-2100. J Environ
Qual35: 1461-1469.
Domke, G.M., J.E. Smith, and C.W. Woodall. (2011) Accounting for density reduction and structural loss in
standing dead trees: Implications for forest biomass and carbon stock estimates in the United States. Carbon
Balance and Management. 6:14.
Domke, G.M., Woodall, C.W., Smith, J.E., Westfall, J.A., McRoberts, R.E. 2012. Consequences of alternative
tree-level biomass estimation procedures on U.S. forest carbon stock estimates. Forest Ecology and Management.
270: 108-116
Domke, G.M., Woodall, C.W., Walters, B.F., Smith, J.E. 2013. From models to measurements: comparing down
dead wood carbon stock estimates in the U.S. forest inventory. PLoS ONE 8(3): e59949.
Eleazer, W.E., W.S. Odle, III, Y.S. Wang, and M.A. Barlaz (1997) "Biodegradability of municipal solid waste
components in laboratory-scale landfills." Env. Sci. Tech. 31(3):911-917.
EPA (2012) Inventory ofU. S. Greenhouse Gas Emissions and Sinks: 1990-2010. EPA, Office of Atmospheric
Programs. Washington, DC.
References 11-47
-------
EPA (2006) Municipal solid waste in the United States: 2005 Facts and figures. Office of Solid Waste, U.S.
Environmental Protection Agency. Washington, DC. (5306P) EPA530-R-06-011. Available online at
.
FAO (2010) Global forest resources assessment 2010. United Nations, Food and Agriculture Organization. FAO
Forestry Paper 163. Rome, Italy. 340p.
Prayer, W.E., and G.M. Furnival (1999) "Forest Survey Sampling Designs: A History." Journal of Forestry 97(12):
4-10.
Freed, R. (2004) Open-dump and Landfill timeline spreadsheet (unpublished). ICF International. Washington, DC.
Hair, D. (1958) "Historical forestry statistics of the United States." Statistical Bull. 228. U.S. Department of
Agriculture Forest Service, Washington, DC.
Hair. D. and A.H. Ulrich (1963) The Demand and price situation for forest products - 1963. U.S. Department of
Agriculture Forest Service, Misc Publication No. 953. Washington, DC.
Harmon, M.E., C.W. Woodall, B. Fasth, J. Sexton, M. Yatkov. (2011) Differences between standing and downed
dead tree wood density reduction factors: A comparison across decay classes and tree species. Res. Paper. NRS-15.
Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 40 p.
Heath, L.S.; Smith, J.E.; Skog, K.; Nowak, D.; Woodall, C. 2011. Managed forest carbon estimates for the U.S.
Greenhouse Gas Inventory, 1990-2008. Journal of Forestry April/May 2011: 167-173.
Heath, L.S. (2012). Using FIA data to inform United States forest carbon national-level accounting needs: 1990-
2010. P. 149-160 in Camp, A.E.; Irland, L.C.; Carroll, C.J.W. Long-term Silvicultural & Ecological Studies:
Results for Science and Management, Volume 2. Yale University School of Forestry & Environmental Studies,
Global Institute of Sustainable Forestry. GISF Research Paper 013.
Heath, L. S., and J.E. Smith (2000) "Soil Carbon Accounting and Assumptions for Forestry and Forest-related
Land Use Change." In The Impact of Climate Change on America's Forests. Joyce, L.A., and Birdsey, R. A. Gen.
Tech. Rep. RMRS-59. Rocky Mountain Research Station, Forest Service, U.S. Department of Agriculture. Fort
Collins, CO, 89-101.
Heath, L.S., J.E., Smith, and R.A. Birdsey (2003) Carbon Trends in U. S. Forestlands: A Context for the Role of
Soils in Forest Carbon Sequestration. In J. M. Kimble, L. S. Heath, R. A. Birdsey, and R. Lai, editors. The Potential
ofU. S. Forest Soils to Sequester Carbon and Mitigate the Greenhouse Effect. Lewis Publishers (CRC Press). Boca
Raton, FL, 35-45.
Heath, L. S., J. E. Smith, C. W. Woodall, D. L. Azuma, and K. L. Waddell (2011) Carbon stocks on forestlands of
the United States, with emphasis on USDA Forest Service ownership. Ecosphere 2(1), article 6, 21 p.
Howard, J. L. (2007) U.S. timber production, trade, consumption, and price statistics 1965 to 2005. Res. Pap. FPL-
RP-637. Madison, WI: USDA, Forest Service, Forest Products Laboratory.
Howard, J. L. (2003) U.S. timber production, trade, consumption, and price statistics 1965 to 2002. Res. Pap. FPL-
RP-615. Madison, WI: USDA, Forest Service, Forest Products Laboratory. Available online at
.
Howard, J. L., Westby, R. M. (2013) U.S. timber production, trade, consumption, and price statistics 1965 to
2011. Res. Pap. FPL-RP-676. Madison, WI: USDA, Forest Service, Forest Products Laboratory.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. The Intergovernmental
Panel on Climate Change, National Greenhouse Gas Inventories Programme, J. Penman, et al., eds. August 13,
2004. Available online at .
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
11-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Jenkins, J.C., D.C. Chojnacky, L.S. Heath, and R.A. Birdsey (2003) "National-scale biomass estimators for United
States tree species." Forest Science 49(1): 12-35.
Johnson, D. W., and P. S. Curtis (2001) "Effects of Forest Management on Soil C and N Storage: Meta Analysis."
Forest Ecology and Management 140:227-238.
Melosi, M. (2000) The Sanitary City. Johns Hopkins University Press. Baltimore, MD.
Melosi, M. (1981) Garbage in The Cities: Refuse Reform and the Environment: 1880-1980. Texas A&M Press.
Micales, J.A. and K.E. Skog (1997) "The decomposition of forest products in landfills." International
Biodeterioration & Bio degradation. 39(2-3): 145-158.
NASA CMS (2013). NASA Carbon Monitoring System, http://carbon.nasa.gov/
National Association of State Foresters (2007a) State Forestry Statistics 1998 Report. Available online at
. March 2008.
National Association of State Foresters (2007b) State Forestry Statistics 2002 Report. Available online at
. March 2008.
National Association of State Foresters (2007c) State Forestry Statistics 2004 Report. Available online at
. March 2008.
Perry, C.H., C.W. Woodall, and M. Schoeneberger (2005) Inventorying trees in agricultural landscapes: towards an
accounting of "working trees". In: "Moving Agroforestry into the Mainstream." Proc. 9th N. Am. Agroforestry
Conf., Brooks, K.N. and Ffolliott, P.P. (eds). 12-15 June 2005, Rochester, MN [CD-ROM]. Dept. of Forest
Resources, Univ. Minnesota, St. Paul, MN, 12 p. Available online at . (verified
23 Sept 2006).
Phillips, D.L., S.L. Brown, P.E. Schroeder, and R.A. Birdsey (2000) "Toward Error Analysis of Large-Scale Forest
Carbon Budgets." Global Ecology andBiogeography 9:305-313.
Russell, M., D'Amato, A.W., Schulz, B.K., Woodall, C.W., Domke, G.M., Bradford, J.B. In Review. A
generalized framework for approximating understory vegetation biomass and carbon stocks using national forest
inventory data in the United States. Forest Ecology and Management.
Skog, K.E. (2008) "Sequestration of carbon in harvested wood products for the United States." Forest Products
Journal 58:56-72.
Skog, K.E., and G.A. Nicholson (1998) "Carbon Cycling Through Wood Products: The Role of Wood and Paper
Products in Carbon Sequestration." Forest Products Journal 48:75-83.
Skog, K.E., K. Pingoud, and J.E. Smith (2004) "A method countries can use to estimate changes in carbon stored in
harvested wood products and the uncertainty of such estimates." Environmental Management 33(Suppl. 1):S65-S73.
Smith, J. (2013) Estimates of Forest Carbon Stocks and Flux: 1990-2013. E-mail correspondence between ICF and
Jim Smith, USDA Forest Service. September 18, 2013.
Smith, J.E., and L.S.Heath (2010) "Exploring the assumed invariance of implied emission factors for forest biomass
in greenhouse gas inventories." Environmental Science & Policy 13:55-62.
Smith, J.E., and L.S. Heath (2002) "A model of forest floor carbon mass for United States forest types." Res. Paper
NE-722. USDA Forest Service, Northeastern Research Station, Newtown Square, PA.
Smith, J. E., L. S. Heath, and J. C. Jenkins (2003) Forest Volume-to-Biomass Models and Estimates of Mass for Live
and Standing Dead Trees of U.S. Forests. General Technical Report NE-298, USDA Forest Service, Northeastern
Research Station, Newtown Square, PA.
Smith, J.E., L.S. Heath, and M.C. Nichols (2010) U.S. Forest Carbon Calculation Tool User's Guide: Forestland
Carbon Stocks and Net Annual Stock Change. General Technical Report NRS-13 revised, U.S. Department of
Agriculture Forest Service, Northern Research Station, 34p.
Smith, J. E., L. S. Heath, and P. B. Woodbury (2004) "How to estimate forest carbon for large areas from inventory
data." Journal of Forestry 102:25-31.
References 11-49
-------
Smith, W. B., P. D. Miles, C. H. Perry, and S. A. Pugh (2009) Forest Resources of the United States, 2007. General
Technical Report WO-78, U.S. Department of Agriculture Forest Service, Washington Office.
Steer, Henry B. (1948) Lumber production in the United States. Misc. Pub. 669, U.S. Department of Agriculture
Forest Service. Washington, DC.
Ulrich, Alice (1985) U.S. Timber Production, Trade, Consumption, and Price Statistics 1950-1985. Misc. Pub.
1453, U.S. Department of Agriculture Forest Service. Washington, DC.
Ulrich, A.H. (1989) U.S. Timber Production, Trade, Consumption, and Price Statistics, 1950-1987. USDA
Miscellaneous Publication No. 1471, U.S. Department of Agriculture Forest Service. Washington, DC, 77.
USDA (1991) State Soil Geographic (STATSGO) Data Base Data use information. Miscellaneous Publication
Number 1492, National Soil Survey Center, Natural Resources Conservation Service, U.S. Department of
Agriculture, Fort Worth, TX.
USDA Forest Service (2013a) Forest Inventory and Analysis National Program: Program Features. U.S. Department
of Agriculture Forest Service. Washington, DC. Available online at < http://fia.fs.fed.us/program-features/>.
Accessed 10 September 2013.
USDA Forest Service. (2013b) Forest Inventory and Analysis National Program: FIA Data Mart. U.S. Department
of Agriculture Forest Service. Washington, DC. Available online at . Accessed 10 September 2013.
USDA Forest Service. (2013c) Forest Inventory and Analysis National Program, FIA library: Field Guides, Methods
and Procedures. U.S. Department of Agriculture Forest Service. Washington, DC. Available online at
. Accessed 10 September 2013.
USDA Forest Service (2013d) Forest Inventory and Analysis National Program, FIA library: Database
Documentation. U.S. Department of Agriculture, Forest Service, Washington Office. Available online at <
http://fia.fs.fed.us/library/database-documentation/>. Accessed 10 September 2013.
USDA Forest Service (2008) Forest Inventory and Analysis National Program, FIA library: Database
Documentation. U.S. Department of Agriculture, Forest Service, Washington Office. Available online at <
http://www.fia.fs.fed.us/library/database-documentation/>. Accessed 15 December 2009.
USDA Forest Service (1992) "1984-1990 Wildfire Statistics." Prepared by State and Private Forestry Fire and
Aviation Management Staff. Facsimile from Helene Cleveland, USDA Forest Service, to ICF International. January
30, 2008.
USDC Bureau of Census (1976) Historical Statistics of the United States, Colonial Times to 1970, Vol. 1.
Washington, DC.
Waddell, K., and B. Hiserote. (2005) The PNW-FIA Integrated Database User Guide: A database of forest inventory
information for California, Oregon, and Washington. Forest Inventory and Analysis Program, Pacific Northwest
Research Station, Portland, Oregon, USA.Woodall, C.W., Amacher, M.C., Bechtold, W.A., Coulston, J.W., Jovan,
S., Perry, C.H., Randolph, K.C., Schulz, B.K., Smith, G.C., Tkacz, B, and Will-Wolf, S. (2011) "Status and future of
the forest health indicators program of the USA" Environmental Monitoring and Assessment, 177:419-436.
Westfall, J.A., Woodall, C.W., Hatfield, M.A. (2013) A statistical power analysis of woody carbon flux from forest
inventory data. Climatic Change. 118:919-931.
Wilson, B.T., Woodall, C.W., Griffith, D. (2013) Imputing forest carbon stock estimates from inventory plots to a
nationally continuous coverage. Carbon Balance and Management. 8:1.
Woodall, C.W. (2012) Where did the U.S. forest biomass/carbon go? Journal of Forestry. 110:113-114.
Woodall, C.W., Amacher, M.C., Bechtold, W.A., Coulston, J.W., Jovan, S., Perry, C.H., Randolph, K.C., Schulz,
B.K., Smith, G.C., Tkacz, B., Will-Wolf, S. (201 Ib) Status and future of the forest health indicators program of the
United States. Environmental Monitoring and Assessment. Ill: 419-436.
Woodall, C.W., B.L. Conkling, M.C. Amacher, J.W. Coulston, S. Jovan, C.H. Perry, B. Schulz, G.C. Smith, S. Will
Wolf. (2010).. The Forest Inventory and Analysis Database Version 4.0: Database Description and Users Manual for
11-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Phase 3. Gen. Tech. Rep. NRS-61. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern
Research Station. 180 p.
Woodall, C.W., Domke, G.M., MacFarlane, D.W., Oswalt, C.M. (2012) Comparing Field- and Model-Based
Standing Dead Tree Carbon Stock Estimates Across Forests of the United States. Forestry 85(1): 125-133.
Woodall, C.W., Domke, G.M., Riley, K, Oswalt, C.M., Crocker, S.J. Yohe, G.W. 2013. Developing a framework
for assessing global change risks to forest carbon stocks. PLOS One. 8: e73222.
Woodall, C.W., L.S. Heath, G.M. Domke, and M.C. Nichols. (2011a) Methods and equations for estimating
aboveground volume, biomass, and carbon for trees in the U.S. forest inventory, 2010. Gen. Tech. Rep. NRS-88.
Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 30 p.
Woodall, C.W., and V.J. Monleon (2008) Sampling protocol, estimation, and analysis procedures for the down
woody materials indicator of the FIA program. Gen. Tech. Rep. NRS-22. Newtown Square, PA: U.S. Department of
Agriculture, Forest Service, Northern Research Station. 68 p.
Woodall, C.W., Walters, B.F., Oswalt, S.N., Domke, G.M., Toney, C., Gray, A.N. (2013) Biomass and carbon
attributes of downed woody materials in forests of the United States. Forest Ecology and Management 305: 48-59.
Woodbury, P.B., L.S. Heath, and J.E. Smith (2007) Effects of land use change on soil carbon cycling in the
conterminous United States from 1900 to 2050, Global Biogeochem. Cycles, 21, GB3006,
doi: 10.1029/2007GB002950.
Woodbury, P.B., Heath, L.S., and Smith, J.E. (2006) "Land Use Change Effects on Forest Carbon Cycling
Throughout the Southern United States." Journal of Environmental Quality, 35:1348-1363.
Forest Land Remaining Forest Land: Non-CO2 Emissions from
Forest Fires
Alaska Department of Natural Resources (2008) Division of Forestry. "Fire Statistics." Available online at
. October 2008.
Alaska Interagency Coordination Center (AICC) (2013) "2012 Alaska Fire Season: Wildland Fire Summary and
Statistics Annual Report." Available online at < http://fire.ak.blm.gov/content/aicc/stats/firestats.pdf>. November
2013.
Heath, L. (2008) Phone communication between Kim Klunich, EPA, and Linda Heath, USDA Forest Service.
November 24, 2008.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. The Intergovernmental
Panel on Climate Change, National Greenhouse Gas Inventories Programme, J. Penman, et al., eds. August 13,
2004. Available online at .
National Association of State Foresters (2011) State Forestry Statistics Spreadsheet. Available online at
. November 2011.
National Association of State Foresters (2008) State Forestry Statistics 2006 Report. Available online at
. February 2009.
National Association of State Foresters (2004) State Forestry Statistics 2004 Report. Available online at
. October 2008.
National Association of State Foresters (2002) State Forestry Statistics 2002 Report. Available online at
. October 2008.
National Association of State Foresters (1998) State Forestry Statistics 1998 Report. Available online at
. October 2008.
References 11-51
-------
National Interagency Fire Center (2013) "Fire Information—Wildland Fire Statistics. Prescribed Fires (1998-2012)
and Wildland Fires and Acres (1960-2012)." Available online at
. November 2013.
Smith, J. (2013a) Estimates of Forest Carbon Stocks and Flux: 1990-2013. E-mail correspondence between ICF and
Jim Smith, USDA Forest Service. September 18, 2013.Smith, J. (2013b) Estimates of Forest Carbon Stocks by
State: 1990-2013. E-mail correspondence between ICF and Jim Smith, USDA Forest Service. November 7, 2013.
Smith, J. (2009) E-mail correspondence between Jean Kim, ICF, and Jim Smith, USDA Forest Service, January 30,
2009.
Smith, J. (2008a) E-mail correspondence between Jean Kim, ICF, and Jim Smith, USDA Forest Service, December
3, 2008.
Smith, J. (2008b) E-mail correspondence between Jean Kim, ICF, and Jim Smith, USDA Forest Service, December
8, 2008.
Smith, J. (2008c) E-mail correspondence between Jean Kim, ICF, and Jim Smith, USDA Forest Service, December
16, 2008.
USDA Forest Service (2010a) Forest Inventory and Analysis National Program: User Information. U.S. Department
of Agriculture Forest Service. Washington, DC. Available online at . Accessed 07 October 2010.
USDA Forest Service. (2010b) Forest Inventory and Analysis National Program: FIA Data Mart. U.S. Department
of Agriculture Forest Service. Washington, DC. Available online at . Accessed 07 October 2010.
USDA Forest Service. (2010c) Forest Inventory and Analysis National Program, FIA library: Field Guides, Methods
and Procedures. U.S. Department of Agriculture Forest Service. Washington, DC. Available online at
. Accessed 07 October 2010.
USDA Forest Service (2010d) Forest Inventory and Analysis National Program, FIA library: Database
Documentation. U.S. Department of Agriculture Forest Service, Washington Office. Available online at
. Accessed 07 October 2010.
USDA Forest Service (1992) "1984-1990 Wildfire Statistics." Prepared by State and Private Forestry Fire and
Aviation Management Staff. Facsimile from Helene Cleveland, USDA Forest Service, to ICF International.
January 30, 2008.
Forest Land Remaining Forest Land: N2O Fluxes from Soils
Albaugh, T.J., Allen, H.L., Fox, T.R. (2007) Historical Patterns of Forest Fertilization in the Southeastern United
States from 1969 to 2004. Southern Journal of Applied Forestry, 31, 129-137(9).
Binkley, D. (2004) Email correspondence regarding the 95% CI for area estimates of southern pine plantations
receiving N fertilizer (±20%) and the rate applied for areas receiving N fertilizer (100 to 200 pounds/acre). Dan
Binkley, Department of Forest, Rangeland, and Watershed Stewardship, Colorado State University and Stephen Del
Grosso, Natural Resource Ecology Laboratory, Colorado State University. September 19, 2004.
Binkley, D., R. Carter, and H.L. Allen (1995) Nitrogen Fertilization Practices in Forestry. In: Nitrogen Fertilization
in the Environment, P.E. Bacon (ed.), Marcel Decker, Inc., New York.
Briggs, D. (2007) Management Practices on Pacific Northwest West-Side Industrial Forest Lands, 1991-2005: With
Projections to 2010. Stand Management Cooperative, SMC Working Paper Number 6, College of Forest Resources,
University of Washington, Seattle.
Fox, T.R., H. L.Allen, T.J. Albaugh, R. Rubilar, and C.A. Carlson (2007) Tree Nutrition and Forest Fertilization of
Pine Plantations in the Southern United States. Southern Journal of Applied Forestry. 31, 5-11.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
11-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
USDA Forest Service (2001) U.S. Forest Facts and Historical Trends. FS-696. U.S. Department of Agriculture
Forest Service, Washington, DC. Available online at .
Cropland Remaining Cropland, Land Converted to Cropland,
Grassland Remaining Grassland, and Land Converted to
Grassland: Changes in Agricultural Soil Carbon Stocks
Allmaras, R.R., H.H. Schomberg, C.L. Douglas, Jr., and T.H. Dao (2000) "Soil organic carbon sequestration
potential of adopting conservation tillage in U.S. croplands." JSoil Water Conserv 55:365-373.
Armentano, T.V. and J.T.A. Verhoeven (1990) "Biogeochemical Cycles: Global," in B.C. Patten, et al. (eds.);
Wetlands and Shallow Continental Water Bodies. SPB Academic Publishing. The Hague, the Netherlands, Vol. I,
281-311.
Bastian, R. (2007) Personal Communication. Robert Bastian, Office of Water, U.S. Environmental Protection
Agency, Washington, DC and Victoria Thompson, ICF International. July 20, 2007.
Brady, N.C. and R.R. Weil (1999) The Nature and Properties of Soils. Prentice Hall. Upper Saddle River, NJ, 881.
CTIC (1998) "1998 Crop Residue Management Executive Summary." Conservation Technology Information
Center. West Lafayette, IN.
Daly, C., R.P. Neilson, and D.L. Phillips (1994) "A Statistical-Topographic Model for Mapping Climatological
Precipitation Over Mountainous Terrain." Journal of Applied Meteorology 33:140-158.
Dean, W. E., and E. Gorham (1998) Magnitude and significance of carbon burial in lakes, reservoirs, and peatlands.
Geology 26:535-538.
Easter, M., S. Williams, and S. Ogle. (2008) Gap-filling NRI data for the Soil C Inventory. Natural Resource
Ecology Laboratory, Colorado State University, Fort Collins, CO. Report provided to the U.S. Environmental
Protection Agency, Tom Wirth.
Edmonds, L., R. L. Kellogg, B. Kintzer, L. Knight, C. Lander, J. Lemunyon, D. Meyer, D.C. Moffitt, and J.
Schaefer (2003) "Costs associated with development and implementation of Comprehensive Nutrient Management
Plans." Part I—Nutrient management, land treatment, manure and wastewater handling and storage, and
recordkeeping. Natural Resources Conservation Service, U.S. Department of Agriculture. Available online at
.
EPA (1999) Biosolids Generation, Use and Disposal in the United States. Office of Solid Waste, U.S.
Environmental Protection Agency. Available online at .
EPA (1993) Federal Register. Part II. Standards for the Use and Disposal of Sewage Sludge; Final Rules. U.S.
Environmental Protection Agency, 40 CFR Parts 257, 403, and 503.
ERS (19%%) Agricultural Resources—Inputs Situation and Outlook Report. AR-9. Economic Research Service, U.S.
Department of Agriculture.
ERS (1997) Cropping Practices Survey Data—1995. Economic Research Service, United States Department of
Agriculture. Available online at .
Eve, M. (2001) E-mail correspondence. MarlenEve, Natural Resources Ecology Laboratory, Colorado State
University and Barbara Braatz and Caren Mintz, ICF International. Statistics on U.S. organic soil areas cultivated in
1982, 1992, and 1997, which were extracted from the 1997 National Resources Inventory. September 21, 2001.
Euliss, N., and R. Gleason (2002) Personal communication regarding wetland restoration factor estimates and
restoration activity data. Ned Euliss and Robert Gleason of the U.S. Geological Survey, Jamestown, ND, to Stephen
Ogle of the National Resource Ecology Laboratory, Fort Collins, CO. August 2002.
Grant, W.R. andR.D. Krenz (1985) U. S. grain sorghum production practices and costs. Staff Report No. AGES
851024. National Economics Division, Economics Research Service, U.S. Department of Agriculture.
References 11-53
-------
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. The Intergovernmental
Panel on Climate Change, National Greenhouse Gas Inventories Programme, J. Penman, et al., eds. August 13,
2004. Available online at .
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency, Paris, France.
Kellogg, R.L., C.H. Lander, D.C. Moffitt, and N. Gollehon (2000) Manure Nutrients Relative to the Capacity of
Cropland and Pastureland to Assimilate Nutrients: Spatial and Temporal Trends for the United States. U.S.
Department of Agriculture, Washington, DC. Publication number npsOO-0579.
McFarland, MJ. (2001) Biosolids Engineering, New York: McGraw-Hill, p. 2.12.
Metherell, A.K., L.A. Harding, C.V. Cole, and W.J. Parton (1993) "CENTURY Soil Organic Matter Model
Environment." Agroecosystem version 4.0. Technical documentation, GPSR Tech. Report No. 4, USDA/ARS, Ft.
Collins, CO.
NASS (2004) Agricultural Chemical Usage: 2003 Field Crops Summary. Report AgChl(04)a. National Agricultural
Statistics Service, U.S. Department of Agriculture, Washington, DC. Available online at
.
NASS (1999) Agricultural Chemical Usage: 1998 Field Crops Summary. Report AgCHl(99). National Agricultural
Statistics Service, U.S. Department of Agriculture, Washington, DC. Available online at
.
NASS (1992) Agricultural Chemical Usage: 1991 Field Crops Summary. Report AgChl(92). National Agricultural
Statistics Service, U.S. Department of Agriculture, Washington, DC. Available online at
.
NEBRA (2007) A National Biosolids Regulation, Quality, End Use & Disposal Survey. North East Biosolids and
Residuals Association, July 21, 2007
NRAES (1992) On-Farm Composting Handbook (NRAES-54). Natural Resource, Agriculture, and Engineering
Service. Available online at .
NRCS (1999) 5*0/7 Taxonomy: A basic system of soil classification for making and interpreting soil surveys, 2nd
Edition. Agricultural Handbook Number 436, Natural Resources Conservation Service, U.S. Department of
Agriculture, Washington, DC.
NRCS (1997) "National Soil Survey Laboratory Characterization Data," Digital Data, Natural Resources
Conservation Service, U.S. Department of Agriculture. Lincoln, NE.
NRCS (1981) Land Resource Regions and Major Land Resource Areas of the United States, USDA Agriculture
Handbook 296, United States Department of Agriculture, Natural Resources Conservation Service, National Soil
Survey Cente., Lincoln, NE, pp. 156.
Nusser, S.M., J.J. Goebel (1997) The national resources inventory: a long term monitoring programme.
Environmental and Ecological Statistics, 4, 181-204.
Ogle, S.M., F.J. Breidt, M. Easter, S. Williams, K. Killian, and K. Paustian (2009) "Scale and uncertainty in
modeled soil organic carbon stock changes for US croplands using a process-based model." Global Change Biology,
in press.
Ogle, S.M., F.J. Breidt, M. Easter, S. Williams and K. Paustian. (2007) "Empirically-Based Uncertainty Associated
with Modeling Carbon Sequestration Rates in Soils." Ecological Modeling 205:453-463.
Ogle, S.M., F.J. Breidt, and K. Paustian. (2006) "Bias and variance in model results due to spatial scaling of
measurements for parameterization in regional assessments." Global Change Biology 12:516-523.
11-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Ogle, S.M., M.D. Eve, FJ. Breidt, and K. Paustian (2003) "Uncertainty in estimating land use and management
impacts on soil organic carbon storage for U.S. agroecosystems between 1982 and 1997." Global Change Biology
9:1521-1542.
Ogle, S., M. Eve, M. Sperrow, FJ. Breidt, and K. Paustian (2002) Agricultural Soil C Emissions, 1990-2001:
Documentation to Accompany EPA Inventory Tables. Natural Resources Ecology Laboratory, Fort Collins, CO.
Provided in an e-mail from Stephen Ogle, NREL to Barbara Braatz, ICF International. September 23, 2002
Parton, W.J., D.S. Ojima, C.V. Cole, and D.S. Schimel (1994) "A General Model for Soil Organic Matter
Dynamics: Sensitivity to litter chemistry, texture and management," in Quantitative Modeling of Soil Forming
Processes. Special Publication 39, Soil Science Society of America, Madison, WI, 147-167.
Parton, W.J., D.S. Schimel, C.V. Cole, D.S. Ojima (1987) "Analysis of factors controlling soil organic matter levels
in Great Plains grasslands." Soil Science Society of America Journal 51:1173-1179.
Parton, W.J., J.W.B. Stewart, C.V. Cole. (1988) "Dynamics of C, N, P, and S in grassland soils: a model."
Biogeochemistry 5:109-131.
Potter, C. S., J.T. Randerson, C.B. Fields, P.A. Matson, P.M. Vitousek, H.A. Mooney, and S.A. Klooster. (1993)
"Terrestrial ecosystem production: a process model based on global satellite and surface data." Global
Biogeochemical Cycles 7:811-841.
Reilly, J.M. and K.O. Fuglie. (1998) "Future yield growth in field crops: What evidence exists?" Soil Till Res
47:275-290.
USDA (1996) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651.
Natural Resources Conservation Service, U.S. Department of Agriculture. July 1996.
USDA (1966) Consumption of Commercial Fertilizers and Primary Plant Nutrients in the United States, 1850-1964
and By States, 1945-1964. Statistical Bulletin Number 375, Statistical Reporting Service, U.S. Department of
Agriculture.
USDA (1957) Fertilizer Used on Crops and Pastures in the United States—1954 Estimates. Statistical Bulletin
Number 216, Agricultural Research Service, U.S. Department of Agriculture.
USDA (1954) Fertilizer Use and Crop Yields in the United States. Agricultural Handbook Number 68, the Fertilizer
Work Group, U.S. Department of Agriculture.
USDA-FSA (2010) Conservation Reserve Program Monthly Summary - September 2010. U.S. Department of
Agriculture, Farm Service Agency, Washington, DC, Available online at
.
USDA-NRCS (2000) Digital Data And Summary Report: 1997 National Resources Inventory. Revised December
2000. Resources Inventory Division, Natural Resources Conservation Service, United States Department of
Agriculture, Beltsville, MD.
Peatlands Remaining Peatlands: CO2 and N2O Emissions from
Peatlands Remaining Peatlands
Apodaca, L. (2011) Email correspondence. Lori Apodaca, Peat Commodity Specialist, USGS and Emily Rowan,
ICF International. November.
Apodaca, L. (2008) E-mail correspondence. Lori Apodaca, Peat Commodity Specialist, USGS and Emily Rowan,
ICF International. October and November.
Cleary, I, N. Roulet and T.R. Moore (2005) "Greenhouse gas emissions from Canadian peat extraction, 1990-2000:
A life-cycle analysis." Ambio 34:456-461.
Division of Geological & Geophysical Surveys (DGGS), Alaska Department of Natural Resources (1997-2011)
Alaska's Mineral Industry Report (1997-2010). Alaska Department of Natural Resources, Fairbanks, AK.
Available online at .
References 11-55
-------
Harbo, L.A. (2013) Oral communication: L.A. Harbo, Mineral Specialist, Alaska Office of Economic Development.
July 19. As cited in United States Geological Survey (USGS) (2013) 2012 Minerals Yearbook: Peat. United States
Geological Survey, Reston, VA. Available online at
.
Harbo, L.A. (2012) Oral communication: L.A. Harbo, Mineral Specialist, Alaska Office of Economic Development.
September 5. As cited in United States Geological Survey (USGS) (2012) 2011Minerals Yearbook: Peat. United
States Geological Survey, Reston, VA. Available online at
.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Szumigala, D.J. (2011) Phone conversation. Dr. David Szumigala, Division of Geological and Geophysical Surveys,
Alaska Department of Natural Resources and Emily Rowan, ICF International. January 18th, 2011.
Szumigala, D.J. (2008) Phone conversation. Dr. David Szumigala, Division of Geological and Geophysical Surveys,
Alaska Department of Natural Resources and Emily Rowan, ICF International. October 17th, 2008.
Szumigala, D.J., R.A. Hughes and L.A. Harbo (2010) Alaska's Mineral Industry 2009: A Summary. Division of
Geological & Geophysical Surveys, Alaska Department of Natural Resources, Fairbanks, AK. Available online at
.
United States Geological Survey (USGS) (1996-2013) Mineral Commodity Summaries: Peat (1996-2012). United
States Geological Survey, Reston, VA. Available online at
.
United States Geological Survey (USGS) (1991-2013) Minerals Yearbook: Peat (1994-2012). United States
Geological Survey, Reston, VA. Available online at
.
United States Geological Survey (USGS) (2013) 2012 Minerals Yearbook: Peat [Advance Release]. United States
Geological Survey, Reston, VA. Available online at
.
United States Geological Survey (USGS) (2012) 2011 Minerals Yearbook: Peat [AdvancedRelease]. United States
Geological Survey, Reston, VA. Available online at
.
AAPFCO (2008 through 2013) Commercial Fertilizers. Association of American Plant Food Control Officials.
University of Missouri. Columbia, MO.
AAPFCO (1995 through 2000a, 2002 through 2007) Commercial Fertilizers. Association of American Plant Food
Control Officials. University of Kentucky. Lexington, KY.
AAPFCO (2000b) 1999-2000 Commercial Fertilizers Data, ASCII files. Available from David Terry, Secretary,
AAPFCO.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Me, Cortney (2009) Email correspondence. Cortney Me, ERG and Tom Wirth, U.S. Environmental Protection
Agency on the amount of urea used in aircraft deicing. January 7, 2009.
11-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Tepordei, V.V. (1997 through 2006) "Crashed Stone," InMinerals Yearbook. U.S. Department of the Interior/U.S.
Geological Survey. Washington, DC. Available online at
.
Tepordei, Valentin V. (2003b) Personal communication. Valentin Tepordei, U.S. Geological Survey and ICF
Consulting, August 18, 2003.
Tepordei, V.V. (1996) "Crashed Stone," InMinerals Yearbook 1994. U.S. Department of the Interior/Bureau of
Mines, Washington, DC. Available online at
http://minerals.usgs.gov/minerals/pubs/commodity/stone_crashed/index.html#mis>. Accessed August 2000.
Tepordei, V.V. (1995) "Crashed Stone," InMinerals Yearbook 1993. U.S. Department of the Interior/Bureau of
Mines, Washington, DC. pp. 1107-1147.
Tepordei, V. V. (1994) "Crashed Stone," InMinerals Yearbook 1992. U.S. Department of the Interior/Bureau of
Mines, Washington, DC. pp. 1279-1303.
Tepordei, V.V. (1993) "Crashed Stone," InMinerals Yearbook 1991. U.S. Department of the Interior/Bureau of
Mines, Washington, DC. pp. 1469-1511.
Terry, D. (2007) Email correspondence. David Terry, Fertilizer Regulatory program, University of Kentucky and
David Berv, ICF International. September 7, 2007.
TVA (1991 through 1994) Commercial Fertilizers. Tennessee Valley Authority, Muscle Shoals, AL.
TVA (1992) Fertilizer Summary Data 1992. Tennessee Valley Authority, Muscle Shoals, AL.
U.S. EPA. (2000) Preliminary Data Summary: Airport Deicing Operations (Revised). EPA-821-R-00-016. August
2000.
USGS (2013) Mineral Industry Surveys: Crushed Stone and Sand and Gravel in the First Quarter of 2013, U.S.
Geological Survey, Reston, VA. Available online at
.
USGS (2012) Mineral Industry Surveys: Crushed Stone and Sand and Gravel in the First Quarter of 2012, U.S.
Geological Survey, Reston, VA. Available online at
.
USGS (2011) Mineral Industry Surveys: Crushed Stone and Sand and Gravel in the First Quarter of 2011, U.S.
Geological Survey, Reston, VA. Available online at
.
USGS (2010) Mineral Industry Surveys: Crushed Stone and Sand and Gravel in the First Quarter of 2010, U.S.
Geological Survey, Reston, VA. Available online at
.
USGS (2009) Mineral Industry Surveys: Crushed Stone and Sand and Gravel in the First Quarter of 2009, U.S.
Geological Survey, Reston, VA. Available online at
.
USGS (2008) Mineral Industry Surveys: Crushed Stone and Sand and Gravel in the First Quarter of 2008, U.S.
Geological Survey, Reston, VA. Available online at
.
USGS (2007) Mineral Industry Surveys: Crushed Stone and Sand and Gravel in the First Quarter of 2007. U.S.
Geological Survey, Reston, VA. Available online at
.
West, Tristram O. (2008) Email correspondence. Tristram West, Environmental Sciences Division, Oak Ridge
National Laboratory, U.S. Department of Energy and Nikhil Nadkarni, ICF International on suitability of liming
emission factor for the entire United States. June 9, 2008.
West, T.O., and A.C. McBride (2005) "The contribution of agricultural lime to carbon dioxide emissions in the
United States: dissolution, transport, and net emissions," Agricultural Ecosystems & Environment 108:145-154.
References 11-57
-------
Willett, Jason C. (2013a) "Crashed Stone," In Minerals Yearbook 2011. U.S. Department of the Interior/US.
Geological Survey, Washington, D.C. Available online at
. Accessed May 2013.
Willett, Jason C. (2013b) Personal Communication. Jason Willet, U.S. Geological Survey and ICF International.
September 9, 2013.
Willett, J.C. (201 la) "Crashed Stone," In Minerals Yearbook 2009. U.S. Department of the Interior/U.S. Geological
Survey, Washington, DC. Available online at
. Accessed August 2011.
Willett, Jason C. (20 lib) "Crashed Stone," In Minerals Yearbook 2010. U.S. Department of the Interior/U.S.
Geological Survey, Washington, D.C. Available online at
. Accessed September 2012.
Willett, Jason C. (2010) "Crashed Stone," In Minerals Yearbook 2008. U.S. Department of the Interior/U.S.
Geological Survey, Washington, D.C. Available online at
. Accessed August 2010.
Willett, J.C. (2009) "Crashed Stone," InMinerals Yearbook 2007. U.S. Department of the Interior/U.S. Geological
Survey, Washington, DC. Available online at
. Accessed August 2009.
Willett, J.C. (2007a) "Crashed Stone," In. Minerals Yearbook 2005. U.S. Department of the Interior/U.S. Geological
Survey, Washington, DC. Available online at
. Accessed August 2007.
Willett, J.C. (2007b) "Crashed Stone," InMinerals Yearbook 2006. U.S. Department of the Interior/U.S. Geological
Survey, Washington, DC. Available online at
. Accessed August 2008.
Settlements Remaining Settlements: N2O Fluxes from Soils
Albaugh, T.J., Allen, H.L., Fox, T.R. (2007) Historical Patterns of Forest Fertilization in the Southeastern United
States from 1969 to 2004. Southern Journal of Applied Forestry, 31, 129-137(9)
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Ruddy B.C., D.L. Lorenz, and O.K. Mueller (2006) County-level estimates of nutrient inputs to the land surface of
the conterminous United States, 1982-2001. Scientific Investigations Report 2006-5012. U.S. Department of the
Interior.
Settlements Remaining Settlements: Changes in Carbon Stocks
in Urban Trees
deVries, R.E. (1987) A Preliminary Investigation of the Growth and Longevity of Trees in Central Park. M.S.
thesis, Rutgers University, New Brunswick, NJ.
Dwyer, J.F., D.J. Nowak, M.H. Noble, and S.M. Sisinni (2000) Connecting People with Ecosystems in the 21st
Century: An Assessment of Our Nation's Urban Forests. General Technical Report PNW-GTR-490, U.S.
Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, OR.
Fleming, L.E. (1988) Growth Estimation of Street Trees in Central New Jersey. M.S. thesis, Rutgers University,
New Brunswick, NJ.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
11-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Nowak, D.J. (2012) Phone conference regarding Changes in Carbon Stocks in Urban Trees estimation methodology.
David Nowak, USD A and Nick Devonshire, ICF International. October 31, 2012.
Nowak, D.J., and E.J. Greenfield (2012) Tree and impervious cover in the United States. Journal of Landscape and
Urban Planning (107) pp. 21-30.
Nowak, D.J., E.J. Greenfield, R.E. Hoehn, and E. Lapoint (2013) Carbon Storage and Sequestration by Trees in
Urban and Community Areas of the United States. Environmental Pollution 178: 229-236. March 12, 2013. .
Nowak, D.J. (2011) Phone conference regarding Changes in Carbon Stocks in Urban Trees estimation methodology.
David Nowak, USD A, Jennifer Jenkins, EPA, and Mark Flugge and Nikhil Nadkarni, ICF International. January 4,
2011.
Nowak, D.J. (2007a) "New York City's Urban Forest." USDA Forest Service. Newtown Square, PA, February
2007.
Nowak, D.J. (2007b) Personal Communication. David Nowak, USDA Forest Service and Susan Asam, ICF
International. September 25, 2007.
Nowak, D.J. (2007c) E-mail correspondence regarding revised sequestration values and standard errors for
sequestration values. David Nowak, USDA Forest Service to Susan Asam, ICF International. October 31, 2007.
Nowak, D.J. (2002a) E-mail correspondence containing information on possible urban tree carbon and forest carbon
overlap. David Nowak, USDA Forest Service to Barbara Braatz, ICF International. January 10, 2002.
Nowak, D.J. (2002b) E-mail correspondence regarding significant digits. David Nowak, USDA Forest Service to
Barbara Braatz, ICF International. October 29, 2002.
Nowak, D.J. (1994) "Atmospheric Carbon Dioxide Reduction by Chicago's Urban Forest." In: Chicago's Urban
Forest Ecosystem: Results of the Chicago Urban Forest Climate Project. E.G. McPherson, D.J. Nowak, and R.A.
Rowntree (eds.). General Technical Report NE-186. U.S. Department of Agriculture Forest Service, Radnor, PA.
pp. 83-94.
Nowak, D.J. (1986) "Silvics of an Urban Tree Species: Norway Maple (Acer platanoides L.)." M.S. thesis, College
of Environmental Science and Forestry, State University of New York, Syracuse, NY.
Nowak, D.J. andD.E. Crane (2002) "Carbon Storage and Sequestration by Urban Trees in the United States."
Environmental Pollution 116(3):381-389.
Nowak, D. J., D.E. Crane, J.C. Stevens, and M. Ibarra (2002) Brooklyn's Urban Forest. General Technical Report
NE-290. U.S. Department of Agriculture Forest Service, Newtown Square, PA.
Nowak, D.J., J.T. Walton, L.G. Kaya, and J.F. Dwyer (2005) "The Increasing Influence of Urban Environments on
U.S. Forest Management." Journal of Forestry 103(8):377-382.
Smith, W.B. and S.R. Shifley (1984) Diameter Growth, Survival, and Volume Estimates for Trees in Indiana and
Illinois. Research Paper NC-257. North Central Forest Experiment Station, U.S. Department of Agriculture Forest
Service, St. Paul, MN.
US Census (2012) "A national 2010 urban area file containing a list of all urbanized areas and urban clusters
(including Puerto Rico and the Island Areas) sorted by UACE code." US Census Bureau, Geography Division.
Wirth, T (2013) E-mail correspondence regarding "U.S. Total Land Area 1990-2012" data between Tom Wirth, U.S.
Environmental Protection Agency Climate Change Division and Leslie Chinery, ICF International. November 25,
2013.
Other: Changes in Yard Trimming and Food Scrap Carbon
Stocks in Landfills
Barlaz, M.A. (2008) "Re: Corrections to Previously Published Carbon Storage Factors." Memorandum to Randall
Freed, ICF International. February 28, 2008.
References 11-59
-------
Barlaz, M.A. (2005) "Decomposition of Leaves in Simulated Landfill." Letter report to Randall Freed, ICF
Consulting. June 29, 2005.
Barlaz, M.A. (1998) "Carbon Storage during Biodegradation of Municipal Solid Waste Components in Laboratory-
Scale Landfills." Global Biogeochemical Cycles 12:373-380.
Eleazer, W.E., W.S. Odle, Y. Wang, and M.A. Barlaz (1997) "Biodegradability of Municipal Solid Waste
Components in Laboratory-Scale Landfills." Environmental Science Technology 31:911-917.
EPA (2013) Municipal Solid Waste Generation, Recycling, and Disposal in the United States: 2011 Facts and
Figures. U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC.
Available online at .
EPA (2006 through 2011) Municipal So lid Waste in the United States: Facts andFigures. U.S. Environmental
Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC. Available online at
.
EPA (2005a) Municipal Solid Waste in the United States: 2003 Data Tables. U.S. Environmental Protection
Agency, Office of Solid Waste and Emergency Response, Washington, DC. Available online at
.
EPA (2005b) Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures
for 2003. U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington,
DC. Available online at < http://www.epa.gov/osw/nonhaz/municipal/pubs/msw03rpt.pdf>.
EPA (2003) Characterization of Municipal Solid Waste in the United States: 2001 Update. (Draft.) U.S.
Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC.
EPA (1999) Characterization of Municipal Solid Waste in the United States: 1998 Update. U.S. Environmental
Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. The Intergovernmental Panel
on Climate Change, National Greenhouse Gas Inventories Programme, J. Penman et al. (eds.). Available online at
.
Oshins, C. and D. Block (2000) "Feedstock Composition at Composting Sites." Biocycle 41(9):31-34.
Schneider, S. (2007, 2008) E-mail correspondence. Shelly Schneider, Franklin Associates, A Division of ERG and
Sarah Shapiro, ICF International.
Tchobanoglous, G., H. Theisen, and S.A. Vigil (1993) Integrated Solid Waste Management, 1st edition. McGraw-
Hill, NY. Cited by Barlaz (1998).Barlaz, M.A. (2008) "Re: Corrections to Previously Published Carbon Storage
Factors." Memorandum to Randall Freed, ICF International. February 28, 2008.
40 CFR Part 60, Subpart Cc (2005) Emission Guidelines and Compliance Times for Municipal Solid Waste
Landfills, 60.30c~60.36c, Code of Federal Regulations, Title 40. Available online at
.
40 CFR Part 60, Subpart WWW (2005) Standards of Performance for Municipal Solid Waste Landfills, 60.750-
60.759, Code of Federal Regulations, Title 40. Available online at
.
11-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Barlaz, M.A. (2006) "Forest Products Decomposition in Municipal Solid Waste Landfills." Waste Management,
26(4): 321-333.
Barlaz, M.A. (1998) "Carbon Storage During Biodegradation of Municipal Solid Waste Components in Laboratory-
scale Landfills." Global Biogeochemical Cycles, 12(2): 373-380, June 1998.
BioCycle (2010) "The State of Garbage in America" By L. Arsova, R. Van Haaren, N. Goldstein, S. Kaufman, and
N. Themelis. BioCycle. December 2010. Available online at .
BioCycle (2008) "The State of Garbage in America" By L. Arsova, R. Van Haaren, N. Goldstein, S. Kaufman, and
N. Themelis. BioCycle. December 2008. Available online at .
BioCycle (2006) "15th Annual BioCycle Nationwide Survey: The State of Garbage in America" By P. Simmons, N.
Goldstein, S. Kaufman, N. Goldstein, N. Themelis, and J. Thompson. BioCycle. April 2006.
Bronstein, K., Coburn, J., and R. Schmeltz (2012) "Understanding the EPA's Inventory of U.S. Greenhouse Gas
Emissions and Sinks and Mandatory GHG Reporting Program for Landfills: Methodologies, Uncertainties,
Improvements and Deferrals." Prepared for the U.S. EPA International Emissions Inventory Conference, August
2012, Tampa, Florida. Available online at < http://www.epa.gov/ttnchiel/conference/ei20/session3/kbronstein.pdf>.
Czepiel, P., B. Mosher, P. Crill, and R. Harriss (1996) "Quantifying the Effect of Oxidation on Landfill Methane
Emissions." Journal of Geophysical Research, 101(D11):16721-16730.
EIA (2007) Voluntary Greenhouse Gas Reports for EIA Form 1605B (Reporting Year 2006). Available online at
.
EPA (2012) Landfill Gas-to-Energy Project Database. Landfill Methane and Outreach Program. July 2012.
EPA (2011) Municipal Solid Waste Generation, Recycling, and Disposal in the United States Tables and Figures for
2010. December 2011. Available online at <
http://www.epa.gov/osw/nonhaz/municipal/pubs/2010_MSW_Tables_and_Figures_508.pdf>.
EPA (2010) Municipal Solid Waste Generation, Recycling, and Disposal in the United States Detailed Tables and
Figures for 2009. December 2010. Available online at
.
EPA (2008) Compilation of Air Pollution Emission Factors, Publication AP-42, Draft Section 2.4 Municipal Solid
Waste Landfills. October 2008.
EPA (1998) Compilation of Air Pollution Emission Factors, Publication AP-42, Section 2.4 Municipal Solid Waste
Landfills. November 1998.
EPA (1993) Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress, U.S.
Environmental Protection Agency, Office of Air and Radiation. Washington, DC. EPA/430-R-93-003. April 1993.
EPA (1988) National Survey of Solid Waste (Municipal) Landfill Facilities, U.S. Environmental Protection Agency.
Washington, DC. EPA/530-SW-88-011. September 1988.
ERG (2013) Draft Production Data Supplied by ERG for 1990-2012 for Pulp and Paper, Fruits and Vegetables, and
Meat. August.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change and Forestry, The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, J. Penman, M. Gytarsky, T. Hiraishi, T.
Krug, D. Kruger, R. Pipatti, L. Buendia, K. Miwa, T. Ngara, K. Tanabe, and F. Wagner (eds.). Hayama, Kanagawa,
Japan.
Mancinelli, R. and C. McKay (1985) "Methane-Oxidizing Bacteria in Sanitary Landfills." Proc. First Symposium on
Biotechnical Advances in Processing Municipal Wastes for Fuels and Chemicals, Minneapolis, MN, 437-450.
August.
References 11-61
-------
Peer, R., S. Thorneloe, and D. Epperson (1993) "A Comparison of Methods for Estimating Global Methane
Emissions from Landfills." Chemosphere, 26(l-4):387-400.
RTI (2013) Review of State of Garbage Data Used in the U.S. Non-CO2 Greenhouse Gas Inventory for Landfills.
Memorandum prepared by K. Weitz and K. Bronstein (RTI) for R. Schmeltz (EPA), November 25, 2013.
RTI (2011) Updated Research on Methane Oxidation in Landfills. Memorandum prepared by K. Weitz (RTI) for R.
Schmeltz (EPA), January 14, 2011.
RTI (2004) Documentation for Changes to the Methodology for the Inventory of Methane Emissions from Landfills.
Memorandum prepared by M. Branscome and J. Coburn (RTI) to E. Scheehle (EPA), August 26, 2004.
Solid Waste Association of North America (SWANA) (1998) Comparison of Models for Predicting Landfill
Methane Recovery. Publication No. GR-LG0075. March 1998.
U.S. Census Bureau (2013) Population Division. Table 1: Annual Estimates of the Resident Population for the
United States, Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2012 (NST-EST2012-01). September 2013.
Available online at < http://www.census.gov/popest/data/national/totals/2012/index.html>
U.S. Census Bureau (2009). Population Division. Table 1. Annual Estimates of the Resident Population for the
United States, Regions, States, and Puerto Rico: April 1, 2000 to July 1, 2009 (NST-EST2009-01). December 2009.
Available online at < http://www.census.gov/popest/data/historical/2000s/vintage_2009/index.html>.
Waste Business Journal (WBJ) (2010). Directory of Waste Processing & Disposal Sites 2010.
Ahn et al. (2010) N2O Emissions from Activated Sludge Processes, 2008-2009: Results of a National Monitoring
Survey in the United States. Environ. Sci. Technol. 44: 4505-4511.
Aguiar and Bartram (2008) Planned Revisions of the Industrial Wastewater Inventory Emission Estimates for the
1990-2007 Inventory. August 10, 2008.
Aguiar et al. (2013) Revisions to the Petroleum Wastewater Inventory. October 2013.
Beecher et al. (2007) "A National Biosolids Regulation, Quality, End Use & Disposal Survey, Preliminary Report."
Northeast Biosolids and Residuals Association, April 14, 2007.
Benyahia, F., M. Abdulkarim, A. Embaby, and M. Rao. (2006) Refinery Wastewater Treatment: A true
Technological Challenge. Presented at the Seventh Annual U. A.E. University Research Conference.
Bicknell et al. (2013) Revisions to Pulp and Paper Wastewater Inventory. October 2013.
DOE (2013) US Department of Energy Bioenergy Technologies Office. Biofuels Basics. Available online at
. Accessed September 2013.
Donovan (1996) Siting an Ethanol Plant in the Northeast. C.T. Donovan Associates, Inc. Report presented to
Northeast Regional Biomass Program (NRBP). (April). Available online at .
Accessed October 2006.
EIA. (2013) Energy Information Administration. U.S. Refinery and Blender Net Production of Crude Oil and
Petroleum Products (Thousand Barrels). Available online at: http://tonto.eia.doe.gov/dnav/pet/hist/mttrpusla.htm.
Accessed: October 2012.
EPA (1997) Estimates of Global Greenhouse Gas Emissions from Industrial and Domestic Wastewater Treatment.
EPA-600/R-97-091. Office of Policy, Planning, and Evaluation, U.S. Environmental Protection Agency.
Washington, DC,. September 1997.
EPA (1996) 1996 Clean Water Needs Survey Report to Congress. Assessment of Needs for Publicly Owned
Wastewater Treatment Facilities, Correction of Combined Sewer Overflows, and Management of Storm Water and
Nonpoint Source Pollution in the United States. Office of Wastewater Management, U.S. Environmental Protection
Agency. Washington, DC. Available online at . Accessed
July 2007.
11-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
EPA (1993) Development Document for the Proposed Effluent Limitations Guidelines and Standards for the Pulp,
Paper and Paperboard Point Source Category. EPA-821-R-93-019. Office of Water, U.S. Environmental
Protection Agency. Washington, DC. October 1993.
EPA (1992) Clean Watersheds Needs Survey 1992 - Report to Congress. Office of Wastewater Management, U.S.
Environmental Protection Agency. Washington, DC.
EPA (1975) Development Document for Interim Final and Proposed Effluent Limitations Guidelines and New
Source Performance Standards for the Fruits, Vegetables, and Specialties Segment of the Canned and Preserved
Fruits and Vegetables Point Source Category. United States Environmental Protection Agency, Office of Water.
EPA-440/1-75-046. Washington DC, October 1975.
EPA (1974) Development Document for Effluent Limitations Guidelines and New Source Performance Standards
for the Apple, Citrus, and Potato Processing Segment of the Canned and Preserved Fruits and Vegetables Point
Source Category. Office of Water, U.S. Environmental Protection Agency, Washington, DC, EPA-440/1 -74-027-a.
March 1974.
Composting
BioCycle (2010) The State of Garbage in America. Prepared by Rob van Haaren, Nickolas Themelis and Nora
Goldstein. Available online at
EPA (2013) Municipal Solid Waste in the United States: 2011 Facts and Figures. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency, Washington, DC. Available online at
.
EPA (2011) Municipal Solid Waste in the United States: 2010 Facts and Figures. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency, Washington, DC. Available online at
.
EPA (2007) Municipal Solid Waste in the United States: 2006 Facts and Figures. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency, Washington, DC. Available online at
.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. Volume 5: Waste, Chapter 4:
Biological Treatment of Solid Waste, Table 4.1. The National Greenhouse Gas Inventories Programme, The
Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds.).
Hayama, Kanagawa, Japan. Available online at < http://www.ipcc-
nggip.iges.or.jp/public/2006gl/pdf/5_Volume5/V5_4_Ch4_Bio_Treat.pdf>.
U.S. Census Bureau (2013) Population Estimates: National Totals: Vintage 2012. Available online at <
http://www.census.gov/popest/data/national/totals/2012/files/NST_EST2012_ALLDATA.csv>.
U.S. Composting Council (2010). Yard Trimmings Bans: Impact and Support. Prepared by Stuart Buckner,
Executive Director, U.S, Composting Council. Available online at
Waste Sources of Indirect Greenhouse Gas Emissions
EPA (2013) "1970 - 2013 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available
online at .
EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and
the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency. Research Triangle Park, NC. October 1997.
References 11-63
-------
Annexes to the Inventory of U.S. GHG Emissions and Sinks
The following seven annexes provide additional information related to the material presented in the main body of
this report as directed in the UNFCCC Guidelines on Reporting and Review (GE.03-60887). Annex 1 contains an analysis
of the key categories of emissions discussed in this report and a review of the methodology used to identify those key
categories. Annex 2 describes the methodologies used to estimate CC>2 emissions from fossil fuel combustion, the carbon
content of fossil fuels, and the amount of carbon stored in products from non-energy uses of fossil fuels. Annex 3
discusses the methodologies used for a number of individual source categories in greater detail than was presented in the
main body of the report and includes explicit activity data and emission factor tables. Annex 4 presents the IPCC
reference approach for estimating CC>2 emissions from fossil fuel combustion. Annex 5 addresses the criteria for the
inclusion of an emission source category and discusses some of the sources that are excluded from U. S. estimates. Annex
6 provides a range of additional information that is relevant to the contents of this report. Annex 7 provides data on the
uncertainty of the emission estimates included in this report. Finally, Annex 8 provides information on the QA/QC
methods and procedures used in the development of the Inventory.
ANNEX 1 Key Category Analysis 2
ANNEX 2 Methodology and Data for Estimating C02 Emissions from Fossil Fuel Combustion 27
2.1. Methodology for Estimating Emissions of C02 from Fossil Fuel Combustion 27
2.2. Methodology for Estimating the Carbon Content of Fossil Fuels 59
2.3. Methodology for Estimating Carbon Emitted from Non-Energy Uses of Fossil Fuels 98
ANNEX 3 Methodological Descriptions for Additional Source or Sink Categories 125
3.1. Methodology for Estimating Emissions of ChU, N20, and Indirect Greenhouse Gases from Stationary Combustion 125
3.2. Methodology for Estimating Emissions of ChU, N20, and Indirect Greenhouse Gases from Mobile Combustion and
Methodology for and Supplemental Information on Transportation-Related GHG Emissions 132
3.3. Methodology for Estimating Emissions from Commercial Aircraft Jet Fuel Consumption 164
3.4. Methodology for Estimating ChU Emissions from Coal Mining 168
3.5. Methodology for Estimating ChU and C02 Emissions from Natural Gas Systems 175
3.6. Methodology for Estimating ChU and C02 Emissions from Petroleum Systems 202
3.7. Methodology for Estimating C02, N20 and ChU Emissions from the Incineration of Waste 209
3.8. Methodology for Estimating Emissions from International Bunker Fuels used by the U.S. Military 214
3.9. Methodology for Estimating HFC and RFC Emissions from Substitution of Ozone Depleting Substances 219
3.10. Methodology for Estimating CH4 Emissions from Enteric Fermentation 240
3.11. Methodology for Estimating CH4and N20 Emissions from Manure Management 258
3.12. Methodology for Estimating N20 Emissions and Soil Organic C Stock Changes from Agricultural Soil Management
(Cropland and Grassland) 287
3.13. Methodology for Estimating Net Carbon Stock Changes in Forest Lands Remaining Forest Lands 338
3.14. Methodology for Estimating CH4 Emissions from Landfills 367
ANNEX 4 IPCC Reference Approach for Estimating C02 Emissions from Fossil Fuel Combustion 385
ANNEX 5 Assessment of the Sources and Sinks of Greenhouse Gas Emissions Not Included 395
ANNEX 6 Additional Information 397
6.1. Global Warming Potential Values 397
6.2. Ozone Depleting Substance Emissions 406
6.3. Sulfur Dioxide Emissions 407
6.4. Complete List of Source Categories 409
6.5. Constants, Units, and Conversions 410
6.6. Abbreviations 412
6.7. Chemical Formulas 417
ANNEX 7 Uncertainty 421
7.1. Overview 421
7.2. Methodology and Results 421
7.3. Planned Improvements 428
7.4. Additional Information on Uncertainty Analyses by Source 429
ANNEX 8 QA/QC Procedures 450
8.1. Background 450
8.2. Purpose 450
8.3. Assessment Factors 451
AT
-------
ANNEX 1 Key Categoiy Analysis
The United States has identified national key categories based on the estimates presented in this report. The
IPCC's Good Practice Guidance (IPCC 2000) describes a key category as a "[category] that is prioritized within the
national inventory system because its estimate has a significant influence on a country's total inventory of direct
greenhouse gases in terms of the absolute level of emissions, the trend in emissions, or both." By definition, key categories
are sources or sinks that have the greatest contribution to the absolute overall level of national emissions in any of the
years covered by the time series. In addition, when an entire time series of emission estimates is prepared, a determination
of key categories must also account for the influence of the trends of individual categories. Therefore, a trend assessment
is conducted to identify source and sink categories for which significant uncertainty in the estimate would have
considerable effects on overall emission trends. Finally, a qualitative evaluation of key categories should be performed, in
order to capture any key categories that were not identified in either of the quantitative analyses, but can be considered key
because of the unique country-specific estimation methods.
The methodology for conducting a key category analysis, as defined by IPCC's Good Practice Guidance (IPCC
2000), IPCC's Good Practice Guidance for Land Use, Land-Use Change, and Forestry (IPCC 2003), and IPCC's 2006
Guidelines for National Greenhouse Gas Inventories IPCC (2006); includes:
• Tier 1 approach (including both level and trend assessments);
• Tier 2 approach (including both level and trend assessments, and incorporating uncertainty analysis); and
• Qualitative approach.
This Annex presents an analysis of key categories, both for sources only and also for sources and sinks (i.e.,
including LULUCF); discusses Tier 1, Tier 2, and qualitative approaches to identifying key categories; provides level and
trend assessment equations; and provides a brief statistical evaluation of IPCC's quantitative methodologies for defining
key categories. Table A- 1 presents the key categories for the United States (including and excluding LULUCF categories)
using emissions and uncertainty data in this report, and ranked according to their sector and global warming potential-
weighted emissions in 2012. The table also indicates the criteria used in identifying these categories (i.e., level, trend, Tier
1, Tier 2, and/or qualitative assessments).
A-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-1: Key Source Categories for the United States 0990-20121
IPCC Source Categories
Gas
TieM
Level Trend Level Trend
Without Without With With
LULUCF LULUCF LULUCF LULUCF
Tier 2
Level Trend Level Trend
Without Without With With
LULUCF LULUCF LULUCF LULUCF
Qual
2012
Emissions
(Tg C02 Eq.)
Energy
IC02 Emissions from Stationary Combustion - Coal - Electricity
Generation
C02 Emissions from Mobile Combustion: Road
IC02 Emissions from Stationary Combustion - Gas - Electricity
Generation
C02 Emissions from Stationary Combustion - Gas - Industrial
C02 Emissions from Stationary Combustion - Oil - Industrial
C02 Emissions from Stationary Combustion - Gas -
Residential
IC02 Emissions from Stationary Combustion -1
Commercial
C02 Emissions from Mobile Combustion: Aviation
C02 Emissions from Non-Energy Use of Fuels
C02 Emissions from Mobile Combustion: Other
C02 Emissions from Stationary Combustion - Coal - Industrial
C02 Emissions from Stationary Combustion - Oil - Residential
IC02 Emissions from Stationary Combustion - Oil - U.S.
Territories
C02 Emissions from Mobile Combustion: Marine
C02 Emissions from Stationary Combustion - Oil -
Commercial
C02 Emissions from Natural Gas Systems
C02 Emissions from Stationary Combustion - Oil - Electricity
C02 Emissions from Stationary Combustion - Coal -
Commercial
IC02 Emissions from Stationary Combustion - Coal -
Residential
ChU Emissions from Natural Gas Systems
Fugitive Emissions from Coal Mining
ChU Emissions from Petroleum Systems
Non-C02 Emissions from Stationary Combustion - Residential
Non-C02 Emissions from Stationary Combustion - Electricity
Generation
N20 Emissions from Mobile Combustion: Road
12.6
A-3
-------
Non-C02 Emissions from Stationary Combustion - Industrial
International Bunker Fuels0
N20
Several
•
•
•
112.8
Industrial Processes
C02 Emissions from Iron and Steel Production & Metallurgical
Coke Production
C02 Emissions from Cement Production
N20 Emissions from Adipic Acid Production
Emissions from Substitutes for Ozone Depleting Substances
SFe Emissions from Electrical Transmission and Distribution
HFC-23 Emissions from HCFC-22 Production
PFC Emissions from Aluminum Production
C02
C02
N20
HiGWP
HiGWP
HiGWP
HiGWP
54.3
35.1
5.8
146.8
6.0
4.3
25
Agriculture
H4 Emissions from Enteric Fermentation
CH4 Emissions from Manure Management
Direct N20 Emissions from Agricultural Soil Management
Indirect N20 Emissions from Applied Nitrogen
CH4
CH4
N20
N20
141.0
52.9
260.9
45.7
Waste
CH4 Emissions from Landfills
CH4
102.8
Land Use, Land Use Change, and Forestry
C02 Emissions from Land Converted to Cropland
C02 Emissions from Grassland Remaining Grassland
C02 Emissions from Landfilled Yard Trimmings and Food
Scraps
C02 Emissions from Cropland Remaining Cropland
C02 Emissions from Urban Trees
C02 Emissions from Changes in Forest Carbon Stocks
CH4 Emissions from Forest Fires
N20 Emissions from Forest Fires
16.8
6.7
(13.2)
(26.5)
(88.4)
(866.5)
15.3
12.5
Subtotal Without LULUCF
6,324.6
Total Emissions Without LULUCF
6,487.8
Percent of Total Without LULUCF
97%
Subtotal With LULUCF
5,379.1
Total Emissions With LULUCF
5,546.3
Percent of Total With LULUCF
97%
'Qualitative criteria.
b Emissions from this source not included in totals.
Note: Parentheses indicate negative values (or sequestration). Table A- 2 provides a complete listing of source categories by IPCC sector, along with notations on the criteria used in identifying key categories, without
LULUCF sources and sinks. Similarly, Table A- 3 provides a complete listing of source and sink categories by IPCC sector, along with notations on the criteria used in identifying key categories, including LULUCF sources
and sinks. The notations refer specifically to the year(s) in the inventory time series (i.e., 1990 to 2012) in which each source category reached the threshold for being a key category based on either a Tier 1 or Tier 2 level
assessment.
A-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
In addition to conducting Tier 1 and 2 level and trend assessments, a qualitative assessment of the source
categories, as described in the IPCC's Good Practice Guidance (IPCC 2000), was conducted to capture any key categories
that were not identified by any quantitative method. One additional key category, international bunker fuels, was
identified using this qualitative assessment. International bunker fuels are fuels consumed for aviation or marine
international transport activities, and emissions from these fuels are reported separately from totals in accordance with
IPCC guidelines. If these emissions were included in the totals, bunker fuels would qualify as a key category according to
the Tier 1 approach. The amount of uncertainty associated with estimation of emissions from international bunker fuels
also supports the qualification of this source category as key, which would qualify it as a key category according to the
Tier 2 approach.
Table A- 2: U.S Greenhouse Gas Inventory Source Categories without LULUGF
IPCC Source Categories
Direct GHG
2012
Emissions
(Tg C02 Key
Eq.) Category?
ID
Criteria"
Level in
which
year(s)?b
Energy
C02 Emissions from Stationary Combustion - Coal - Electricity
Generation
C02 Emissions from Mobile Combustion: Road
C02 Emissions from Stationary Combustion - Gas - Electricity
Generation
C02 Emissions from Stationary Combustion - Gas - Industrial
C02 Emissions from Stationary Combustion - Oil - Industrial
C02 Emissions from Stationary Combustion - Gas - Residential
C02 Emissions from Stationary Combustion - Gas - Commercial
C02 Emissions from Mobile Combustion: Aviation
C02 Emissions from Non-Energy Use of Fuels
C02 Emissions from Mobile Combustion: Other
C02 Emissions from Stationary Combustion - Coal - Industrial
C02 Emissions from Stationary Combustion - Oil - Residential
C02 Emissions from Stationary Combustion - Oil - U.S. Territories
C02 Emissions from Mobile Combustion: Marine
C02 Emissions from Stationary Combustion - Oil - Commercial
C02 Emissions from Natural Gas Systems
C02 Emissions from Stationary Combustion - Oil - Electricity
Generation
C02 Emissions from Incineration of Waste
C02 Emissions from Stationary Combustion - Coal - Commercial
C02 Emissions from Stationary Combustion - Coal - U.S. Territories
C02 Emissions from Stationary Combustion - Gas - U.S. Territories
C02 Emissions from Petroleum Systems
C02 Emissions from Stationary Combustion - Geothermal Energy
C02 Emissions from Stationary Combustion - Coal - Residential
Cm Emissions from Natural Gas Systems
Fugitive Emissions from Coal Mining
Cm Emissions from Petroleum Systems
Fugitive Emissions from Abandoned Underground Coal Mines
Non-C02 Emissions from Stationary Combustion - Residential
Non-C02 Emissions from Stationary Combustion - Industrial
Cm Emissions from Mobile Combustion: Road
Non-C02 Emissions from Stationary Combustion - Commercial
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
CH4
CH4
CH4
CH4
CH4
CH4
CH4
CH4
1,511.2
1,469.8
492.2
434.7
265.2
224.8
156.9
145.1
110.3
84.5
74.3
64.1
44.7
40.1
36.4
35.2
18.8
12.2
4.1
3.4
1.4
0.4
0.4
0.0
129.9
55.8
31.7
4.7
3.1
1.2
1.2
0.8
Li Ti L2 T2
LiTiL2T2
LiTiL2T2
Li Ti L2
LiTiL2T2
Li Ti L2
Li Ti L2
LiTiL2T2
LiTiL2T2
UTi
Li Ti L2 T2
Li Ti T2
UTi
UTi
UTi
LiL2
Li Ti T2
Ti
T2
LiTiL2T2
LiTiL2T2
LiL2T2
L2T2
1990,2012
1990,2012
1990,2012
1990,2012
1990,2012
1990,2012
1990,2012
1990,2012
1990,2012
1990i,
2012i
1990,2012
1990i,
2012i
2012i
1990i,
2012i
1990i,
2012i
1990,2012
1990i
1990,2012
1990,2012
1990,20122
19902
A-5
-------
Non-CC>2 Emissions from Stationary Combustion - Electricity
Generation
Cm Emissions from Mobile Combustion: Other
Non-C02 Emissions from Stationary Combustion - U.S. Territories
Cm Emissions from Mobile Combustion: Aviation
Cm Emissions from Mobile Combustion: Marine
Cm Emissions from Incineration of Waste
Non-C02 Emissions from Stationary Combustion - Electricity
Generation
N20 Emissions from Mobile Combustion: Road
Non-C02 Emissions from Stationary Combustion - Industrial
N20 Emissions from Mobile Combustion: Other
N20 Emissions from Mobile Combustion: Aviation
Non-C02 Emissions from Stationary Combustion - Residential
N20 Emissions from Mobile Combustion: Marine
N20 Emissions from Incineration of Waste
Non-C02 Emissions from Stationary Combustion - Commercial
Non-C02 Emissions from Stationary Combustion - U.S. Territories
International Bunker Fuels0
cm
cm
cm
cm
cm
cm
N20
N20
N20
N20
N20
N20
N20
N20
N20
N20
Several
0.5
0.4
0.1
+
+
+
18.3
12.6
2.5
2.0
1.4
0.8
0.6
0.4
0.3
0.1
112.8
TiL2T2 19902,
20122
LiTiL2T2 1990
T2
Q
Industrial Processes
C02 Emissions from Iron and Steel Production & Metallurgical Coke
Production
C02 Emissions from Cement Production
C02 Emissions from Lime Production
C02 Emissions from Ammonia Production
C02 Emissions from Other Process Uses of Carbonates
C02 Emissions from Urea Consumption for Non-Ag Purposes
C02 Emissions from Petrochemical Production
C02 Emissions from Aluminum Production
C02 Emissions from Soda Ash Production and Consumption
C02 Emissions from Carbon Dioxide Consumption
C02 Emissions from Titanium Dioxide Production
C02 Emissions from Ferroalloy Production
C02 Emissions from Zinc Production
C02 Emissions from Glass Production
C02 Emissions from Phosphoric Acid Production
C02 Emissions from Lead Production
C02 Emissions from Silicon Carbide Production and Consumption
Cm Emissions from Petrochemical Production
Cm Emissions from Iron and Steel Production & Metallurgical Coke
Production
Cm Emissions from Ferroalloy Production
Cm Emissions from Silicon Carbide Production and Consumption
N20 Emissions from Nitric Acid Production
N20 Emissions from Adipic Acid Production
N20 Emissions from Product Uses
Emissions from Substitutes for Ozone Depleting Substances
SFe Emissions from Electrical Transmission and Distribution
HFC-23 Emissions from HCFC-22 Production
PFC, HFC, and SFe Emissions from Semiconductor Manufacture
PFC Emissions from Aluminum Production
SFe Emissions from Magnesium Production and Processing
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
cm
cm
cm
cm
N20
N20
N20
HiGWP
HiGWP
HiGWP
HiGWP
HiGWP
HiGWP
54.3
35.1
13.3
9.4
8.0
5.2
3.5
3.4
2.7
1.8
1.7
1.7
1.4
1.2
1.1
0.5
0.2
3.1
0.6
+
+
15.3
5.8
4.4
146.8
6.0
4.3
3.7
2.5
1.7
LiTiL2T2 1990,2012,
Li 1990i
Ti
LiTiL2T2 2012
TiT2
LiTiT2 1990i
Ti
Agriculture
Cm Emissions from Enteric Fermentation
cm
141.0
Li L2 1990, 2012
A-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Cm Emissions from Manure Management
Cm Emissions from Rice Cultivation
Cm Emissions from Field Burning of Agricultural Residues
Direct N20 Emissions from Agricultural Soil Management
Indirect ivbO Emissions from Applied Nitrogen
N20 Emissions from Manure Management
N20 Emissions from Field Burning of Agricultural Residues
cm
cm
cm
N20
N20
N20
N20
52.9
7.4
0.3
260.9
45.7
18.0
0.1
LiTiL2T2 2012
LiTiL2T2 1990,2012
LiL2T2 1990,2012
Waste
Cm Emissions from Landfills
Cm Emissions from Wastewater Treatment
Cm Emissions from Composting
N20 Emissions from Wastewater Treatment
N20 Emissions from Composting
cm
cm
cm
N20
N20
102.8
12.8
1.6
5.0
1.8
LiTiL2T2 1990,2012
a For the ID criteria, L refers to a key category identified through a level assessment; T refers to a key category identified through a trend assessment and the
subscripted number refers to either a Tier 1 or Tier 2 assessment (e.g., I.2 designates a source is a key category for a Tier 2 level assessment).
b If the source is a key category for both Li and I.2 (as designated in the ID criteria column), it is a key category for both assessments in the years provided unless
noted by a subscript, in which case it is a key category for that assessment in that year only (.e.g., 1990 designates a source is a key category for the Tier 2
assessment only in 1990).
'Emissions from these sources not included in totals.
+ Does not exceed 0.05 Tg C02 Eq.
Note: LULUCF sources and sinks are not included in this analysis.
A-7
-------
Table A- 3: U.S Greenhouse Gas Inventory Source Categories with LULUGF
IPCC Source Categories
Direct GHG
2012
Emissions
(Tg C02 Key
Eq.) Category?
ID
Criteria3
Level in
which
year(s)?b
Energy
C02 Emissions from Stationary Combustion - Coal - Electricity
Generation
C02 Emissions from Mobile Combustion: Road
C02 Emissions from Stationary Combustion - Gas - Electricity
Generation
C02 Emissions from Stationary Combustion - Gas - Industrial
C02 Emissions from Stationary Combustion - Oil - Industrial
C02 Emissions from Stationary Combustion - Gas - Residential
C02 Emissions from Stationary Combustion - Gas - Commercial
C02 Emissions from Mobile Combustion: Aviation
C02 Emissions from Non-Energy Use of Fuels
C02 Emissions from Mobile Combustion: Other
C02 Emissions from Stationary Combustion - Coal - Industrial
C02 Emissions from Stationary Combustion - Oil - Residential
C02 Emissions from Stationary Combustion - Oil - U.S. Territories
C02 Emissions from Mobile Combustion: Marine
C02 Emissions from Stationary Combustion - Oil - Commercial
C02 Emissions from Natural Gas Systems
C02 Emissions from Stationary Combustion - Oil - Electricity
Generation
C02 Emissions from Incineration of Waste
C02 Emissions from Stationary Combustion - Coal - Commercial
C02 Emissions from Stationary Combustion - Coal - U.S. Territories
C02 Emissions from Stationary Combustion - Gas - U.S. Territories
C02 Emissions from Petroleum Systems
C02 Emissions from Stationary Combustion - Geothermal Energy
C02 Emissions from Stationary Combustion - Coal - Residential
Cm Emissions from Natural Gas Systems
Fugitive Emissions from Coal Mining
Cm Emissions from Petroleum Systems
Fugitive Emissions from Abandoned Underground Coal Mines
Non-C02 Emissions from Stationary Combustion - Residential
Non-C02 Emissions from Stationary Combustion - Industrial
Cm Emissions from Mobile Combustion: Road
Non-C02 Emissions from Stationary Combustion - Commercial
Non-C02 Emissions from Stationary Combustion - Electricity
Generation
Cm Emissions from Mobile Combustion: Other
Non-C02 Emissions from Stationary Combustion - U.S. Territories
Cm Emissions from Mobile Combustion: Aviation
Cm Emissions from Mobile Combustion: Marine
Cm Emissions from Incineration of Waste
Non-C02 Emissions from Stationary Combustion - Electricity
Generation
N20 Emissions from Mobile Combustion: Road
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
cm
cm
cm
cm
cm
cm
cm
cm
cm
cm
cm
cm
cm
cm
N20
N20
1,511.2
1,469.8
492.2
434.7
265.2
224.8
156.9
145.1
110.3
84.5
74.3
64.1
44.7
40.1
36.4
35.2
18.8
12.2
4.1
3.4
1.4
0.4
0.4
0.0
129.9
55.8
31.7
4.7
3.1
1.2
1.2
0.8
0.5
0.4
0.1
+
+
+
18.3
12.6
LiTiL2T2
Li Ti L2 T2
Li Ti L2 T2
LiL2
Li Ti L2 T2
Li Ti L2
Li Ti L2
Li Ti L2 T2
Li Ti L2 T2
UTi
LiTiL2T2
UTi
UTi
UTi
UTi
LiL2
Li Ti T2
Ti
LiTiL2T2
LiTiL2T2
LiTiL2T2
L2T2
Ti L2 T2
Li Ti L2 T2
1990,2012
1990,2012
1990i,2012
1990,2012
1990,2012
1990,2012
1990,2012
1990,2012,
1990,2012
1990i,
2012i
1990,2012
1990i,
2012i
1990i,
2012i
1990i,
2012i
1990i,
2012i
1990,2012
199d
1990,2012
1990,2012
1990,2012
19902
19902,
20122
1990
A-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Non-CC>2 Emissions from Stationary Combustion - Industrial
N20 Emissions from Mobile Combustion: Other
N20 Emissions from Mobile Combustion: Aviation
Non-C02 Emissions from Stationary Combustion - Residential
N20 Emissions from Mobile Combustion: Marine
N20 Emissions from Incineration of Waste
Non-C02 Emissions from Stationary Combustion - Commercial
Non-C02 Emissions from Stationary Combustion - U.S. Territories
International Bunker Fuels0
N20
N20
N20
N20
N20
N20
N20
N20
Several
2.5
2.0
1.4
0.8
0.6
0.4
0.3
0.1
112.8
Q
Industrial Processes
C02 Emissions from Iron and Steel Production & Metallurgical Coke
Production
C02 Emissions from Cement Production
C02 Emissions from Lime Production
C02 Emissions from Ammonia Production
C02 Emissions from Other Process Uses of Carbonates
C02 Emissions from Urea Consumption for Non-Ag Purposes
C02 Emissions from Petrochemical Production
C02 Emissions from Aluminum Production
C02 Emissions from Soda Ash Production and Consumption
C02 Emissions from Carbon Dioxide Consumption
C02 Emissions from Titanium Dioxide Production
C02 Emissions from Ferroalloy Production
C02 Emissions from Zinc Production
C02 Emissions from Glass Production
C02 Emissions from Phosphoric Acid Production
C02 Emissions from Lead Production
C02 Emissions from Silicon Carbide Production and Consumption
Cm Emissions from Petrochemical Production
Cm Emissions from Iron and Steel Production & Metallurgical Coke
Production
Cm Emissions from Ferroalloy Production
Cm Emissions from Silicon Carbide Production and Consumption
N20 Emissions from Nitric Acid Production
N20 Emissions from Adipic Acid Production
N20 Emissions from Product Uses
Emissions from Substitutes for Ozone Depleting Substances
SFe Emissions from Electrical Transmission and Distribution
HFC-23 Emissions from HCFC-22 Production
PFC, HFC, and SFe Emissions from Semiconductor Manufacture
PFC Emissions from Aluminum Production
SFe Emissions from Magnesium Production and Processing
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
cm
cm
cm
cm
N20
N20
N20
HiGWP
HiGWP
HiGWP
HiGWP
HiGWP
HiGWP
54.3
35.1
13.3
9.4
8.0
5.2
3.5
3.4
2.7
1.8
1.7
1.7
1.4
1.2
1.1
0.5
0.2
3.1
0.6
+
+
15.3
5.8
4.4
146.8
6.0
4.3
3.7
2.5
1.7
LiTiL2T2 1990, 2012i
Li 1990i,
2012i
Ti
LiTiL2T2 2012
TiT2
UTi 199d
Ti
Agriculture
Cm Emissions from Enteric Fermentation
Cm Emissions from Manure Management
Cm Emissions from Rice Cultivation
Cm Emissions from Field Burning of Agricultural Residues
Direct N20 Emissions from Agricultural Soil Management
Indirect ivbO Emissions from Applied Nitrogen
N20 Emissions from Manure Management
N20 Emissions from Field Burning of Agricultural Residues
cm
cm
cm
cm
N20
N20
N20
N20
141.0
52.9
7.4
0.3
260.9
45.7
18.0
0.1
LiL2 1990,2012
LiTiL2T2 1990i, 2012
LiL2 1990,2012
LiL2 1990,2012
Waste
A-9
-------
Cm Emissions from Landfills
CH4 Emissions from Wastewater Treatment
CH4 Emissions from Composting
ixhO Emissions from Wastewater Treatment
ixhO Emissions from Composting
CH4
CH4
CH4
N20
N20
102.8
12.8
1.6
5.0
1.8
Li Ti L2 T2
1990,2012
Land Use, Land Use Change, and Forestry
C02 Emissions from Land Converted to Cropland
C02 Emissions from Grassland Remaining Grassland
C02 Emissions from Liming of Agricultural Soils
C02 Emissions from Urea Fertilization
C02 Emissions from Wetlands Remaining Wetlands
C02 Emissions from Land Converted to Grassland
C02 Emissions from Landfilled Yard Trimmings and Food
Scraps
C02 Emissions from Cropland Remaining Cropland
C02 Emissions from Urban Trees
C02 Emissions from Changes in Forest Carbon Stocks
ChU Emissions from Forest Fires
ixhO Emissions from Forest Fires
ixhO Emissions from Settlement Soils
ixhO Emissions from Forest Soils
ixhO Emissions from Wetlands Remaining Wetlands
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
CH4
N20
N20
N20
N20
16.8
6.7
3.9
3.4
0.8
(8.5)
(13.2)
(26.5)
(88.4)
(866.5)
5.3
2.5
1.5
0.4
+
Ti L2 T2
L2T2
Ti L2 T2
Li Ti L2 T2
Li Ti L2 T2
Li Ti L2 T2
Ti L2 T2
Ti L2 T2
1990, 2012
1990, 2012
19902
1990,2012
1990,2012
1990,2012
2012
2012
a For the ID criteria, L refers to a key category identified through a level assessment; T refers to a key category identified through a trend assessment and the
subscripted number refers to either a Tier 1 or Tier 2 assessment (e.g., L2 designates a source is a key category for a Tier 2 level assessment).
b If the source is a key category for both Li and L2 (as designated in the ID criteria column), it is a key category for both assessments in the years provided unless
noted by a subscript, in which case it is a key category only for that assessment in only that year (.e.g., 19902 designates a source is a key category for the Tier 2
assessment only in 1990).
'Emissions from these sources not included in totals.
+ Does not exceed 0.05 Tg C02 Eq.
Note: Parentheses indicate negative values (or sequestration).
Evaluation of Key Categories
Level Assessment
When using a Tier 1 approach for the level assessment, a predetermined cumulative emissions threshold is used
to identify key categories. When source and sink categories are sorted in order of decreasing absolute emissions, those
that fall at the top of the list and cumulatively account for 95 percent of emissions are considered key categories. The 95
percent threshold in the IPCC Good Practice Guidance (IPCC 2000) was designed to establish a general level where the
key category analysis covers approximately 75 to 92 percent of inventory uncertainty.
Including the Tier 2 approach provides additional insight into why certain source categories are considered key,
and how to prioritize inventory improvements. In the Tier 2 approach, the level assessment for each category from the Tier
1 approach is multiplied by its percent relative uncertainty. If the uncertainty reported is asymmetrical, the absolute value
of the larger uncertainty is used. Uncertainty is not estimated for the following sources: CC>2 emissions from stationary
combustion - geothermal energy; CC>2 emissions from mobile combustion by mode of transportation; CH4 and N2O
emissions from mobile combustion by mode of off-road transportation; and CH4 from the incineration of waste. While
CC>2 emissions from geothermal energy are included in the overall emissions estimate, they are not an official IPCC source
category. As a result, there are no guidelines to associate uncertainty with the emissions estimate; therefore, an uncertainty
analysis was not conducted. The uncertainty associated with CC>2 from mobile combustion is applied to each mode's
emissions estimate, and the uncertainty associated with off-road vehicle CH4 and N2O emissions are applied to both CH4
and N2(D emissions from aviation, marine, and other sources. No uncertainty was associated with CH4 emissions from
waste incineration because emissions are less than 0.05 Gg CH4 and an uncertainty analysis was not conducted. When
source and sink categories are sorted in decreasing order of this calculation, those that fall at the top of the list and
cumulatively account for 90 percent of emissions are considered key categories. The key categories identified by the Tier
2 level assessment may differ from those identified by the Tier 1 assessment. The final set of key categories includes all
A-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
source and sink categories identified as key by either the Tier 1 or the Tier 2 assessment, keeping in mind that the two
assessments are not mutually exclusive.
It is important to note that a key category analysis can be sensitive to the definitions of the source and sink
categories. If a large source category is split into many subcategories, then the subcategories may have contributions to
the total inventory that are too small for those source categories to be considered key. Similarly, a collection of small,
non-key source categories adding up to less than 5 percent of total emissions could become key source categories if those
source categories were aggregated into a single source category. The United States has attempted to define source and
sink categories by the conventions which would allow comparison with other international key categories, while still
maintaining the category definitions that constitute how the emissions estimates were calculated for this report. As such,
some of the category names used in the key category analysis may differ from the names used in the main body of the
report. Additionally, the United States accounts for some source categories, including fossil fuel feedstocks, international
bunkers, and emissions from U.S. territories, that are derived from unique data sources using country-specific
methodologies.
Table A- 4 through Table A- 7 contain the 1990 and 2012 level assessments for both with and without LULUCF
sources and sinks, and contain further detail on where each source falls within the analysis. Tier 1 key categories are
shaded dark gray. Additional key categories identified by the Tier 2 assessment are shaded light gray.
Trend Assessment
The Tier 1 approach for trend assessment is defined as the product of the source or sink category level
assessment and the absolute difference between the source or sink category trend and the total trend. In turn, the source or
sink category trend is defined as the change in emissions from the base year to the current year, as a percentage of current
year emissions from that source or sink category. The total trend is the percentage change in total inventory emissions
from the base year to the current year.
Thus, the source or sink category trend assessment will be large if the source or sink category represents a large
percentage of emissions and/or has a trend that is quite different from the overall inventory trend. To determine key
categories, the trend assessments are sorted in decreasing order, so that the source or sink categories with the highest trend
assessments appear first. The trend assessments are summed until the threshold of 95 percent is reached; all categories
that fall within that cumulative 95 percent are considered key categories.
For the Tier 2 approach, the trend assessment for each category from the Tier 1 approach is multiplied by its
percent relative uncertainty. If the uncertainty reported is asymmetrical, the larger uncertainty is used. When source and
sink categories are sorted in decreasing order of this calculation, those that fall at the top of the list and cumulatively
account for 90 percent of emissions are considered key categories. The key categories identified by the Tier 2 trend
assessment may differ from those identified by the Tier 1 assessment. The final set of key categories includes all source
and sink categories identified as key by either the Tier 1 or the Tier 2 assessment, keeping in mind that the two
assessments are not mutually exclusive.
Table A- 8 and Table A- 9 contain the 1990 through 2012 trend assessment for both with and without LULUCF
sources and sinks, and contain further detail on where each source falls within the analysis. Tier 1 key categories are
shaded dark gray. Additional key categories identified by the Tier 2 assessment are shaded light gray.
Table A- 4:1990 Key Source Category TieM and Tier 2 Analysis—Level Assessment, without LULUCF
1990 Estimate Tier 1 Level Cumulative Tier 2 Level
IPCC Source Categories Direct GHG (Tg C02 Eq.) Assessment Total Uncertainty Assessment
C02 Emissions from Stationary Combustion - Coal - Electricity C02 1,547.6 0.25 0.25 10% 0.024
Generation
C02 Emissions from Mobile Combustion: Road C02 1,188.9 0.19 0.44 7% 0.013
C02 Emissions from Stationary Combustion - Gas - Industrial C02 408.9 0.07 0.51 7% 0.005
C02 Emissions from Stationary Combustion - Oil - Industrial C02 280.9 0.05 0.55 20% 0.009
Direct MO Emissions from Agricultural Soil Management MO 240.7 0.04 0.59 28% 0.011
C02 Emissions from Stationary Combustion - Gas - Residential C02 238.0 0.04 0.63 7% 0.003
C02 Emissions from Mobile Combustion: Aviation C02 187.4 0.03 0.66 7% 0.002
C02 Emissions from Stationary Combustion - Gas - Electricity C02 175.3 0.03 0.69 5% 0.001
Generation
Cm Emissions from Natural Gas Systems Cm 156.4 0.03 0.71 30% 0.007
C02 Emissions from Stationary Combustion - Coal - Industrial C02 155.3 0.02 0.74 16% 0.004
CH4 Emissions from Landfills CH4 147.8 0.02 0.76 56% 0.013
-------
C02 Emissions from Stationary Combustion - Gas - Commercial
Cm Emissions from Enteric Fermentation
C02 Emissions from Non-Energy Use of Fuels
C02 Emissions from Iron and Steel Production & Metallurgical
Coke Production
C02 Emissions from Stationary Combustion - Oil - Electricity
Generation
C02 Emissions from Stationary Combustion - Oil - Residential
Fugitive Emissions from Coal Mining
C02 Emissions from Mobile Combustion: Other
C02 Emissions from Stationary Combustion - Oil - Commercial
C02 Emissions from Mobile Combustion: Marine
Indirect ixhO Emissions from Applied Nitrogen
N20 Emissions from Mobile Combustion: Road
C02 Emissions from Natural Gas Systems
HFC-23 Emissions from HCFC-22 Production
CKU Emissions from Petroleum Systems
C02 Emissions from Cement Production
CKU Emissions from Manure Management
C02 Emissions from Stationary Combustion - Oil - U.S.
Territories
SFe Emissions from Electrical Transmission and Distribution
PFC Emissions from Aluminum Production
N20 Emissions from Nitric Acid Production
N20 Emissions from Adipic Acid Production
N20 Emissions from Manure Management
CKU Emissions from Wastewater Treatment
C02 Emissions from Ammonia Production
C02 Emissions from Stationary Combustion - Coal -
Commercial
C02 Emissions from Lime Production
C02 Emissions from Incineration of Waste
CKU Emissions from Rice Cultivation
Non-C02 Emissions from Stationary Combustion - Electricity
Generation
C02 Emissions from Aluminum Production
Fugitive Emissions from Abandoned Underground Coal Mines
SFe Emissions from Magnesium Production and Processing
C02 Emissions from Other Process Uses of Carbonates
Non-C02 Emissions from Stationary Combustion - Residential
N20 Emissions from Product Uses
CKU Emissions from Mobile Combustion: Road
C02 Emissions from Urea Consumption for Non-Ag Purposes
N20 Emissions from Wastewater Treatment
C02 Emissions from Petrochemical Production
Non-C02 Emissions from Stationary Combustion - Industrial
C02 Emissions from Stationary Combustion - Coal - Residential
PFC, HFC, and SFe Emissions from Semiconductor
Manufacture
C02 Emissions from Soda Ash Production and Consumption
CKU Emissions from Petrochemical Production
C02 Emissions from Ferroalloy Production
N20 Emissions from Mobile Combustion: Aviation
Non-C02 Emissions from Stationary Combustion - Industrial
C02
CKU
C02
C02
C02
C02
CKU
C02
C02
C02
N20
N20
C02
MFCs
CKU
C02
CKU
C02
SF6
PFCs
N20
N20
N20
CKU
C02
C02
C02
C02
CKU
N20
C02
CKU
SF6
C02
CKU
N20
CKU
C02
N20
C02
N20
C02
Several
C02
CH4
C02
N20
CH4
142.1
137.9
120.8
99.8
97.5
97.4
81.1
73.3
64.9
44.5
41.4
40.3
37.7
36.4
35.8
33.3
31.5
27.2
26.7
18.4
18.2
15.8
14.4
13.2
13.0
12.0
11.4
8.0
7.7
7.4
6.8
6.0
5.4
4.9
4.6
4.4
4.2
3.8
3.5
3.4
3.3
3.0
2.9
2.7
2.3
2.2
1.8
1.6
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.78
0.81
0.82
0.84
0.86
0.87
0.88
0.90
0.91
0.91
0.92
0.93
0.93
0.94
0.95
0.95
0.96
0.96
0.96
0.97
0.97
0.97
0.97
0.98
0.98
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
7%
18%
35%
17%
8%
6%
35%
7%
5%
7%
151%
27%
30%
10%
149%
6%
20%
11%
25%
6%
38%
4%
24%
27%
7%
15%
3%
14%
96%
171%
2%
26%
12%
20%
225%
24%
16%
10%
100%
27%
211%
NE
5%
6%
10%
12%
2%
49%
0.002
0.004
0.007
0.003
0.001
0.001
0.005
0.001
0.001
<0.001
0.010
0.002
0.002
0.001
0.009
<0.001
0.001
<0.001
0.001
<0.001
0.001
<0.001
0.001
0.001
<0.001
<0.001
<0.001
<0.001
0.001
0.002
<0.001
<0.001
<0.001
<0.001
0.002
<0.001
<0.001
<0.001
0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
C02 Emissions from Phosphoric Acid Production
C02 Emissions from Glass Production
C02 Emissions from Carbon Dioxide Consumption
N20 Emissions from Mobile Combustion: Other
C02 Emissions from Titanium Dioxide Production
Non-C02 Emissions from Stationary Combustion - Residential
ChU Emissions from Iron and Steel Production & Metallurgical
Coke Production
Non-C02 Emissions from Stationary Combustion - Commercial
C02 Emissions from Stationary Combustion - Coal - U.S.
Territories
C02 Emissions from Zinc Production
ixhO Emissions from Mobile Combustion: Marine
C02 Emissions from Lead Production
ixhO Emissions from Incineration of Waste
C02 Emissions from Stationary Combustion - Geothermal
Energy
C02 Emissions from Petroleum Systems
Non-C02 Emissions from Stationary Combustion - Commercial
C02 Emissions from Silicon Carbide Production and
Consumption
ixhO Emissions from Composting
Non-C02 Emissions from Stationary Combustion - Electricity
Generation
Emissions from Substitutes for Ozone Depleting Substances
CKU Emissions from Composting
CKU Emissions from Mobile Combustion: Other
ChU Emissions from Field Burning of Agricultural Residues
ixhO Emissions from Field Burning of Agricultural Residues
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
CKU Emissions from Mobile Combustion: Aviation
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
CKU Emissions from Silicon Carbide Production and
Consumption
CKU Emissions from Mobile Combustion: Marine
CKU Emissions from Ferroalloy Production
CKU Emissions from Incineration of Waste
C02 Emissions from Stationary Combustion - Gas - U.S.
Territories
Note: LULUCF sources and sinks are not included in this analysis.
a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical,
NE Uncertainty not estimated.
+ Does not exceed 0.05 Tg C02 Eq.
C02 1.6 <0.01
C02 1.5 <0.01
C02 1.4 <0.01
N20 1.3 <0.01
C02 1.2 <0.01
N20 1.1 <0.01
CKU 1.0 <0.01
CKU 0.9 <0.01
C02 0.6 <0.01
C02 0.6 <0.01
N20 0.6 <0.01
C02 0.5 <0.01
N20 0.5 <0.01
C02 0.4 <0.01
C02 0.4 <0.01
N20 0.4 <0.01
C02 0.4 <0.01
N20 0.4 <0.01
CKU 0.3 <0.01
Several 0.3 <0.01
CH4 0.3 <0.01
CH4 0.3 <0.01
CH4 0.3 <0.01
N20 0.1 <0.01
N20 0.1 <0.01
CH4 0.1 <0.01
CH4 + <0.01
CH4 + <0.01
CH4 + <0.01
CKU + <0.01
CH4 + <0.01
C02 + <0.01
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
21%
5%
40%
1%
13%
205%
22%
138%
19%
17%
16%
15%
313%
NA
149%
38%
9%
50%
3%
14%
50%
1%
42%
32%
203%
8%
57%
9%
4%
11%
NE
17%
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
the uncertainty given here is the larger and always positive.
Table A- 5: 1990 KeySourceCategoryTieM and Tier 2 Analysis— Level Assessment, with LULUCF
1990 Estimate Tier 1 Level
IPCC Source Categories Direct GHG (Tg C02 Eq.) Assessment
(C02 Emissions from Stationary Combustion - Coal - Electricity
Generation
C02 Emissions from Mobile Combustion: Road
C02 Emissions from Changes in Forest Carbon Stocks
C02 Emissions from Stationary Combustion - Gas - Industrial
C02 1,547.6 0.22
C02 1,188.9 0.17
C02 704.6 0.10
C02 408.9 0.06
Cumulative
Total
0.22
0.38
0.48
0.54
Uncertainty
10%
7%
15%
7%
Tier 2 Level
Assessment
0.021
0.011
0.015
0.004
A-13
-------
C02 Emissions from Stationary Combustion - Oil - Industrial
Direct ixhO Emissions from Agricultural Soil Management
C02 Emissions from Stationary Combustion - Gas - Residential
C02 Emissions from Mobile Combustion: Aviation
C02 Emissions from Stationary Combustion - Gas - Electricity
Generation
Cm Emissions from Natural Gas Systems
C02 Emissions from Stationary Combustion - Coal - Industrial
ChU Emissions from Landfills
C02 Emissions from Stationary Combustion - Gas - Commercial
ChU Emissions from Enteric Fermentation
C02 Emissions from Non-Energy Use of Fuels
C02 Emissions from Iron and Steel Production & Metallurgical
Coke Production
C02 Emissions from Stationary Combustion - Oil - Electricity
Generation
C02 Emissions from Stationary Combustion - Oil - Residential
Fugitive Emissions from Coal Mining
C02 Emissions from Mobile Combustion: Other
C02 Emissions from Stationary Combustion - Oil - Commercial
C02 Emissions from Urban Trees
C02 Emissions from Cropland Remaining Cropland
C02 Emissions from Mobile Combustion: Marine
Indirect N20 Emissions from Applied Nitrogen
N20 Emissions from Mobile Combustion: Road
C02 Emissions from Natural Gas Systems
HFC-23 Emissions from HCFC-22 Production
ChU Emissions from Petroleum Systems
C02 Emissions from Cement Production
CKU Emissions from Manure Management
C02 Emissions from Stationary Combustion - Oil - U.S.
Territories
C02 Emissions from Land Converted to Cropland
SFe Emissions from Electrical Transmission and Distribution
C02 Emissions from Landfilled Yard Trimmings and Food
Scraps
PFC Emissions from Aluminum Production
N20 Emissions from Nitric Acid Production
N20 Emissions from Adipic Acid Production
N20 Emissions from Manure Management
ChU Emissions from Wastewater Treatment
C02 Emissions from Ammonia Production
C02 Emissions from Stationary Combustion - Coal -
Commercial
C02 Emissions from Lime Production
C02 Emissions from Grassland Remaining Grassland
C02 Emissions from Incineration of Waste
ChU Emissions from Rice Cultivation
Non-C02 Emissions from Stationary Combustion - Electricity
Generation
C02 Emissions from Land Converted to Grassland
C02 Emissions from Aluminum Production
Fugitive Emissions from Abandoned Underground Coal Mines
SFe Emissions from Magnesium Production and Processing
C02
N20
C02
C02
C02
ChU
C02
ChU
C02
ChU
C02
C02
C02
C02
ChU
C02
C02
C02
C02
C02
N20
N20
C02
MFCs
ChU
C02
ChU
C02
C02
SF6
C02
PFCs
N20
N20
N20
ChU
C02
C02
C02
C02
C02
CH4
N20
C02
C02
ChU
SF6
280.9
240.7
238.0
187.4
175.3
156.4
155.3
147.8
142.1
137.9
120.8
99.8
97.5
97.4
81.1
73.3
64.9
60.4
51.9
44.5
41.4
40.3
37.7
36.4
35.8
33.3
31.5
27.2
269
26.7
24.2
18.4
18.2
15.8
14.4
13.2
13.0
12.0
11.4
9.6
8.0
7.7
7.4
7.3
6.8
6.0
5.4
0.04
0.03
0.03
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.58
0.61
0.65
0.67
0.70
0.72
0.74
0.76
0.78
0.80
0.82
0.83
0.85
0.86
0.87
0.88
0.89
0.90
0.91
0.91
0.92
0.93
0.93
0.94
0.94
0.95
0.95
0.95
096
0.96
0.96
0.97
0.97
0.97
0.97
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.99
20%
28%
7%
7%
5%
30%
16%
56%
7%
18%
35%
17%
8%
6%
35%
7%
5%
47%
167%
7%
151%
27%
30%
10%
149%
6%
20%
11%
•
77%
25%
60%
6%
38%
4%
24%
27%
7%
15%
3%
529%
14%
96%
171%
108%
2%
26%
12%
0.008
0.010
0.002
0.002
0.001
0.007
0.003
0.012
0.001
0.003
0.006
0.002
0.001
0.001
0.004
0.001
<0.001
0.004
0.012
<0.001
0.009
0.002
0.002
0.001
0.007
<0.001
0.001
<0.001
0.003
0.001
0.002
<0.001
0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
0.007
<0.001
0.001
0.002
0.001
<0.001
<0.001
<0.001
A-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
C02 Emissions from Other Process Uses of Carbonates
C02 Emissions from Liming of Agricultural Soils
Non-C02 Emissions from Stationary Combustion - Residential
ixhO Emissions from Product Uses
Cm Emissions from Mobile Combustion: Road
C02 Emissions from Urea Consumption for Non-Ag Purposes
ixhO Emissions from Wastewater Treatment
C02 Emissions from Petrochemical Production
Non-C02 Emissions from Stationary Combustion - Industrial
C02 Emissions from Stationary Combustion - Coal - Residential
PFC, HFC, and SFe Emissions from Semiconductor
Manufacture
C02 Emissions from Soda Ash Production and Consumption
CKU Emissions from Forest Fires
C02 Emissions from Urea Fertilization
CKU Emissions from Petrochemical Production
C02 Emissions from Ferroalloy Production
ixhO Emissions from Forest Fires
ixhO Emissions from Mobile Combustion: Aviation
Non-C02 Emissions from Stationary Combustion - Industrial
C02 Emissions from Phosphoric Acid Production
C02 Emissions from Glass Production
C02 Emissions from Carbon Dioxide Consumption
ixhO Emissions from Mobile Combustion: Other
C02 Emissions from Titanium Dioxide Production
Non-C02 Emissions from Stationary Combustion - Residential
C02 Emissions from Wetlands Remaining Wetlands
ixhO Emissions from Settlement Soils
CKU Emissions from Iron and Steel Production & Metallurgical
Coke Production
Non-C02 Emissions from Stationary Combustion - Commercial
C02 Emissions from Stationary Combustion - Coal - U.S.
Territories
C02 Emissions from Zinc Production
ixhO Emissions from Mobile Combustion: Marine
C02 Emissions from Lead Production
ixhO Emissions from Incineration of Waste
C02 Emissions from Stationary Combustion - Geothermal
Energy
C02 Emissions from Petroleum Systems
Non-C02 Emissions from Stationary Combustion - Commercial
C02 Emissions from Silicon Carbide Production and
Consumption
ixhO Emissions from Composting
Non-C02 Emissions from Stationary Combustion - Electricity
Generation
Emissions from Substitutes for Ozone Depleting Substances
CKU Emissions from Composting
CKU Emissions from Mobile Combustion: Other
CKU Emissions from Field Burning of Agricultural Residues
ixhO Emissions from Field Burning of Agricultural Residues
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
C02
C02
CKU
N20
CKU
C02
N20
C02
N20
C02
Several
C02
CKU
C02
CKU
C02
N20
N20
CKU
C02
C02
C02
N20
C02
N20
C02
N20
CKU
CKU
C02
C02
N20
C02
N20
C02
C02
N20
C02
N20
CH4
Several
CH4
CH4
CKU
N20
N20
4.9
4.7
4.6
4.4
4.2
3.8
3.5
3.4
3.3
3.0
2.9
2.7
2.5
2.4
2.3
2.2
2.0
1.8
1.6
1.6
1.5
1.4
1.3
1.2
1.1
1.0
1.0
1.0
0.9
0.6
0.6
0.6
0.5
0.5
0.4
0.4
0.4
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.1
0.1
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
20%
106%
225%
24%
16%
10%
100%
27%
211%
NE
5%
6%
176%
43%
10%
12%
144%
2%
49%
21%
5%
40%
1%
13%
205%
30%
163%
22%
138%
19%
17%
16%
15%
313%
NA
149%
38%
9%
50%
3%
14%
50%
1%
42%
32%
203%
<0.001
0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-15
-------
N20 Emissions from Forest Soils
Cm Emissions from Mobile Combustion: Aviation
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
ChU Emissions from Silicon Carbide Production and
Consumption
ChU Emissions from Mobile Combustion: Marine
ChU Emissions from Ferroalloy Production
ixhO Emissions from Wetlands Remaining Wetlands
ChU Emissions from Incineration of Waste
C02 Emissions from Stationary Combustion - Gas - U.S.
Territories
N20 0.1 <0.01 1.00
ChU 0.1 <0.01 1.00
ChU + <0.01 1.00
ChU + <0.01 1.00
ChU + <0.01 1.00
ChU + <0.01 1.00
N20 + <0.01 1.00
ChU + <0.01 1.00
C02 + <0.01 1.00
211%
8%
57%
9%
4%
11%
73%
NE
17%
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
NE Uncertainty not estimated.
+ Does not exceed 0.05 Tg C02 Eq.
Table A- 6: 2012 Key Source Category Tier land Tier 2 Analysis— Level Assessment, without LULUCF
IPCC Source Categories
C02 Emissions from Stationary Combustion - Coal -
Electricity Generation
C02 Emissions from Mobile Combustion: Road
C02 Emissions from Stationary Combustion - Gas -
Electricity Generation
C02 Emissions from Stationary Combustion - Gas -
Industrial
C02 Emissions from Stationary Combustion - Oil - Industrial
Direct ixhO Emissions from Agricultural Soil Management
C02 Emissions from Stationary Combustion - Gas -
Residential
C02 Emissions from Stationary Combustion - Gas -
Commercial
Emissions from Substitutes for Ozone Depleting
Substances
C02 Emissions from Mobile Combustion: Aviation
ChU Emissions from Enteric Fermentation
ChU Emissions from Natural Gas Systems
C02 Emissions from Non-Energy Use of Fuels
ChU Emissions from Landfills
C02 Emissions from Mobile Combustion: Other
C02 Emissions from Stationary Combustion - Coal -
Industrial
C02 Emissions from Stationary Combustion - Oil -
Residential
Fugitive Emissions from Coal Mining
C02 Emissions from Iron and Steel Production &
Metallurgical Coke Production
ChU Emissions from Manure Management
Indirect N20 Emissions from Applied Nitrogen
C02 Emissions from Stationary Combustion - Oil - U.S.
Territories
C02 Emissions from Mobile Combustion: Marine
C02 Emissions from Stationary Combustion - Oil -
Commercial
C02 Emissions from Natural Gas Systems
C02 Emissions from Cement Production
2012 Estimate Tier 1 Level Cumulative
Direct GHG (Tg C02 Eq.) Assessment Total
C02 1,511.2 0.23 0.23
C02 1,469.8 0.23 0.46
C02 492.2 0.08 0.54
C02 434.7 0.07 0.60
C02 265.2 0.04 0.64
N20 260.9 0.04 0.68
C02 224.8 0.03 0.72
C02 156.9 0.02 0.74
Several 146.8 0.02 0.76
C02 145.1 0.02 0.79
ChU 141.0 0.02 0.81
ChU 129.9 0.02 0.83
C02 110.3 0.02 0.85
ChU 102.8 0.02 0.86
C02 84.5 0.01 0.87
C02 74.3 0.01 0.89
C02 64.1 0.01 0.90
ChU 55.8 0.01 0.90
C02 54.3 0.01 0.91
ChU 52.9 0.01 0.92
N20 45.7 0.01 0.93
C02 44.7 0.01 0.94
C02 40.1 0.01 0.94
C02 36.4 0.01 0.95
C02 35.2 0.01 0.95
C02 35.1 0.01 0.96
Uncertainty
10%
7%
5%
7%
20%
28%
7%
7%
14%
7%
18%
30%
35%
56%
7%
16%
6%
35%
17%
20%
151%
11%
7%
5%
30%
6%
Tier 2 Level
Assessment
0.022
0.015
0.004
0.005
0.008
0.011
0.002
0.002
0.003
0.001
0.004
0.006
0.006
0.009
0.001
0.002
0.001
0.003
0.001
0.002
0.011
0.001
<0.001
<0.001
0.002
<0.001
A-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Cm Emissions from Petroleum Systems
C02 Emissions from Stationary Combustion - Oil - Electricity
Generation
Non-C02 Emissions from Stationary Combustion - Electricity
Generation
ixhO Emissions from Manure Management
ixhO Emissions from Nitric Acid Production
C02 Emissions from Lime Production
ChU Emissions from Wastewater Treatment
ixhO Emissions from Mobile Combustion: Road
C02 Emissions from Incineration of Waste
C02 Emissions from Ammonia Production
C02 Emissions from Other Process Uses of Carboantes
ChU Emissions from Rice Cultivation
SFe Emissions from Electrical Transmission and Distribution
ixhO Emissions from Adipic Acid Production
C02 Emissions from Urea Consumption for Non-Ag
Purposes
ixhO Emissions from Wastewater Treatment
Fugitive Emissions from Abandoned Underground Coal
Mines
ixhO Emissions from Product Uses
HFC-23 Emissions from HCFC-22 Production
C02 Emissions from Stationary Combustion - Coal -
Commercial
PFC, HFC, and SFe Emissions from Semiconductor
Manufacture
C02 Emissions from Petrochemical Production
C02 Emissions from Aluminum Production
C02 Emissions from Stationary Combustion - Coal - U.S.
Territories
Non-C02 Emissions from Stationary Combustion -
Residential
ChU Emissions from Petrochemical Production
C02 Emissions from Soda Ash Production and
Consumption
PFC Emissions from Aluminum Production
Non-C02 Emissions from Stationary Combustion - Industrial
ixhO Emissions from Mobile Combustion: Other
C02 Emissions from Carbon Dioxide Consumption
ixhO Emissions from Composting
C02 Emissions from Titanium Dioxide Production
SFe Emissions from Magnesium Production and Processing
C02 Emissions from Ferroalloy Production
CKU Emissions from Composting
C02 Emissions from Stationary Combustion - Gas - U.S.
Territories
C02 Emissions from Zinc Production
ixhO Emissions from Mobile Combustion: Aviation
C02 Emissions from Glass Production
Non-C02 Emissions from Stationary Combustion - Industrial
ChU Emissions from Mobile Combustion: Road
C02 Emissions from Phosphoric Acid Production
Non-C02 Emissions from Stationary Combustion -
Residential
CHU
Electricity C02
Electricity ixhO
N20
N20
C02
ChU
N20
C02
C02
lantes C02
ChU
listribution SFe
N20
,g C02
N20
Coal ChU
N20
MFCs
1- C02
31.7
18.8
18.3
18.0
15.3
13.3
12.8
12.6
12.2
9.4
8.0
7.4
6.0
5.8
5.2
5.0
4.7
4.4
4.3
4.1
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.96
0.97
0.97
0.97
0.98
0.98
0.98
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
149%
8%
171%
24%
38%
3%
27%
27%
14%
7%
20%
96%
25%
4%
10%
100%
26%
24%
10%
15%
0.007
<0.001
0.005
0.001
0.001
<0.001
0.001
0.001
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
Several
C02
C02
C02
CH4
3.7
3.5
3.4
3.4
3.1
<0.01
<0.01
<0.01
<0.01
<0.01
0.99
0.99
0.99
0.99
0.99
5%
27%
2%
19%
225%
<0.001
<0.001
<0.001
<0.001
0.001
ChU
C02
PFCs
N20
N20
C02
N20
C02
SF6
C02
ChU
C02
C02
N20
C02
ChU
ChU
C02
N20
3.1
2.7
2.5
2.5
2.0
1.8
1.8
1.7
1.7
1.7
1.6
1.4
1.4
1.4
1.2
1.2
1.2
1.1
0.8
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
10%
6%
6%
211%
1%
40%
50%
13%
12%
12%
50%
17%
17%
2%
5%
49%
16%
21%
205%
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-17
-------
Non-C02 Emissions from Stationary Combustion -
Commercial
Cm Emissions from Iron and Steel Production &
Metallurgical Coke Production
ixhO Emissions from Mobile Combustion: Marine
C02 Emissions from Lead Production
Non-C02 Emissions from Stationary Combustion - Electricity
Generation
CKU Emissions from Mobile Combustion: Other
C02 Emissions from Petroleum Systems
C02 Emissions from Stationary Combustion - Geothermal
Energy
ixhO Emissions from Incineration of Waste
Non-C02 Emissions from Stationary Combustion -
Commercial
ChU Emissions from Field Burning of Agricultural Residues
C02 Emissions from Silicon Carbide Production and
Consumption
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
ixhO Emissions from Field Burning of Agricultural Residues
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
CKU Emissions from Mobile Combustion: Aviation
CKU Emissions from Mobile Combustion: Marine
CKU Emissions from Ferroalloy Production
CKU Emissions from Silicon Carbide Production and
Consumption
CKU Emissions from Incineration of Waste
C02 Emissions from Stationary Combustion - Coal -
Residential
Note: LULUCF sources and sinks are not included in this analysis.
CKU
CKU
N20
C02
CKU
CKU
C02
C02
N20
N20
CKU
C02
N20
N20
CKU
CKU
CKU
CKU
CKU
CKU
C02
0.8
0.6
0.6
0.5
0.5
0.4
0.4
0.4
0.4
0.3
0.3
0.2
0.1
0.1
0.1
+
+
+
+
+
0.0
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
138%
22%
16%
15%
3%
1%
149%
NA
313%
38%
42%
9%
203%
32%
57%
8%
4%
11%
9%
NE
NE
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
NE Uncertainty not estimated.
+ Does not exceed 0.05 Tg C02 Eq.
Table A- 7: 2012 Key Source Category Tier 1 and Tier 2 Analysis— Level Assessment with LULUGF
IPCC Source Categories
C02 Emissions from Stationary Combustion - Coal -
Electricity Generation
C02 Emissions from Mobile Combustion: Road
C02 Emissions from Changes in Forest Carbon Stocks
C02 Emissions from Stationary Combustion - Gas -
Electricity Generation
C02 Emissions from Stationary Combustion - Gas -
Industrial
C02 Emissions from Stationary Combustion - Oil - Industrial
Direct ixhO Emissions from Agricultural Soil Management
C02 Emissions from Stationary Combustion - Gas -
Residential
C02 Emissions from Stationary Combustion - Gas -
Commercial
Emissions from Substitutes for Ozone Depleting
Substances
C02 Emissions from Mobile Combustion: Aviation
Direct GHG
C02
C02
C02
C02
C02
C02
N20
C02
C02
Several
C02
2012
Estimate (Tg
C02 Eq.)
1,511.2
1,469.8
866.5
492.2
434.7
265.2
260.9
224.8
156.9
146.8
145.1
Tierl
Level Cumulative
Assessment
0.20
0.19
0.11
0.07
0.06
0.04
0.03
0.03
0.02
0.02
0.02
Total
0.20
0.39
0.51
0.57
0.63
0.67
0.70
0.73
0.75
0.77
0.79
Uncertainty
10%
7%
15%
5%
7%
20%
28%
7%
7%
14%
7%
Tier 2 Level
Assessment
0.019
0.013
0.018
0.003
0.004
0.007
0.010
0.002
0.001
0.003
0.001
A-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Cm Emissions from Enteric Fermentation
ChU Emissions from Natural Gas Systems
C02 Emissions from Non-Energy Use of Fuels
ChU Emissions from Landfills
C02 Emissions from Urban Trees
C02 Emissions from Mobile Combustion: Other
C02 Emissions from Stationary Combustion - Coal -
Industrial
C02 Emissions from Stationary Combustion - Oil -
Residential
Fugitive Emissions from Coal Mining
C02 Emissions from Iron and Steel Production &
Metallurgical Coke Production
CKU Emissions from Manure Management
Indirect N20 Emissions from Applied Nitrogen
C02 Emissions from Stationary Combustion - Oil - U.S.
Territories
C02 Emissions from Mobile Combustion: Marine
C02 Emissions from Stationary Combustion - Oil -
Commercial
C02 Emissions from Natural Gas Systems
C02 Emissions from Cement Production
ChU Emissions from Petroleum Systems
C02 Emissions from Cropland Remaining Cropland
C02 Emissions from Stationary Combustion - Oil - Electricity
Generation
Non-C02 Emissions from Stationary Combustion - Electricity
N20 Emissions from Manure Management
C02 Emissions from Land Converted to Cropland
N20 Emissions from Nitric Acid Production
C02 Emissions from Lime Production
C02 Emissions from Landfilled Yard Trimmings and Food
Scraps
ChU Emissions from Wastewater Treatment
N20 Emissions from Mobile Combustion: Road
N20 Emissions from Forest Fires
C02 Emissions from Incineration of Waste
C02 Emissions from Ammonia Production
C02 Emissions from Land Converted to Grassland
C02 Emissions from Other Process Uses of Carbonates
ChU Emissions from Rice Cultivation
C02 Emissions from Grassland Remaining Grassland
SFe Emissions from Electrical Transmission and Distribution
N20 Emissions from Adipic Acid Production
C02 Emissions from Urea Consumption for Non-Ag
Purposes
N20 Emissions from Wastewater Treatment
Fugitive Emissions from Abandoned Underground Coal
Mines
N20 Emissions from Product Uses
HFC-23 Emissions from HCFC-22 Production
C02 Emissions from Stationary Combustion - Coal -
Commercial
ChU
ChU
C02
ChU
C02
C02
C02
C02
ChU
C02
ChU
N20
C02
C02
C02
C02
C02
ChU
C02
C02
N20
N20
C02
p|_|
N20
C02
C02
ChU
N20
N20
C02
C02
C02
C02
CH4
C02
SF6
N20
C02
N20
CH4
N20
MFCs
C02
141.0
129.9
110.3
102.8
88.4
84.5
74.3
64.1
55.8
54.3
52.9
45.7
44.7
40.1
36.4
35.2
35.1
31.7
26.5
18.8
18.3
18.0
16.8
HC O
15.3
13.3
13.0
12.8
12.6
12.5
12.2
9.4
8.5
8.0
7.4
6.7
6.0
5.8
5.2
5.0
4.7
4.4
4.3
4.1
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
-------
C02 Emissions from Liming of Agricultural Soils
RFC, HFC, and SFe Emissions from Semiconductor
Manufacture
C02 Emissions from Petrochemical Production
C02 Emissions from Urea Fertilization
C02 Emissions from Aluminum Production
C02 Emissions from Stationary Combustion - Coal - U.S.
Territories
Non-C02 Emissions from Stationary Combustion -
Residential
CKU Emissions from Petrochemical Production
C02 Emissions from Soda Ash Production and
Consumption
PFC Emissions from Aluminum Production
Non-C02 Emissions from Stationary Combustion - Industrial
ixhO Emissions from Mobile Combustion: Other
C02 Emissions from Carbon Dioxide Consumption
ixhO Emissions from Composting
C02 Emissions from Titanium Dioxide Production
SFe Emissions from Magnesium Production and Processing
C02 Emissions from Ferroalloy Production
CKU Emissions from Composting
ixhO Emissions from Settlement Soils
C02 Emissions from Stationary Combustion - Gas - U.S.
Territories
C02 Emissions from Zinc Production
ixhO Emissions from Mobile Combustion: Aviation
C02 Emissions from Glass Production
Non-C02 Emissions from Stationary Combustion - Industrial
CKU Emissions from Mobile Combustion: Road
C02 Emissions from Phosphoric Acid Production
C02 Emissions from Wetlands Remaining Wetlands
Non-C02 Emissions from Stationary Combustion -
Residential
Non-C02 Emissions from Stationary Combustion -
Commercial
ChU Emissions from Iron and Steel Production &
Metallurgical Coke Production
ixhO Emissions from Mobile Combustion: Marine
C02 Emissions from Lead Production
Non-C02 Emissions from Stationary Combustion - Electricity
Generation
CKU Emissions from Mobile Combustion: Other
C02 Emissions from Petroleum Systems
C02 Emissions from Stationary Combustion - Geothermal
Energy
ixhO Emissions from Incineration of Waste
ixhO Emissions from Forest Soils
Non-C02 Emissions from Stationary Combustion -
Commercial
CKU Emissions from Field Burning of Agricultural Residues
C02 Emissions from Silicon Carbide Production and
Consumption
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
C02
Several
C02
C02
C02
C02
CKU
CKU
C02
PFCs
N20
N20
C02
N20
C02
SF6
C02
CKU
N20
C02
C02
N20
C02
CKU
CKU
C02
C02
N20
CKU
CKU
N20
C02
CKU
CKU
C02
C02
N20
N20
N20
CH4
C02
N20
3.9
3.7
3.5
3.4
3.4
3.4
3.1
3.1
2.7
2.5
2.5
2.0
1.8
1.8
1.7
1.7
1.7
1.6
1.5
1.4
1.4
1.4
1.2
1.2
1.2
1.1
0.8
0.8
0.8
0.6
0.6
0.5
0.5
0.4
0.4
0.4
0.4
0.4
0.3
0.3
0.2
0.1
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
106%
5%
27%
43%
2%
19%
225%
10%
6%
6%
211%
1%
40%
50%
13%
12%
12%
50%
163%
17%
17%
2%
5%
49%
16%
21%
30%
205%
138%
22%
16%
15%
3%
1%
149%
NA
313%
211%
38%
42%
9%
203%
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
N20 Emissions from Field Burning of Agricultural Residues
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
CKU Emissions from Mobile Combustion: Aviation
CKU Emissions from Mobile Combustion: Marine
CKU Emissions from Ferroalloy Production
CKU Emissions from Silicon Carbide Production and
Consumption
N20 Emissions from Wetlands Remaining Wetlands
CKU Emissions from Incineration of Waste
C02 Emissions from Stationary Combustion - Coal -
Residential
N20
CKU
CKU
CKU
CKU
CKU
N20
CKU
C02
0.1 <0.01
0.1 <0.01
+ <0.01
+ <0.01
+ <0.01
+ <0.01
+ <0.01
+ <0.01
0.0 <0.01
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
32%
57%
8%
4%
11%
9%
73%
NE
NE
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
* Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
NE Uncertainty not estimated.
+ Does not exceed 0.05 Tg C02 Eq.
Table A-8:1990-2012 Key Source Category Tierl and 2 Analysis—Trend Assessment, without LULUCF
IPCC Source Categories
C02 Emissions from Stationary Combustion - Gas -
Electricity Generation
C02 Emissions from Mobile Combustion: Road
Emissions from Substitutes for Ozone Depleting
Substances
C02 Emissions from Stationary Combustion - Coal -
Electricity Generation
C02 Emissions from Stationary Combustion - Coal -
Industrial
C02 Emissions from Stationary Combustion - Oil -
Electricity Generation
Cm Emissions from Landfills
C02 Emissions from Mobile Combustion: Aviation
C02 Emissions from Iron and Steel Production &
Metallurgical Coke Production
C02 Emissions from Stationary Combustion - Oil -
Residential
HFC-23 Emissions from HCFC-22 Production
Cm Emissions from Natural Gas Systems
C02 Emissions from Stationary Combustion - Oil -
Commercial
N20 Emissions from Mobile Combustion: Road
Fugitive Emissions from Coal Mining
C02 Emissions from Stationary Combustion - Oil -
Industrial
C02 Emissions from Stationary Combustion - Gas -
Residential
SFe Emissions from Electrical Transmission and
Distribution
Cm Emissions from Manure Management
PFC Emissions from Aluminum Production
C02 Emissions from Stationary Combustion - Oil -
U.S. Territories
C02 Emissions from Non-Energy Use of Fuels
N20 Emissions from Adipic Acid Production
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
Direct N20 Emissions from Agricultural Soil
Direct GHG
C02
C02
Several
C02
C02
C02
cm
C02
C02
C02
MFCs
cm
C02
N20
cm
C02
C02
SF6
cm
PFCs
C02
C02
N20
N20
N20
1990 2012
Estimate Estimate
(Tg C02 Eq.)(Tg C02 Eq.)
175.3
1,188.9 1
0.3
1,547.6 1
155.3
97.5
147.8
187.4
99.8
97.4
36.4
156.4
64.9
40.3
81.1
280.9
238.0
26.7
31.5
18.4
27.2
120.8
15.8
7.4
240.7
492.2
,469.8
146.8
,511.2
74.3
18.8
102.8
145.1
54.3
64.1
4.3
129.9
36.4
12.6
55.8
265.2
224.8
6.0
52.9
2.5
44.7
110.3
5.8
18.3
260.9
Tier 1 Trend
Assessment
0.05
0.04
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
Tier 2 Trend
Assessment
0.002
0.002
0.003
0.002
0.002
0.001
0.004
0.001
0.001
<0.001
0.001
0.002
<0.001
0.001
0.002
0.001
<0.001
0.001
0.001
<0.001
<0.001
0.001
<0.001
0.003
<0.001
%
Contribution
to Trend
19.5
14.4
9.2
6.5
5.5
5.2
3.2
3.2
3.1
2.4
2.1
2.1
2.0
1.9
1.8
1.7
1.5
1.4
1.3
1.1
1.0
1.0
0.7
0.7
0.6
Cumulative
Total
19
34
43
50
55
60
64
67
70
72
74
76
78
80
82
84
85
87
88
89
90
91
92
92
93
A-21
-------
Management
C02 Emissions from Stationary Combustion - Gas -
Commercial
C02 Emissions from Stationary Combustion - Coal -
Commercial
C02 Emissions from Mobile Combustion: Other
C02 Emissions from Stationary Combustion - Gas -
Industrial
C02 Emissions from Mobile Combustion: Marine
Cm Emissions from Petroleum Systems
C02 Emissions from Ammonia Production
C02 Emissions from Natural Gas Systems
SFe Emissions from Magnesium Production and
Processing
C02 Emissions from Incineration of Waste
C02 Emissions from Aluminum Production
N20 Emissions from Nitric Acid Production
Cm Emissions from Mobile Combustion: Road
IC02 Emissions from Stationary Combustion - Coal -
Residential
N20 Emissions from Manure Management
C02 Emissions from Other Process Uses of
Carbonates
Cm Emissions from Enteric Fermentation
C02 Emissions from Stationary Combustion - Coal -
U.S. Territories
Indirect N20 Emissions from Applied Nitrogen
Non-C02 Emissions from Stationary Combustion -
Residential
Fugitive Emissions from Abandoned Underground
Coal Mines
C02 Emissions from Stationary Combustion - Gas -
U.S. Territories
N20 Emissions from Wastewater Treatment
C02 Emissions from Lime Production
N20 Emissions from Composting
C02 Emissions from Urea Consumption for Non-Ag
Purposes
Cm Emissions from Composting
Cm Emissions from Wastewater Treatment
Non-C02 Emissions from Stationary Combustion -
Industrial
C02 Emissions from Zinc Production
Cm Emissions from Petrochemical Production
PFC, HFC, and SFe Emissions from Semiconductor
Manufacture
Cm Emissions from Rice Cultivation
N20 Emissions from Mobile Combustion: Other
C02 Emissions from Ferroalloy Production
C02 Emissions from Phosphoric Acid Production
N20 Emissions from Mobile Combustion: Aviation
C02 Emissions from Titanium Dioxide Production
Non-C02 Emissions from Stationary Combustion -
Industrial
Cm Emissions from Iron and Steel Production &
Metallurgical Coke Production
Non-C02 Emissions from Stationary Combustion -
Residential
C02
C02
C02
C02
C02
cm
C02
C02
SF6
C02
C02
N20
cm
C02
N20
C02
cm
C02
N20
cm
cm
C02
N20
C02
N20
C02
cm
cm
N20
C02
cm
Several
cm
N20
C02
C02
N20
C02
cm
cm
N20
142.1
12.0
73.3
408.9
445
35.8
13.0
37.7
5.4
8.0
6.8
18.2
4.2
3.0
14.4
4.9
137.9
0.6
41.4
4.6
6.0
+
3.5
11.4
0.4
3.8
0.3
13.2
3.3
0.6
2.3
2.9
7.7
1.3
2.2
1.6
1.8
1.2
1.6
1.0
1.1
156.9
4.1
84.5
434.7
401
31.7
9.4
35.2
1.7
12.2
3.4
15.3
1.2
0.0
18.0
8.0
141.0
3.4
45.7
3.1
4.7
1.4
5.0
13.3
1.8
5.2
1.6
12.8
2.5
1.4
3.1
3.7
7.4
2.0
1.7
1.1
1.4
1.7
1.2
0.6
0.8
<0.01
<0.01
<0.01
<0.01
<001
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.001
<0.001
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.5
0.5
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
94
94
95
95
95
96
96
96
97
97
97
97
97
98
98
98
98
98
98
98
99
99
99
99
99
99
99
99
99
99
99
99
99
99
99
100
100
100
100
100
100
A-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
C02 Emissions from Glass Production
C02 Emissions from Carbon Dioxide Consumption
C02 Emissions from Cement Production
C02 Emissions from Silicon Carbide Production and
Consumption
N20 Emissions from Product Uses
C02 Emissions from Soda Ash Production and
Consumption
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
Non-C02 Emissions from Stationary Combustion -
Commercial
Cm Emissions from Mobile Combustion: Other
N20 Emissions from Incineration of Waste
Non-C02 Emissions from Stationary Combustion -
Commercial
C02 Emissions from Petrochemical Production
Non-C02 Emissions from Stationary Combustion -
U.S. Territories
N20 Emissions from Mobile Combustion: Marine
Cm Emissions from Mobile Combustion: Aviation
Cm Emissions from Field Burning of Agricultural
Residues
Non-C02 Emissions from Stationary Combustion -
U.S. Territories
Cm Emissions from Silicon Carbide Production and
Consumption
C02 Emissions from Stationary Combustion -
Geothermal Energy
C02 Emissions from Lead Production
Cm Emissions from Ferroalloy Production
C02 Emissions from Petroleum Systems
N20 Emissions from Field Burning of Agricultural
Residues
Cm Emissions from Mobile Combustion: Marine
Cm Emissions from Incineration of Waste
C02
C02
C02
C02
N20
C02
cm
cm
cm
N20
N20
C02
N20
N20
cm
cm
cm
cm
C02
C02
cm
C02
N20
cm
cm
1.5
1.4
33.3
0.4
4.4
2.7
0.3
0.9
0.3
0.5
0.4
3.4
0.1
0.6
0.1
0.3
+
+
0.4
0.5
+
0.4
0.1
+
+
1.2
1.8
35.1
0.2
4.4
2.7
0.5
0.8
0.4
0.4
0.3
3.5
0.1
0.6
+
0.3
0.1
+
0.4
0.5
+
0.4
0.1
+
+
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
.1 100
Note: LULUCF sources and sinks are not included in this analysis.
+ Does not exceed 0.05 Tg C02 Eq.
Table A- 9:1990-2012 Key Source Category Tier 1 and 2 Analysis—Trend Assessment, with LULUCF
2012
1990 Estimate Percent Cumulative
Estimate (Tg C02 Tier 1 Trend Tier 2 Trend Contribution to Contribution to
IPCC Source Categories Direct GHG (Tg C02 Eq.) Eq.) Assessment Assessment Trend (%) Trend (%)
C02 Emissions from Stationary Combustion -
Gas - Electricity Generation
C02 Emissions from Mobile Combustion: Road
Emissions from Substitutes for Ozone
Depleting Substances
C02 Emissions from Stationary Combustion -
Coal - Electricity Generation
C02 Emissions from Changes in Forest
Carbon Stocks
C02 Emissions from Stationary Combustion -
Coal - Industrial
C02 Emissions from Stationary Combustion -
Oil - Electricity Generation
Cm Emissions from Landfills
C02
C02
Several
C02
C02
C02
C02
cm
1751—
1,188.9
0.3
1,547.6
704.6
155.3
97.5
147.8
492.2
1,469.8
146.8
1,511.2
866.5
74.3
18.8
102.8
0.04
0.03
0.02
0.02
0.02
0.01
0.01
0.01
0.002
0.002
0.003
0.002
0.003
0.002
0.001
0.004
16.6
11.3
8.0
7.1
6.5
4.9
4.6
2.9
17
28
36
43
49
54
59
62
A-23
-------
CC>2 Emissions from Mobile Combustion:
Aviation
C02 Emissions from Iron and Steel Production
& Metallurgical Coke Production
C02 Emissions from Stationary Combustion -
Oil - Residential
Cm Emissions from Natural Gas Systems
HFC-23 Emissions from HCFC-22 Production
C02 Emissions from Stationary Combustion -
Oil - Industrial
C02 Emissions from Stationary Combustion -
Oil - Commercial
Fugitive Emissions from Coal Mining
N20 Emissions from Mobile Combustion: Road
C02 Emissions from Cropland Remaining
Cropland
C02 Emissions from Stationary Combustion -
Gas - Residential
C02 Emissions from Urban Trees
SFe Emissions from Electrical Transmission
and Distribution
Cm Emissions from Manure Management
C02 Emissions from Non-Energy Use of Fuels
PFC Emissions from Aluminum Production
C02 Emissions from Stationary Combustion -
Oil - U.S. Territories
C02 Emissions from Landfilled Yard
Trimmings and Food Scraps
Cm Emissions from Forest Fires
C02 Emissions from Land Converted to
Cropland
N20 Emissions from Adipic Acid Production
Non-C02 Emissions from Stationary
Combustion - Electricity Generation
N20 Emissions from Forest Fires
C02 Emissions from Stationary Combustion -
Coal - Commercial
C02 Emissions from Mobile Combustion:
Marine
C02 Emissions from Mobile Combustion:
Other
Cm Emissions from Petroleum Systems
C02 Emissions from Stationary Combustion -
Direct N20 Emissions from Agricultural Soil
Management
Cm Emissions from Enteric Fermentation
C02 Emissions from Natural Gas Systems
C02 Emissions from Ammonia Production
SFe Emissions from Magnesium Production
and Processing
N20 Emissions from Nitric Acid Production
C02 Emissions from Aluminum Production
C02 Emissions from Incineration of Waste
C02 Emissions from Grassland Remaining
Grassland
Cm Emissions from Mobile Combustion: Road
C02 Emissions from Stationary Combustion -
Coal - Residential
C02 Emissions from Other Process Uses of
C02
C02
C02
cm
MFCs
C02
C02
cm
N20
C02
C02
C02
SF6
cm
C02
PFCs
C02
C02
cm
C02
N20
N20
N20
C02
C02
C02
cm
C02
N20
cm
C02
C02
SF6
N20
C02
C02
C02
cm
C02
C02
187.4
99.8
97.4
156.4
36.4
280.9
64.9
81.1
40.3
51.9
238.0
60.4
26.7
31.5
120.8
18.4
27.2
24.2
2.5
26.9
15.8
7.4
2.0
12.0
44.5
73.3
35.8
142.1
240.7
137.9
37.7
13.0
5.4
18.2
6.8
8.0
9.6
4.2
3.0
4.9
145.1
54.3
64.1
129.9
4.3
265.2
36.4
55.8
12.6
26.5
224.8
88.4
6.0
52.9
110.3
2.5
44.7
13.0
15.3
16.8
5.8
18.3
12.5
4.1
40.1
84.5
31.7
156.9
260.9
141.0
35.2
9.4
1.7
15.3
3.4
12.2
6.7
1.2
+
8.0
0.01
0.01
0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.001
0.001
<0.001
0.002
<0.001
0.001
<0.001
0.001
0.001
0.007
<0.001
0.002
0.001
0.001
0.001
<0.001
<0.001
0.001
0.003
0.001
<0.001
0.003
0.002
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.003
<0.001
<0.001
<0.001
2.9
2.8
2.1
2.0
1.9
1.8
1.8
1.6
1.6
1.6
1.5
1.3
1.2
1.1
1.0
0.9
0.9
0.7
0.7
0.6
0.6
0.6
0.6
0.5
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
65
68
70
72
74
75
77
79
80
82
83
85
86
87
88
89
90
91
91
92
92
93
94
94
94
95
95
95
96
96
96
97
97
97
97
97
98
98
98
98
A-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Carbonates
N20 Emissions from Manure Management N20 14.4 18.0 <0.01 <0.001 0.2 98
C02 Emissions from Stationary Combustion - C02 0.6 3.4 <0.01 <0.001 0.1 98
Coal - U.S. Territories
Non-C02 Emissions from Stationary CH4 4.6 3.1 <0.01 0.001 0.1
Indirect N20 Emissions from Applied Nitrogen N20 41.4 45.7 <0.01 <0.001 0.1 99
Fugitive Emissions from Abandoned CH4 6.0 4.7 <0.01 <0.001 0.1 99
Underground Coal Mines
C02 Emissions from Stationary Combustion - C02 + 1.4 <0.01 <0.001 0.1 99
Gas - U.S. Territories
N20 Emissions from Composting N20 0.4 1.8 <0.01 <0.001 0.1 99
N20 Emissions from Wastewater Treatment N20 3.5 5.0 <0.01 <0.001 0.1 99
CH4 Emissions from Composting CH4 0.3 1.6 <0.01 <0.001 0.1 99
C02 Emissions from Urea Consumption for C02 3.8 5.2 <0.01 <0.001 0.1 99
Non-Ag Purposes
C02 Emissions from Lime Production C02 11.4 13.3 <0.01 <0.001 0.1 99
CH4 Emissions from Wastewater Treatment CH4 13.2 12.8 <0.01 <0.001 0.1 99
Non-C02 Emissions from Stationary N20 3.3 2.5 <0.01 <0.001 0.1 99
Combustion - Industrial
C02 Emissions from Liming of Agricultural C02 4.7 3.9 <0.01 <0.001 0.1 99
Soils
C02 Emissions from Urea Fertilization C02 2.4 3.4 <0.01 <0.001 <0.1 99
C02 Emissions from Stationary Combustion - C02 408.9 434.7 <0.01 <0.001 <0.1 99
Gas-Industrial
CH4 Emissions from Rice Cultivation CH4 7.7 7.4 <0.01 <0.001 <0.1 99
C02 Emissions from Zinc Production C02 0.6 1.4 <0.01 <0.001 <0.1 99
C02 Emissions from Land Converted to C02 7.3 8.5 <0.01 <0.001 <0.1 99
Grassland
CH4 Emissions from Petrochemical Production CH4 2.3 3.1 <0.01 <0.001 <0.1 100
PFC, HFC, and SF6 Emissions from Several 2.9 3.7 <0.01 <0.001 <0.1 100
Semiconductor Manufacture
C02 Emissions from Ferroalloy Production C02 2.2 1.7 <0.01 <0.001 <0.1 100
N20 Emissions from Mobile Combustion: N20 1.3 2.0 <0.01 <0.001 <0.1 100
Other
C02 Emissions from Phosphoric Acid C02 1.6 1.1 <0.01 <0.001 <0.1 100
Production
N20 Emissions from Mobile Combustion: N20 1.8 1.4 <0.01 <0.001 <0.1 100
Aviation
C02 Emissions from Titanium Dioxide C02 1.2 1.7 <0.01 <0.001 <0.1 100
Production
Non-C02 Emissions from Stationary CH4 1.6 1.2 <0.01 <0.001 <0.1 100
Combustion - Industrial
N20 Emissions from Settlement Soils N20 1.0 1.5 <0.01 <0.001 <0.1 100
Non-C02 Emissions from Stationary N20 1.1 0.8 <0.01 <0.001 <0.1 100
Combustion - Residential
CH4 Emissions from Iron and Steel Production CH4 1.0 0.6 <0.01 <0.001 <0.1 100
& Metallurgical Coke Production
C02 Emissions from Glass Production C02 1.5 1.2 <0.01 <0.001 <0.1 100
C02 Emissions from Carbon Dioxide C02 1.4 1.8 <0.01 <0.001 <0.1 100
Consumption
N20 Emissions from Forest Soils N20 0.1 0.4 <0.01 <0.001 <0.1 100
N20 Emissions from Product Uses N20 4.4 4.4 <0.01 <0.001 <0.1 100
C02 Emissions from Wetlands Remaining C02 1.0 0.8 <0.01 <0.001 <0.1 100
Wetlands
C02 Emissions from Cement Production C02 33.3 35.1 <0.01 <0.001 <0.1 100
C02 Emissions from Silicon Carbide C02 0.4 0.2 <0.01 <0.001 <0.1 100
Production and Consumption
C02 Emissions from Soda Ash Production and C02 2.7 2.7 <0.01 <0.001 <0.1 100
Consumption
-------
Non-CC>2 Emissions from Stationary
Combustion - Commercial
Non-C02 Emissions from Stationary
Combustion - Electricity Generation
N20 Emissions from Incineration of Waste
Cm Emissions from Mobile Combustion:
Other
C02 Emissions from Petrochemical Production
Non-C02 Emissions from Stationary
Combustion - Commercial
N20 Emissions from Mobile Combustion:
Marine
Non-C02 Emissions from Stationary
Combustion - U.S. Territories
Cm Emissions from Field Burning of
Agricultural Residues
Cm Emissions from Mobile Combustion:
Aviation
Non-C02 Emissions from Stationary
Combustion - U.S. Territories
C02 Emissions from Stationary Combustion -
Geothermal Energy
C02 Emissions from Lead Production
Cm Emissions from Silicon Carbide
Production and Consumption
C02 Emissions from Petroleum Systems
Cm Emissions from Ferroalloy Production
N20 Emissions from Field Burning of
Agricultural Residues
N20 Emissions from Wetlands Remaining
Wetlands
Cm Emissions from Incineration of Waste
Cm Emissions from Mobile Combustion:
Marine
cm
cm
N20
cm
C02
N20
N20
N20
cm
cm
cm
C02
C02
cm
C02
cm
N20
N20
cm
cm
0.9
0.3
0.5
0.3
3.4
0.4
0.6
0.1
0.3
0.1
+
0.4
0.5
+
0.4
+
0.1
+
+
+
0.8
0.5
0.4
0.4
3.5
0.3
0.6
0.1
0.3
+
0.1
0.4
0.5
+
0.4
+
0.1
+
+
+
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
<0.001 <0.
1 100
1 100
1 100
1 100
1 100
1 100
1 100
1 100
1 100
1 100
1 100
1 100
1 100
1 100
1 100
1 100
1 100
1 100
1 100
1 100
+ Does not exceed 0.05 Tg C02 Eq.
References
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Negara,
andK. Tanabe (eds.). Hayman, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, J. Penman, et al. (eds.). Available online
at . August 13, 2004.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories,
Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories Programme.
A-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
ANNEX 2 Methodology and Data for Estimating C02
Emissions from Fossil Fuel Combustion
2.1. Methodology for Estimating Emissions of C02 from Fossil Fuel Combustion
Carbon dioxide (CCh) emissions from fossil fuel combustion were estimated using a "bottom-up" methodology
characterized by seven steps. These steps are described below.
Step 1: Determine Total Fuel Consumption by Fuel Type and Sector
The bottom-up methodology used by the United States for estimating CC>2 emissions from fossil fuel combustion
is conceptually similar to the approach recommended by the Intergovernmental Panel on Climate Change (IPCC) for
countries that intend to develop detailed, sectoral -based emission estimates in line with a Tier 2 method in the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories (IPCC 2006). Total consumption data and adjustments to
consumption are presented in Columns 2 through 13 of Table A- 10.
Adjusted consumption data are presented in Columns 2 through 8 of Table A- 1 1 through Table A- 33 with totals
by fuel type in Column 8 and totals by end-use sector in the last rows. Fuel consumption data for the bottom-up approach
were obtained directly from the Energy Information Administration (EIA) of the U.S. Department of Energy. These data
were first gathered in physical units, and then converted to their energy equivalents (see the Constants, Units, and
Conversions Annex). The EIA data were collected through a variety of consumption surveys at the point of delivery or
use and qualified with survey data on fuel production, imports, exports, and stock changes. Individual data elements were
supplied by a variety of sources within EIA. Most information was taken from published reports, although some data were
drawn from unpublished energy studies and databases maintained by EIA.
Energy consumption data were aggregated by sector (i.e., residential, commercial, industrial, transportation,
electricity generation, and U.S. territories), primary fuel type (e.g., coal, natural gas, and petroleum), and secondary fuel
type (e.g., motor gasoline, distillate fuel, etc.). The 2012 total adjusted energy consumption across all sectors, including
territories, and energy types was 71,607.7 trillion British thermal units (TBtu), as indicated in the last entry of Column 13
in Table A- 10. This total excludes fuel used for non-energy purposes and fuel consumed as international bunkers, both of
which were deducted in earlier steps.
Electricity consumption information was allocated to each sector based on EIA's distribution of electricity retail
sales to ultimate customers (i.e., residential, commercial, industrial, and other). Because the "other" fuel use includes sales
to both the commercial and transportation sectors, EIA' s limited transportation electricity use data were subtracted from
"other" electricity use and also reported separately. This total was consequently combined with the commercial electricity
data. Further information on these electricity end uses is described in EIA's Annual Energy Review (EIA 20 12).
There are also three basic differences between the consumption data presented in Table A- 10 through Table A-
33 and those recommended in the IPCC emission inventory methodology.
First, consumption data in the U.S. Inventory are presented using higher heating values (HHV)1 rather than the
lower heating values (LHV)2 reflected in the IPCC emission inventory methodology. This convention is followed because
data obtained from EIA are based on FIHV. Of note, however, is that EIA renewable energy statistics are often published
using LHV. The difference between the two conventions relates to the treatment of the heat energy that is consumed in the
process of evaporating the water contained in the fuel. The simplified convention used by the International Energy
Agency for converting from HHV to LHV is to multiply the energy content by 0.95 for petroleum and coal and by 0.9 for
natural gas.
1 Also referred to as Gross Calorific Values (GCV).
2 Also referred to as Net Calorific Values (NCV).
A-27
-------
Second, while EIA's energy use data for the United States includes only the 50 U.S. states and the District of
Columbia, the data reported to the UNFCCC are to include energy consumption within U.S. territories. Therefore,
consumption estimates for U.S. territories were added to domestic consumption of fossil fuels. Energy consumption data
from U.S. territories are presented in Column 7 of Table A- 10 through Table A- 33. It is reported separately from
domestic sectoral consumption, because it is collected separately by EIA with no sectoral disaggregation.
Third, there were a number of modifications made in this report that may cause consumption information herein
to differ from figures given in the cited literature. These are (1) the reallocation of select amounts of coking coal,
petroleum coke, natural gas, residual fuel oil, and other oil (>401 F) for processes accounted for in the Industrial Processes
chapter, (2) corrections for synthetic natural gas production, (3) subtraction of other fuels used for non-energy purposes,
and (4) subtraction of international bunker fuels. These adjustments are described in the following steps.
Step 2: Subtract uses accounted for in the Industrial Processes chapter.
Portions of the fuel consumption data for seven fuel categories—coking coal, distillate fuel, industrial other coal,
petroleum coke, natural gas, residual fuel oil, and other oil (>401 F)—were reallocated to the Industrial Processes chapter,
as these portions were consumed as raw materials during non-energy related industrial processes. Emissions from these
fuels used as raw materials are presented in the Industrial Processes chapter, and are removed from the energy and non-
energy consumption estimates within the Energy chapter.
• Coking coal is used as a raw material (specifically as a reducing agent) in the blast furnace process to
produce iron and steel, lead, and zinc and therefore is not used as a fuel for this process.
• Similarly, petroleum coke is used in multiple processes as a raw material, and is thus not used as a fuel in
those applications. The processes in which petroleum coke is used include (1) ferroalloy production, (2)
aluminum production (for the production of C anodes and cathodes), (3) titanium dioxide production (in the
chloride process), (4) ammonia production, and (5) silicon carbide.
• Natural gas consumption is used for the production of ammonia, and blast furnace and coke oven gas used in
iron and steel production.
• Residual fuel oil and other oil (>40 IF) are both used in the production of C black.
• Natural gas, distillate fuel, coal, and metallurgical coke are used to produce pig iron through the reduction of
iron ore in the production of iron and steel.
Step 3: Adjust for Conversion of Fossil Fuels and Exports
First, a portion of industrial "other" coal that is accounted for in EIA coal combustion statistics is actually used to
make "synthetic natural gas" via coal gasification at the Dakota Gasification Plant, a synthetic natural gas plant. The plant
produces synthetic natural gas and by-product CC>2. The synthetic natural gas enters the natural gas distribution system.
Since October 2000, a portion of the CC>2 produced by the coal gasification plant has been exported to Canada by pipeline.
The remainder of the CC>2 by-product from the plant is released to the atmosphere. The energy in this synthetic natural gas
enters the natural gas distribution stream, and is accounted for in EIA natural gas combustion statistics. Because this
energy of the synthetic natural gas is already accounted for as natural gas combustion, this amount of energy is deducted
from the industrial coal consumption statistics to avoid double counting. The exported CCh is not emitted to the
atmosphere in the United States, and therefore the energy used to produce this amount of CC>2 is subtracted from industrial
other coal.
Step 4: Adjust Sectoral Allocation of Distillate Fuel Oil and Motor Gasoline
EPA conducted a separate bottom-up analysis of transportation fuel consumption based on data from the Federal
Highway Administration (FHWA). The FHWA data indicated that the amount of distillate and motor gasoline
consumption allocated to the transportation sector in the EIA statistics should be adjusted. Therefore, for the estimates
presented in the U.S. Inventory, the transportation sector's distillate fuel and motor gasoline consumption was adjusted
upward to match the value obtained from the bottom-up analysis. As the total distillate and motor gasoline consumption
Fuel consumption by U.S. territories (i.e., American Samoa, Guam, Puerto Rico, U.S. Virgin Islands, Wake Island, and other U.S.
Pacific Islands) is included in this report
A-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
estimate from EIA are considered to be accurate at the national level, the distillate and motor gasoline consumption totals
for the residential, commercial, and industrial sectors were adjusted downward proportionately.
Step 5: Subtract Consumption for Non-Energy Use
U.S. aggregate energy statistics include consumption of fossil fuels for non-energy purposes. Depending on the
end-use, non-energy uses of fossil fuels can result in long term storage of some or all of the C contained in the fuel. For
example, asphalt made from petroleum can sequester up to 100 percent of the C contained in the petroleum feedstock for
extended periods of time. Other non-energy fossil fuel products, such as lubricants or plastics also store C, but can lose or
emit some of this C when they are used and/or burned as waste. As the emission pathways of C used for non-energy
purposes are vastly different than fuel combustion, these emissions are estimated separately in the Carbon Emitted in
Products from Non-Energy Uses of Fossil Fuels section in this chapter. Therefore, the amount of fuels used for non-
energy purposes, shown in Table A-34, was subtracted from total fuel consumption.
Step 6: Subtract Consumption of International Bunker Fuels
Emissions from international transport activities, or international bunker fuel consumption, are not included in
national totals and instead reported separately, as required by the IPCC (IPCC/UNEP/OECD/TEA 1997) and UNFCCC
inventory reporting guidelines (UNFCCC, 2006). EIA energy statistics, however, include these bunker fuels—jet fuel for
aircraft, and distillate fuel oil and residual fuel oil for marine shipping—as part of fuel consumption by the transportation
end-use sector. Therefore, the amount of consumption for international bunker fuels was estimated and subtracted from
total fuel consumption (see Table A-35). Emissions from international bunker fuels have been estimated separately and
not included in national totals.4
Step 7: Determine the C Content of All Fuels
The C content of combusted fossil fuels was estimated by multiplying adjusted energy consumption (Columns 2
through 8 of Table A- 11 through Table A- 33) by fuel-specific C content coefficients (see Table A- 36 and Table A- 37)
that reflect the amount of C per unit of energy in each fuel. The C content coefficients used in the U.S. inventory were
derived by EIA from detailed fuel information and are similar to the C content coefficients contained in the IPCC's default
methodology (IPCC/UNEP/OECD/TEA 1997), with modifications reflecting fuel qualities specific to the United States.
Step 8: Estimate COi Emissions
Actual CC>2 emissions in the United States were summarized by major fuel (i.e., coal, petroleum, natural gas,
geothermal) and consuming sector (i.e., residential, commercial, industrial, transportation, electricity generation, and U.S.
territories). Emission estimates are expressed in teragrams of carbon dioxide equivalents (Tg CC>2 Eq.). To convert from
C content to CC>2 emissions, the fraction of C that is oxidized was applied. This fraction was 100 percent based on
guidance in IPCC (2006).
To determine total emissions by final end-use sector, emissions from electricity generation were distributed to
each end-use sector according to its share of aggregate electricity consumption (see Table A-38). This pro-rated approach
to allocating emissions from electricity generation may overestimate or underestimate emissions for particular sectors due
to differences in the average C content of fuel mixes burned to generate electricity.
To provide a more detailed accounting of emissions from transportation, fuel consumption data by vehicle type
and transportation mode were used to allocate emissions by fuel type calculated for the transportation end-use sector.
Additional information on the allocation is available in Annex 3.2.
[BEGIN BOX]
Box 1. Uses of Greenhouse Gas Reporting Program Data in Reporting Emissions from Industrial Sector Fossil Fuel
Combustion
Refer to the International Bunker Fuels section of the Energy chapter and Annex 3.3 for a description of the methodology for
distinguishing between international and domestic fuel consumption.
A-29
-------
As described in the calculation methodology, total fossil fuel consumption for each year is based on aggregated
end-use sector consumption published by the EIA. The availability of facility-level combustion emissions through EPA's
Greenhouse Gas Reporting Program (GHGRP) has provided an opportunity to better characterize the industrial sector's
energy consumption and emissions in the United States, through a disaggregation of EIA's industrial sector fuel
consumption data from select industries.
For EPA's GHGRP 2010, 2011, and 2012 reporting years, facility-level fossil fuel combustion emissions
reported through the GHGRP were categorized and distributed to specific industry types by utilizing facility-reported
NAICS codes (as published by the U.S. Census Bureau), and associated data available from EIA's 2010 Manufacturing
Energy Consumption Survey (MECS). As noted previously in this report, the definitions and provisions for reporting fuel
types in EPA's GHGRP include some differences from the inventory's use of EIA national fuel statistics to meet the
UNFCCC reporting guidelines. The IPCC has provided guidance on aligning facility-level reported fuels and fuel types
published in national energy statistics, which guided this exercise. ^
This year's effort represents an attempt to align, reconcile, and coordinate the facility-level reporting of fossil
fuel combustion emissions under EPA's GHGRP with the national-level approach presented in this report. Consistent
with recommendations for reporting the inventory to the UNFCCC, progress was made on certain fuel types for specific
industries and has been included in the Common Reporting Format (CRF) tables that are submitted to the UNFCCC along
with this report. However, a full mapping was not completed this year due to fuel category differences between national
statistics published by EIA and facility-level reported GHGRP data. Furthermore, given that calendar year 2010 was the
first year in which emissions data were reported to EPA's GHGRP, the current inventory's examination only focused on
2010, 201 land, 2012. For the current exercise, the efforts in reconciling fuels focused on standard, common fuel types
(e.g., natural gas, distillate fuel oil, etc.) where the fuels in EIA's national statistics aligned well with facility-level
GHGRP data. For these reasons, the current information presented in the CRF tables should be viewed as an initial attempt
at this exercise. Additional efforts will be made for future inventory reports to improve the mapping of fuel types, and
examine ways to reconcile and coordinate any differences between facility-level data and national statistics. Additionally,
J J J J '
in order to expand this effort through the full time series presented in this report, further analyses will be conducted linking
GHGRP facility-level reporting with the information published by EIA in its MECS data, other available MECS survey
years , and any further informative sources of data. It is believed that the current analysis has led to improvements in the
presentation of data in the Inventory, but further work will be conducted, and future improvements will be realized in
subsequent Inventory reports.
Additionally, to assist in the disaggregation of industrial fuel consumption, EIA will now synthesize energy
consumption data using the same procedure as is used for the last historical (benchmark) year of the Annual Energy
Outlook (AEO). This procedure reorganizes the most recent data from the Manufacturing Energy Consumption Survey
(MECS) (conducted every four years) into the nominal data submission year using the same energy-economy integrated
model used to produce the AEO projections, the National Energy Modeling System (NEMS). EIA believes this
"nowcasting" technique provides an appropriate estimate of energy consumption for the CRF.
To address gaps in the time series, EIA performs a NEMS model projection, using the MECS baseline sub-sector
energy consumption. The NEMS model accounts for changes in factors that influence industrial sector energy
consumption, and has access to data which may be more recent than MECS, such as industrial sub-sector macro industrial
output (i.e., shipments) and fuel prices. By evaluating the impact of these factors on industrial subsector energy
consumption, NEMS can anticipate changes to the energy shares occurring post-MECS and can provide a way to
appropriately disaggregate the energy-related emissions data into the CRF.
While the fuel consumption values for the various manufacturing sub-sectors are not directly surveyed for all
years, they represent EIA's best estimate of historical consumption values for non-MECS years. Moreover, as an integral
part of each AEO publication, this synthetic data series is likely to be maintained consistent with all available EIA and
non-EIA data sources even as the underlying data sources evolve for both manufacturing and non-manufacturing
industries alike.
See Section 4 "Use of Facility-Level Data in Good Practice National Greenhouse Gas Inventories" of the IPCC
meeting report, and specifically the section on using facility-level data in conjunction with energy data, at .
See < http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html>.
A-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Other sectors' fuel consumption (commercial, residential, transportation) will be benchmarked with the latest
aggregate values from the Monthly Energy Review.7 EIA will work with the U.S. Environmental Protection Agency to
back cast these values to 1990.
[END BOX]
http://www.eia.gov/totalenergy/data/monthly/.
A-31
-------
Table A-10:2012 Energy Consumption Data by Fuel Type (TBtu) and Adjusted Energy Consumption Data
1 23456789
10
11
12
13
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Coking Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
NE 43.6
NE
43.6
4,241.5 2,960.0
949.7 516.7
438.9 323.2
7.7 1.2
503.1 145.5
15.0
31.4
0.4
5,191.3 3,520.2
Total
Ind.
917.4
122.4
795.0
8,512.5
7,699.2
826.7
1,154.3
2.0
2,229.4
130.5
86.9
7.0
(0.0)
161.6
453.9
287.2
84.4
704.3
1,480.8
14.7
60.1
15.3
17,129.0
Consumption
Trans.
NE
NE
777.2
25,150.9
25.1
5,978.3
2,901.4
33.7
123.2
15,418.6
670.5
25,928.1
(TBtu)"
Elec.
15,821.2
15,821.2
9,286.8
219.2
52.9
NA
76.7
89.6
53.1
25,380.3
Terr.
36.9
36.9
27.1
738.1
132.9
56.4
7.2
11.9
1.0
242.3
163.8
122.6
802.1
Total
16,819.0
43.6
122.4
795.0
NE
15,821.2
36.9
25,805.0
35,273.8
826.7
25.1
8,080.5
2,957.8
18.2
2,923.7
254.8
15,762.8
949.4
(0.0)
284.2
453.9
287.2
84.4
794.3
1,480.8
14.7
60.1
15.3
53.1
77,951.0
Adjustments (TBtu)b
Bunker
Fuel
1,467.4
91.7
916.3
459.5
1,467.4
Unadjusted NEU Consumption
Ind. Trans. Terr.
132.7
122.4
10.3
311.8
4,184.6 123.2 123.6
826.7
17.5
2,003.9
130.5 123.2 1.0
161.6 122.6
453.9
287.2
45.9
66.3
161.1
14.7
15.3
4,629.1 123.2 123.6
Total Adjusted
Consumption
16,686.3
43.6
784.7
15,821.2
36.9
25,493.2
29,375.0
25.1
7,971.4
2,041.5
18.2
919.8
15,762.8
490.0
(0.0)
38.5
728.0
1,319.8
60.1
53.1
71,607.7
'" Expressed as gross calorific values (i.e., higher heating values).
b Adjustments are subtracted from total consumption estimates and include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
NE (Not Estimated)
NA (Not Available)
A-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-11:2012 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
NE 43.6
NE
43.6
4,241.5 2,960.0
949.7 516.7
438.9 323.2
7.7 1.2
503.1 145.5
15.0
31.4
0.4
5,191.3 3,520.2
Adjusted Consumption
Ind. Trans.
784.7
784.7
8,200.7
3,514.6
1,136.8
2.0
225.5
86.9
7.0
(0.0)
38.5
638.0
1,319.8
60.1
12,499.9
NE
NE
777.2
23,560.3
25.1
5,886.7
1,985.2
33.7
15,418.6
211.1
24,337.5
(TBtu)a
Elec.
15,821.2
15,821.2
9,286.8
219.2
52.9
NA
76.7
89.6
53.1
25,380.3
Terr. Total
36.9 16,686.3
43.6
784.7
15,821.2
36.9 36.9
27.1 25,493.2
614.4 29,375.0
25.1
132.9 7,971.4
56.4 2,041.5
7.2 18.2
11.9 919.8
242.3 15,762.8
163.8 490.0
(0.0)
38.5
728.0
1,319.8
60.1
53.1
678.4 71,607.7
Emissions'1 (Tg
Res. Comm. Ind.
NE 4.1
NE
4.1
224.8 156.9
64.1 36.4
32.5 23.9
0.6 0.1
31.0 9.0
1.1
2.4
(0.0)
288.9 197.4
74.3
74.3
434.7
265.2
84.1
0.1
13.9
6.2
0.5
(0.0)
2.7
65.1
88.0
4.5
774.2
C02 Eq.) from
Trans.
NE 1
NE
1
41.2
1,698.3
1.7
435.4
143.4
2.1
1,099.9
15.8
Energy Use
Elec.
,511.2
,511.2
492.2
18.8
3.9
5.8
9.1
0.4
1,739.5 2,022.7
Terr.
3.4
3.4
1.4
44.7
9.8
4.1
0.5
0.7
17.3
12.3
49.6
Total
1,593.0
4.1
74.3
NE
1,511.2
3.4
1,351.2
2,127.6
1.7
589.5
147.4
1.3
56.8
1,124.5
36.8
(0.0)
2.7
74.3
88.0
4.5
0.4
5,072.3
" Expressed as gross calorific values (i.e., higher heating values).
b Adjustments are subtracted from total consumption estimates and include biofuels,
NE (Not Estimated)
NA (Not Available)
conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
A-33
-------
Table A-12:2011 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
NE 61.7
NE
61.7
4,804.6 3,216.1
1,031.8 635.1
521.9 414.2
18.5 3.2
491.3 142.1
21.6
53.7
0.2
5,836.3 3,912.9
Adjusted Consumption
Ind. Trans.
866.1 NE
866.1
NE
7,873.4 733.5
3,583.1 23,706.1
27.1
1,213.2 5,899.3
2,029.9
3.6
176.5 32.9
125.0 15,459.0
72.1 258.0
0.0
27.3
585.9
1,323.4
56.1
12,322.6 24,439.7
(TBtu)"
Elec.
18,035.2
18,035.2
7,712.2
303.0
64.2
NA
93.1
145.7
52.3
26,102.6
Terr.
36.9
36.9
27.1
614.4
132.9
56.4
7.2
11.9
242.2
163.8
678.3
Total
18,999.9
61.7
866.1
18,035.2
36.9
24,366.9
29,873.5
27.1
8,245.7
2,086.3
32.5
854.8
15,847.8
640.6
0.0
27.3
731.8
1,323.4
56.1
52.3
73,292.5
Emissions'1 (Tg
Res. Comm. Ind.
NE 5.8
NE
5.8
254.7 170.5
70.3 45.2
38.6 30.6
1.4 0.2
30.3 8.8
1.5
4.0
0.0
324.9 221.5
82.0
82.0
417.3
269.4
89.7
0.3
10.9
8.9
5.4
0.0
1.9
59.8
88.3
4.2
768.7
C02 Eq.) from
Trans.
NE 1
NE
1
38.9
1,709.0
1.9
436.3
146.6
2.0
1,102.8
19.4
Energy Use
Elec.
,722.7
,722.7
408.8
26.6
4.7
7.0
14.9
0.4
1,747.9 2,158.5
Terr.
3.4
3.4
1.4
44.7
9.8
4.1
0.5
0.7
17.3
12.3
49.6
Total
1,813.9
5.8
82.0
NE
1,722.7
3.4
1,291.5
2,165.3
1.9
609.8
150.7
2.4
52.8
1,130.6
48.1
0.0
1.9
74.7
88.3
4.2
0.4
5,271.1
" Expressed as gross calorific values (i.e., higher heating values).
b Adjustments are subtracted from total consumption estimates and include biofuels,
NE (Not Estimated)
NA (Not Available)
conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
A-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-13:2010 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
NE 69.7
NE
69.7
4,878.1 3,164.7
1,119.5 650.9
560.2 394.4
29.1 4.8
530.1 140.1
49.5
61.7
0.4
5,997.6 3,885.2
Adjusted Consumption
Ind. Trans.
951.6 NE
951.6
NE
7,683.2 719.0
3,704.1 23,960.2
27.0
1,121.4 5,765.5
2,097.5
7.3
219.7 29.5
247.9 15,768.5
57.3 272.2
(0.2)
77.8
620.9
1,324.0
28.0
12,338.9 24,679.1
(TBtu)"
Elec.
19,133.5
19,133.5
7,527.6
378.3
80.3
NA
154.1
143.9
51.9
27,091.3
Terr.
36.9
36.9
27.8
614.4
132.9
56.4
7.2
11.9
242.2
163.8
679.1
Total
20,191.6
69.7
951.6
19,133.5
36.9
24,000.4
30,427.2
27.0
8,054.7
2,153.8
48.5
931.3
16,308.1
709.0
(0.2)
77.8
765.2
1,324.0
28.0
51.9
74,671.1
Emissions'1 (Tg
Res. Comm. Ind.
NE 6.6
NE
6.6
258.6 167.7
76.3 46.4
41.4 29.2
2.1 0.4
32.7 8.6
3.5
4.6
0.0
334.8 220.7
90.1
90.1
407.2
278.3
82.9
0.5
13.6
17.7
4.3
(0.0)
5.4
63.4
88.3
2.1
775.6
C02 Eq.) from
Trans.
NE 1
NE
1
38.1
1,726.9
1.9
426.4
151.5
1.8
1,124.9
20.4
Energy Use
Elec.
,827.6
,827.6
399.0
32.2
5.9
11.6
14.7
0.4
1,765.0 2,259.2
Terr.
3.4
3.4
1.5
44.7
9.8
4.1
0.5
0.7
17.3
12.3
49.6
Total
1,927.7
6.6
90.1
NE
1,827.6
3.4
1,272.1
2,204.8
1.9
595.7
155.6
3.5
57.5
1,163.4
53.2
(0.0)
5.4
78.1
88.3
2.1
0.4
5,404.9
" Expressed as gross calorific values (i.e., higher heating values).
b Adjustments are subtracted from total consumption estimates and include biofuels,
NE (Not Estimated)
NA (Not Available)
conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
A-35
-------
Table A-14:2009 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 2345678
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
NE 73.4
NE
73.4
4,883.1 3,186.6
1,139.4 675.2
564.7 387.8
27.7 4.2
547.1 138.9
72.8
71.3
0.3
6,022.5 3,935.1
Adjusted Consumption
Ind. Trans.
877.3
877.3
7,125.1
3,549.9
1,018.4
4.4
201.7
333.8
67.3
(0.8)
63.8
618.0
1,321.1
(77.8)
11,552.2
NE
NE
714.9
23,732.2
26.6
5,539.3
2,134.2
28.0
15,818.3
185.7
24,447.0
(TBtu)"
Elec.
18,225.3
18,225.3
7,022.4
389.9
70.1
NA
181.0
138.9
51.2
25,688.9
Terr.
36.9
36.9
27.4
535.5
83.4
61.5
8.0
15.0
202.0
165.7
599.8
Total
19,212.8
73.4
877.3
18,225.3
36.9
22,959.4
30,022.1
26.6
7,663.6
2,195.7
44.3
930.7
16,426.9
670.9
(0.8)
63.8
757.2
1,321.1
(77.8)
51.2
72,245.6
Emissions'1 (Tg
Res. Comm. Ind.
NE 6.9
NE
6.9
258.8 168.9
77.5 48.1
41.8 28.7
2.0 0.3
33.8 8.6
5.2
5.4
0.0
336.4 223.9
83.0
83.0
377.6
266.8
75.3
0.3
12.4
23.8
5.1
(0.1)
4.5
63.1
88.1
(5.8)
727.5
C02 Eq.) from
Trans.
NE
NE
37.9
1,709.8
1.8
409.7
154.1
1.7
1,128.5
13.9
1,747.7
Energy Use
Elec.
1,740.9
1,740.9
372.2
33.0
5.2
13.6
14.2
0.4
2,146.4
Terr. Total
3.4 1,834.2
6.9
83.0
NE
1,740.9
3.4 3.4
1.5 1,216.9
39.0 2,174.2
1.8
6.2 566.8
4.4 158.6
0.6 3.2
0.9 57.4
14.4 1,171.9
12.4 50.4
(0.1)
4.5
77.3
88.1
(5.8)
0.4
43.8 5,225.7
a Expressed as gross calorific values (i.e., higher heating values).
b Adjustments are subtracted from total consumption estimates and include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
NE (Not Estimated)
NA (Not Available)
A-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-15:2000 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
NE 80.8
NE
80.8
5,010.1 3,228.4
1,182.8 649.5
608.8 338.2
21.3 4.4
552.7 158.0
77.7
71.0
0.3
6,192.9 3,958.7
Adjusted
Ind.
1,081.5
1,081.5
7,571.4
4,012.2
1,137.1
3.8
226.7
424.5
131.5
0.1
76.5
642.6
1,423.0
(53.7)
12,665.0
Consumption
Trans.
NE
NE
692.1
24,686.5
28.3
6,106.9
2,396.1
40.1
15,843.9
271.3
25,378.7
(TBtu)"
Elec.
20,513.0
20,513.0
6,828.9
467.7
73.1
NA
240.4
154.2
50.6
27,860.2
Terr. Total
36.9 21,712.0
80.8
1,081.5
20,513.0
36.9 36.9
29.3 23,360.2
492.3 31,491.1
28.3
110.3 8,374.4
35.0 2,431.1
5.9 35.4
15.7 993.3
136.1 16,482.1
189.3 903.5
0.1
76.5
797.1
1,423.0
(53.7)
50.6
558.4 76,613.9
Emissions'1 (Tg
Res. Comm. Ind.
NE 7.6
NE
7.6
265.5 171.1
80.7 46.0
45.0 25.0
1.6 0.3
34.1 9.7
5.5
5.3
0.0
346.2 224.7
102.4
102.4
401.3
300.4
84.1
0.3
14.0
30.3
9.9
0.0
5.4
65.6
94.9
(4.0)
804.1
C02 Eq.) from
Trans.
NE
NE
36.7
1,779.8
2.0
451.6
173.0
2.5
1,130.3
20.4
1,816.5
Energy Use
Elec.
1,959.4
1,959.4
361.9
39.2
5.4
18.1
15.7
0.4
2,360.9
Terr.
3.4
3.4
1.6
36.0
8.2
2.5
0.4
1.0
9.7
14.2
41.0
Total
2,072.8
7.6
102.4
NE
1,959.4
3.4
1,238.1
2,282.1
2.0
619.3
175.6
2.6
61.3
1,175.8
67.8
0.0
5.4
81.4
94.9
(4.0)
0.4
5,593.4
a Expressed as gross calorific values (i.e., higher heating values).
b Adjustments are subtracted from total consumption estimates and include biofuels, conversion of fossil fuels, non-energy use (seeTable A-34), and international bunker fuel consumption (see Table A-35).
NE (Not Estimated)
NA (Not Available)
A-37
-------
Table A-16:2007 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456781
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
7.8 70.0
7.8
70.0
4,835.4 3,085.1
1,224.9 680.6
697.3 368.7
43.9 9.2
483.7 121.4
105.6
75.4
0.4
6,068.1 3,835.7
Adjusted Consumption
Ind. Trans.
1,130.8 NE
1,130.8
NE
7,521.3 663.5
4,580.8 25,834.8
31.6
1,185.5 6,439.7
2,485.0
13.4
379.1 21.9
528.4 16,470.5
130.4 386.1
1.8
89.7
704.7
1,482.6
65.2
13,232.9 26,498.2
(TBtu)"
Elec.
20,807.7
20,807.7
7,005.2
657.1
89.3
NA
396.6
171.2
49.9
28,520.0
Terr.
36.9
36.9
26.7
551.7
136.5
55.5
5.2
11.6
157.0
185.9
615.3
Total
22,053.2
7.8
70.0
1,130.8
20,807.7
36.9
23,137.2
33,529.9
31.6
8,917.0
2,540.4
71.8
1,017.7
17,261.4
1,174.4
1.8
89.7
876.3
1,482.6
65.2
49.9
78,770.2
Emissions'1 (Tg
Res. Comm. Ind.
0.7 6.7
0.7
6.7
256.3 163.5
84.6 48.7
51.6 27.3
3.2 0.7
29.8 7.5
7.6
5.7
0.0
341.6 218.9
107.0
107.0
398.6
341.9
87.7
1.0
23.4
37.9
9.8
0.1
6.3
72.0
98.9
4.8
847.5
C02 Eq.) from
Trans.
NE
NE
35.2
1,869.5
2.2
476.3
179.5
1.4
1,181.2
29.0
1,904.7
Energy Use
Elec.
1,987.3
1,987.3
371.3
53.9
6.6
29.8
17.5
0.4
2,412.8
Terr.
3.4
3.4
1.4
40.4
10.1
4.0
0.4
0.7
11.3
14.0
45.2
Total
2,105.1
0.7
6.7
107.0
NE
1,987.3
3.4
1,226.3
2,438.9
2.2
659.5
183.5
5.3
62.8
1,238.0
88.2
0.1
6.3
89.5
98.9
4.8
0.4
5,770.8
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-17:2006 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456781
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
6.4 64.8
6.4
64.8
4,475.9 2,901.7
1,204.9 677.7
693.0 390.5
66.4 15.2
445.5 123.2
73.3
75.3
0.3
5,687.2 3,644.2
Adjusted Consumption
Ind. Trans.
1,188.8 NE
1,188.8
NE
7,323.2 625.0
4,699.4 25,766.7
33.4
1,199.4 6,358.6
2,523.8
29.6
369.7 27.5
566.7 16,517.0
176.4 306.3
0.6
70.0
720.5
1,496.2
70.3
13,211.4 26,391.7
(TBtu)"
Elec.
20,461.9
20,461.9
6,375.1
648.1
73.7
NA
360.5
213.9
49.7
27,534.8
Terr.
36.9
36.9
26.1
620.9
90.2
76.1
4.4
6.6
188.8
254.8
683.9
Total
21,758.7
6.4
64.8
1,188.8
20,461.9
36.9
21,727.0
33,617.7
33.4
8,805.3
2,599.9
115.5
972.5
17,345.9
1,173.3
0.6
70.0
934.6
1,496.2
70.3
49.7
77,153.2
Emissions'1 (Tg
Res. Comm. Ind.
0.6 6.2
0.6
6.2
237.3 153.8
83.6 48.5
51.3 28.9
4.9 1.1
27.5 7.6
5.2
5.7
0.0
321.5 208.6
112.6
112.6
388.2
350.9
88.7
2.2
22.8
40.4
13.2
0.0
4.9
73.6
99.8
5.2
851.8
C02 Eq.) from
Trans.
NE
NE
33.1
1,857.8
2.3
470.3
182.3
1.7
1,178.2
23.0
1,890.9
Energy Use
Elec.
1,953.7
1,953.7
338.0
54.4
5.4
27.1
21.8
0.4
2,346.4
Terr.
3.4
3.4
1.4
45.5
6.7
5.5
0.3
0.4
13.5
19.1
50.3
Total
2,076.6
0.6
6.2
112.6
NE
1,953.7
3.4
1,151.8
2,440.7
2.3
651.2
187.8
8.5
60.0
1,237.4
88.1
0.0
4.9
95.4
99.8
5.2
0.4
5,669.5
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-39
-------
Table A-18:2005 Energy Consumption Data and C0? Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
8.4 97.0
8.4
97.0
4,946.4 3,073.2
1,369.2 716.0
772.6 404.7
83.8 21.6
512.9 131.4
42.2
115.8
0.3
6,324.0 3,886.2
Adjusted
Ind.
1,219.1
1,219.1
7,329.7
4,324.0
1,128.9
39.1
349.6
328.2
237.4
8.3
98.1
702.1
1,429.4
2.8
12,872.8
Consumption
Trans.
NE
NE
623.9
25,865.2
35.4
6,193.8
2,621.7
28.2
16,729.7
256.4
26,489.1
(TBtu)"
Elec.
20,737.2
20,737.2
6,014.5
1,234.5
114.6
NA
876.5
243.5
50.1
28,036.4
Terr. Total
32.7 22,094.5
8.4
97.0
1,219.1
20,737.2
32.7 32.7
24.3 22,012.0
623.2 34,132.2
35.4
121.3 8,735.9
66.0 2,687.7
5.8 150.2
0.8 1,022.8
194.2 17,294.3
235.2 1,721.3
8.3
98.1
945.9
1,429.4
2.8
50.1
680.2 78,288.9
Emissions'1 (Tg
Res. Comm. Ind.
0.8 9.3
0.8
9.3
262.2 162.9
94.9 51.3
57.1 29.9
6.1 1.6
31.7 8.1
3.0
8.7
0.0
357.9 223.5
115.3
115.3
388.5
323.8
83.5
2.9
21.6
23.3
17.8
0.6
6.9
71.7
95.4
0.2
827.6
C02 Eq.) from
Trans.
NE
NE
33.1
1,858.7
2.4
458.1
189.3
1.7
1,187.8
19.3
1,891.7
Energy Use
Elec.
1,983.8
1,983.8
318.8
99.2
8.5
65.8
24.9
0.4
2,402.1
Terr.
3.0
3.0
1.3
45.7
9.0
4.8
0.4
0.0
13.8
17.7
50.0
Total
2,112.3
0.8
9.3
115.3
NE
1,983.8
3.0
1,166.7
2,473.5
2.4
646.1
194.1
11.0
63.1
1,227.9
129.3
0.6
6.9
96.6
95.4
0.2
0.4
5,752.9
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-19:2004 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
11.4 102.9
11.4
102.9
4,980.8 3,201.0
1,474.6 766.7
878.1 447.0
84.8 20.5
511.7 152.0
24.4
122.5
0.3
6,466.9 4,070.6
Adjusted Consumption
Ind. Trans.
1,262.0
1,262.0
7,913.5
4,193.3
1,139.6
28.2
372.7
203.2
204.7
10.6
111.2
715.3
1,483.3
(75.6)
13,368.8
NE
NE
602.0
25,589.7
31.2
5,917.7
2,584.8
19.1
16,850.4
186.4
26,191.7
(TBtu)"
Elec.
20,305.0
20,305.0
5,594.9
1,212.4
111.3
NA
879.0
222.1
50.5
27,162.9
Terr. Total
32.0 21,713.3
11.4
102.9
1,262.0
20,305.0
32.0 32.0
24.6 22,316.9
653.6 33,890.2
31.2
134.4 8,628.1
68.8 2,653.6
6.0 139.5
0.8 1,056.3
198.6 17,276.7
245.0 1,637.6
10.6
111.2
937.6
1,483.3
(75.6)
50.5
710.2 77,971.0
Emissions'1 (Tg C02 Eq.) from
Res. Comm. Ind. Trans.
1.1 9.8
1.1
9.8
264.1 169.7
102.7 54.9
64.9 33.1
6.2 1.5
31.6 9.4
1.7
9.2
0.0
367.9 234.4
118.3
118.3
419.6
314.1
84.3
2.1
23.0
14.4
15.4
0.7
7.8
73.0
99.0
(5.6)
851.9
NE
NE
31.9
1,839.2
2.2
437.7
186.7
1.2
1,197.6
14.0
1,871.2
Energy Use
Elec.
1,943.1
1,943.1
296.7
96.9
8.2
66.0
22.7
0.4
2,337.0
Terr.
2.9
2.9
1.3
47.9
9.9
5.0
0.4
0.0
14.1
18.4
52.2
Total
2,075.1
1.1
9.8
118.3
NE
1,943.1
2.9
1,183.4
2,455.8
2.2
638.1
191.6
10.2
65.2
1,227.9
123.0
0.7
7.8
95.7
99.0
(5.6)
0.4
5,714.7
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-41
-------
Table A-20:2003 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
12.2 82.0
12.2
82.0
5,209.4 3,260.9
1,466.1 762.0
851.3 453.1
70.3 18.6
544.5 156.9
22.0
111.1
0.3
6,687.8 4,104.9
Adjusted Consumption
Ind. Trans.
1,248.8
1,248.8
7,845.1
3,948.6
1,055.5
24.1
326.8
119.1
176.4
7.5
110.4
701.9
1,477.3
(50.4)
13,042.5
NE
NE
627.4
24,940.9
30.2
5,710.9
2,482.5
17.9
16,600.4
99.1
25,568.4
(TBtu)"
Elec.
20,184.7
20,184.7
5,246.2
1,205.0
161.0
NA
869.4
174.7
49.2
26,685.2
Terr. Total
33.9 21,561.7
12.2
82.0
1,248.8
20,184.7
33.9 33.9
26.9 22,216.0
621.8 32,944.5
30.2
120.5 8,352.3
76.1 2,558.5
10.7 123.7
10.5 1,056.6
210.1 16,951.6
193.9 1,450.0
7.5
110.4
876.8
1,477.3
(50.4)
49.2
682.6 76,771.4
Emissions'1 (Tg C02 Eq.) from
Res. Comm. Ind. Trans.
1.2 7.8
1.2
7.8
275.9 172.7
101.8 54.5
63.0 33.5
5.1 1.4
33.7 9.7
1.6
8.3
0.0
378.8 235.0
117.0
117.0
415.4
296.5
78.1
1.8
20.2
8.5
13.2
0.5
7.7
71.7
98.6
(3.7)
828.9
NE 1
NE
1
33.2
1,790.6
2.1
422.4
179.3
1.1
1,178.3
7.4
Energy Use
Elec.
,931.0
,931.0
277.8
95.0
11.9
65.3
17.8
0.4
1,823.8 2,304.2
Terr.
3.1
3.1
1.4
45.3
8.9
5.5
0.8
0.7
14.9
14.6
49.9
Total
2,060.1
1.2
7.8
117.0
NE
1,931.0
3.1
1,176.4
2,383.7
2.1
617.7
184.8
9.1
65.3
1,203.2
108.9
0.5
7.7
89.5
98.6
(3.7)
0.4
5,620.5
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-21:2002 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
12.2 89.8
12.2
89.8
4,995.0 3,212.5
1,358.9 645.4
761.8 393.4
59.9 15.9
537.1 140.8
15.2
79.8
0.2
6,366.1 3,947.7
Adjusted
Ind.
1,243.7
1,243.7
8,086.3
3,785.2
1,048.4
13.8
393.3
104.1
146.1
7.5
111.9
696.3
1,399.4
(135.7)
13,115.1
Consumption
Trans.
NE
NE
698.9
24,961.1
33.7
5,595.9
2,565.5
14.3
16,523.8
227.9
25,660.0
(TBtu)"
Elec.
19,782.8
19,782.8
5,766.8
961.3
127.4
NA
658.7
175.2
49.4
26,560.4
Terr.
10.8
10.8
22.8
556.8
92.8
61.8
8.2
11.2
189.4
193.6
590.4
Total
21,139.3
12.2
89.8
1,243.7
19,782.8
10.8
22,782.3
32,268.7
33.7
8,019.7
2,627.3
97.9
1,096.7
16,832.5
1,306.1
7.5
111.9
871.7
1,399.4
(135.7)
49.4
76,239.7
Emissions'1 (Tg C02 Eq.) from
Res. Comm. Ind. Trans.
1.2 8.6
1.2
8.6
264.7 170.3
93.9 46.1
56.3 29.1
4.4 1.2
33.2 8.7
1.1
6.0
0.0
359.8 224.9
116.6
116.6
428.6
283.9
77.5
1.0
24.3
7.4
11.0
0.5
7.8
71.1
93.4
(10.1)
829.1
NE
NE
37.0
1,793.6
2.3
413.9
185.3
0.9
1,174.1
17.1
1,830.6
Energy Use
Elec.
1,889.9
1,889.9
305.7
76.8
9.4
49.5
17.9
0.4
2,272.7
Terr. Total
1.0 2,017.2
1.2
8.6
116.6
NE
1,889.9
1.0 1.0
1.2 1,207.5
40.6 2,334.9
2.3
6.9 593.1
4.5 189.7
0.6 7.2
0.7 67.8
13.5 1,196.0
14.5 98.1
0.5
7.8
89.0
93.4
(10.1)
0.4
42.8 5,559.9
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-43
-------
Table A-22:2001 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
12.0 96.9
12.0
96.9
4,889.0 3,097.3
1,463.0 718.6
842.2 471.4
95.1 31.4
525.7 142.7
3.1
69.9
0.2
6,364.0 3,912.7
Adjusted
Ind.
1,358.4
1,358.4
7,949.0
3,910.8
1,181.8
23.2
372.1
24.2
146.7
6.1
131.6
683.3
1,417.3
(75.4)
13,218.2
Consumption
Trans.
NE
NE
658.0
24,449.8
34.9
5,417.0
2,626.3
13.7
16,198.3
159.5
25,107.8
(TBtu)"
Elec.
19,613.7
19,613.7
5,458.1
1,276.6
170.5
NA
1,002.8
103.2
46.9
26,395.2
Terr. Total
3.8 21,084.8
12.0
96.9
1,358.4
19,613.7
3.8 3.8
22.9 22,074.3
632.2 32,450.9
34.9
109.4 8,192.3
98.9 2,725.2
0.9 150.6
7.0 1,061.2
187.6 16,413.2
228.4 1,607.2
6.1
131.6
786.7
1,417.3
(75.4)
46.9
658.9 75,656.8
Emissions'1 (Tg C02 Eq.) from Energy Use
Res. Comm. Ind. Trans. Elec.
1.1 9.2
1.1
9.2
259.1 164.2
101.8 51.5
62.3 34.9
7.0 2.3
32.5 8.8
0.2
5.2
0.0
362.0 224.9
127.8
127.8
421.3
293.2
87.4
1.7
23.0
1.7
11.0
0.4
9.2
69.8
94.6
(5.6)
842.3
NE
NE
34.9
1,754.3
2.4
400.6
189.7
0.8
1,148.7
12.0
1,789.2
1,869.8
1,869.8
289.3
98.5
12.6
75.3
10.5
0.4
2,257.9
Terr.
0.4
0.4
1.2
46.2
8.1
7.1
0.1
0.4
13.3
17.2
47.8
Total
2,008.4
1.1
9.2
127.8
NE
1,869.8
0.4
1,170.0
2,345.4
2.4
605.9
196.8
11.0
65.6
1,164.0
120.7
0.4
9.2
80.3
94.6
(5.6)
0.4
5,524.1
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-23:2000 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
11.4 91.9
11.4
91.9
5,104.6 3,251.5
1,427.5 694.0
778.0 422.2
94.6 29.7
554.9 150.4
91.6
0.2
6,543.4 4,037.4
Adjusted Consumption
Ind. Trans.
1,348.8 NE
1,348.8
NE
8,656.0 672.0
3,575.0 24,649.1
36.3
1,003.7 5,442.4
2,700.3
15.6
468.7 11.9
16,014.8
184.1 443.5
3.8
171.6
697.3
1,431.2
(401.2)
13,579.8 25,321.1
(TBtu)"
Elec.
20,220.2
20,220.2
5,293.4
1,144.3
174.8
NA
870.8
98.6
48.1
26,706.0
Terr.
10.3
10.3
12.7
471.7
71.3
74.1
2.4
8.0
185.1
130.9
494.6
Total
21,682.4
11.4
91.9
1,348.8
20,220.2
10.3
22,990.2
31,961.5
36.3
7,892.5
2,774.3
142.2
1,193.9
16,199.8
1,720.8
3.8
171.6
796.2
1,431.2
(401.2)
48.1
76,682.2
Emissions'1 (Tg
Res. Comm. Ind.
1.1 8.8
1.1
8.8
270.7 172.5
98.8 49.6
57.5 31.2
6.9 2.2
34.4 9.3
6.9
0.0
370.7 230.8
127.3
127.3
459.1
267.5
74.2
1.1
29.0
13.8
0.3
12.0
71.2
95.5
(29.7)
853.9
C02 Eq.) from
Trans.
NE
NE
35.6
1,769.1
2.5
402.5
195.0
0.7
1,135.0
33.3
1,804.7
Energy Use
Elec.
1,927.4
1,927.4
280.8
88.4
12.9
65.4
10.1
0.4
2,296.9
Terr.
0.9
0.9
0.7
34.2
5.3
5.3
0.2
0.5
13.1
9.8
35.9
Total
2,065.5
1.1
8.8
127.3
NE
1,927.4
0.9
1,219.4
2,307.6
2.5
583.7
200.4
10.4
74.0
1,148.1
129.2
0.3
12.0
81.3
95.5
(29.7)
0.4
5,592.8
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-45
-------
Table A-24:1999 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
14.0 102.5
14.0
102.5
4,834.9 3,115.0
1,342.1 613.9
705.0 373.4
111.2 26.9
526.0 140.2
73.3
0.1
6,191.0 3,831.5
Adjusted Consumption
Ind. Trans.
1,372.8
1,372.8
8,424.6
3,577.9
983.4
12.8
395.9
150.9
6.4
182.5
719.8
1,414.1
(287.9)
13,375.3
NE
NE
675.3
24,058.4
39.2
5,251.3
2,664.8
14.3
15,913.1
175.7
24,733.8
(TBtu)"
Elec.
19,279.5
19,279.5
4,902.1
1,211.4
140.1
NA
958.7
112.5
50.6
25,443.6
Terr. Total
10.2 20,779.0
14.0
102.5
1,372.8
19,279.5
10.2 10.2
21,952.0
461.0 31,264.7
39.2
79.4 7,532.6
59.5 2,724.4
3.7 154.7
8.3 1,084.6
164.0 16,077.2
146.0 1,504.6
6.4
182.5
832.4
1,414.1
(287.9)
50.6
471.2 74,046.3
Emissions'1 (Tg C02 Eq.) from
Res. Comm. Ind. Trans.
1.3 9.8
1.3
9.8
256.3 165.1
92.8 43.8
52.1 27.6
8.1 2.0
32.5 8.7
5.5
0.0
350.5 218.7
129.9
129.9
446.6
269.2
121
0.9
24.5
11.3
0.4
12.8
73.5
94.3
(21.3)
845.7
NE
NE
35.8
1,725.0
2.7
388.4
192.5
0.9
1,127.4
13.2
1,760.8
Energy Use
Elec.
1,836.4
1,836.4
259.9
93.8
10.4
72.0
11.5
0.4
2,190.5
Terr.
0.9
0.9
33.5
5.9
4.3
0.3
0.5
11.6
11.0
34.5
Total
1,978.3
1.3
9.8
129.9
NE
1,836.4
0.9
1,163.8
2,258.2
2.7
557.1
196.8
11.3
67.1
1,139.0
113.0
0.4
12.8
85.0
94.3
(21.3)
0.4
5,400.7
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-25:1998 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
11.5 93.4
11.5
93.4
4,646.1 3,083.0
1,207.3 609.1
675.1 375.1
108.3 31.2
423.9 117.6
85.2
0.1
5,865.0 3,785.5
Adjusted Consumption
Ind. Trans.
1,470.8
1,470.8
8,826.0
3,470.7
1,027.9
22.1
271.6
173.3
4.0
147.0
707.7
1,431.0
(313.9)
13,767.5
NE
NE
666.1
23,278.6
35.5
4,955.2
2,608.0
17.6
15,583.4
78.9
23,944.7
(TBtu)"
Elec.
19,215.7
19,215.7
4,674.9
1,306.2
135.7
NA
1,047.0
123.6
50.4
25,247.2
Terr. Total
10.5 20,802.0
11.5
93.4
1,470.8
19,215.7
10.5 10.5
21,896.1
445.4 30,317.4
35.5
71.9 7,240.8
59.9 2,667.8
6.3 167.8
5.9 836.7
160.3 15,743.8
141.1 1,525.5
4.0
147.0
831.4
1,431.0
(313.9)
50.4
456.0 73,065.9
Emissions'1 (Tg
Res. Comm. Ind.
1.1 8.9
1.1
8.9
246.0 163.3
84.0 43.7
49.9 27.7
7.9 2.3
26.1 7.2
6.4
0.0
331.1 215.9
139.1
139.1
467.4
262.4
76.0
1.6
16.7
13.0
0.3
10.3
72.3
95.5
(23.3)
868.9
C02 Eq.) from
Trans.
NE
NE
35.3
1,670.9
2.5
366.5
188.4
1.1
1,106.6
5.9
1,706.2
Energy Use
Elec.
1,828.2
1,828.2
247.6
101.3
10.0
78.6
12.6
0.4
2,177.4
Terr.
1.0
1.0
32.4
5.3
4.3
0.5
0.4
11.4
10.6
33.4
Total
1,978.3
1.1
8.9
139.1
NE
1,828.2
1.0
1,159.5
2,194.7
2.5
535.5
192.7
12.3
51.6
1,118.0
114.6
0.3
10.3
84.9
95.5
(23.3)
0.4
5,332.8
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-47
-------
Table A-26:1997 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
16.0 129.4
16.0
129.4
5,092.9 3,285.3
1,333.5 655.1
785.9 398.9
92.9 24.6
454.8 120.2
111.2
0.1
6,442.5 4,069.8
Adjusted Consumption
Ind. Trans.
1,457.6
1,457.6
9,032.5
3,896.2
1,057.3
18.8
429.9
240.1
9.1
4.6
164.5
639.9
1,435.0
(102.9)
14,386.4
NE
NE
780.3
22,693.8
39.7
4,802.2
2,553.8
14.2
15,147.5
136.5
23,474.1
(TBtu)"
Elec.
18,904.5
18,904.5
4,125.5
926.8
110.6
NA
714.6
101.6
50.2
24,007.1
Terr. Total
10.4 20,518.0
16.0
129.4
1,457.6
18,904.5
10.4 10.4
22,316.6
445.3 29,950.8
39.7
81.6 7,236.4
62.1 2,615.9
4.0 140.3
6.5 1,025.7
160.0 15,307.5
131.1 1,333.5
9.1
4.6
164.5
741.6
1,435.0
(102.9)
50.2
455.7 72,835.6
Emissions'1 (Tg C02 Eq.) from
Res. Comm. Ind. Trans.
1.5 12.3
1.5
12.3
270.1 174.2
93.0 47.1
58.1 29.5
6.8 1.8
28.1 7.4
8.4
0.0
364.6 233.6
137.6
137.6
479.0
290.1
78.2
1.4
26.5
18.0
0.6
0.3
11.5
65.3
95.7
(7.6)
906.7
NE
NE
41.4
1,628.5
2.7
355.2
184.4
0.9
1,075.0
10.3
1,669.8
Energy Use
Elec.
1,797.0
1,797.0
218.8
72.2
8.2
53.7
10.4
0.4
2,088.4
Terr.
1.0
1.0
32.4
6.0
4.5
0.3
0.4
11.4
9.8
33.4
Total
1,949.5
1.5
12.3
137.6
NE
1,797.0
1.0
1,183.4
2,163.3
2.7
535.2
188.9
10.3
63.3
1,086.4
100.1
0.6
0.3
11.5
75.7
95.7
(7.6)
0.4
5,296.5
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-27:1996 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
16.6 121.6
16.6
121.6
5,354.4 3,226.3
1,396.6 718.3
839.0 437.6
88.8 21.0
468.7 122.4
137.2
0.1
6,767.5 4,066.2
Adjusted Consumption
Ind. Trans.
1,454.9
1,454.9
9,020.3
3,912.3
1,049.1
18.3
401.7
284.7
7.0
13.7
177.5
638.2
1,434.9
(112.8)
14,387.5
NE
NE
736.9
22,502.3
37.4
4,599.0
2,556.0
15.6
14,979.4
314.9
23,239.2
(TBtu)"
Elec.
18,429.0
18,429.0
3,862.4
817.4
109.4
NA
628.4
79.6
48.9
23,157.7
Terr. Total
10.3 20,032.4
16.6
121.6
1,454.9
18,429.0
10.3 10.3
22,200.4
434.6 29,781.4
37.4
76.5 7,110.5
78.5 2,634.5
3.0 131.1
7.3 1,015.8
151.4 15,130.8
118.0 1,483.1
7.0
13.7
177.5
717.9
1,434.9
(112.8)
48.9
445.0 72,063.1
Emissions'1 (Tg
Res. Comm. Ind.
1.6 11.6
1.6
11.6
283.9 171.1
97.5 51.8
62.1 32.4
6.5 1.5
28.9 7.5
10.3
0.0
383.0 234.5
137.4
137.4
478.3
291.5
77.6
1.3
24.8
21.4
0.5
1.0
12.4
65.2
95.7
(8.4)
907.2
C02 Eq.) from
Trans.
NE
NE
39.1
1,615.0
2.6
340.1
184.6
1.0
1,063.0
23.6
1,654.0
Energy Use
Elec.
1,752.4
1,752.4
204.8
63.4
8.1
47.2
8.1
0.4
2,021.0
Terr.
1.0
1.0
31.6
5.7
5.7
0.2
0.5
10.7
8.9
32.5
Total
1,903.9
1.6
11.6
137.4
NE
1,752.4
1.0
1,177.2
2,150.7
2.6
525.9
190.3
9.6
62.6
1,073.8
111.4
0.5
1.0
12.4
73.3
95.7
(8.4)
0.4
5,232.2
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-49
-------
Table A-28:1995 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
Total Coal
Res. Comm.
17.5 116.8
17.5
116.8
4,954.2 3,096.0
1,260.9 694.0
791.8 419.1
74.3 22.1
394.8 108.7
2.5
141.5
0.1
6,232.5 3,906.8
Adjusted Consumption (TBtu)a
Ind. Trans. Elec.
1,526.9
1,526.9
8,722.5
3,538.2
967.9
15.4
403.4
27.2
286.2
5.3
14.5
169.0
600.7
1,369.5
(320.9)
13,787.7
NE 17,466.3
NE
17,466.3
724.0 4,302.0
21,935.2 754.6
39.6
4,383.3 108.1
2,428.8 NA
17.7
14,678.5
387.3 566.0
80.6
45.6
22,659.2 22,568.5
Terr.
10.2
10.2
461.8
89.5
75.7
3.6
5.6
146.7
140.7
472.0
Total
19,137.7
17.5
116.8
1,526.9
17,466.3
10.2
21,798.6
28,644.7
39.6
6,759.6
2,504.5
115.4
930.2
14,854.9
1,521.6
5.3
14.5
169.0
681.4
1,369.5
(320.9)
45.6
69,626.6
Emissions'1 (Tg C02 Eq.) from
Res. Comm. Ind. Trans.
1.7 11.2
1.7
11.2
262.7 164.2
88.3 50.1
58.6 31.0
5.4 1.6
24.4 6.7
0.2
10.6
0.0
352.7 225.5
144.4
144.4
462.5
263.2
71.6
1.1
24.9
1.9
21.5
0.4
1.1
11.8
61.3
91.4
(23.8)
870.2
NE
NE
38.4
1,571.2
2.7
324.2
172.2
1.1
1,041.8
29.1
1,609.5
Energy Use
Elec.
1,660.7
1,660.7
228.1
58.7
8.0
42.5
8.2
0.3
1,947.9
Terr.
0.9
0.9
33.6
6.6
5.4
0.3
0.3
10.4
10.6
34.5
Total
1,819.0
1.7
11.2
144.4
NE
1,660.7
0.9
1,155.9
2,065.1
2.7
499.9
177.6
8.4
57.4
1,054.3
114.3
0.4
1.1
11.8
69.6
91.4
(23.8)
0.3
5,040.4
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-29:1994 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
Total Coal
Res. Comm.
20.8 118.1
20.8
118.1
4,959.8 2,962.0
1,305.3 745.9
856.7 447.1
64.9 19.5
383.7 107.3
171.9
0.1
6,286.0 3,826.0
Adjusted Consumption
Ind. Trans.
1,594.9
1,594.9
8,290.3
3,697.9
975.8
16.9
423.1
368.4
6.1
18.7
169.4
594.9
1,404.0
(279.2)
13,583.1
NE
NE
708.5
21,492.3
38.1
4,187.0
2,473.8
34.0
14,401.3
358.1
22,200.8
(TBtu)"
Elec.
17,260.9
17,260.9
3,977.3
1,058.8
120.1
NA
869.0
69.7
53.0
22,350.0
Terr.
10.0
10.0
506.3
118.8
65.8
3.0
7.3
147.4
164.1
516.3
Total
19,004.7
20.8
118.1
1,594.9
17,260.9
10.0
20,897.9
28,806.5
38.1
6,705.5
2,539.5
104.3
955.3
14,548.7
1,931.5
6.1
18.7
169.4
664.7
1,404.0
(279.2)
53.0
68,762.2
Emissions'1 (Tg C02 Eq.) from
Res. Comm. Ind. Trans.
2.0 11.3
2.0
11.3
262.9 157.0
91.8 54.0
63.4 33.1
4.8 1.4
23.7 6.6
12.9
0.0
356.7 222.3
150.7
150.7
439.4
274.6
72.2
1.2
26.1
27.7
0.4
1.4
11.9
60.7
93.7
(20.7)
864.7
NE
NE
37.6
1,539.3
2.6
309.7
175.5
2.1
1,022.5
26.9
1,576.9
Energy Use
Elec.
1,638.8
1,638.8
210.8
81.3
8.9
65.3
7.1
0.4
1,931.2
Terr. Total
0.9 1,803.7
2.0
11.3
150.7
NE
1,638.8
0.9 0.9
1,107.6
36.9 2,077.9
2.6
8.8 495.9
4.7 180.2
0.2 7.6
0.4 59.0
10.5 1,033.0
12.3 145.0
0.4
1.4
11.9
67.9
93.7
(20.7)
0.4
37.8 4,989.6
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-51
-------
Table A-30:1993 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
Total Coal
Res. Comm.
25.7 117.3
25.7
117.3
5,063.3 2,923.3
1,348.5 743.4
883.3 447.2
75.6 14.0
389.6 109.2
172.7
0.2
6,437.5 3,783.9
Adjusted Consumption
Ind. Trans.
1,585.0
1,585.0
8,272.5
3,588.7
989.9
13.1
412.2
382.9
0.2
21.2
166.1
614.6
1,384.6
(396.0)
13,446.1
NE
NE
644.7
20,921.0
38.4
3,889.4
2,368.4
20.2
14,237.0
367.5
21,565.7
(TBtu)"
Elec.
17,195.9
17,195.9
3,537.5
1,123.8
86.5
NA
958.6
78.6
57.3
21,914.5
Terr.
9.6
9.6
459.9
104.9
62.1
3.8
4.9
128.3
155.9
469.5
Total
18,933.5
25.7
117.3
1,585.0
17,195.9
9.6
20,441.3
28,185.1
38.4
6,401.1
2,430.5
106.5
936.2
14,365.3
2,037.7
0.2
21.2
166.1
693.4
1,384.6
(396.0)
57.3
67,617.2
Emissions'1 (Tg C02 Eq.) from
Res. Comm. Ind. Trans.
2.5 11.3
2.5
11.3
268.4 155.0
94.9 53.8
65.3 33.1
5.5 1.0
24.0 6.7
13.0
0.0
365.8 220.1
149.8
149.8
438.6
267.4
73.2
1.0
25.4
28.8
0.0
1.6
11.6
62.8
92.4
(29.3)
855.7
NE
NE
34.2
1,502.1
2.7
287.6
168.2
1.2
1,014.8
27.6
1,536.3
Energy Use
Elec.
1,632.5
1,632.5
187.5
86.4
6.4
72.0
8.0
0.4
1,906.9
Terr. Total
0.9 1,796.9
2.5
11.3
149.8
NE
1,632.5
0.9 0.9
1,083.7
33.6 2,038.2
2.7
7.8 473.4
4.4 172.6
0.3 7.8
0.3 57.8
9.1 1,023.9
11.7 153.0
0.0
1.6
11.6
70.8
92.4
(29.3)
0.4
34.5 4,919.2
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-31:1992 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
Total Coal
Res. Comm.
25.6 116.6
25.6
116.6
4,804.6 2,871.2
1,365.8 788.9
931.4 481.7
65.0 11.1
369.4 106.9
189.1
0.1
6,196.0 3,776.7
Adjusted Consumption
Ind. Trans.
1,554.6
1,554.6
8,125.3
3,758.7
1,028.5
9.8
441.8
323.9
0.2
27.4
75.7
161.3
626.7
1,418.4
(354.8)
13,438.6
NE
NE
608.1
20,442.6
41.1
3,665.7
2,343.8
19.4
13,972.5
400.1
21,050.7
(TBtu)"
Elec.
16,465.6
16,465.6
3,511.5
990.7
73.5
NA
872.2
45.0
55.1
21,022.9
Terr. Total
8.8 18,171.1
25.6
116.6
1,554.6
16,465.6
8.8 8.8
19,920.7
444.9 27,791.7
41.1
91.8 6,272.6
61.3 2,405.1
3.3 89.2
11.9 949.4
122.1 14,094.7
154.6 1,939.8
0.2
27.4
75.7
161.3
671.8
1,418.4
(354.8)
55.1
453.7 65,938.6
Emissions'1 (Tg C02 Eq.) from
Res. Comm. Ind. Trans.
2.5 11.3
2.5
11.3
254.5 152.1
96.5 57.3
68.9 35.6
4.8 0.8
22.8 6.6
14.2
0.0
353.5 220.6
147.4
147.4
430.5
279.5
76.1
0.7
27.3
24.3
0.0
2.0
5.4
11.3
64.0
94.6
(26.3)
857.4
NE
NE
32.2
1,470.4
2.8
271.1
166.6
1.2
998.6
30.0
1,502.7
Energy Use
Elec.
1,569.6
1,569.6
186.0
75.5
5.4
65.5
4.6
0.4
1,831.5
Terr.
0.8
0.8
32.5
6.8
4.4
0.2
0.7
8.7
11.6
33.3
Total
1,731.6
2.5
11.3
147.4
NE
1,569.6
0.8
1,055.4
2,011.6
2.8
463.9
171.0
6.5
58.7
1,007.4
145.7
0.0
2.0
5.4
11.3
68.6
94.6
(26.3)
0.4
4,799.0
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-53
-------
Table A-32:1991 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
Total Coal
Res. Comm.
25.4 115.5
25.4
115.5
4,667.2 2,795.4
1,381.5 903.6
931.0 517.7
72.3 12.1
378.1 108.2
53.7
211.9
6,074.0 3,814.5
Adjusted Consumption
Ind. Trans.
1,602.7
1,602.7
7,827.8
3,480.5
1,050.8
11.4
342.2
122.0
270.9
(0.1)
39.0
(25.9)
147.0
587.6
1,385.9
(450.2)
12,911.1
NE
NE
620.3
19,715.8
41.7
3,449.7
2,373.6
21.1
13,605.3
224.4
20,336.1
(TBtu)"
Elec.
16,249.7
16,249.7
3,377.4
1,198.3
83.6
NA
1,085.3
29.3
54.5
20,879.8
Terr.
7.7
7.7
425.4
71.4
78.2
2.8
13.8
124.7
134.6
433.2
Total
18,001.0
25.4
115.5
1,602.7
16,249.7
7.7
19,288.1
27,105.1
41.7
6,104.1
2,451.8
98.6
863.5
13,905.6
1,927.2
(0.1)
39.0
(25.9)
147.0
616.9
1,385.9
(450.2)
54.5
64,448.7
Emissions'1 (Tg C02 Eq.) from
Res. Comm. Ind. Trans.
2.4 11.1
2.4
11.1
247.3 148.1
97.5 65.6
68.9 38.3
5.3 0.9
23.3 6.7
3.8
15.9
347.2 224.8
152.1
152.1
414.7
259.2
111
0.8
21.1
8.7
20.3
(0.0)
2.9
(1.8)
10.3
60.0
92.5
(33.3)
826.0
NE
NE
32.9
1,414.7
2.9
255.1
168.8
1.3
969.7
16.9
1,447.6
Energy Use
Elec.
1,548.2
1,548.2
178.9
90.7
6.2
81.5
3.0
0.4
1,818.2
Terr. Total
0.7 1,714.6
2.4
11.1
152.1
NE
1,548.2
0.7 0.7
1,021.8
30.9 1,958.6
2.9
5.3 451.4
5.6 174.4
0.2 7.2
0.9 53.3
8.9 991.1
10.1 144.7
(0.0)
2.9
(1.8)
10.3
63.0
92.5
(33.3)
0.4
31.6 4,695.4
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 33:1990 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
Total Coal
Res. Comm.
31.1 124.5
31.1
124.5
4,490.9 2,682.2
1,375.2 891.4
959.2 525.4
63.9 11.8
352.1 102.3
22.1
229.8
5,897.2 3,698.1
Adjusted Consumption (TBtu)a Emissions'1 (Tg C02 Eq.) from Energy Use
Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Elec. Terr. Total
1,640.5 NE 16,261.0 7.0 18,064.0 3.0 12.0 155.3 NE 1,547.6 0.6 1,718.4
31.1 3.0 3.0
124.5 12.0 12.0
1,640.5 1,640.5 155.3 155.3
NE NE NE
16,261.0 16,261.0 1,547.6 1,547.6
7.0 7.0 0.6 0.6
7,716.4 679.9 3,308.5 18,877.9 238.0 142.1 408.9 36.0 175.3 1,000.3
3,780.5 20,326.1 1,289.4 374.8 28,037.5 97.4 64.9 280.9 1,457.9 97.5 27.2 2,025.9
45.0 45.0 3.1 3.1
1,098.5 3,554.8 96.5 74.0 6,308.4 70.9 38.9 81.2 262.9 7.1 5.5 466.5
2,590.1 NA 61.0 2,651.1 184.2 4.3 188.6
12.3 2.6 90.6 4.7 0.9 0.9 0.2 6.6
380.2 22.9 14.4 871.9 21.8 6.3 23.5 1.4 0.9 53.9
36.7 13,813.0 101.0 13,972.8 1.6 2.6 983.7 7.2 995.1
364.1 300.3 1,162.6 121.8 2,178.7 17.3 27.3 22.6 87.3 9.2 163.6
0.2 0.2 0.0 0.0
50.9 50.9 3.8 3.8
53.7 53.7 3.8 3.8
125.2 125.2 8.8 8.8
591.2 30.4 621.5 60.4 3.1 63.5
1,436.5 1,436.5 95.8 95.8
(369.0) (369.0) (27.3) (27.3)
52.7 52.7 0.4 0.4
13,137.3 21,006.0 20,911.6 381.9 65,032.0 338.3 219.0 845.1 1,494.0 1,820.8 27.9 4,745.1
a Expressed as gross calorific values (i.e.:
b Consumption and/or emissions of select
NE (Not Estimated)
NA (Not Available)
higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-34), and international bunker fuel consumption (see Table A-35).
fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-55
-------
Table A-34: Unadjusted Non-Energy Fuel Consumption tTBtul
Sector/Fuel Type
Industry
Industrial Coking Coal
Industrial Other Coal
Natural Gas to Chemical
Plants, Other Uses
Asphalt & Road Oil
LPG
Lubricants
Pentanes Plus
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Still Gas
Petroleum Coke
Special Naphtha
Other (Wax/Misc.)
Distillate Fuel Oil
Waxes
Miscellaneous Products
Transportation
Lubricants
U.S. Territories
Lubricants
Other Petroleum (Misc.
Prod.)
Total
1990
4,544.0
+
8.2
305.9
1,170.2
1,201.4
186.3
125.2
347.8
753.9
36.71
123.1 1
107.1 1
7.0
33.3
137.81
176.0
176.0 1
86.7
0.7
86.0
4,806.7
1995
5,089.7
37.8
11.3
371.0
1,178.2
1,586.9
177.8
169.0
373.0
801.0
47.9
120.6
70.8
6.8
40.6
97.1
167.9
167.9
90.8
2.0
88.8
5,348.5
1996
5,151.0
24.1
11.4
388.7
1,175.9
1,652.0
172.5
177.5
479.3
729.7
2.2
118.6
74.5
6.8
48.7
89.0
163.0
163.0
121.7
1.5
120.2
5,435.7
1997
5,376.1
0.0
11.2
406.4
1,223.6
1,670.4
182.3
164.5
536.4
861.2
12.1
87.5
72.3
6.8
43.7
97.8
172.1
172.1
131.6
2.5
129.1
5,679.8
1998
5,632.6
10.9
10.4
426.6
1,262.6
1,744.4
190.8
147.0
584.0
818.7
6.2
150.8
107.3
11.7
42.4
119.0
180.2
180.2
135.0
1.3
133.8
5,947.9
1999
5,843.7
40.1
11.1
413.5
1,324.4
1,820.7
192.8
182.5
502.1
811.1
23.0
216.0
145.4
11.7
37.4
111.9
182.1
182.1
139.3
1.4
138.0
6,165.1
2000
5,576.8
53.6
12.4
401.7
1,275.7
1,759.3
189.9
171.6
613.5
722.2
17.0
98.7
97.4
11.7
33.1
119.2
179.4
179.4
152.2
3.1
149.1
5,908.3
2001
5,263.7
24.8
11.3
391.8
1,256.9
1,642.3
174.0
131.6
493.7
662.5
49.3
174.3
78.5
11.7
36.3
124.9
164.3
164.3
80.3
0.0
80.3
5,508.4
2002 2003 2004 2005 2006 2007 2008 2009 2010
5,425.7 5,342.9 5,847.8 5,483.3 5,470.0 5,225.2 4,770.3 4,510.2 4,764.6
40.3 51.9 167.8 80.5 62.9 2.3 29.2 6.4 64.8
12.0 11.9 11.9 11.9 11.9 11.9 11.9 11.9 10.3
380.7 345.3 306.6 270.4 233.4 233.6 233.6 233.6 311.8
1,240.0 1,219.5 1,303.8 1,323.2 1,261.2 1,197.0 1,012.0 873.1 877.8
1,766.3 1,701.6 1,768.5 1,659.5 1,734.6 1,726.7 1,596.6 1,748.0 1,901.6
171.9 159.0 161.0 160.2 156.1 161.2 149.6 134.5 149.5
111.9 110.4 111.2 98.1 70.1 89.7 76.5 63.8 77.7
582.6 613.0 749.4 698.7 628.9 562.5 477.2 471.9 490.6
632.1 699.4 779.5 708.0 790.6 744.1 647.8 424.8 452.5
61.7 59.0 62.9 67.7 57.2 44.2 47.3 133.9 147.8
145.8 122.8 218.3 186.9 213.6 201.2 225.1 180.7 61.0
102.4 80.5 51.0 62.5 70.1 78.0 84.9 46.2 26.1
11.7 11.7 11.7 11.7 17.5 17.5 17.5 17.5 17.5
32.2 31.1 30.8 31.4 26.2 21.9 19.1 12.2 17.1
134.2 126.0 113.4 112.8 136.0 133.5 142.0 151.8 158.7
162.4 150.1 152.1 151.3 147.4 152.2 141.3 127.1 141.2
162.4 150.1 152.1 151.3 147.4 152.2 141.3 127.1 141.2
140.2 123.5 110.8 121.9 133.4 108.4 132.1 59.6 123.6
3.0 4.9 5.1 4.6 6.2 5.9 2.7 1.0 1.0
137.2 118.6 105.7 117.3 127.2 102.5 129.4 58.5 122.6
5,728.3 5,616.5 6,110.7 5,756.6 5,750.8 5,485.9 5,043.7 4,696.8 5,029.4
2011
4,729.4
60.8
10.3
311.8
859.5
1,996.1
141.8
27.3
487.3
388.5
163.6
62.4
22.6
17.5
15.1
164.7
133.9
133.9
123.6
1.0
122.6
4,987.0
2012
4,629.1
122.4
10.3
311.8
826.7
2,003.9
130.5
45.9
453.9
287.2
161.1
66.3
14.7
17.5
15.3
161.6
123.2
123.2
123.6
1.0
122.6
4,876.0
Note: These values are unadjusted non-energy fuel use provided by EIA. They have not yet been adjusted to remove petroleum feedstock exports and processes accounted for in the Industrial Processes Chapter.
+ Does not exceed 0.05 TBtu.
Table A-35: International Bunker Fuel Consumption (TBtu)
Fuel Type
Marine Residual Fuel Oil
Marine Distillate Fuel Oil &
Other
Aviation Jet Fuel
Total
1990
715.7
158.ol
539.4
1,413.1
1995
523.2
125.7
703.4
1,352.3
1996
536.4
114.1
718.3
1,368.8
1997
575.2
125.5
754.4
1,455.1
1998
594.8
158.8
748.8
1,502.4
1999
489.7
113.6
796.9
1,400.3
2000
444.1
85.9
880.1
1,410.0
2001
426.0
72.4
799.7
1,298.1 1
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
448.9 471.8 553.1 581.0 599.4 607.5 654.6 604.8 619.8 518.4
82.6 103.9 143.6 126.9 119.3 111.3 122.2 111.0 128.2 107.4
774.8 783.0 797.7 853.1 855.6 872.7 796.8 749.1 865.4 919.9
,306.3 1,358.7 1,494.4 1,561.0 1,574.2 1,591.5 1,573.6 1,464.9 1,613.4 1,545.7
2012
459.5
91.7
916.3
1,467.4
Note: Further information on the calculation of international bunker fuel consumption of aviation jet fuel is provided in Annex 3.3: Methodology for Estimating Emissions from Commercial Aircraft Jet Fuel Consumption.
A-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 36: Key Assumptions for Estimating Clh Emissions
C Content Coefficient
Fuel Type (Tg C/QBtu)
Coal
Residential Coal
Commercial Coal
Industrial Coking Coal
Industrial Other Coal
Electric Power Coal
U.S. Territory Coal (bit)
Pipeline Natural Gas
Flare Gas a
Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil No. 1
Distillate Fuel Oil No. 2"
Distillate Fuel Oil No. 4
Jet Fuel
Kerosene
LPG (energy use)
LPG (non-energy use)
Lubricants
Motor Gasoline
Residual Fuel Oil No. 5
Residual Fuel Oil No. 6 b
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Misc. Products (Territories)
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
[a]
[a]
[a]
[a]
[a]
25.14
[a]
14.92
20.55
18.86
19.98
20.17
20.47
[a]
19.96
[a]
[a]
20.20
[a]
19.89
20.48
18.87
[a]
[a]
[a]
20.00
18.55
20.17
19.10
27.85
18.20
19.74
[a]
19.80
2.05
a Flare gas is not used in the C02 from fossil fuel combustion calculations and is presented for informational purposes only.
b Distillate fuel oil No.2 and residual fuel oil No. 6 are used in the C02 from fossil fuel combustion calculations, and other oil types are presented for informational
purposes only. An additional discussion on the derivation of these carbon content coefficients is presented in Annex 2.2.
Sources: C coefficients from EIA (2009b) and EPA 201 Oa.
[a] These coefficients vary annually due to fluctuations in fuel quality (see Table A- 37)
A-57
-------
Table A- 37: Annually Variable C Content Coefficients by Year tTg C/QBtul
Fuel Type
1990
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Residential Coal 26.20
Commercial Coal 26.00
Industrial Coking Coal 31.00
Industrial Other Coal 25.82
Electric Power Coal 25.96
Pipeline Natural Gas 14.45
LPG (energy use) 16.86
LPG (non-energy use) 17.06
Motor Gasoline 19.42
Jet Fuel 19.40
MoGas Blend Components 19.42
Misc. Products 20.15
Unfinished Oils 20.15
Crude Oil 20.15
26.13 26.04 25.90 26.07 25.98 26.01 26.00 25.98 26.04 25.91 26.09 26.29 25.94 25.71* 25.71* 25.71* 25.71* 25.71*
26.13 26.04 25.90 26.07 25.98 26.01 26.00 25.98 26.04 25.91 26.09 26.29 25.94 25.71 25.71 25.71 25.71 25.71
31.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00
25.75 25.75 25.79 25.80 25.74 25.66 25.57 25.55 25.56 25.80 25.84 25.82 25.82 25.82 25.82 25.82 25.82
25.93 25.95 25.98 26.00 26.00 26.05 26.09 26.10 26.09 26.04 26.05 26.05 26.05 26.05 26.05 26.05
14.46 14.44 14.46 14.47 14.46 14.46 14.44 14.46 14.46 14.46 14.46 14.46 14.46 14.46 14.46 14.46
16.82 16.84 16.81 16.86 16.89 16.87 16.85 16.86 16.84 16.84 16.83 16.82 16.83 16.83 16.83 16.83 16.83
17.08 17.07 17.09 17.10 17.09 17.09 17.07 17.06 17.06 17.05 17.06 17.06 17.06 17.06 17.06
19.37 19.32 19.33 19.34 19.38 19.36 19.38 19.36 19.45 19.56 19.46 19.46 19.46 19.46 19.46
19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70
19.37 19.32 19.33 19.34 19.38 19.36 19.38 19.36 19.45 19.56 19.46 19.46 19.46 19.46 19.46
20.22 20.17 20.22 20.27 20.28 20.25 20.31 20.31 20.28 20.28 20.31 20.31 20.31 20.31 20.31
20.22 20.17 20.22 20.27 20.28 20.25 20.31 20.31 20.28 20.28 20.31 20.31 20.31 20.31 20.31
20.22 20.17 20.22 20.27 20.28 20.25 20.31 20.31 20.28 20.28 20.31 20.31 20.31 20.31 20.31
25.80
25.93 25.93
14.46 14.46
16.82
17.09 17.10
19.36 19.35
19.34 19.70
19.36 19.35
17.08
19.36
19.70
19.36
20.21 20.23 20.22
20.21 20.23 20.22
20.21 20.23 20.22
*U.S. EIA discontinued collection of residential sector coal consumption data in 2008, because consumption of coal in the residential sector is extremely limited. Therefore, the number cited here is developed from
commercial/institutional consumption.
Source: EPA (201 Oa)
Table A-38: Electricity Consumption by End-Use Sector (Billion Kilowatt-Hours)
End-Use Sector
Residential
Commercial
Industrial
Transportation
Total
1990
924
838
1,070
5
2,837
1995
1,043
953
1,163
5
3,164
1996
1,083
980
1,186
5
3,254
1997
1,076
1,027
1,194
5
3,302
1998
1,130
1,078
1,212
5
3,425
1999
1,145
1,104
1,230
5
3,484
2000
1,192
1,159
1,235
5
3,592
2001
1,202
1,191
1,159
6
3,557
2002
1,265
1,205
1,156
6
3,632
2003
1,276
1,199
1,181
7
3,662
2004
1,292
1,230
1,186
7
3,716
2005
1,359
1,275
1,169
8
3,811
2006
1,352
1,300
1,158
7
3,817
2007
1,392
1,336
1,154
8
3,890
2008
1,380
1,336
1,141
8
3,865
2009
1,364
1,307
1,044
8
3,724
2010
1,446
1,330
1,103
8
3,886
2011
1,423
1,328
1,124
8
3,883
2012
1,375
1,327
1,123
7
3,832
Note: Does not include the U.S. territories.
Source: EIA (2014)
A-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
2.2. Methodology for Estimating the Carbon Content of Fossil Fuels
This sub-annex presents the background and methodology for estimating the carbon (C) content of fossil fuels
combusted in the United States. The C content of a particular fossil fuel represents the maximum potential emissions to
the atmosphere if all C in the fuel is oxidized during combustion. The C content coefficients used in past editions of this
report were developed using methods first outlined in the U.S. Energy Information Administration's (EIA) Emissions of
Greenhouse Gases in the United States: 1987-1992 (1994) and were developed primarily by EIA. For this report, EPA
has updated many of the C content coefficients based on carbon dioxide emission factors developed for the Mandatory
Reporting of Greenhouse Gases Rule, signed in September 2009 (EPA, 2009b, 2010). This sub-annex describes an
updated methodology for estimating the C content of natural gas, and presents a time-series analysis of changes in U.S. C
content coefficients for coal, petroleum products and natural gas. A summary of C content coefficients used in this report
appears in Table A- 39.
Though the methods for estimating C contents for coal, natural gas, and petroleum products differ in their
details, they each follow the same basic approach. First, because C coefficients are presented in terms of mass per unit
energy (i.e., teragrams C per quadrillion Btu or Tg C/QBtu), those fuels that are typically described in volumetric units
(petroleum products and natural gas) are converted to units of mass using an estimated density. Second, C contents are
derived from fuel sample data, using descriptive statistics to estimate the C share of the fuel by weight. The heat content
of the fuel is then estimated based on the sample data, or where sample data are unavailable or unrepresentative, by default
values that reflect the characteristics of the fuel as defined by market requirements. A discussion of each fuel appears
below.
The C content of coal is described first because approximately one-third of all U.S. C emissions from fossil fuel
combustion are associated with coal consumption. The methods and sources for estimating the C content of natural gas
are provided next. Approximately one-fifth of U.S. greenhouse gas emissions from fossil fuel combustion are attributable
to natural gas consumption. Finally, this sub-annex examines C contents of petroleum products. U.S. energy consumption
statistics account for more than 20 different petroleum products.
A-59
-------
Table A- 39: Carbon Content Coefficients Used in this Report tTg Carhon/QBtul
Fuel Type
Coal
Residential Coal3
Commercial Coal3
Industrial Coking Coal3
Industrial Other Coal3
Utility Coal3'"
Pipeline Natural Gasc
Flare Gas
Petroleum
Asphalt and Road Oil
Aviation Gasoline
Distillate Fuel Oil No. 1
Distillate Fuel Oil No. 2
Distillate Fuel Oil No. 4
Jet Fuel3
Kerosene
LPG (energy use)c
LPG (non-energy use)c
Lubricants
Motor Gasoline0
Residual Fuel No. 5
Residual Fuel No. 6
Other Petroleum
Av. Gas Blend Comp.
Mo. Gas Blend Compc
Crude Oilc
Misc. Products0
Misc. Products (Terr.)
Naphtha (<401 deg. F)
Other oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils0
Waxes
Other Wax and Misc.
Geothermal
1990
26.20
26.20
25.53
25.82
25.96
14.45
15.31
20.55
18.86
19.98
20.17
20.47
19.40
19.96
16.86
17.06
20.20
19.42
19.89
20.48
18.87
19.42
20.15
20.15
20.15
18.55
20.17
19.10
27.85
18.20
19.74
20.15
19.80
19.80
2.05
1995
26.13
26.13
25.57
25.80
25.93
14.46
15.31
20.55
18.86
19.98
20.17
20.47
19.34
19.96
16.82
17.09
20.20
19.36
19.89
20.48
18.87
19.36
20.21
20.21
20.21
18.55
20.17
19.10
27.85
18.20
19.74
20.21
19.80
19.80
2.05
1996 1997
26.04 25.90
26.04 25.90
25.56 25.59
25.75 25.75
25.93 25.93
14.46 14.46
15.31 15.31
20.55 20.55
18.86 18.86
19.98 19.98
20.17 20.17
20.47 20.47
19.70 19.70
19.96 19.96
16.82 16.84
17.10 17.08
20.20 20.20
19.35 19.36
19.89 19.89
20.48 20.48
18.87 18.87
19.35 19.36
20.23 20.22
20.23 20.22
20.23 20.22
18.55 18.55
20.17 20.17
19.10 19.10
27.85 27.85
18.20 18.20
19.74 19.74
20.23 20.22
19.80 19.80
19.80 19.80
2.05 2.05
1998 1999
26.07 25.98
26.07 25.98
25.62 25.59
25.79 25.80
25.95 25.98
14.44 14.46
15.31 15.31
20.55 20.55
18.86 18.86
19.98 19.98
20.17 20.17
20.47 20.47
19.70 19.70
19.96 19.96
16.81 16.86
17.08 17.07
20.20 20.20
19.37 19.32
19.89 19.89
20.48 20.48
18.87 18.87
19.37 19.32
20.22 20.17
20.22 20.17
20.22 20.17
18.55 18.55
20.17 20.17
19.10 19.10
27.85 27.85
18.20 18.20
19.74 19.74
20.22 20.17
19.80 19.80
19.80 19.80
2.05 2.05
2000
26.01
26.01
25.63
25.74
26.00
14.47
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.89
17.09
20.20
19.33
19.89
20.48
18.87
19.33
20.22
20.22
20.22
18.55
20.17
19.10
27.85
18.20
19.74
20.22
19.80
19.80
2.05
2001 2002
26.00 25.98
26.00 25.98
25.63 25.65
25.66 25.57
26.00 26.05
14.46 14.46
15.31 15.31
20.55 20.55
18.86 18.86
19.98 19.98
20.17 20.17
20.47 20.47
19.70 19.70
19.96 19.96
16.87 16.85
17.10 17.09
20.20 20.20
19.34 19.38
19.89 19.89
20.48 20.48
18.87 18.87
19.34 19.38
20.27 20.28
20.27 20.28
20.27 20.28
18.55 18.55
20.17 20.17
19.10 19.10
27.85 27.85
18.20 18.20
19.74 19.74
20.27 20.28
19.80 19.80
19.80 19.80
2.05 2.05
2003 2004
26.04 25.91
26.04 25.91
25.63 25.63
25.55 25.56
26.09 26.10
14.44 14.46
15.31 15.31
20.55 20.55
18.86 18.86
19.98 19.98
20.17 20.17
20.47 20.47
19.70 19.70
19.96 19.96
16.86 16.84
17.09 17.07
20.20 20.20
19.36 19.38
19.89 19.89
20.48 20.48
18.87 18.87
19.36 19.38
20.25 20.31
20.25 20.31
20.25 20.31
18.55 18.55
20.17 20.17
19.10 19.10
27.85 27.85
18.20 18.20
19.74 19.74
20.25 20.31
19.80 19.80
19.80 19.80
2.05 2.05
2005 2006
26.09 26.29
26.09 26.29
25.60 25.60
25.80 25.84
26.09 26.04
14.46 14.46
15.31 15.31
20.55 20.55
18.86 18.86
19.98 19.98
20.17 20.17
20.47 20.47
19.70 19.70
19.96 19.96
16.84 16.83
17.06 17.06
20.20 20.20
19.36 19.45
19.89 19.89
20.48 20.48
18.87 18.87
19.36 19.45
20.31 20.28
20.31 20.28
20.31 20.28
18.55 18.55
20.17 20.17
19.10 19.10
27.85 27.85
18.20 18.20
19.74 19.74
20.31 20.28
19.80 19.80
19.80 19.80
2.05 2.05
2007 2008
25.94 25.71*
25.94 25.71
25.61 25.61
25.82 25.82
26.05 26.05
14.46 14.46
15.31 15.31
20.55 20.55
18.86 18.86
19.98 19.98
20.17 20.17
20.47 20.47
19.70 19.70
19.96 19.96
16.82 16.83
17.05 17.06
20.20 20.20
19.56 19.46
19.89 19.89
20.48 20.48
18.87 18.87
19.56 19.46
20.28 20.31
20.28 20.31
20.28 20.31
18.55 18.55
20.17 20.17
19.10 19.10
27.85 27.85
18.20 18.20
19.74 19.74
20.28 20.31
19.80 19.80
19.80 19.80
2.05 2.05
2009 2010
25.71* 25.71*
25.71 25.71
25.61 25.61
25.82 25.82
26.05 26.05
14.46 14.46
15.31 15.31
20.55 20.55
18.86 18.86
19.98 19.98
20.17 20.17
20.47 20.47
19.70 19.70
19.96 19.96
16.83 16.83
17.06 17.06
20.20 20.20
19.46 19.46
19.89 19.89
20.48 20.48
18.87 18.87
19.46 19.46
20.31 20.31
20.31 20.31
20.31 20.31
18.55 18.55
20.17 20.17
19.10 19.10
27.85 27.85
18.20 18.20
19.74 19.74
20.31 20.31
19.80 19.80
19.80 19.80
2.05 2.05
2011 2012
25.71* 25.71*
25.71 25.71
25.61 25.61
25.82 25.82
26.05 26.05
14.46 14.46
15.31 15.31
20.55 20.55
18.86 18.86
19.98 19.98
20.17 20.17
20.47 20.47
19.70 19.70
19.96 19.96
16.83 16.83
17.06 17.06
20.20 20.20
19.46 19.46
19.89 19.89
20.48 20.48
18.87 18.87
19.46 19.46
20.31 20.31
20.31 20.31
20.31 20.31
18.55 18.55
20.17 20.17
19.10 19.10
27.85 27.85
18.20 18.20
19.74 19.74
20.31 20.31
19.80 19.80
19.80 19.80
2.05 2.05
*U.S. EIA discontinued collection of residential sector coal consumption data in 2008, because consumption of coal in the residential sector is extremely limited. Therefore, the number cited here is developed from
commercial/institutional consumption.
A-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
a C contents vary annually based on changes in annual mix of production and end-use consumption of coal from each producing state.
b C content for utility coal used in the electric power calculations. All coefficients based on higher heating value. Higher heating value (gross heating value) is the total amount of heat released when a fuel is burned. Coal,
crude oil, and natural gas all include chemical compounds of carbon and hydrogen. When those fuels are burned, the carbon and hydrogen combine with oxygen in the air to produce C02 and water. Some of the energy
released in burning goes into transforming the water into steam and is usually lost. The amount of heat spent in transforming the water into steam is counted as part of gross heat content. Lower heating value (net heating
value), in contrast, does not include the heat spent in transforming the water into steam. Using a simplified methodology based on International Energy Agency defaults, higher heating value can be converted to lower
heating value for coal and petroleum products by multiplying by 0.95 and for natural gas by multiplying by 0.90. Carbon content coefficients are presented in higher heating value because U.S. energy statistics are reported
by higher heating value.
c C contents vary annually based on changes in fuel composition.
A-61
-------
Coal
Approximately one-third of all U.S. CC>2 emissions from fossil fuel combustion are associated with coal
consumption. Although the IPCC guidelines provide C contents for coal according to rank, it was necessary to develop C
content coefficients by consuming sector to match the format in which coal consumption is reported by EIA. Because the
C content of coal varies by the state in which it was mined and by coal rank, and because the sources of coal for each
consuming sector vary by year, the weighted average C content for coal combusted in each consuming sector also varies
over time. A time series of C contents by coal rank and consuming sector appears in Table A- 40.8
Methodology
The methodology for developing C contents for coal by consuming sector consists of four steps. An additional
step has been taken to calculate C contents by coal rank to facilitate comparison with IPCC default values.
Step 1. Determine carbon contents by rank and by state of origin
C contents by rank and state of origin are estimated on the basis of 7,092 coal samples, 6,588 of which were
collected by the U.S. Geological Survey (USGS 1998) and 504 samples that come from the Pennsylvania State University
database (PSU 2010). These coal samples are classified according to rank and state of origin. For each rank in each state,
the average heat content and C content of the coal samples are calculated based on the proximate (heat) and ultimate
(percent carbon) analyses of the samples. Dividing the C content (reported in pounds CCb) by the heat content (reported
in million Btu or MMBtu) yields an average C content coefficient. This coefficient is then converted into units of Tg
C/QBtu.
Step 2. Determine weighted average carbon content by state
C contents by rank and origin calculated in Step 1 are then weighted by the annual share of state production
that was each rank. State production by rank is obtained from the EIA. This step yields a single carbon content per
state that varies annually based on production. However, most coal-producing states produce only one rank of coal.
For these states the weighted factor equals the carbon content calculated in Step 1 and is constant across the time
series.
Step 3. Allocate sectoral consumption by state of origin
U.S. energy statistics9 through 2007 provide data on the origin of coal used in four areas: 1) the electric
power industry, 2) industrial coking, 3) all other industrial uses, and 4) the residential and commercial end-use
sectors.10 Because U.S. energy statistics do not provide the distribution of coal rank consumed by each consuming
sector, it is assumed that each sector consumes a representative mixture of coal ranks from a particular state that
matches the mixture of all coal produced in that state during the year. Thus, the weighted state-level factor
developed in Step 2 is applied.
Step 4. Weight sectoral carbon contents to reflect the rank and state of origin of coal consumed
Sectoral C contents are calculated by multiplying the share of coal purchased from each state by the state's
weighted C content estimated in Step 2. The resulting partial C contents are then totaled across all states to generate a
national sectoral C content.
Csector = SstatelxCstatel + Sstate2xCstate2 +... . + Sstate50xCstate50
where,
For a comparison to earlier estimated carbon contents please see Chronology and Explanation of Changes in Individual Carbon
Content Coefficients of Fossil Fuels near the end of this annex.
9 U.S. Energy Information Administration (EIA). Coal Distribution -Annual (2001-2008); and Coal Industry Annual (1990-2000).
Beginning in 2008, the EIA collects and reports data on commercial and institutional coal consumption, rather than residential and
commercial consumption. Thus, the residential / commercial coal coefficient reported in Table A- 39 for 2009 represents the mix of coal
consumed by commercial and institutional users. Currently, only an extremely small amount of coal is consumed in the U.S. Residential
Sector.
A-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The C content by consuming sector;
The portion of consuming sector coal consumption attributed to production from a given state;
The estimated weighted C content of all ranks produced in a given state.
A-63
-------
Table A- 40: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank tTg C/QBtul 0990-20121
Consuming Sector _ 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Electric Power 2511 25l3 25l3 25l3 25l5 25l8 261)0 261)0 26~05 26~09 2610 26~09 26~04 26~05 26~05 261)5 261)5 26~05 261T
Industrial Coking 25.53 25.57 25.56 25.59 25.62 25.59 25.63 25.63 25.65 25.63 25.63 25.60 25.60 25.61 25.61 25.61 25.61 25.61 25.61
Other Industrial 25.82 25.80 25.75 25.75 25.79 25.80 25.74 25.66 25.57 25.55 25.56 25.80 25.84 25.82 25.82 25.82 25.82 25.82 25.82
Residential/ Commercial 26.20 26.13 26.04 25.90 26.07 25.98 26.01 26.00 25.98 26.04 25.91 26.09 26.29 25.94 25.71 25.71 25.71 25.71 25.71
Coal Rank
Anthracite 28~28 28~28 28~28 28~28 28~28 28~28 28~28 28~28 28~28 2^28 2^28 2^28 2^28 2^28 2^28 2^28 2^28 2^28
Bituminous 25.38 25.42 25.43 25.43 25.43 25.44 25.45 25.46 25.46 25.45 25.45 25.45 25.45 25.45 25.44 25.44 25.44 25.44 25.44
Sub-bituminous 26.50 26.50 26.50 26.50 26.50 26.50 26.49 26.50 26.50 26.50 26.50 26.50 26.50 26.50 26.50 26.50 26.50 26.50 26.50
Lignite _ 26.58 26.59 26.58 26.59 26.59 26.60 26.61 26.62 26.63 26.62 26.62 26.62 26.62 26.64 26.65 26.65 26.65 26.65 26.65
a In 2008, the EIA began collecting consumption data for commercial and institutional consumption rather than commercial and residential consumption.
Sources: C content coefficients calculated from USGS (1998) and PSU (2010); data presented in EPA (201 Ob).
A-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Step 5. Develop national-level carbon contents by rank for comparison to IPCC defaults
Although not used to calculate emissions, national-level C contents by rank are more easily compared to C
contents of other countries than are sectoral C contents. This step requires weighting the state-level C contents by rank
developed under Step 1 by overall coal production by state and rank. Each state-level C content by rank is multiplied by
the share of national production of that rank that each state represents. The resulting partial C contents are then summed
across all states to generate an overall C content for each rank.
Nrank = Prankl x Crankl + Prank2 x Crank2 +... + Pranknx Crankn
where,
Nrank = The national C content by rank;
Prank = The portion of U. S. coal production of a given rank attributed to each state; and
Crank = The estimated C content of a given rank in each state.
Data Sources
The ultimate analysis of coal samples was based on the 7,092 coal samples, 6,588 of which are from USGS
(1998) and 504 that come from the Pennsylvania State University Coal Database (PSU 2010). Data contained in the
USGS's CoalQual Database are derived primarily from samples taken between 1973 and 1989, and were largely reported
in State Geological Surveys. Data in the PSU Coal Database are mainly from samples collected by PSU since 1967 and
are housed at the PSU Sample Bank. Only the subset of PSU samples that are whole-seam channel samples are included
in the development of carbon factors in order to increase data accuracy.
Data on coal consumption by sector and state of origin, as well as coal production by state and rank, were
obtained from EIA. The EIA's Annual Coal Report is the source for state coal production by rank from 2001-2008. In
prior years, the EIA reported this data in its Coal Industry Annual. Data for coal consumption by state of origin and
consuming sector for 2001 to 2008 was obtained from the EIA's Coal Distribution —Annual. For 1990-2000, end-use data
was obtained from the Coal Industry Annual.
Uncertainty
C contents vary considerably by state. Bituminous coal production and sub-bituminous coal production
represented 47.3 percent and 46.1 percent of total U.S. supply in 2008, respectively. State average C content coefficients
for bituminous coal vary from a low of 85.59 kg CC>2 per MMBtu in Texas to a high of 105.21 kg CC>2 per MMBtu in
Montana. However, Texas bituminous coal is considered anomalous, ^ has not been produced since 2004 and production
since 1990 peaked at just 446,000 short tons in 1996. The next lowest average emission factor for bituminous coal is
found in Western Kentucky (91.36 kg CCh per MMBtu). In 2000, Montana produced no bituminous coal and Western
Kentucky production accounted for just 4.5 percent of overall bituminous production. In 2008, more than 60 percent of
bituminous coal was produced in three states: West Virginia, Kentucky (predominantly from the Eastern production
region), and Pennsylvania, and this share has remained fairly constant since 1990. These three states show a variation in C
content for bituminous coals of +0.7 percent, based on more than 2,000 samples (see Table A-41).
Similarly, the C content coefficients for sub-bituminous coal range from 91.29 kg CC>2 per MMBtu in Utah to
98.10 kg CC>2 per MMBtu in Alaska. However, Utah has no recorded production of sub-bituminous coal since 1990.
Production of sub-bituminous coal in Alaska has made up less than 0.7 percent of total sub-bituminous production since
1990, with even this small share declining over time. Wyoming has represented between 75 percent and 87 percent of
total sub-bituminous coal production in the United States in each year since 1990. Thus, the C content coefficient for
Wyoming (97.22 kg CC>2 per MMBtu), based on 455 samples, dominates the national average.
The interquartile range of C content coefficients among samples of sub-bituminous coal in Wyoming was +1.5
percent from the mean. Similarly, this range among samples of bituminous coal from West Virginia, Kentucky, and
Pennsylvania was +1.2 percent or less for each state. The large number of samples and the low variability within the
sample set of the states that represent the predominant source of supply of U.S. coal suggest that the uncertainty in this
factor is very low, on the order of +1.0 percent.
11 See, for example: San Filipo, 1999. USGS. (U.S. Geological Survey Open-File Report 99-301), Ch. 4.
A-65
-------
For comparison, J. Quick (2010) completed an analysis similar in methodology to that used here, in order to
generate national average carbon emission factors as well as county-level factors. This study's rank-based national
average factors have a maximum deviation from the factors developed in this Inventory report of -0.55 percent, which is
for lignite (range: -0.55 to +0.1 percent). This corroboration further supports the assertion of minimal uncertainty in the
application of the rank-based factors derived for the purposes of this Inventory.
Table A-41: Variability in Carbon Content Coefficients by Rank Across States [Kilograms Clh Per MMBtul
State
Alabama
Alaska
Arizona
Arkansas
Colorado
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maryland
Massachusetts
Michigan
Mississippi
Missouri
Montana
Nevada
New Mexico
North Dakota
Ohio
Oklahoma
Pennsylvania
Tennessee
Texas
Utah
Virginia
Washington
West Virginia
Wyoming
U.S. Average
Number of
Samples
951
91
15
80
318
35
1
57
146
100
29
897
1
47
3
3
8
111
309
2
185
202
674
63
861
61
64
169
470
18
612
503
7,092
Bituminous
92.84
98.33
93.94
96.36
94.37
95.01
-
92.33
92.65
91.87
90.91
92.61
-
94.29
-
92.88
-
91.71
105.21
94.41
94.29
-
91.84
92.33
93.33
92.82
85.59
95.75
93.51
94.53
93.84
94.80
93.13
Sub-
bituminous Anthracite
-
98.10
97.34
-
96.52
-
94.90
-
-
-
-
-
-
-
114.82
-
-
-
97.73 103.60
-
94.89 103.92
93.97
-
-
103.68
-
94.19
91.29
98.54
97.36 102.53
-
97.22
96.94 104.29
Lignite
99.10
98.65
-
94.97
101.10
-
-
-
-
-
-
-
96.01
-
-
-
98.19
-
99.40
99.86
-
99.48
-
-
-
-
94.47
-
-
106.55
-
-
98.63
Notes: - Indicates No Sample Data Available.
Sources: Calculated from USGS (1998), and PSU (2010);
data presented in EPA (2010).
Natural Gas
Natural gas is predominantly composed of methane, which is 75 percent C by weight and contains 14.2 Tg
C/QBtu (higher heating value), but it may also contain many other compounds that can lower or raise its overall C content.
These other compounds may be divided into two classes: 1) natural gas liquids (NGLs), and 2) non-hydrocarbon gases.
The most common NGLs are ethane (X^Hs), propane (CsHs), butane (C/JTio), and, to a lesser extent, pentane (CjHn) and
hexane (CeHn). Because the NGLs have more C atoms than CLU (which has only one), their presence increases the
overall C content of natural gas. NGLs have a commercial value greater than that of CH/i, and therefore are usually
separated from raw natural gas at gas processing plants and sold as separate products. Ethane is typically used as a
A-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
petrochemical feedstock, propane and butane have diverse uses, and natural gasoline12 contributes to the gasoline/naphtha
"octane pool," used primarily to make motor gasoline.
Raw natural gas can also contain varying amounts of non-hydrocarbon gases, such as CC>2, nitrogen, helium and
other noble gases, and hydrogen sulfide. The share of non-hydrocarbon gases is usually less than 5 percent of the total,
but there are individual natural gas reservoirs where the share can be much larger. The treatment of non-hydrocarbon
gases in raw gas varies. Hydrogen sulfide is always removed. Inert gases are removed if their presence is substantial
enough to reduce the energy content of the gas below pipeline specifications (see Step 1, below). Otherwise, inert gases
will usually be left in the natural gas. Because the raw gas that is usually flared (see Step 2, below) contains NGLs and
CC>2, it will typically have a higher overall C content than gas that has been processed and moved to end-use customers via
transmission and distribution pipelines.
Methodology
The methodology for estimating the C contents of pipeline and flared natural gas can be described in five steps.
Step 1. Define pipeline-qualify natural gas
In the United States, pipeline-quality natural gas is required to have an energy content greater than 970 Btu per
cubic foot, but less than 1,100 Btu per cubic foot. Hydrogen sulfide content must be negligible. Typical pipeline-quality
natural gas is about 95 percent CH4, 3 percent NGLs, and 2 percent non-hydrocarbon gases, of which approximately half is
CO2.
However, there remains a range of gas compositions that are consistent with pipeline specifications. The
minimum C content coefficient for natural gas would match that for pure CH4, which equates to an energy content of
1,005 Btu per standard cubic foot. Gas compositions with higher or lower Btu content tend to have higher C emission
factors, because the "low" Btu gas has a higher content of inert gases (including CC>2 offset with more NGLs), while
"high" Btu gas tends to have more NGLs.
flared:
Step 2. Define flared gas
Every year, a certain amount of natural gas is flared in the United States. There are several reasons that gas is
• There may be no market for some batches of natural gas, the amount may be too small or too variable, or the
quality might be too poor to justify treating the gas and transporting it to market (such is the case when gas
contains large shares of CCh). Most natural gas that is flared for these reasons is "rich" associated gas, with
relatively high energy content, high NGL content, and a high C content.
• Gas treatment plants may flare substantial volumes of natural gas because of "process upsets," because the gas is
"off spec," or possibly as part of an emissions control system. Gas flared at processing plants may be of variable
quality.
Data on the energy content of flare gas, as reported by states to EIA, indicate an average energy content of 1,130
Btu per standard cubic foot (EIA 1994). Flare gas may have an even higher energy content than reported by EIA since
rich associated gas can have energy contents as high as 1,300 to 1,400 Btu per cubic foot.
Step 3. Determine a relationship between carbon content and heat content
A relationship between C content and heat content may be used to develop a C content coefficient for natural gas
consumed in the United States. In 1994, EIA examined the composition (including C contents) of 6,743 samples of
pipeline-quality natural gas from utilities and/or pipeline companies in 26 cities located in 19 states. To demonstrate that
these samples were representative of actual natural gas "as consumed" in the United States, their heat content was
compared to that of the national average. For the most recent year, the average heat content of natural gas consumed in the
United States was 1,029 Btu per cubic foot, and has varied by less than 1 percent (1,027 to 1,029 Btu per cubic foot) over
the past 5 years. Meanwhile, the average heat content of the 6,743 samples was 1,027 Btu per cubic foot, and the median
heat content was 1,031 Btu per cubic foot. Thus, the average heat content of the sample set falls well within the typical
12 A term used in the gas processing industry to refer to a mixture of liquid hydrocarbons (mostly pentanes and heavier hydrocarbons)
extracted from natural gas.
A-67
-------
range of natural gas consumed in the United States, suggesting that these samples continue to be representative of natural
gas "as consumed" in the United States. The average and median composition of these samples appear in Table A-42.
Table A-42: Composition of Natural Gas (Percent)
Compound
Methane
Ethane
Propane
Higher Hydrocarbons
Non-hydrocarbons
Higher Heating Value (Btu per cubic foot)
Average
93.07
3.21
0.59
0.32
2.81
1,027
Median
95.00
2.79
0.48
0.30
1.43
1,031
Source: Gas Technology Institute (1992).
Carbon contents were calculated for a series of sub-samples based on their CCh content and heat content. Carbon
contents were calculated for the groups of samples with less than 1.0 percent (n=5,181) and less than 1.5 percent CC>2 only
(n=6,522) and those with less than 1.0 or 1.5 percent CC>2 and less than 1,050 Btu/cf (n=4,888 and 6,166, respectively).
These stratifications were chosen to exclude samples with CC>2 content and heat contents outside the range of pipeline-
quality natural gas. In addition, hexane was removed from the samples since it is usually stripped out of raw natural gas
before delivery because it is a valuable natural gas liquid used as a feedstock for gasoline. The average carbon contents
for the four separate sub-samples are shown below in Table A-43.
Table A-43: Carbon Content of Pipeline-Quality Natural Gas by CQ2 and Heat Content (Tg C/QBtu)
Sample Average Carbon Content
Full Sample 14.48
<1.0%C02 14.43
<1.5%C02 14.47
< 1.0%C02 and <1,050 Btu/cf 14.42
< 1.5 %CQ2 and <1,050 Btu/cf 14.47
Source: EPA (2010).
Step 4. Apply carbon content coefficients developed in Step 3 to pipeline natural gas
A regression analysis was performed on the sub-samples in to further examine the relationship between carbon
content and heat content. The regression used carbon content as the dependent variable and heat content as the
independent variable. The resulting R-squared values for each of the sub-samples ranged from 0.79 for samples with
less than 1.5 percent CC>2 and under 1,050 Btu/cf to 0.91 for samples containing less than 1.0 percent CC>2 only. However,
the sub-sample with less than 1.5 percent CC>2 and 1,050 Btu/cf was chosen as the representative sample for two reasons.
First, it most accurately reflects the range of CC>2 content and heat content of pipeline quality natural gas. Secondly, the R-
squared value, although it is the lowest of the sub-groups tested, remains relatively high. This high R-squared indicates a
low percentage of variation in C content as related to heat content. The regression for this sub-sample resulted in the
following equation:
C Content = (0.011 x Heat Content) + 3.5341
This equation was used to estimate the annual predicted carbon content of natural gas from 1990 to 2010 based
on the EIA's national average pipeline-quality gas heat content for each year. The table of average C contents for each
year is shown below in Table A-44.
Table A-44: Carbon Content Coefficients for Natural Gas tTg Carhon/QBtul
Fuel Type 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Natural Gas 14.45 14.46 14.46 14.46 14.44 14.46 14.47 14.46 14.46 14.44 14.46 14.46 14.46 14.46 14.46 14.46 14.46 14.46 14.46
Source: EPA (2010)
R-squared represents the percentage of variation in the dependent variable (in this case carbon content) explained by variation in the
independent variables.
A-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Step 5. Apply carbon content coefficients developed in Step 3 to flare gas
Selecting a C content coefficient for flare gas was much more difficult than for pipeline natural gas, because of
the uncertainty of its composition and of the combustion efficiency of the flare. Because EIA estimates the heat content of
flare gas at 1,130 Btu per cubic foot, the average C content for samples with more than 1,100 Btu per cubic foot (n=18)
was chosen as the relevant sub-sample from which to calculate a flare gas carbon content. The sample dataset did not
include any samples with more than 1,130 Btu per cubic foot.
Hexane was not removed from flare gas samples since it is assumed that natural gas liquids are present in
samples with higher heat contents. Carbon contents were calculated for each sample with a heat content of more than
1,100 Btu per cubic foot. The simple average C content for the sample sub-set representing flare gas is shown below in
Table A-45.
Table A-45: Carbon Content of Flare Gas tTg C/QBtul
Relevant Sub-Sample Average Carbon Content
>1,100 Bin/of 15.31
Source: EPA (2010)
Data Sources
Natural gas samples were obtained from the Gas Technology Institute (1992). Average heat content data for
natural gas consumed in the United States was taken from EIA (2009a).
Uncertainty
The assignment of C content coefficients for natural gas, and particularly for flare gas, requires more subjective
judgment than the methodology used for coal. This subjective judgment may introduce additional uncertainty.
Figure A-l shows the relationship between the calculated C content for each natural gas sample and its energy
content. This figure illustrates the relatively restricted range of variation in both the energy content (which varies by about
6 percent from average) and the C emission coefficient of natural gas (which varies by about 5 percent). Thus, the
knowledge that gas has been sold via pipeline to an end-use consumer allows its C emission coefficient to be predicted
with an accuracy of +5.0 percent.
A-69
-------
Figure A-1: Carbon Content for Samples of Pipeline-Quality Natural Gas Included in the Gas Technology Institute
Database
10.0
= National Average
14.0
\
970
990
1,010 1,030 1,050 1,070
Energy Content (Btu per Cubic Foot)
I
1,090
I
1,110
n
1,130
Source: EIA(1994) Energy Infer mate n Administration, Emissions of Greenhouse Gases in the United States 19B7-1992, U.S. Department of
Energy, Washington, DC, November, 1994, DOE/EIAQ573, Appendix A
Natural gas suppliers may achieve the same overall energy content from a wide variety of methane, higher
hydrocarbon, and non-hydrocarbon gas combinations. Thus, the plot reveals large variations in C content for a single Btu
value. In fact, the variation in C content for a single Btu value may be nearly as great as the variation for the whole
sample. As a result, while energy content has some predictive value, the specific energy content does not substantially
improve the accuracy of an estimated C content coefficient beyond the +5.0 percent offered with the knowledge that it is
of pipeline-quality.
The plot of C content also reveals other interesting anomalies. Samples with the lowest emissions coefficients
tend to have energy contents of about 1,000 Btu per cubic foot. They are composed of almost pure CH/i. Samples with a
greater proportion of NGLs (e.g., ethane, propane, and butane) tend to have energy contents greater than 1,000 Btu per
cubic foot, along with higher emissions coefficients. Samples with a greater proportion of inert gases tend to have lower
energy content, but they usually contain CC>2 as one of the inert gases and, consequently, also tend to have higher emission
coefficients (see left side of Figure A-1).
For the full sample (n=6,743), the average C content of a cubic foot of gas was 14.48 Tg C/QBtu (see Table A-
44). Additionally, a regression analysis using the full sample produced a predicted C content of 14.49 Tg C/QBtu based
on a heat content of 1,029 Btu/cf (the average heat content in the U.S. for the most recent year). However, these two
values include an upward influence on the resulting carbon content that is caused by inclusion in the sample set of the
samples that contain large amounts of inert carbon dioxide and those samples with more than 1,050 Btu per cubic foot that
contain an unusually large amount of NGLs. Because typical gas consumed in the United States does not contain such a
large amount of carbon dioxide or natural gas liquids, a carbon content of 14.47 Tg C/QBtu, based on samples with less
than 1.5 percent carbon dioxide and less than 1,050 Btu per cubic foot, better represents the pipeline-quality fuels typically
consumed.
A-70 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Petroleum
There are four critical determinants of the C content coefficient for a petroleum -based fuel:
• The density of the fuel (e.g., the weight in kilograms of one barrel of fuel);
• The fraction by mass of the product that consists of hydrocarbons, and the fraction of non -hydrocarbon
impurities;
• The specific types of "families" of hydrocarbons that make up the hydrocarbon portion of the fuel; and
• The heat content of the fuel.
l = (Dfuelx Sfhel) / Efhel
where,
Cfuei = The C content coefficient of the fuel;
Dfuei = The density of the fuel;
S^i = The share of the fuel that is C; and
EM = The heat content of the fuel.
Most of the density, carbon share or heat contents applied to calculate the carbon coefficients for petroleum
products that are described in this sub-Annex and applied to this emissions inventory have been updated for this edition of
the report. These changes have been made where necessary to increase the accuracy of the underlying data or to align the
petroleum properties data used in this report with that developed for use in the Mandatory Reporting of Greenhouse Gases
Rule (EPA 2009b).
Petroleum products vary between 5.6 degrees API gravity (dense products such as asphalt and road oil) and 247
degrees (ethane). 14 This is a range in density of 60 to 1 50 kilograms per barrel, or +50 percent. The variation in C content,
however, is much smaller (+5 to 7 percent) for products produced by standard distillation refining: ethane is 80 percent C
by weight, while petroleum coke is 90 to 92 percent C. This tightly bound range of C contents can be explained by basic
petroleum chemistry (see below). Additional refining can increase carbon contents. Calcined coke, for example, is formed
by heat treating petroleum coke to about 1600 degrees Kelvin (calcining), to expel volatile materials and increase the
percentage of elemental C. This product can contain as much as 97 to 99 percent carbon. Calcined coke is mainly used in
the aluminum and steel industry to produce C anodes.
Petroleum Chemistry
Crude oil and petroleum products are typically mixtures of several hundred distinct compounds, predominantly
hydrocarbons. All hydrocarbons contain hydrogen and C in various proportions. When crude oil is distilled into
petroleum products, it is sorted into fractions by the boiling temperature of these hundreds of organic compounds. Boiling
temperature is strongly correlated with the number of C atoms in each molecule. Petroleum products consisting of
relatively simple molecules and few C atoms have low boiling temperatures, while larger molecules with more C atoms
have higher boiling temperatures.
Products that boil off at higher temperatures are usually more dense, which implies greater C content as well.
Petroleum products with higher C contents, in general, have lower energy content per unit mass and higher energy content
per unit volume than products with lower C contents. Empirical research led to the establishment of a set of quantitative
relationships between density, energy content per unit weight and volume, and C and hydrogen content. Figure A-2
compares C content coefficients calculated on the basis of the derived formula with actual C content coefficients for a
range of crude oils, fuel oils, petroleum products, and pure hydrocarbons. The actual fuel samples were drawn from the
sources described below in the discussions of individual petroleum products.
14 API gravity is an arbitrary scale expressing the gravity or density of liquid petroleum products, as established by the American
Petroleum Institute (API). The measuring scale is calibrated in terms of degrees API. The higher the API gravity, the lighter the
compound. Light crude oils generally exceed 38 degrees API and heavy crude oils are all crude oils with an API gravity of 22 degrees or
below. Intermediate crude oils fall in the range of 22 degrees to 38 degrees API gravity. API gravity can be calculated with the
following formula: API Gravity = (141.5/Specific Gravity) — 131.5. Specific gravity is the density of a material relative to that of water.
At standard temperature and pressure, there are 62.36 pounds of water per cubic foot, or 8.337 pounds water per gallon.
A-71
-------
Figure A-2:
Density
Estimated and Actual Relationships Between Petroleum Carbon Content Coefficients and Hydrocarbon
24 -I
22 -
I?
5 "I
u ®
•E O
b -S
.a a!
20 -
19 -
1 Reformat?
• Lig ht Fiefo rrnate
Heavy R?forrnate
•Catalytic Naphthas
i-hexane
i-pentane
n-butare j_ butane
• Propylere
-|- Fro pare
1
) 15
I
30
I
45
I
60
+ =
I I
75 90
Paraffin
I
Hyd rooarbo re
I I I
105 120 135 15
16
Hydrocarbon Density (API Gravity)
Source: Car ton content factors for paraffins a re calculated based on the properties of hydrocarbons in V. Guthrie (ed.), Petroleum Produtfs
Handbook (New York: McGraw Hill, 1960) p. 33. Carbon content factors from otter petroleum products are drawn from sources described
below. Relationship between density and emission factors based on the relationship between density and energy content in U.S. Department of
Commerce, National Bureau of Standards, Thermal Properties ofPetroSeiim Products, Miscellaneous Publication, No. 97 [Washington, DC.,
1929), pp. 16-21, and relationship between energy content and fuel composition inS. Ringen, J. Lanurn, and P.P. Miknis, "Calculating Heating
Values from the Elemental Composition of Fossil Fuels,'Fuei Vol. 58 (January 1979), p.69.
The derived empirical relationship between C content per unit heat and density is based on the types of
hydrocarbons most frequently encountered. Petroleum fuels can vary from this relationship due to non-hydrocarbon
impurities and variations in molecular structure among classes of hydrocarbons. In the absence of more exact information,
this empirical relationship offers a good indication of C content.
Non-hydrocarbon Impurities
Most fuels contain a certain share of non-hydrocarbon material. This is also primarily true of crude oils and fuel
oils. The most common impurity is sulfur, which typically accounts for between 0.5 and 4 percent of the mass of most
crude oils, and can form an even higher percentage of heavy fuel oils. Some crude oils and fuel oils also contain
appreciable quantities of oxygen and nitrogen, typically in the form of asphaltenes or various acids. The nitrogen and
oxygen content of crude oils can range from near zero to a few percent by weight. Lighter petroleum products have much
lower levels of impurities, because the refining process tends to concentrate all of the non-hydrocarbons in the residual oil
fraction. Light products usually contain less than 0.5 percent non-hydrocarbons by mass. Thus, the C content of heavy
fuel oils can often be several percent lower than that of lighter fuels, due entirely to the presence of non-hydrocarbons.
A-72 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Variations in Hydrocarbon Classes
Hydrocarbons can be divided into five general categories, each with a distinctive relationship between density
and C content and physical properties. Refiners tend to control the mix of hydrocarbon types in particular products in
order to give petroleum products distinct properties. The main classes of hydrocarbons are described below.
Paraffins. Paraffins are the most common constituent of crude oil, usually comprising 60 percent by mass.
Paraffins are straight-chain hydrocarbons with the general formula CnH2n+2- Paraffins include ethane (C2He), propane
(CsHs), butane (C4Hio), and octane (CsHis). As the chemical formula suggests, the C content of the paraffins increases
with their C number: ethane is 79.89 percent C by weight, octane 84.12 percent. As the size of paraffin molecules
increases, the C content approaches the limiting value of 85.7 percent asymptotical (see Figure A-3).
Cycloparaffms. Cycloparaffins are similar to paraffins, except that the C molecules form ring structures rather
than straight chains, and consequently require two fewer hydrogen molecules than paraffins. Cycloparaffins always have
the general formula CnH2n and are 85.63 percent C by mass, regardless of molecular size.
Olefms. Olefins are a very reactive and unstable form of paraffin: a straight chain with two carbon atoms double
bonded together (thus are unsaturated) compared to the carbon atoms in a paraffin (which are saturated with hydrogen).
They are never found in crude oil but are created in moderate quantities by the refining process. Gasoline, for example,
may contain between 2 and 20 percent olefins. They also have the general formula CnH2n, and hence are also always 85.63
percent C by weight. Propylene (CsHe), a common intermediate petrochemical product, is an olefin.
Aromatics. Aromatics are very reactive hydrocarbons that are relatively uncommon in crude oil (10 percent or
less). Light aromatics increase the octane level in gasoline, and consequently are deliberately created by catalytic
reforming of heavy naphtha. Aromatics also take the form of ring structures with some double bonds between C atoms.
The most common aromatics are benzene (CeHe), toluene (CyHs), and xylene (CsHio). The general formula for aromatics
is CnH2n-6. Benzene is 92.26 percent C by mass, while xylene is 90.51 percent C by mass and toluene is 91.25 percent C by
mass. Unlike the other hydrocarbon families, the C content of aromatics declines asymptotically toward 85.7 percent with
increasing C number and density (see Figure A-3).
Polynuclear Aromatics. Polynuclear aromatics are large molecules with a multiple ring structure and few
hydrogen atoms, such as naphthalene (CioHs and 93.71 percent C by mass) and anthracene (CnHio and 97.7 percent C).
They are relatively rare but do appear in heavier petroleum products.
Figure A-3 illustrates the share of C by weight for each class of hydrocarbon. Hydrocarbon molecules containing
2 to 4 C atoms are all natural gas liquids; hydrocarbons with 5 to 10 C atoms are predominantly found in naphtha and
gasoline; and hydrocarbon compounds with 12 to 20 C atoms comprise "middle distillates," which are used to make diesel
fuel, kerosene and jet fuel. Larger molecules which can be vacuum distilled may be used as lubricants, waxes, and residual
fuel oil or cracked and blended into the gasoline or distillate pools.
A-73
-------
Figure A-3: Carbon Content of Pure Hydrocarbons as a Function of Carbon Number
100 -i
I
-Q
C
1
95 -
90 -
35 -
80 -
75 -
70
1 Paraffins
* Cyclo paraffins
VAroiriatics
Benzene
Toluene
Xylene
Cyclopentane
*> ****
.
n- pe ntane •
'Butene
1 Propane
'Ethane
> Methane
Gasoline Jet Fuel
LPG Naphtha Ke rose re Diesel
fttftttttttttttttt
Lube Oil Fuel Oil
1
5
1
10
I
15
I
20
I
25
I
30 3
M urn be r o f Carbo n Ate rns In Molecule
Source: J.M.Hunt,f!s Irate Hm Gso chemisty and Geology (San Francsco, CA,W.H.FreenrianardOompany,197S),pp.31-S7.
If nothing is known about the composition of a particular petroleum product, assuming that it is 85.7 percent C
by mass is not an unreasonable first approximation. Since denser products have higher C numbers, this guess would be
most likely to be correct for crude oils and fuel oils. The C content of lighter products is more affected by the shares of
paraffins and aromatics in the blend.
Energy Content of Petroleum Products
The exact energy content (gross heat of combustion) of petroleum products is not generally known. EIA
estimates energy consumption in Btu on the basis of a set of industry-standard conversion factors. These conversion
factors are generally accurate to within 3 to 5 percent.
Individual Petroleum Products
The United States maintains data on the consumption of more than 20 separate petroleum products and product
categories. The C contents, heat contents, and density for each product are provided below in Table A-46. A description
of the methods and data sources for estimating the key parameters for each individual petroleum product appears below.
A-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-46: Carbon Content Coefficients and Underlying Data for Petroleum Products
2008 Carbon Content Gross Heat of Combustion
Fuel (Tg C/QBtu) (MMBtu/Barrel)
Motor Gasoline
LPG(total)
LPG (energy use)
LPG (non-energy use)
Jet Fuel
Distillate Fuel No. 1
Distillate Fuel No. 2
Distillate Fuel No. 4
Residual Fuel No. 5
Residual Fuel No. 6
Asphalt and Road Oil
Lubricants
Naphtha (< 400 deg. F)a
Other Oils (>400 deg. F)a
Aviation Gas
Kerosene
Petroleum Coke
Special Naphtha
Petroleum Waxes
Still Gas
Crude Oil
Unfinished Oils
Miscellaneous Products
Pentones Plus
19.46
16.97
16.83
17.06
19.70
19.98
20.17
20.47
19.89
20.48
20.55
20.20
18.55
20.17
18.86
19.96
27.85
19.74
19.80
18.20
20.31
20.31
20.31
19.10
(See a)
(See b)
(See b)
(See b)
5.670
5.822
5.809
6.135
5.879
6.317
6.636
6.065
5.248
5.825
5.048
5.825
6.024
5.248
5.537
6.000
5.800
5.825
5.796
4.620
Density
(API Gravity)
(See a)
(See b)
(See b)
(See b)
42.0
35.3
35.8
23.2
33.0
15.5
5.6
25.7
62.4
35.8
69.0
35.3
-
52.0
43.3
-
31.2
31.2
31.2
81.3
Percent
Carbon
(See a)
(See b)
(See b)
(See b)
86.30
86.40
87.30
86.47
85.67
84.67
83.47
85.80
84.11
87.30
85.00
86.40
92.28
84.75
85.30
77.70
85.49
85.49
85.49
83.63
a = Calculation of the carbon content coefficient for motor gasoline in 2008 uses separate higher heating values for conventional and reformulated gasoline of
5.253 and 5.150, respectively (EIA 2008a). Densities and carbon shares (percent carbon) are annually variable and separated by both fuel formulation and
grade, see Motor Gasoline and Blending Components, below, for details.
b = LPG is a blend of multiple paraffinic hydrocarbons: ethane, propane, isobutane, and normal butane, each with their own heat content, density and C content,
see Table A-49.
a Petrochemical feedstocks have been split into naphthas and other oils for this inventory report. Parameters presented are for naphthas with a boiling temperature less
than 400 degrees Fahrenheit. Other oils are petrochemical feedstocks with higher boiling points. They are assumed to have the same characteristics as distillate fuel oil no.
2.
- No sample data available
Sources: EIA (1994), EIA (2009a), EPA (2009b), and EPA (2010).
Motor Gasoline and Motor Gasoline Blending Components
Motor gasoline is a complex mixture of relatively volatile hydrocarbons with or without small quantities of
additives, blended to form a fuel suitable for use in spark-ignition engines.15 "Motor Gasoline" includes conventional
gasoline; all types of oxygenated gasoline, including gasohol; and reformulated gasoline; but excludes aviation gasoline.
Gasoline is the most widely used petroleum product in the United States, and its combustion accounts for nearly
20 percent of all U.S. CC>2 emissions. EIA collects consumption data (i.e., "petroleum products supplied" to end-users) for
several types of finished gasoline over the 1990 through 2008 time period: regular, mid-grade and premium conventional
gasoline (all years) and regular, mid-grade and premium reformulated gasoline (November 1994 to 2008). Leaded and
oxygenated gasoline are not separately included in the data used for this report.
15 Motor gasoline, as defined in ASTM Specification D 4814 or Federal Specification W-G-1690C, is characterized as having a boiling
range of 122 degrees to 158 degrees Fahrenheit at the 10-percent recovery point to 365 degrees to 374 degrees Fahrenheit at the 90-
percent recovery point.
Oxygenated gasoline volumes are included in the conventional gasoline data provided by EIA from 2007 onwards. Leaded gasoline
was included in total gasoline by EIA until October 1993.
A-75
-------
The American Society for Testing and Materials (ASTM) standards permit a broad range of densities for
gasoline, ranging from 50 to 70 degrees API gravity, or 111.52 to 112.65 kilograms per barrel (EIA 1994), which implies a
range of possible C and energy contents per barrel. Table A- 47 reflects changes in the density of gasoline over time and
across grades and formulations of gasoline through 2008.
Table A- 47: Motor Gasoline Density,1990 - 2012 (Degrees API)
Fuel Grade
Conventional
Low Octane
High Octane
Conventional
Low Octane
High Octane
Reformulated
Low Octane
High Octane
Reformulated
Low Octane
High Octane
1990 1995
-Winter Grade
62 0 59 8
59.0 58.0
-Summer Grade
58 2 56 1
55 5 55 1
-Winter Grade
NA 61.9
NA 59.9
-Summer Grade
NA 585
NA 56.7
1996
606
585
569
553
6? 3
60S
58 0
578
1997
615
593
571
564
6?1
6?1
588
584
1998
61 a
600
576
557
6? 7
614
584
585
1999
61 6
603
577
574
6? 7
61 0
584
578
2000
616
597
568
558
6? 7
61 1
584
583
2001
61 7
591
57?
555
6? 6
61 0
588
58?
2002
61 6
590
565
557
61 9
61 8
58?
580
2003
618
599
568
560
6?1
619
591
587
2004
6? 4
607
574
570
6? 7
61 8
581
589
2005
6? 6
609
579
570
6? 8
61 8
584
581
2006
6? 7
600
578
574
6? 3
617
587
590
2007
631
603
575
569
6?1
6?1
585
593
2008
630
609
586
580
6? 4
6? 5
591
598
2009
630
609
586
580
6? 4
6? 5
591
598
2010
630
609
586
580
6? 4
6? 5
591
598
2011
630
609
586
580
6? 4
6? 5
591
598
2012
630
609
586
580
6? 4
6? 5
591
598
Notes: NA - Not Applicable, fuel type was not analyzed.
Source: National Institute of Petroleum and Energy Research (1990 through 2012).
The density of motor gasoline increased across all grades through 1994, partly as a result of the leaded gasoline
phase-out. In order to maintain the "anti-knock" quality and octane ratings of gasoline in the absence of lead, the portion
of aromatic hydrocarbons blended into gasoline through the refining process was increased. As discussed above, aromatic
hydrocarbons have a lower ratio of hydrogen to C than other hydrocarbons typically found in gasoline, and therefore
increase fuel density.
The trend in gasoline density was reversed beginning in 1996 with the development of fuel additives that raised
oxygen content. In 1995, a requirement for reformulated gasoline in non-attainment areas implemented under the Clean
Air Act Amendments further changed the composition of gasoline consumed in the United States. Through 2005, methyl
tertiary butyl ether (MTBE), ethanol, ethyl tertiary butyl ether (ETBE) and tertiary amyl methyl ether (TAME) were added
to reformulated and sometimes to conventional gasoline to boost its oxygen content, reduce its toxics impacts and increase
its octane. The increased oxygen reduced the emissions of carbon monoxide and unburned hydrocarbons. These oxygen-
rich blending components are also much lower in C than standard gasoline. The average gallon of reformulated gasoline
consumed in 2005 contained over 10 percent MTBE and 0.6 percent TAME (by volume). The characteristics of
reformulated fuel additives appear in Table A-48.
Table A-48: Characteristics of Major Reformulated Fuel Additives
Additive Density (Degrees API) Carbon Share (Percent)
MTBE 58~668.13
ETBE 58.5 70.53
TAME 51.2 70.53
DIPE 62.7 70.53
Ethanol (100%) 45.8 52.14
Source: EPA, 2009b.
Since 2005, due to concerns about the potential environmental consequences of the use of MTBE in fuels, there
has been a shift away from the addition of MTBE, TAME, ETBE and DIPE and towards the use of ethanol as a fuel
oxygenate.17 Ethanol, also called ethyl alcohol, is an anhydrous alcohol with molecular formula C2H5OH. Ethanol has a
The annual motor gasoline carbon contents that are applied for this inventory do not include the carbon contributed by the
ethanol contained in reformulated fuels. Ethanol is a bio fuel, and net carbon fluxes from changes in biogenic carbon reservoirs in
croplands are accounted for in the estimates for Land Use, Land-Use Change and Forestry.
A-76 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
lower carbon share than other oxygenates, approximately 52 percent compared to about 70 percent for MTBE and TAME.
The density of ethanol was calculated by fitting density data at 10 degree intervals to a polynomial of order two and then
using the fit to interpolate the value of the density at 15 degrees Celsius. A common fuel mixture of 10 percent denatured
ethanol (denatured by 2 percent hydrocarbons) and 90 percent gasoline, known as E10, is widely used in the United States
and does not require any modification to vehicle engines or fuel systems. The average gallon of reformulated alcohol
blend gasoline in 2008 contained 8.6 percent ethanol (by volume). As of 2010, ten States require the use of ethanol-
blended fuel. Ethanol blends up to E85 (85 percent ethanol, 15 percent gasoline) are in use in the United States but can
only be used in specially designed vehicles called flexible fuel vehicles (FFVs). Most ethanol fuel in the United States is
produced using corn as feedstock, although production pathways utilizing agricultural waste, woody biomass and other
resources are in development.
Methodology
Step 1. Disaggregate U.S. gasoline consumption by grade and type
Separate monthly data for U.S. sales to end users of finished gasoline by product grade and season for both
standard gasoline and reformulated gasoline were obtained from the EIA.
Step 2. Develop carbon content coefficients for each grade and type
Annual C content coefficients for each gasoline grade, type and season are derived from four parameters for each
constituent of the finished gasoline blend: the volumetric share of each constituent,20 the density of the constituent, share
of the constituent that is C; and the energy content of a gallon of the relevant formulation of gasoline. The percent by
mass of each constituent of each gasoline type was calculated using percent by volume data from NTPER and the density
of each constituent. The ether additives listed in Table A-48 are accounted for in both reformulated fuels and conventional
fuels, to the extent that they were present in the fuel. From 2006 onward, reformulated fuel mass percentages are
calculated from their constituents, net of the share provided by ethanol. C content coefficients were then derived from the
calculated percent by mass values by weighting the carbon share of each constituent by its contribution to the total mass of
the finished motor gasoline product.
Step 3. Weight overall gasoline carbon content coefficient for consumption of each grade and type
The C content for each grade, type and season of fuel is multiplied by the share of annual consumption
represented by the grade and fuel type during the relevant time period. Individual coefficients are then summed and
totaled to yield an overall C content coefficient for each year.
Data Sources
Data for the density of motor gasoline were derived from the National Institute for Petroleum and Energy
Research (NTPER) (1990 through 2009). Data on the characteristics of reformulated gasoline, including C share, were also
taken fromNIPER (1990 through 2009).
Standard heat contents for motor gasoline of 5.253 MMBtu per barrel conventional gasoline and 5.150 MMBtu
per barrel reformulated gasoline22 were adopted from EIA (2009a).
Uncertainty
The uncertainty underlying the C content coefficients for motor gasoline has three underlying sources: the
uncertainty in the averages published by NIPER, uncertainty in the C shares assumed in the EPA's analysis to be
representative of the constituent hydrocarbon classes within gasoline (aromatics, olefins and saturates) and uncertainty in
the heat contents applied.
18 Ethanol.org. http://www.ethanol.org/index.php?id=79&parentid=26. Retrieved 2-19-2010.
19 "Ethanol Market Penetration". Alternative Fuels and Advanced Vehicles Data Center, US DOE.
http://www.afdc.energy.gov/afdc/ethanol/market.html. Retrieved 2-19-2010.
20 Calculations account for the properties of the individual constituents of gasoline, including, as applicable to the fuel grade and
type: aromatics (excluding benzene), olefms, benzene, saturates, MTBE, TAME, ETBE, DIPE and ethanol.
21 Saturates are assumed to be octane and aromatics are assumed to be toluene.
The reformulated gasoline heat content is applied to both reformulated blends containing ethers and those containing ethanol.
A-77
-------
A variable number of samples are used each year to determine the average percent by volume share of each
hydrocarbon within each grade, season and formulation of gasoline that are obtained from NIPER. The total number of
samples analyzed for each seasonal NIPER report varies from approximately 730 to over 1,800 samples over the period
from 1990 through 2008. The number of samples analyzed that underlie the calculation of the average make-up of each
seasonal formulation and grade varies from approximately 50 to over 400, with the greatest number of samples each
season being of conventional, regular or premium gasoline. Further, not all sample data submitted to NIPER contains data
for each of the properties, such that the number of samples underlying each constituent average value for each season,
grade and formulation may be variable within the single gasoline type (e.g., of the 1,073 samples for which some data was
obtained for gasoline sold in Winter 1995 through 1996, benzene content was provided for all samples, while olefin,
aromatic and saturate content was provided for just 736 of those samples).
The distribution of sample origin collected for the NIPER report and the calculation of national averages are not
reflective of sales volumes. The publication of simple, rather than sales-weighted averages to represent national average
values increases the uncertainty in their application to the calculation of carbon content factors for the purposes of this
inventory. Further, data for each sample is submitted voluntarily, which may also affect their representativeness.
Additionally, because the simple average constituent shares are calculated based upon data that have been
renormalized to account for the share of ethers and alcohols, total average volume shares may not equal 100 percent.
The simple average for each hydrocarbon constituent is contained within a range of values that are as wide as -
637+74.5 percent of the mean across the Winter 2007 through 2008 and -51.37+49.6 percent across the Summer 2008
samples of conventional, regular grade gasoline. However, these wide ranges exist for benzene, which generally accounts
for only 1 percent, by volume, of each gallon. In contrast, saturates, the class of hydrocarbon that contribute the largest
share, by volume, ranges only -6.57+6.4 percent for the same set of Winter samples and -8.87+15.7 percent for the Summer
samples.
Secondly, EPA's calculation of C content factors for each gasoline type includes the following assumptions: for
the purposes of assigning a carbon share to each compound in the blend, aromatic content (other than benzene) is assumed
to be toluene and saturated hydrocarbons are assumed to be octane. All olefins have the same carbon share because they
all have a molecular formula in the form CnH2n, so the C share applied to the olefin portion of the total gasoline blend does
not increase the level of uncertainty in the calculation. These assumptions are based upon the use of octane and octane
isomers as the primary saturates and toluene as the primary non-benzene aromatic in U.S. motor gasoline blends. The
octane rating of a particular blend is based upon the equivalent iso-octane to heptane ratio, which is achieved through
significant octane content relative to the other saturates. Aside from benzene, U.S. gasolines will include toluene as a
major aromatic component, so toluene may be assumed a reasonable representative of total non-benzene aromatic content
(EPA 2009a).
For each hydrocarbon category, the assumed C content lies within a range of possible values for all such
hydrocarbons. Among saturated hydrocarbons, the C share of octane (84.12 percent) is at the high end of the range while
ethane is represents the low end of the range (79.89 percent C). Total saturates constitute from 40 to 95 percent by volume
of a given gasoline blend. For aromatics, toluene (91.25 percent C) lies in the middle of the possible range. This range is
bounded by cumene (89.94 percent C) and naphthalene (93.71 percent C). Total aromatics may make up between 3 and
50 percent by volume of any given gasoline blend. The range of these potential values contributes to the uncertainty
surrounding the final calculated C factors.
However, as demonstrated above in Figure A-3, the amount of variation in C content of gasoline is restricted by
the compounds in the fuel to +4 percent. Further, despite variation in sampling survey response, sample size and annually
variable fuel formulation requirements, the observed variation in the annual weighted motor gasoline coefficients
estimated for this inventory is +0.8 percent over 1990 through 2008.
The third primary contributor to uncertainty is the assumed heat content. The heat contents are industry
standards established many years ago. The heat contents are standard conversion factors used by EIA to convert
volumetric energy data to energy units. Because the heat contents of fuels change over time, without necessarily and
directly altering their volume, the conversion of known volumetric data to energy units may introduce bias. Thus, a more
precise approach to estimating emissions factors would be to calculate C content per unit of volume, rather than per unit of
energy. Adopting this approach, however, makes it difficult to compare U.S. C content coefficients with those of other
nations.
The changes in density of motor gasoline over the last decade suggest that the heat content of the fuels is also
changing. However, that change within any season grade has been less than 1 percent over the decade. Of greater concern
A-78 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
is the use of a standardized heat content across grades that show a variation in density of +1.5 percent from the mean for
conventional gasoline and +1.0 percent for reformulated fuels.
Jef Fuel
Jet fuel is a refined petroleum product used in jet aircraft engines. There are two classes of jet fuel used in the
United States: "naphtha-based" jet fuels and "kerosene-based" jet fuels. In 1989, 13 percent of U.S. consumption was
naphtha-based fuel, with the remainder kerosene-based jet fuel. In 1993, the U.S. Department of Defense began a
conversion from naphtha-based JP-4 jet fuel to kerosene-based jet fuel, because of the possibility of increased demand for
reformulated motor gasoline limiting refinery production of naphtha-based jet fuel. By 1996, naphtha-based jet fuel
represented less than one-half of one percent of all jet fuel consumption. The C content coefficient for jet fuel used in this
report prior to 1996 represents a consumption-weighted combination of the naphtha-based and kerosene-based
coefficients. From 1996 to 2008, only the kerosene-based portion of total consumption is considered significant.
Methodology
Step 1. Estimate the carbon content for naphtha-based jet fuels
Because naphtha-based jet fuels are used on a limited basis in the United States, sample data on its characteristics
are limited. The density of naphtha-based jet fuel (49 degrees) was estimated as the central point of the acceptable API
gravity range published by ASTM. The heat content of the fuel was assumed to be 5.355 MMBtu per barrel based on EIA
industry standards. The C fraction was derived from an estimated hydrogen content of 14.1 percent (Martel and Angello
1977), and an estimated content of sulfur and other non-hydrocarbons of 0.1 percent.
Step 2. Estimate the carbon content for kerosene-based jet fuels
The density of kerosene-based jet fuels was estimated at 42 degrees API and the carbon share at 86.3 percent.
The density estimate was based on 38 fuel samples examined by NIPER. Carbon share was estimated on the basis of a
hydrogen content of 13.6 percent found in fuel samples taken in 1959 and reported by Martel and Angello, and on an
assumed sulfur content of 0.1 percent. The EIA's standard heat content of 5.670 MMBtu per barrel was adopted for
kerosene-based jet fuel.
Step 3. Weight the overall jet fuel carbon content coefficient for consumption of each type of fuel (1990-1995
only)
For years 1990 through 1995, the C content for each jet fuel type (naphtha-based, kerosene-based) is multiplied
by the share of overall consumption of that fuel type, as reported by EIA (2009a). Individual coefficients are then summed
and totaled to yield an overall C content coefficient. Only the kerosene-based C coefficient is reflected in the overall jet
fuel coefficient for 1996 through 2008.
Data Sources
Data on the C content of naphtha-based jet fuel was taken from C.R. Martel and L.C. Angello (1977). Data on
the density of naphtha-based jet fuel was taken from ASTM (1985). Standard heat contents for kerosene and naphtha-
based jet fuels were adopted from EIA (2009a). Data on the C content of kerosene-based jet fuel is based on C.R. Martel
and L.C. Angello (1977) and the density is derived from NIPER (1993).
Uncertainty
Variability in jet fuel is relatively small with the average C share of kerosene-based jet fuel varying by less than
+1 percent and the density varying by +1 percent. This is because the ratio of fuel mass to useful energy must be tightly
bounded to maximize safety and range. There is more uncertainty associated with the density and C share of naphtha-
based jet fuel because sample data were unavailable and default values were used. This uncertainty has only a small
impact on the overall uncertainty of the C content coefficient for jet fuels, however, because naphtha-based jet fuel
represents a small and declining share of total jet fuel consumption in the United States and is treated as negligible when
calculating carbon content factors for 1996 onwards.
Distillate Fuel
Distillate fuel is a general classification for diesel fuels and fuel oils. Products known as No. 1, No. 2, and No. 4
diesel fuel are used in on-highway diesel engines, such as those in trucks and automobiles, as well as off-highway engines,
such as those in railroad locomotives and agricultural machinery. No. 1, No. 2, and No. 4 fuel oils are also used for space
heating and electric power generation.
A-79
-------
Methodology
For this inventory, separate C coefficients have been estimated for each of the three distillates, although the level
of aggregation of U.S. energy statistics requires that a single coefficient is used to represent all three grades in inventory
calculations. In past inventories, the emission coefficient was only determined for distillate No. 2. Distillate No. 2
remains the representative grade applied to the distillate class for calculation purposes. Coefficients developed for No. 1
and No. 4 distillate are provided for informational purposes. The C share each distillate is drawn from Perry's Chemical
Engineers' Handbook, 8th Ed. (Green & Perry 2008). Each C share was combined with individual heat contents of 5.822,
5.809 and 6.135 MMBtu per barrel, respectively for distillates No. 1, No. 2 and No. 4, and densities of 35.3, 35.8 and 23.2
degrees API to calculate C coefficients for each distillate type.
Data Sources
Densities for distillate Nos. 1 and 2 were derived from Alliance of Automobile Manufacturers, Diesel Survey -
Winter 2008 (AAM 2009). Densities are based on four, and 144 samples, respectively. The density of distillate fuel oil
No. 4 is taken from Perry's Chemical Engineer's Handbook, 8th Ed. (Green & Perry, 2008), Table 24-6.
Heat contents are adopted from EPA (2009b). And carbon shares for each distillate are from Perry's Chemical
Engineers' Handbook (Green & Perry 2008), Table 24-6.
Uncertainty
The primary source of uncertainty for the estimated C content of distillate fuel is the selection of No. 2 distillate
as the typical distillate fuel oil or diesel fuel. No.2 fuel oil is generally consumed for home heating. No.l distillate is
generally less dense and if it is consumed in large portions for mobile sources, the application of the C content estimated
for No. 2 for this report is likely to be too high when applied to both No. 1 and No. 2 distillates. The opposite is true of the
application of a coefficient based upon the properties of No. 2 to the consumption of No. 4 distillate, which is of a
significantly higher density and thus, has a higher C coefficient despite its lower C share. The overall effect on
uncertainty from applying a single factor will depend on the relative annual consumption of each distillate.
The densities applied to the calculation of each carbon factor are an underlying a source of uncertainty. While
the density of No. 1 distillate is based upon just four samples, the factor applied to all distillates in the inventory estimates
(that for No. 2 oil) is based on a much larger sample size (144). Given the range of densities for these three distillate fuel
classes (0.1342 to 0.1452 MT/bbl at 60°F), the uncertainty associated with the assumed density of distillate fuels is
predominately a result of the use of No. 2 to represent all distillate consumption. There is also a small amount of
uncertainty in the No. 2 distillate density itself. This is due to the possible variation across seasonal diesel formulations
and fuel grades and between stationary and transport applications within the No. 2 distillate classification. The range of
the density of the samples of No. 2 diesel (regular grade, 15ppm sulfur) is ±2.5 percent from the mean, while the range in
density across the small sample set of No. 1 diesel is -2.1 to +1.6 percent of the mean. Samples from AAM (2009) of
Premium No. 2 diesel (n=5) and higher sulfur (500 ppm S) regular diesel (n=2), which are also consumed in the U.S., each
have nominally higher average densities (+1.3 percent and +0.6 percent, respectively) than do the low-sulfur regular diesel
samples that underlie the density applied in this inventory.
The use of the 144 AAM samples to define the density of No. 2 distillate (and those four samples used to define
that of No. 1 distillate) may introduce additional uncertainty because the samples were collected from just one season of
on-road fuel production (Winter 2008). Despite the limited sample frame, the average No. 2 density calculated from the
samples is applied to the calculation of a uniform C coefficient applicable for all years of the inventory and for all types of
distillate consumption. The ASTM standards for each grade of diesel fuel oil do not include a required range in which the
density must lie, and the density (as well as heat content and carbon share) may vary according to the additives in each
seasonal blend and the sulfur content of each sub-grade.
However, previous studies also show relatively low variation in density across samples of No. 2 and across all
distillates, supporting the application of a single No. 2 density to all U.S. distillate consumption. The average density
calculated from samples analyzed by the EIA in 1994 (n=7) differs only very slightly from the value applied for the
purposes of this inventory (-0.12 percent for No. 2 distillate). Further, the difference between the mean density applied to
this inventory (No. 2 only) and that calculated from EIA samples of all distillates, regardless of grade, is also near zero (-
0.06 percent, based on n=14, of distillates No. l,No. 2 and No. 4 combined).
A C share of 87.30 percent is applied to No. 2 distillate, while No. 1 and No. 4 have C shares estimated at 86.40
and 86.47 percent, respectively. Again, the application of parameters specific to No. 2 to the consumption of all three
distillates contributes to an increased level of uncertainty in the overall coefficient and emissions estimate and its broad
application. For comparison, four No. 1 fuel oil samples obtained by EIA (1994) contained an average of 86.19 percent C,
A-80 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
while seven samples No. 2 fuel oil from the same EIA analysis showed an average of 86.60 percent C. Additionally, three
samples of No. 4 distillate indicate an average C share of 85.81 percent. The range of C share observed across the seven
No. 2 samples is 86.1 to 87.5 percent, and across all samples (all three grades, n=14) the range is 85.3 to 87.5 percent C.
There also exists an uncertainty of +1 percent in the share of C in No. 2 based on the limited sample size.
Residual Fuel
Residual fuel is a general classification for the heavier oils, known as No. 5 and No. 6 fuel oils, that remain after
the distillate fuel oils and lighter hydrocarbons are distilled away in refinery operations. Residual fuel conforms to ASTM
Specifications D 396 and D 975 and Federal Specification VV-F-815C. No. 5, a residual fuel oil of medium viscosity, is
also known as Navy Special and is defined in Military Specification MIL-F-859E, including Amendment 2 (NATO
Symbol F-770). It is used in steam-powered vessels in government service and inshore power plants. No. 6 fuel oil
includes Bunker C fuel oil and is used for the production of electric power, space heating, vessel bunkering, and various
industrial purposes.
In the United States, electric utilities purchase about one-third of the residual oil consumed. A somewhat larger
share is used for vessel bunkering, and the balance is used in the commercial and industrial sectors. The residual oil
(defined as No. 6 fuel oil) consumed by electric utilities has an energy content of 6.287 MMBtu per barrel (EIA 2008a)
and an average sulfur content of 1 percent (EIA 2001). This implies a density of about 17 degrees API.
Methodology
Because U.S. energy consumption statistics are available only as an aggregate of No. 5 and No. 6 residual oil, a
single coefficient must be used to represent the full residual fuel category. As in earlier editions of this report, residual
fuel oil has been defined as No. 6 fuel oil, due to the majority of residual consumed in the United States being No. 6.
However, for this report, a separate coefficient for fuel oil No. 5 has also been developed for informational purposes.
Densities of 33.0 and 15.5 degrees API were adopted when developing the C content coefficients for Nos. 5 and 6,
respectively (Wauquier, J.-P., ed. 1995; Green & Perry, ed. 2008).
The estimated C share of fuel oil No. 5 is 85.67 percent, based on an average of 12 ultimate analyses of samples
of fuel oil (EIA 1994). An average share of C in No. 6 residual oil of 84.67 percent by mass was used, based on Perry's,
8th Ed. (Green & Perry 2008).
Data Sources
Data on the C share and density of residual fuel oil No. 6 were obtained from Green & Perry, ed. (2008).
Data on the C share of fuel oil No. 5 was adopted from EIA (1994), and the density of No. 5 was obtained from
Wauquier, J.-P., ed. (1995).
Heat contents for both Nos. 5 and 6 fuel oil are adopted from EPA (2009b).
Uncertainty
Beyond the application of a C factor based upon No. 6 oil to all residual oil consumption, the largest source of
uncertainty in estimating the C content of residual fuel centers on the estimates of density. Fuel oils are likely to differ
depending on the application of the fuel (i.e., power generation or as a marine vessel fuel). Slight differences between the
density of residual fuel used by utilities and that used in mobile applications are likely attributable to non-sulfur impurities,
which reduce the energy content of the fuel, but do not greatly affect the density of the product. Impurities of several
percent are commonly observed in residual oil. The extent of the presence of impurities has a greater effect on the
uncertainty of C share estimation than it does on density. This is because these impurities do provide some Btu content to
the fuel, but they are absent of carbon. Fuel oils with significant sulfur, nitrogen and heavy metals contents would have a
different total carbon share than a fuel oil that is closer to pure hydrocarbon. This contributes to the uncertainty of the
estimation of an average C share and C coefficient for these varied fuels.
The 12 samples of residual oil (EIA 1994) cover a density range from 4.3 percent below to 8.2 percent above the
mean density. The observed range of C share in these samples is -2.5 to +1.8 percent of the mean. Overall, the
uncertainty associated with the C content of residual fuel is probably +1 percent.
Liquefied Petroleum Gases (LPG)
EIA identifies four categories of paraffinic hydrocarbons as LPG: ethane, propane, isobutane, and n-butane.
Because each of these compounds is a pure paraffinic hydrocarbon, their C shares are easily derived by taking into account
-------
the atomic weight ofC (12.01) and the atomic weight of hydrogen (1.01). Thus, for example, the C share of propane,
CsHs, is 81.71 percent. The densities and heat contents of the compounds are also well known, allowing C content
coefficients to be calculated directly. Table A-49 summarizes the physical characteristic of LPG.
Table A-49: Physical Characteristics of Liquefied Petroleum Gases
Compound
Ethane
Propane
Isobutane
n-butane
Chemical
Formula
C2H6
C3H8
C4Hio
C4Hio
Density (Barrels
Per Metric Ton)
11.55
12.76
11.42
10.98
Carbon Content
(Percent)
79.89
81.71
82.66
82.66
Energy Content
(MMBtu/Barrel)
3.082
3.836
3.974
4.326
Carbon Content
Coefficient (Tg
C/QBtu)
17.16
16.76
17.77
17.75
Source: Densities - CRC Handbook of Chemistry and Physics (2008/09); Carbon Contents - derived from the atomic weights of the elements; Energy Contents
- EPA (2009b). All values are for the compound in liquid form. The density and energy content of ethane are for refrigerated ethane (-89 degrees C). Values for
n-butane are for pressurized butane (-25 degrees C).
Methodology
Step 1. Assign carbon content coefficients to each pure paraffmic compound
Based on their known physical characteristics, a C content coefficient is assigned to each compound contained in
the U.S. energy statistics category, Liquefied Petroleum Gases.
Step 2. Weight individual LPG coefficients for share of fuel use consumption
A C content coefficient for LPG used as fuel is developed based on the consumption mix of the individual
compounds reported in U.S. energy statistics.
Step 3. Weight individual LPG coefficients for share of non-fuel use consumption
The mix of LPG consumed for non-fuel use differs significantly from the mix of LPG that is combusted. While
the majority of LPG consumed for fuel use is propane, ethane is the largest component of LPG used for non-fuel
applications. A C content coefficient for LPG used for non-fuel applications is developed based on the consumption mix
of the individual compounds reported in U.S. energy statistics.
Step 4. Weight the carbon content coefficients for fuel use and non-fuel use by their respective shares of
consumption
The changing shares of LPG fuel use and non-fuel use consumption appear below in Table A- 50.
Data Sources
Data on C share was derived via calculations based on atomic weights of each element of the four individual
compounds densities are from the CRC Handbook of Chemistry and Physics, 89th Education. The energy content of each
LPG is from the EPA (2009b). LPG consumption was based on data obtained from API (1990 through 2008) and EIA
(2009b). Non-fuel use of LPG was obtained from API (1990 through 2008).
Uncertainty
Because LPG consists of pure paraffinic compounds whose density, heat content and C share are physical
constants, there is limited uncertainty associated with the C content coefficient for this petroleum product. Any
uncertainty is associated with the collection of data tabulating fuel- and non-fuel consumption in U.S. energy statistics.
This uncertainty is likely less than +3 percent.
Tahle A- 50: Consumption and Carhon Content Coefficients of Liquefied Petroleum Gases.1990-2011
1990 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Energy Consumption (QBtu)
Fuel Use
Ethane
Propane
Butane
Isobutane
Non-Fuel Use
0.88
0.04 1
0.77 1
0.06 1
0.01 1
1.35 |
1.31
0.10
1.07
0.07
0.06
1.90
1.16
0.06
1.00
0.06
0.04
1.77
1.25
0.06
1.10
0.05
0.04
1.85
1.22
0.06
1.07
0.06
0.03
1.75
1.26
006
1.12
0.06
0.01
1.80
1.21
0.06
1.08
0.05
0.01
1.70
1.19
0.06
1.07
0.05
0.01
1.74
1.20
0.07
1.09
0.05
0.00
1.78
1.13
0.06
1.02
0.05
0.00
1.67
1.13
0.07
1.02
0.03
0.01
1.80
1.16
0.08
1.02
0.05
0.01
1.96
1.16
0.08
1.02
0.05
0.01
1.96
1.16
0.08
1.02
0.05
0.01
1.96
A-82 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Ethane 0.71
Propane 0.51
Butane 0.11
Isobutene 0.02
1.04
0.65
0.11
0.09
0.96
0.59
0.13
0.09
1.00
0.64
0.12
0.08
0.92
0.63
0.13
0.07
0.97
0.66
0.13
0.03
0.91
0.63
0.12
0.03
0.98
0.63
0.12
0.02
1.03
0.64
0.11
0.01
0.95
0.60
0.12
0.00
1.12
0.60
0.08
0.01
1.22
0.60
0.12
0.03
1.22
0.60
0.12
0.03
1.22
0.60
0.12
0.03
Carbon Content (Tg C/QBtu)
Fuel Use 16.86
Non-Fuel Use 17.06
16.89
17.09
16.87
17.10
16.85
17.09
16.86
17.09
16.84
17.07
16.84
17.06
16.83
17.06
16.82
17.05
16.83
17.06
16.82
17.06
16.82
17.06
16.82
17.06
16.82
17.06
Sources: Fuel use of LPG based on data from EIA (2009b) and API (1990 through 2007). Non-fuel use of LPG from API (1990
through 2008). Volumes converted using the energy contents provided in Table A-49. C contents from EPA (2010).
Aviation Gasoline
Aviation gasoline is used in piston-powered airplane engines. It is a complex mixture of relatively volatile
hydrocarbons with or without small quantities of additives, blended to form a fuel suitable for use in aviation reciprocating
engines. Fuel specifications are provided in ASTM Specification D910 and Military Specification MIL-G-5572. Aviation
gas is a relatively minor contributor to greenhouse gas emissions compared to other petroleum products, representing
approximately 0.1 percent of all consumption.
The ASTM standards for boiling and freezing points in aviation gasoline effectively limit the aromatics content
to a maximum of 25 percent (ASTM D910). Because weight is critical in the operation of an airplane, aviation gas must
have as many Btu per pound (implying a lower density) as possible, given other requirements of piston engines such as
high anti-knock quality.
Methodology
A C content coefficient for aviation gasoline was calculated on the basis of the EIA standard heat content of
5.048 MMBtu per barrel. This implies a density of approximately 69 degrees API gravity or 5.884 pounds per gallon,
based on the relationship between heat content and density of petroleum liquids, as described in Thermal Properties of
Petroleum Products (DOC 1929). To estimate the share of C in the fuel, it was assumed that aviation gasoline is 87.5
percent iso-octane, 9.0 percent toluene, and 3.5 percent xylene. The maximum allowable sulfur content in aviation
gasoline is 0.05 percent, and the maximum allowable lead content is 0.1 percent. These amounts were judged negligible
and excluded for the purposes of this analysis. This yielded a C share of 85.00 percent and a C content coefficient of
18.86 Tg C/QBtu.
Data Sources
Data sources include ASTM (1985). A standard heat content for aviation gas was adopted from EIA (2009a).
Uncertainty
The relationship used to calculate density from heat content has an accuracy of five percent at 1 atm. The
uncertainty associated with the C content coefficient for aviation gasoline is larger than that for other liquid petroleum
products examined because no ultimate analyses of samples are available. Given the requirements for safe operation of
piston-powered aircraft the composition of aviation gas is well bounded and the uncertainty of the C content coefficient is
likely to be +5 percent.
Still Gas
Still gas, or refinery gas, is composed of light hydrocarbon gases that are released as petroleum is processed in a
refinery. The composition of still gas is highly variable, depending primarily on the nature of the refining process and
secondarily on the composition of the product being processed. Petroleum refineries produce still gas from many different
processes. Still gas can be used as a fuel or feedstock within the refinery, sold as a petrochemical feedstock, or purified
and sold as pipeline-quality natural gas. For the purposes of this inventory, the coefficient derived here is only applied to
still gas that is consumed as a fuel. In general, still gas tends to include large amounts of free hydrogen and methane, as
well as smaller amounts of heavier hydrocarbons. Because different refinery operations result in different gaseous by-
products, it is difficult to determine what represents typical still gas.
Methodology
The properties of still gas used to calculate the carbon content are taken from the literature. The carbon share of
still gas was calculated from its net calorific value and carbon content from IPCC (2006). This calculation yields a carbon
share of 77.7 percent. The density of still gas was estimated to be 0.1405 metric tons per barrel based on its heat content
A-83
-------
(from EIA 2008a) and the relationship between heat content and density that is described by the U.S. Department of
Commerce, Bureau of Standards (DOC 1929).
Data Sources
The carbon share of still gas is calculated from data provided by IPCC (2006). Density is estimated at 0.1405
metric tons per barrel, approximately 28.3 degrees API, based on the heat content of 6.00 MMbtu/barrel of still gas from
EIA (2009a).
Uncertainty
The EIA obtained data on four samples of still gas. Table A-51 below shows the composition of those samples.
Table A-51: Composition, Energy Content, and Carbon Content Coefficient for Four Samples of Still Gas
Sample
One
Two
Three
Four
Hydrogen
(%)
12.7
34.7
72.0
17.0
Methane
(%)
28.1
20.5
12.8
31.0
Ethane
(%)
17.1
20.5
10.3
16.2
Propane
(%)
11.9
6.7
3.8
2.4
Btu Per Cubic
Foot
1,388
1,143
672
1,100
Carbon Content
(Tg C/QBtu)
17.51
14.33
10.23
15.99
Sources: EIA (2008b).
Because the composition of still gas is highly heterogeneous, the C content coefficient for this product is highly
uncertain. Gas streams with a large, free-hydrogen content are likely to be used as refinery or chemical feedstocks.
Therefore, the sample cited above with the very high H content of 72 percent (and the lowest calculated C content) is less
likely to be representative of the still gas streams to which the calculated coefficient is applied. The C content coefficient
used for this report is probably at the high end of the plausible range given that it is higher than the greatest sample-based
C content in Table A-51.
Asphalt
Asphalt is used to pave roads. Because most of its C is retained in those roads, it is a small source of carbon
dioxide emissions. It is derived from a class of hydrocarbons called "asphaltenes," which are abundant in some crude oils
but not in others. Asphaltenes have oxygen and nitrogen atoms bound into their molecular structure, so that they tend to
have lower C contents than do other hydrocarbons.
Methodology
Ultimate analyses of twelve samples of asphalts showed an average C content of 83.47 percent. The EIA
standard Btu content for asphalt of 6.636 MMBtu per barrel was assumed. The ASTM petroleum measurement tables
show a density of 5.6 degrees API or 8.605 pounds per gallon for asphalt. Together, these variables generate C content
coefficient of 20.55 Tg C/QBtu.
Data Sources
A standard heat content for asphalt was adopted from EIA (2009a). The density of asphalt was determined by
the ASTM (1985). C share is adopted from analyses in EIA (2008b).
Uncertainty
The share of C in asphalt ranges from 79 to 88 percent by weight. Also present in the mixture are hydrogen and
sulfur, with shares by weight ranging from seven to 13 percent for hydrogen, and from trace levels to eight percent for
sulfur. Because C share and total heat content in asphalts do vary systematically, the overall C content coefficient is likely
to be accurate to +5 percent.
Lubricants
Lubricants are substances used to reduce friction between bearing surfaces, or incorporated into processing
materials used in the manufacture of other products, or used as carriers of other materials. Petroleum lubricants may be
produced either from distillates or residues. Lubricants include all grades of lubricating oils, from spindle oil to cylinder
oil to those used in greases. Lubricant consumption is dominated by motor oil for automobiles, but there is a large range
of product compositions and end uses within this category.
Methodology
A-84 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The ASTM Petroleum Measurement tables give the density of lubricants at 25.6 degrees API, or 0.1428 metric
tons per barrel. Ultimate analysis of a single sample of motor oil yielded a C content of 85.80 percent. A standard heat
content of 6.065 MMBtu per barrel was adopted from EIA. These factors produce a C content coefficient of 20.20 Tg
C/QBtu.
Data Sources
A standard heat content was adopted from the EIA (2009a). The carbon content of lubricants is adopted from
ultimate analysis of one sample of motor oil (EPA 2009a). The density of lubricating oils was determined by ASTM
(1985).
Uncertainty
Uncertainty in the estimated C content coefficient for lubricants is driven by the large range of product
compositions and end uses in this category combined with an inability to establish the shares of the various products
captured under this category in U.S. energy statistics. Because lubricants may be produced from either the distillate or
residual fractions during refineries, the possible C content coefficients range from 19.89 Tg C/QBtu to 21.48 Tg C/QBtu
or an uncertainty band from-1.5 percent to +1.4 percent of the estimated value.
Petrochemical Feedstocks
U.S. energy statistics distinguish between two different kinds of petrochemical feedstocks: those with a boiling
temperature below 400 degrees Fahrenheit, generally called "naphtha," and those with a boiling temperature 401 degrees
Fahrenheit and above, referred to as "other oils" for the purposes of this inventory.
Methodology
The C content of these petrochemical feedstocks are estimated independently according to the following steps.
Step 1. Estimate the carbon content coefficient for naphtha
Because reformed naphtha is used to make motor gasoline (hydrogen is released to raise aromatics content and
octane rating), "straight-run" naphtha is assumed to be used as a petrochemical feedstock. Ultimate analyses of five
samples of naphtha were examined and showed an average C share of 84.11 percent. A density of 62.4 degrees API
gravity was taken from the Handbook of Petroleum Refining Processes, 3rd ed. The standard EIA heat content of 5.248
MMBtu per barrel is used to estimate a C content coefficient of 18.55 Tg C/QBtu.
Step 2. Estimate the carbon content coefficient for petrochemical feedstocks with a boiling temperature 400
degrees Fahrenheit and above ("other oils ")
The boiling temperature of this product places it into the "middle distillate" fraction in the refining process, and
EIA estimates that these petrochemical feedstocks have the same heat content as distillate fuel No. 2. Thus, the C content
coefficient of 20.17 Tg C/QBtu used for distillate fuel No. 2 is also adopted for this portion of the petrochemical
feedstocks category.
Data Sources
Naphthas: Data on the C content was taken from Unzelman (1992). Density is from Meyers (2004). A standard
heat content for naphthas was adopted from EIA (2009a). Other oils: See Distillate Fuel, Distillate No.2.
Uncertainty
Petrochemical feedstocks are not so much distinguished on the basis of chemical composition as on the identity
of the purchaser, who are presumed to be a chemical company or a petrochemical unit co-located on the refinery grounds.
Naphthas are defined, for the purposes of U.S. energy statistics, as those naphtha products destined for use as a
petrochemical feedstock. Because naphthas are also commonly used to produce motor gasoline, there exists a
considerable degree of uncertainty about the exact composition of petrochemical feedstocks.
Different naphthas are distinguished by their density and by the share of paraffins, isoparaffins, olefins,
naphthenes and aromatics contained in the oil. Naphtha from the same crude oil fraction may have vastly different
properties depending on the source of the crude. Two different samples of Egyptian crude, for example, produced two
straight run naphthas having naphthene and paraffin contents (percent volume) that differ by 18.1 and 17.5 percent,
respectively (Matar and Hatch, 2000).
A-85
-------
Naphthas are typically used either as a petrochemical feedstock or a gasoline feedstock, with lighter paraffinic
naphthas going to petrochemical production. Naphthas that are rich in aromatics and naphthenes tend to be reformed or
blended into gasoline. Thus, the product category encompasses a range of possible fuel compositions, creating a range of
possible C shares and densities. The uncertainty associated with the calculated C content of naphthas is primarily a
function of the uncertainty that underlies the average carbon share calculation, which is based on a limited number of
samples. Two additional samples cited by the EIA (1994) have a range of 83.80 to 84.42 percent C.
The uncertainty of the carbon content for other oils is based upon the assumption of distillate oil No. 2 as a
product representative of the ill-defined classification of "other oils," and from the calculation of the C content of No. 2
itself (see "Distillate Fuels," above). While No. 2 distillate is used as a proxy for "other oils" for the purposes of this
inventory's carbon coefficient, important differences exist between these two petroleum products, contributing some
uncertainty to the cross-application. Other oils are defined herein as those "oils with a boiling range equal to or greater
than 401°F that are generally intended for use as a petrochemical feedstock and are not defined elsewhere." For
comparison, various material safety data sheets (MSDSs) published by producers of distillate No. 2 indicate a boiling
range for this product of 320-700 degrees Fahrenheit. The relatively open definition of the classification "other oils"
leaves room for potentially significant variation in the heating value, density and carbon share properties of each feedstock
oil having a boiling point above 400 degrees Fahrenheit, creating a large band of uncertainty beyond that associated with
the C factor for distillate No. 2.
Kerosene
A light petroleum distillate that is used in space heaters, cook stoves, and water heaters and is suitable for use as
a light source when burned in wick-fed lamps, kerosene is drawn from the same petroleum fraction as jet fuel. Kerosene is
generally comparable to No. 1 distillate oil.
Methodology
The average density and C share of kerosene are assumed to be the same as those for distillate No. 1 since the
physical characteristics of the products are very similar. Thus, a density of 35.3 degrees API and average C share of 86.40
percent were applied to a standard heat content for distillate No. 1 of 5.825 MMBtu per barrel to yield a C content
coefficient of 19.96 Tg C/QBtu.
Data Sources
A standard heat content for distillate No. 1 was adopted from EIA (2009a).
Uncertainty
Uncertainty in the estimated C content for kerosene is driven by the selection of distillate No. 1 as a proxy for
kerosene. If kerosene is more like kerosene-based jet fuel, the true C content coefficient is likely to be some 1.3 percent
lower. If kerosene is more aptly compared to No. 2 distillate oil, then the true C content coefficient is likely to be about
1.1 percent higher. While kerosene is a light petroleum distillate, like distillate No. 1, the two oil classes are do have some
variation in their properties. For example, the boiling range of kerosene is 250-550 degrees Fahrenheit, whereas No. 1 oils
typically boil over a range from 350-615 degrees Fahrenheit. The properties of individual kerosenes will vary with their
use and particular crude origin, as well. Both kerosene and fuel oil No. 1 are primarily composed of hydrocarbons having
9 to 16 carbon atoms per molecule. However, kerosene is a straight-run No. 1 fuel oil, additional cracking processes and
additives contribute to the range of possible fuels that make up the broader distillate No. 1 oil category.
Petroleum Coke
Petroleum coke is the solid residue by-product of the extensive processing of crude oil. It is a coal-like solid,
usually has a C content greater than 90 percent, and is used as a boiler fuel and industrial raw material.
Methodology
Ultimate analyses of two samples of petroleum coke showed an average C share of 92.28 percent. The
ASTM standard density of 9.543 pounds per gallon was adopted and the EIA standard energy content of 6.024
MMBtu per barrel assumed. Together, these factors produced an estimated C content coefficient of 27.85 Tg
C/QBtu.
Data Sources
A-86 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
C content was derived from two samples from Martin, S.W. (1960). The density of petroleum coke was taken
from the ASTM (1985). A standard heat content for petroleum coke was adopted from EIA (2009a).
Uncertainty
The uncertainty associated with the estimated C content coefficient of petroleum coke can be traced to two
factors: the use of only two samples to establish C contents and a standard heat content which may be too low. Together,
these uncertainties are likely to bias the C content coefficient upwards by as much as 6 percent.
Special Naphtha
Special naphtha is defined as a light petroleum product to be used for solvent applications, including commercial
hexane and four classes of solvent: Stoddard solvent, used in dry cleaning; high flash point solvent, used as an industrial
paint because of its slow evaporative characteristics; odorless solvent, most often used for residential paints; and high
solvency mineral spirits, used for architectural finishes. These products differ in both density and C percentage, requiring
the development of multiple coefficients.
Methodology
The method for estimating the C content coefficient of special naphtha includes three steps.
Step 1. Estimate the carbon content coefficient for hexane
Hexane is a pure paraffin containing 6 C atoms and 14 hydrogen atoms; thus, it is 83.63 percent C. Its density is
83.7 degrees API or 5.477 pounds per gallon and its derived C content coefficient is 21.40 Tg C/QBtu.
Step 2. Estimate the carbon contents ofnon-hexane special naphthas
The hydrocarbon compounds in special naphthas are assumed to be either paraffinic or aromatic (see discussion
above). The portion of aromatics in odorless solvents is estimated at less than 1 percent, Stoddard and high flash point
solvents contain 15 percent aromatics and high solvency mineral spirits contain 30 percent aromatics (Boldt and Hall
1977). These assumptions, when combined with the relevant densities, yield the C content factors contained in Table A-
52, below.
Table A-52: Characteristics of Non-hexane Special Naphthas
Special Naphtha
Odorless Solvent
Stoddard Solvent
High Flash Point
Mineral Spirits
Aromatic Content
(Percent)
1
15
15
30
Density
(Degrees API)
55.0
47.9
47.6
43.6
Carbon Share
(Percent Mass)
84.51
84.44
84.70
85.83
Carbon Content (Tg
C/QBtu)
19.41
20.11
20.17
20.99
Sources: EIA (2008b) and Boldt and Hall (1977).
Step 3. Develop weighted carbon content coefficient based on consumption of each special naphtha
EIA reports only a single consumption figure for special naphtha. The C contents of the five special naphthas are
weighted according to the following formula: approximately 10 percent of all special naphtha consumed is hexane; the
remaining 90 percent is assumed to be distributed evenly among the four other solvents. The resulting emissions
coefficient for special naphthas is 19.74 Tg C/QBtu.
Data Sources
A standard heat content for special naphtha was adopted from EIA (2009a). Density and aromatic contents were
adopted from Boldt and Hall (1977).
Uncertainty
The principal uncertainty associated with the estimated C content coefficient for special naphtha is the allocation
of overall consumption across individual solvents. The overall uncertainty is bounded on the low end by the C content of
odorless solvent and on the upper end by the C content of hexane. This implies an uncertainty band of —1.7 percent to
+8.4 percent.
A-87
-------
Petroleum Waxes
The ASTM standards define petroleum wax as a product separated from petroleum that is solid or semi-solid at
77 degrees Fahrenheit (25 degrees Celsius). The two classes of petroleum wax are paraffin waxes and microcrystalline
waxes. They differ in the number of C atoms and the type of hydrocarbon compounds. Microcrystalline waxes have
longer C chains and more variation in their chemical bonds than paraffin waxes.
Methodology
The method for estimating the C content coefficient for petroleum waxes includes three steps.
Step 1. Estimate the carbon content of paraffin waxes
For the purposes of this analysis, paraffin waxes are assumed to be composed of 100 percent paraffinic
compounds with a chain of 25 C atoms. The resulting C share for paraffinic wax is 85.23 percent and the density is
estimated at 45 degrees API or 6.684 pounds per gallon.
Step 2. Estimate the carbon content of microcrystalline waxes
Microcrystalline waxes are assumed to consist of 50 percent paraffinic and 50 percent cycloparaffinic
compounds with a chain of 40 C atoms, yielding a C share of 85.56 percent. The density of microcrystalline waxes is
estimated at 36.7 degrees API, based on a sample of 10 microcrystalline waxes found in the Petroleum Products
Handbook.
Step 3. Develop a carbon content coefficient for petroleum waxes by weighting the density and carbon content of
paraffinic and microcrystalline waxes
A weighted average density and C content was calculated for petroleum waxes, assuming that wax consumption
is 80 percent paraffin wax and 20 percent microcrystalline wax. The weighted average C content is 85.30 percent, and the
weighted average density is 6.75 pounds per gallon. EIA's standard heat content for waxes is 5.537 MMBtu per barrel.
These inputs yield a C content coefficient for petroleum waxes of 19.80 Tg C/QBtu.
Data Sources
Density of paraffin wax was taken from ASTM (1985). Density of microcrystalline waxes was derived from 10
samples found in Guthrie (1960). A standard heat content for petroleum waxes was adopted from EIA (2009a).
Uncertainty
Although there is considerable qualitative uncertainty associated with the allocation of petroleum waxes and
microcrystalline waxes, the quantitative variation in the C contents for all waxes is limited to + 1 percent because of the
nearly uniform relationship between C and other elements in petroleum waxes broadly defined.
Crude Oil, Unfinished Oils, and Miscellaneous Products
U.S. energy statistics include several categories of petroleum products designed to ensure that reported refinery
accounts "balance" and cover any "loopholes" in the taxonomy of petroleum products. These categories include crude oil,
unfinished oils, and miscellaneous products. Crude oil is rarely consumed directly, miscellaneous products account for
less than one percent of oil consumption, and unfinished oils are a balancing item that may show negative consumption.
For C accounting purposes, it was assumed that all these products have the same C content as crude oil.
Methodology
EIA reports on the average density and sulfur content of U.S. crude oil purchased by refineries. To develop a
method of estimating C content based on this information, results of ultimate analyses of 182 crude oil samples were
collected. Within the sample set, C content ranged from 82 to 88 percent C, but almost all samples fell between 84 percent
and 86 percent C. The density and sulfur content of the crude oil data were regressed on the C content, producing the
following equation:
Percent C = 76.99 + (10.19 x Specific Gravity) + (-0.76 x Sulfur Content)
A-88 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Absent the term representing sulfur content, the equation had an R-squared of only 0.35.23 When C content was
adjusted to exclude sulfur, the R-squared value rose to 0.65. While sulfur is the most important non-hydrocarbon
impurity, nitrogen and oxygen can also be significant, but they do not seem to be correlated with either density or sulfur
content. Restating these results, density accounts for about 35 percent of the variation in C content, impurities account for
about 30 percent of the variation, and the remaining 35 percent is accounted for by other factors, including (presumably)
the degree to which aromatics and polynuclear aromatics are present in the crude oil. Applying this equation to the 2008
crude oil quality data (30.21 degrees API and 1.47 percent sulfur) produces an estimated C content of 84.79 percent.
Applying the density and C content to the EIA standard energy content for crude oil of 5.800 MMBtu per barrel produced
an emissions coefficient of 20.31 Tg C/QBtu.
Data Sources
C content was derived from 182 crude oil samples, including 150 samples from U.S. National Research Council
(1927). A standard heat content for crude oil was adopted from EIA (2009a).
Uncertainty
The uncertainty of the estimated C content for crude oil centers on the 35 percent of variation that cannot be
explained by density and sulfur content. This variation is likely to alter the C content coefficient by +3 percent. Since
unfinished oils and miscellaneous products are impossible to define, the uncertainty of applying a crude oil C content is
likely to be bounded by the range of petroleum products described in this chapter at +10 percent.
Chronology and Explanation of Changes in Individual Carbon Content Coefficients of Fossil Fuels
Coal
Original 1994 Analysis
A set of 5,426 coal samples from the EIA coal analysis file were used to develop C content estimates. The results
from that sample set appear below in Table A-53. The EIA Coal Analysis File was originally developed by the U.S.
Bureau of Mines and contained over 60,000 coal samples obtained through numerous coal seams throughout the United
States. Many of the samples were collected starting in the 1940s and 1950s through the 1980s and analyzed in U.S.
government laboratories.
Table A-53: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank, 1990 - 2000 [Tg C/QBtul
Consuming Sector
Electric Power
Industrial Coking
Other Industrial
Residential / Commercial
Coal Rank
Anthracite
Bituminous
Sub-bituminous
Lignite
1990
25.68
25.51
25.58
25.92
28.13
25.37
26.24
26.62
1991
25.69
25.51
25.59
26.00
28.13
25.37
26.24
26.62
1992
25.69
25.51
25.62
26.13
28.13
25.37
26.24
26.62
1993
26.71
25.51
25.61
25.97
28.13
25.37
26.24
26.62
1994
25.72
25.52
25.63
25.95
28.13
25.37
26.24
26.62
1995
25.74
25.53
25.63
26.00
28.13
25.37
26.24
26.62
1996
25.74
25.55
25.61
25.92
28.13
25.37
26.24
26.62
1997
25.76
25.56
25.63
26.00
28.13
25.37
26.24
26.62
1998
25.76
25.56
25.63
26.00
28.13
25.37
26.24
26.62
1999
25.76
25.56
25.63
26.00
28.13
25.37
26.24
26.62
2000
25.76
25.56
25.63
26.00
28.13
25.37
26.24
26.62
Sources: Emission factors be consuming sector from B.D. Hong and E.R. Slatnick, "Carbon Dioxide Emission Factors for Coal," U.S. Energy Information
Administration, Quarterly Coal Report, January-March 1994. (Washington, DC, 1994) and Emission factors by rank from Science Applications International
Corporation, "Analysis of the Relationship Between Heat and Carbon Content of U.S. Fuels: Final Task Report," Prepared for the U.S. Energy Information
Administration, Office of Coal, Nuclear, Electric and Alternative Fuels (Washington, DC 1992).
2002 Update
The methodology employed for these estimates was unchanged from previous years; however, the underlying
coal data sample set was updated. A new database, CoalQual 2.0 (1998), compiled by the U.S. Geological Survey was
adopted for the updated analysis. The updated sample set included 6,588 coal samples collected by the USGS and its state
R-squared represents the percentage of variation in the dependent variable (in this case carbon content) explained by variation in the
independent variables.
A-89
-------
affiliates between 1973 and 1989. The decision to switch to the sample data contained in the USGS CoalQual database
from the EIA database was made because the samples contained in the USGS database were collected and analyzed more
recently than those obtained by EIA from the Bureau of Mines. The new coefficients developed in the 2002 revision were
in use through the 1990 through 2007 Inventory and are provided in Table A-54.
A-90 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-54: Carbon Content Coefficients for Coal by Consuming Sector and Coal Ranh.1990 - 2000 tTg C/QBtul
Consuming Sector
Electric Power
Industrial Coking
Other Industrial
Residential/ Commercial
Coal Rank
Anthracite
Bituminous
Sub-bituminous
Lignite
1990
25.68
25.51
25.58
25.92
28.26
25.43
26.50
26.19
1991
25.69
25.51
25.60
26.00
28.26
25.45
26.49
26.21
1992
25.69
25.51
25.62
26.13
28.26
25.44
26.49
26.22
1993
25.71
25.51
25.61
25.97
28.26
25.45
26.48
26.21
1994
25.72
25.52
25.63
25.95
28.26
25.46
26.49
26.24
1995
25.74
25.53
25.63
26.00
28.26
25.47
26.49
26.22
1996
25.74
25.55
25.61
25.92
28.26
25.47
26.49
26.17
1997
25.76
25.56
25.63
26.00
28.26
25.48
26.49
26.20
1998
25.76
25.56
25.63
26.00
28.26
25.47
26.49
26.23
1999
25.76
25.56
25.63
26.00
28.26
25.48
26.49
26.26
2000
25.76
25.56
25.63
26.00
28.26
25.49
26.48
26.30
Sources: Data from USGS, U.S. Coal Quality Database Version 2.0 (1998) and analysis prepared by SAIC 2007.
2007 Update
The analysis of the USGS Coal Qual data was updated in 2007 to make a technical correction that affected the value for lignite and those sectors which consume
lignite Table A-55 contains the annual coefficients that resulted from the 2007 analysis.
Table A-55: Carbon Content Coefficients for Coal by Consuming Sector and Coal Ranh.1990-2007ITg C/QBtul
1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Consuming Sector
Electric Power 25.68 25.74 25.74 25.76 25.76 25.76 25.76 25.76 25.76 25.76 25.76 25.76 25.76 25.76
Industrial Coking 25.51 25.53 25.55 25.56 25.56 25.56 25.56 25.56 25.56 25.56 25.56 25.56 25.56 25.56
Other Industrial 25.58 25.63 25.61 25.63 25.63 25.63 25.63 25.63 25.63 25.63 25.63 25.63 25.63 25.63
Residential/Commercial 25.92 26.00 25.92 26.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00
Coal Rank
Anthracite 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26
Bituminous 25.43 25.47 25.47 25.48 25.47 25.48 25.49 25.49 25.49 25.49 25.49 25.49 25.49 25.49
Sub-bituminous 26.50 26.49 26.49 26.49 26.49 26.49 26.48 26.48 26.48 26.48 26.48 26.48 26.48 26.48
Lignite 26.19 26.22 26.17 26.20 26.23 26.26 26.30 26.30 26.30 26.30 26.30 26.30 26.30 26.57
Sources: Data from USGS, U.S. Coal Quality Database Version 2.0 (1998) and analysis prepared by (SAIC 2007).
A-91
-------
2010 Update
The estimated annual C content coefficients for coal by rank and sector of consumption were updated again in
2010. Sample data from the Energy Institute at Pennsylvania State University (504 samples) were added to the 6,588
USGS samples to create a new database of 7,092 samples. The same analytical method used in the 2002 update was
applied using these additional samples to calculate revised state-level carbon contents for each coal rank and then for
national average consumption by end-use sector and by rank.
Natural Gas
A revised analytical methodology underlies the natural gas coefficients used in this report. Prior to the current
Inventory, descriptive statistics were used to stratify 6,743 samples of pipeline quality natural gas by heat content and then
to determine the average C content of natural gas at the national average heat content (EIA 1994). The same coefficient
was applied to all pipeline natural gas consumption for all years, because U.S. energy statistics showed a range of national
average heat contents of pipeline gas of only 1,025 to 1,031 Btu per cubic foot (1 percent) from 1990 through 1994. A
separate factor was developed in the same manner for all flared gas. In the previous Inventory, a weighted national
average C content was calculated using the average C contents for each sub-sample of gas that conformed with an
individual state's typical cubic foot of natural gas since there is regional variation in energy content. The result was a
weighted national average of 14.47 Tg C/QBtu.
The current Inventory is revised to make use of the same set of samples, but utilizes a regression equation, as
described above, of sample-based heat content and carbon content data in order to calculate annually-variable national
average C content coefficients based on annual national average heat contents for pipeline natural gas and for flare gas. In
addition, the revised analysis calculates an average C content from all samples with less than 1.5 percent CCh and less than
1,050 Btu/cf (samples most closely approximating the makeup of pipeline quality natural gas). The result was identical to
the previous weighted national average of 14.47 Tg C/QBtu. The average C contents from the 1994 calculations are
presented in Table A-56 below for comparison.
Table A-56: Carbon Content of Pipeline-Quality Natural Gas by Energy Content (Tg C/QBtu]
Sample Average Carbon Content
GRI Full Sample 14.51
Greater than 1,000 Btu 14.47
1,025 to 1,035 Btu 14.45
975 to 1,000 Btu 14.73
1,000 to 1,025 Btu 14.43
1,025 to 1,050 Btu 14.47
1,050 to 1,075 Btu 14.58
1,075 to 1,100 Btu 14.65
Greater than 1,100 Btu 14.92
Weighted National Average 14.47
Source: EIA (1994).
Petroleum Products
All of the petroleum product C coefficients except that for Aviation Gasoline Blending Components have been
updated for the current Inventory. EPA is updating these factors to better align the fuel properties data that underlie the
Inventory factors with those published in the Mandatory Reporting of Greenhouse Gases Rule (EPA 2009b), Suppliers of
Petroleum Products (MM) and Stationary Combustion (C) subparts. The coefficients that were applied in the previous
report are provided in Table A-57 below. Specifically, each of the coefficients used in this report have been calculated
from updated density and C share data, largely adopted from analyses undertaken for the Rule (EPA 2009b). In some
cases, the heat content applied to the conversion to a carbon-per-unit-energy basis has also been updated. Additionally,
the category Misc. Products (Territories), which is based upon the coefficients calculated for crude oil, has been allowed to
vary annually with the crude oil coefficient. The petrochemical feedstock category has been eliminated for this report
because the constituent products—naphthas and other oils—are estimated independently. Further, although the level of
aggregation of U.S. energy statistics currently limits the application of coefficients for residual and distillate fuels to these
two generic classifications, individual coefficients for the five major types of fuel oil (Nos. 1, 2, 4, 5 and 6) have been
estimated for the current report and are presented in Table A-46 above. Each of the C coefficients applied in the previous
Inventory is provided below for comparison (Table A-57).
A-92 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-57: Carbon Content Coefficients and Underlying Data for Petroleum Products
2007 Carbon Content Gross Heat of Combustion
Fuel (Tg C/QBtu) (MMBtu/Barrel)
Motor Gasoline
LPG (total)3
LPG (energy use)
LPG (non-energy use)
Jet Fuel
Distillate Fuel
Residual Fuel
Asphalt and Road Oil
Lubricants
Petrochemical Feedstocks
Aviation Gas
Kerosene
Petroleum Coke
Special Naphtha
Petroleum Waxes
Still Gas
Crude Oil
Unfinished Oils
Miscellaneous Products
Pentones Plus
Natural Gasoline
19.33
16.99
17.18
16.76
19.33
19.95
21.49
20.62
20.24
19.37
18.87
19.72
27.85
19.86
19.81
17.51
20.33
20.33
20.33
18.24
18.24
5.219
(See a)
(See a)
(See a)
5.670
5.825
6.287
6.636
6.065
5.248"
5.048
5.670
6.024
5.248
5.537
6.000
5.800
5.825
5.796
4.620
4.620
Density
(API Gravity)
59.1
(See a)
(See a)
(See a)
42.0
35.5
11.0
5.6
25.6
67.1"
69.0
41.4
-
51.2
43.3
-
30.5
30.5
30.5
81.7
81.7
Percent
Carbon
86.60
(See a)
(See a)
(See a)
86.30
86.34
85.68
83.47
85.80
84.11"
85.00
86.01
92.28
84.76
85.29
-
85.49
85.49
85.49
83.70
83.70
a Heat, density and percent carbon values are provided separately for ethane, propane and isobutene.
a LPG is a blend of multiple paraffmic hydrocarbons: ethane, propane, isobutane, and normal butane, each with their own heat content, density and C content,
see Table A-49.
b Parameters presented are for naphthas with a boiling temperature less than 400 degrees Fahrenheit. Petrochemical feedstocks with higher boiling points are
assumed to have the same characteristics as distillate fuel.
- No sample data available
Sources: EIA (1994), EIA (2008a), and SAIC (2007).
Additional revisions to the inventory's C coefficients since 1990 are detailed below.
Jet Fuel
1995 Update
Between 1994 and 1995, the C content coefficient for kerosene-based jet fuel was revised downward from 19.71
Tg C/QBtu to 19.33 Tg C/QBtu. This downward revision was the result of a shift in the sample set used from one
collected between 1959 and 1972 and reported on by Martel and Angello in 1977 to one collected by Boeing in 1989 and
published by Hadaller and Momenthy in 1990. The downward revision was a result of a decrease in density, as well as
slightly lower C shares than in the earlier samples. However, the assumed heat content is unchanged because it is based
on an EIA standard and probably yields a downward bias in the revised C content coefficient.
1990 through 2008 Inventory Update
The coefficient was revised again for the 1990 through 2008 Inventory, returning to Martel and Angello and
NIPER as the source of the carbon share and density data, respectively, for kerosene-based fuels. This change was made in
order to align the coefficients used for this report with the values used in the Mandatory Reporting of Greenhouse Gases
Rule (EPA 2009b). The return to the use of the Martel and Angello and NIPER coefficients was deemed more appropriate
for the Rule as it was considered a more conservative coefficient given the uncertainty and variability in coefficients
across the types of jet fuel in use in the United States. The factor will be revisited in future Inventories in light of data
received from reporting entities in response to the Rule.
A-93
-------
Liquefied Petroleum Gases (LPG)
The C content coefficient of LPG is updated annually to reflect changes in the consumption mix of the
underlying compounds: ethane; propane; isobutane; and normal butane. In 1994, EIA included pentanes plus—assumed to
have the characteristics of hexane—in the mix of compounds broadly described as LPG. In 1995, EIA removed pentanes
plus from this fuel category. Because pentanes plus is relatively rich in C per unit of energy, its removal from the
consumption mix lowered the C content coefficient for LPG from 17.26 Tg C/QBtu to 16.99 Tg C/QBtu. In 1998, EIA
began separating LPG consumption into two categories: energy use and non-fuel use and providing individual coefficients
for each. Because LPG for fuel use typically contains higher proportions of propane than LPG for non-fuel use, the C
content coefficient for fuel use was 1.8 to 2.5 percent higher than the coefficient for non-fuel use in previous Inventories
(see Table A-57).
However, for the current update of the LPG coefficients, the assumptions that underlie the selection of density
and heat content data for each pure LPG compound have been updated, leading to a significant revision of the assumed
properties of ethane. For this report, the physical characteristics of ethane, which constitutes over 90 percent of LPG
consumption for non-fuel uses, have been updated to reflect ethane that is in (refrigerated) liquid form. Previously, the
share of ethane was included using the density and energy content of gaseous ethane. Table A-58, below, compares the
values applied for each of the compounds under the two sets of coefficient calculations. The carbon share of each pure
compound was also updated by using more precise values for each compound's molecular weight.
Due in large part to the revised assumptions for ethane, the weighted C content for non-fuel use is now higher
than that of the weighted coefficient for fuel use, which is dominated by the consumption of more dense propane. Under
the revised assumptions, each annual weighted coefficient for non-fuel LPG consumption is 1.2 to 1.7 percent higher each
year than is that for LPGs consumed for fuel (energy) uses.
Table A-58: Physical Characteristics of Liquefied Petroleum Gases
Compound
Ethane
Propane
Isobutane
n-butane
Chemical
Formula
C2H6
C3H8
C4Hio
C4Hio
1990-2007
Density
(bbl / MT)
16.88
12.44
11.20
10.79
Updated
Density
(bbl / MT)
11.55
12.76
11.42
10.98
1990-2007
Energy Content
(MMBtu/bbl)
2.916
3.824
4.162
4.328
Updated
Energy Content
(MMBtu/bbl)
3.082
3.836
3.974
4.326
1990-2007
C Content
Coefficient (Tg
C/QBtu)
16.25
17.20
17.75
17.72
Updated
C Content
Coefficient (Tg
C/QBtu)
17.16
16.76
17.77
17.75
Sources: Updated: Densities - CRC Handbook of Chemistry and Physics, 89th Ed. (2008/09); Energy Contents - EPA (2009b). All values are for the compound
in liquid form. The density and energy content of ethane are for refrigerated ethane (-89 degrees C). Values for n-butane are for pressurized butane (-25 degrees
C). Values in previous editions of this inventory: Gurthrie (1960).
Motor Gasoline
The C content coefficient for motor gasoline varies annually based on the density of and proportion of additives
in a representative sample of motor gasoline examined each year. However, in 1997 EIA began incorporating the effects
of the introduction of reformulated gasoline into its estimate of C content coefficients for motor gasoline. This change
resulted in a downward step function in C content coefficients for gasoline of approximately 0.3 percent beginning in
1995. In 2005-2006 reformulated fuels containing ethers began to be phased out nationally. Ethanol was added to
gasoline blends as a replacement oxygenate, leading to another shift in gasoline density (see Table A- 47), in the list and
proportion of constituents that form the blend and in the blended C share based on those constituents.
A-94 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-59: Carbon Content Coefficients for Petroleum Products.1990-2007 ITg C/QBtul
Fuel Type
Petroleum
Asphalt and Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel3
Kerosene
LPG (energy use)3
LPG (non-energy use)3
Lubricants
Motor Gasoline3
Residual Fuel
Other Petroleum
Av Gas Blend Comp.
Mo Gas Blend Comp3
Crude Oil3
Misc. Products3
Misc. Products (Terr.)
Naphtha (<401 deg. F)
Other oil (>401 deg. F)
Pentanes Plus
Petrochemical Feed.
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils3
Waxes
Other Wax and Misc.
1990
20.62
18.87
19.95
19.40
19.72
17.21
16.83
20.24
19.41
21.49
18.87
19.41
20.16
20.16
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.16
19.81
19.81
1995
20.62
18.87
19.95
19.34
19.72
17.20
16.87
20.24
19.38
21.49
18.87
19.38
20.23
20.23
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.23
19.81
19.81
1996
20.62
18.87
19.95
19.33
19.72
17.20
16.86
20.24
19.36
21.49
18.87
19.36
20.25
20.25
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.25
19.81
19.81
1997
20.62
18.87
19.95
19.33
19.72
17.18
16.88
20.24
19.35
21.49
18.87
19.35
20.24
20.24
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.24
19.81
19.81
1998
20.62
18.87
19.95
19.33
19.72
17.23
16.88
20.24
19.33
21.49
18.87
19.33
20.24
20.24
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.24
19.81
19.81
1999
20.62
18.87
19.95
19.33
19.72
17.25
16.84
20.24
19.33
21.49
18.87
19.33
20.19
20.19
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.19
19.81
19.81
2000
20.62
18.87
19.95
19.33
19.72
17.20
16.81
20.24
19.34
21.49
18.87
19.34
20.23
20.23
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.23
19.81
19.81
2001
20.62
18.87
19.95
19.33
19.72
17.21
16.83
20.24
19.34
21.49
18.87
19.34
20.29
20.29
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.29
19.81
19.81
2002
20.62
18.87
19.95
19.33
19.72
17.20
16.82
20.24
19.35
21.49
18.87
19.35
20.30
20.30
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.30
19.81
19.81
2003
20.62
18.87
19.95
19.33
19.72
17.21
16.84
20.24
19.33
21.49
18.87
19.33
20.28
20.28
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.28
19.81
19.81
2004
20.62
18.87
19.95
19.33
19.72
17.20
16.81
20.24
19.33
21.49
18.87
19.33
20.33
20.33
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.33
19.81
19.81
2005
20.62
18.87
19.95
19.33
19.72
17.19
16.81
20.24
19.33
21.49
18.87
19.33
20.33
20.33
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.33
19.81
19.81
2006
20.62
18.87
19.95
19.33
19.72
17.19
16.78
20.24
19.33
21.49
18.87
19.33
20.33
20.33
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.33
19.81
19.81
2007
20.62
18.87
19.95
19.33
19.72
17.18
16.76
20.24
19.33
21.49
18.87
19.33
20.33
20.33
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.33
19.81
19.81
a C contents vary annually based on changes in fuel composition.
A-95
-------
References
AAM (2009). Diesel Survey. Alliance of Automobile Manufacturers, Winter 2008.
American Gas Association (1974) Gas Engineer's Handbook, New York, NY, Industrial Press, pp. 3/71, 3.87.
API (1990 through 2008) Sales of Natural Gas Liquids and Liquefied Refinery Gases, American Petroleum Institute.
API (1988) Alcohols and Ethers: A Technical Assessment of Their Applications as Fuels and Fuel Components, American
Petroleum Institute, API 4261.
ASTM (1985) ASTM and Other Specifications for Petroleum Products and Lubricants, American Society for Testing and
Materials. Philadelphia, PA.
Black, F. and L. High (1979) "Methodology for Determining Particulate and Gaseous Diesel Emissions," in, The
Measurement and Control of Diesel Particulate Emissions, Society of Automotive Engineers, p. 128.
Boldt, K. and B.R. Hall (1977) Significance of Tests for Petroleum Products, Philadelphia, PA, American Society for
Testing and Materials, p. 30.
Chemical Rubber Company (CRC), (2008/2009), Handbook of Chemistry and Physics, 89th Ed., editor D. Lide,
Cleveland, OH: CRC Press.
DOC (1929) Thermal Properties of Petroleum Products, U.S. Department of Commerce, National Bureau of Standards.
Washington, DC. pp. 16-21.
EIA (2001-2009b) Coal Distribution - Annual, U.S. Department of Energy, Energy Information Administration.
Washington, DC. DOE/EIA.
EIA (2008a) Monthly Energy Review, September 2007 and Published Supplemental Tables on Petroleum Product detail.
Energy Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2007/9).
EIA (2008b) Documentation for Emissions of Greenhouse Gases in the United States 2006. DOE/EIA-0638(2006).
October 2008.
EIA (2009a) Annual Energy Review, Energy Information Administration, U.S. Department of Energy, Washington, DC.
DOE/EIA-0384(2008).
EIA (2009b) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington,
DC. Available online at <
http://www.eia.doe.go v/oil_gas/petroleum/data_publications/petroleum_supply_annual/psa_volumel/psa_volumel.ht
ml>.
EIA (2001-2009a) Annual Coal Report, U.S. Department of Energy, Energy Information Administration. Washington,
DC. DOE/EIA 0584.
EIA (2001) Cost and Quality of Fuels for Electric Utility Plants 2000, Energy Information Administration. Washington,
DC. August 2001. Available online at .
EIA (1990-2001) Coal Industry Annual, U.S. Department of Energy, Energy Information Administration. Washington,
DC. DOE/EIA 0584.
EIA (1994) Emissions of Greenhouse Gases in the United States 1987-1992, Energy Information Administration, U.S.
Department of Energy. Washington, DC. November, 1994. DOE/EIA 0573.
EIA (1993) Btu Tax on Finished Petroleum Products, Energy Information Administration, Petroleum Supply Division
(unpublished manuscript, April 1993).
EPA (2010) Carbon Content Coefficients Developed for EPA's Inventory of Greenhouse Gases and Sinks. Office of Air
and Radiation, Office of Atmospheric Programs, U. S. Environmental Protection Agency, Washington, D.C.
EPA (2009a), "Industry Overview and Current Reporting Requirements for Petroleum Refining and Petroleum Imports,"
Petroleum Product Suppliers Technical Support Document for the Proposed Mandatory Reporting Rule. Office of Air
and Radiation. 30 January, 2009.
EPA (2009b). Mandatory Reporting of Greenhouse Gases Rule. Federal Register Docket ID EPA-HQ-OAR-2008-0508-
2278, 30 September, 2009.
A-96 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Gas Technology Institute (1992) Database as documented in W.E. Liss, W.H. Thrasher, G.F. Steinmetz, P. Chowdiah, and
A. Atari, Variability of Natural Gas Composition in Select Major Metropolitan Areas of the United States. GRI-
92/0123. March 1992.
Guthrie, V.B. (ed.) (1960) Characteristics of Compounds, Petroleum Products Handbook, p.3-3. New York, NY,
McGraw-Hill.
Hadaller, O.J. and A.M. Momenthy (1990) The Characteristics of Future Fuels, Part 1, "Conventional Heat Fuels".
Seattle, WA, Boeing Corp. September 1990. pp. 46-50.
Intergovernmental Panel on Climate Change (IPCC), 2006 IPCC Guidelines for National Greenhouse Gas Inventories.
Prepared by the National Greenhouse Gas Inventories Programme (Japan, 2006)
Matar, S. and L. Hatch (2000), Chemistry of Petrochemical Processes, 2nd Ed. Gulf Publishing Company: Houston.
Martel, C.R., and L.C. Angello (1977) "Hydrogen Content as a Measure of the Combustion Performance of Hydrocarbon
Fuels," in Current Research in Petroleum Fuels, Volume I. New York, NY, MSS Information Company, p. 116.
Martin, S.W. (1960) "Petroleum Coke," in Virgil Guthrie (ed.), Petroleum Processing Handbook, New York, NY,
McGraw-Hill, pp. 14-15.
Meyers, (2004), Handbook of Petroleum Refining Processes, 3rd ed., NY, NY: McGraw Hill.
National Institute for Petroleum and Energy Research (1990 through 2009) Motor Gasolines, Summer and Motor
Gasolines, Winter.
NIPER (1993). C. Dickson, Aviation Turbine Fuels, 1992, NIPER-179 PPS93/2 (Bartlesville, OK: National Institute for
Petroleum and Energy Research, March 1993).
Pennsylvania State University (PSU) (2010). Coal Sample Bank and Database. Data received by SAIC 18 February 2010
from Gareth Mitchell, The Energy Institute, Pennsylvania State University.
Quick, Jeffrey (2010). "Carbon Dioxide Emission Factors for U.S. Coal by Origin and Destination," Environmental
Science & Technology, Forthcoming.
SAIC (2007) Analysis prepared by Science Applications International Corporation for EPA, Office of Air and Radiation,
Market Policies Branch.
U.S. National Research Council (1927) International Critical Tables of Numerical Data, Physics, Chemistry, and
Technology, New York, NY, McGraw-Hill.
Unzelman, G.H. (1992) "A Sticky Point for Refiners: FCC Gasoline and the Complex Model," Fuel Reformulation,
July/August 1992, p. 29.
USGS (1998) CoalQualDatabase Version 2.0, U.S. Geological Survey.
Wauquier, J., ed. (1995). Petroleum Refining, Crude Oil, Petroleum Products and Process Flowsheets (Editions Technip -
Pans, 1995) pg.225, Table 5.16.
A-97
-------
2.3. Methodology for Estimating Carbon Emitted from Non-Energy Uses of Fossil
Fuels
Carbon (C) storage associated with the non-energy use of fossil fuels was calculated by multiplying each fuel's
potential emissions (i.e., each fuel's total C content) by a fuel-specific storage factor, as listed in Table A-60. The
remaining C—i.e., that which is not stored—is emitted. This sub-annex explains the methods and data sources employed
in developing the storage factors for petrochemical feedstocks (industrial other coal, natural gas for non-fertilizer uses,
LPG, pentanes plus, naphthas, other oils, still gas, special naphtha), asphalt and road oil, lubricants, waxes, and
miscellaneous products. The storage factors24 for the remaining non-energy fuel uses are either based on values
recommended for use by IPCC (2006), or when these were not available, assumptions based on the potential fate of C in
the respective NEU products.
Table A-60: Fuel Types and Percent of C Stored for Non-Energy Uses
Sector/Fuel Type Storage Factor (%)
Industry
Industrial Coking Coala 10
Industrial Other Coalb 70
Natural Gas to Chemical Plantsb 70
Asphalt & Road Oil 100
LPG" 70
Lubricants 9
Pentanes Plusb 70
Naphtha (<401 deg. F)" 70
Other Oil (>401 deg. F)b 70
Still Gasb 70
Petroleum Coke° 30
Special Naphthab 70
Distillate Fuel Oil 50
Waxes 58
Miscellaneous Products 0
Transportation
Lubricants 9
U.S. Territories
Lubricants 9
Other Petroleum (Misc. Prod.) 10
- Not applicable
a Includes processes for which specific coking coal consumption and emission factor data are not available. Consumption of coking coal for production of iron
and steel is covered in the Industrial Processes chapter.
b The storage factor listed is the value for 2012. As described in this annex, the factor varies over time.
c Assumes petroleum coke consumption is for pigments. Consumption of petroleum coke for production of primary aluminum anodes, electric arc furnace
anodes, titanium dioxide, ammonia, urea, and ferroalloys is covered in the Industrial Processes chapter.
The following sections describe the non-energy uses in greater detail, outlining the methods employed and data
used in estimating each storage factor. Several of the fuel types tracked by EIA are used in organic chemical synthesis and
in other manufacturing processes, and are referred to collectively as "petrochemical feedstocks." Because the methods and
data used to analyze them overlap, they are handled as a group and are discussed first. Discussions of the storage factors
for asphalt and road oil, lubricants, waxes, and miscellaneous products follow.
Throughout this section, references to "storage factors" represent the proportion of carbon stored.
A-98 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Petrochemical Feedstocks
Petrochemical feedstocks—industrial other coal, natural gas for non-fertilizer uses, LPG, pentanes plus,
naphthas, other oils, still gas, special naphtha—are used in the manufacture of a wide variety of man-made chemicals and
products. Plastics, rubber, synthetic fibers, solvents, paints, fertilizers, pharmaceuticals, and food additives are just a few
of the derivatives of these fuel types. Chemically speaking, these fuels are diverse, ranging from simple natural gas (i.e.,
predominantly CH-i) to heavier, more complex naphthas and other oils.25
After adjustments for (1) use in industrial processes and (2) net exports, these eight fuel categories constituted
approximately 197.3 Tg CC>2 Eq., or 62 percent, of the 315.8 Tg CC>2 Eq. of non-energy fuel consumption in 2012. For
2012, the storage factor for the eight fuel categories was 70 percent. In other words, of the net consumption, 70 percent
was destined for long-term storage in products—including products subsequently combusted for waste disposal—while
the remaining 30 percent was emitted to the atmosphere directly as CC>2 (e.g., through combustion of industrial by-
products) or indirectly as CC>2 precursors (e.g., through evaporative product use). The indirect emissions include a variety
of organic gases such as volatile organic compounds (VOCs) and carbon monoxide (CO), which eventually oxidize into
CCh in the atmosphere. The derivation of the storage factor is described in the following sections.
Methodology and Data Sources
The petrochemical feedstocks storage factor is equal to the ratio of C stored in the final products to total C
content for the non-energy fossil fuel feedstocks used in industrial processes, after adjusting for net exports of feedstocks.
One aggregate storage factor was calculated to represent all eight fuel feedstock types. The feedstocks were grouped
because of the overlap of their derivative products. Due to the many reaction pathways involved in producing
petrochemical products (or wastes), it becomes extraordinarily complex to link individual products (or wastes) to their
parent fuel feedstocks.
Import and export data for feedstocks were obtained from the Energy Information Administration (EIA) for the
major categories of petrochemical feedstocks. EIA's Petroleum Supply Annual publication tracks imports and exports of
petrochemical feedstocks, including butanes, butylenes, ethane, ethylene, propane, propylene, LPG, and naphthas (i.e.,
most of the large volume primary chemicals produced by petroleum refineries). These imports and exports are already
factored into the U.S. fuel consumption statistics. However, EIA does not track imports and exports of chemical
intermediates and products produced by the chemical industry (e.g., xylenes, vinyl chloride), which are derived from the
primary chemicals produced by the refineries. These products represent very large flows of C derived from fossil fuels
(i.e., fossil C), so estimates of net flows not already considered in EIA's dataset were developed for the entire time series
from 1990 to 2012.
The approach to estimate imports and exports involves three steps, listed here and then described in more detail
below:
Step 1. Identify commodities derived from petrochemical feedstocks, and calculate net import/export for each.
Step 2. Estimate the C content for each commodity.
Step 3. Sum the net C imports/exports across all commodities.
Step 1 relies heavily on information provided by the National Petrochemical and Refiners Association (NPRA)
and U.S. Bureau of the Census (BoC) trade statistics published by the U.S. International Trade Commission (USITC).
NPRA provided a spreadsheet of the ten-digit BoC Harmonized Tariff Schedule (HTS) Commodity Codes used to compile
import-export data for periodic reports issued to NPRA's membership on trade issues. Additional feedstock commodities
were identified by HTS code in the BoC data system and included in the net import/export analysis.
One of the difficulties in analyzing trade data is that a large portion of the outputs from the refining industry are
fuels and fuel components, and it was difficult to segregate these from the outputs used for non-energy uses. The NPRA-
supplied codes identify fuels and fuel components, thus providing a sound basis for isolating net imports/exports of
petrochemical feedstocks. Although MTBE and related ether imports are included in the published NPRA data, these
commodities are not included in the total net imports/exports calculated here, because it is assumed that they are fuel
additives and do not contribute to domestic petrochemical feedstocks. Net exports of MTBE and related ethers are also
not included in the totals, as these commodities are considered to be refinery products that are already accounted for in the
Naphthas are compounds distilled from petroleum containing 4 to 12 carbon atoms per molecule and having a boiling point less than
401° F. "Other oils" are distillates containing 12 to 25 carbon atoms per molecule and having a boiling point greater than 401° F.
A-99
-------
EIA data. Imports and exports of commodities for which production and consumption data are provided by EIA (e.g.,
butane, ethylene, and liquefied petroleum gases) are also not included in the totals, to avoid double-counting.
Another difficulty is that one must be careful to assure that there is not double-counting of imports and exports in
the data set. Other parts of the mass balance (described later) provide information on C flows, in some cases based on
production data and in other cases based on consumption data. Production data relates only to production within the
country; consumption data incorporates information on imports and exports as well as production. Because many
commodities are emissive in their use, but not necessarily their production, consumption data is appropriately used in
calculations for emissive fates. For purposes of developing an overall mass balance on U.S. non-energy uses of C, for
those materials that are non-emissive (e.g., plastics), production data is most applicable. And for purposes of adjusting the
mass balance to incorporate C flows associated with imports and exports, it was necessary to carefully review whether or
not the mass balance already incorporated cross-boundary flows (through the use of consumption data), and to adjust the
import/export balance accordingly.
The BoC trade statistics are publicly available26 and cover a complete time series from 1990 to 2012. These
statistics include information on imports and exports of thousands of commodities. After collecting information on annual
flows of the more than 100 commodities identified by NPRA, Step 2 involves calculating the C content for each
commodity from its chemical formula. In cases where the imports and exports were expressed in units of volume, rather
than mass, they were converted to mass based on the commodities' densities.
Step 3 involves summing the net C imports/exports across all commodities. The results of this step are shown in
Table A-61. As shown in the table, the United States has been a net exporter of chemical intermediates and products
throughout the 1990 to 2012 period.
Table A-61: Net Exports of Petrochemical Feedstochs.1990 - 2012 tTg C0? Eq.l
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Net Exports 12.0 11.1 8.3 1.8 7.3 14.8 20.2 6.5 4.1 8.4 4.5 9.0 7.7 8.3 9.8
After adjusting for imports and exports, the C budget is adjusted for the quantity of C that is used in the
Industrial Processes sector of the Inventory. Fossil fuels used for non-energy purposes in industrial processes—and for
which C emissions and storage have been characterized through mass balance calculations and/or emission factors that
directly link the non-energy use fossil fuel raw material and the industrial process product—are not included in the non-
energy use sector. These industrial processes (and their non-energy use fossil fuel raw materials) include iron and steel
(coal coke), primary aluminum (petroleum coke), titanium oxide (petroleum coke), ferroalloys (petroleum coke), and
ammonia and urea (petroleum coke and natural gas).
For each year of the Inventory, the total C content of non-energy uses was calculated by starting with the EIA
estimate of non-energy use, and reducing it by the adjustment factor for net exports (see Table A-61) to yield net domestic
fuel consumption for non-energy. The balance was apportioned to either stored C or emissive C, based on a storage factor.
The overall storage factor for the feedstocks was determined by developing a mass balance on the C in
feedstocks, and characterizing products, uses, and environmental releases as resulting in either storage or emissions. The
total C in the system was estimated by multiplying net domestic consumption for non-energy by the C content of each of
the feedstocks (i.e., industrial other coal, natural gas for non-fertilizer uses, LPG, pentanes plus, naphthas, other oils, still
gas, special naphtha). C content values for the fuel feedstocks are discussed in the Estimating Emissions from Fossil Fuel
Combustion and Estimating the Carbon Content from Fossil Fuel Combustion Annexes.
Next, C pools and releases in a variety of industrial releases, energy recovery processes, and products were
characterized. The C fate categories are plastics, energy recovery, synthetic rubber, synthetic fibers, organic solvents, C
black, detergents and personal cleansers, industrial non-methane volatile organic compound (NMVOC) emissions,
hazardous waste incineration, industrial toxic chemical (i.e., TRI) releases, pesticides, food additives, antifreeze and
deicers (glycols), and silicones.27
See the U.S International Trade Commission (USITC) Trade Dataweb at .
For the most part, the releases covered by the U.S. Toxic Release Inventory (TRI) represent air emissions or water discharges
associated with production facilities. Similarly, VOC emissions are generally associated with production facilities. These emissions
could have been accounted for as part of the Waste chapter, but because they are not necessarily associated with waste management, they
were included here. Toxic releases are not a "product" category, but they are referred to as such for ease of discussion.
A-100 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The C in each product or waste produced was categorized as either stored or emitted. The aggregate storage
factor is the C-weighted average of storage across fuel types. As discussed later in the section on uncertainty, the sum of
stored C and emitted C (i.e., the outputs of the system) exceeded total C consumption (i.e., the inputs to the system) for
some years in the time series.28 To address this mass imbalance, the storage factor was calculated as C storage divided by
total C outputs (rather than C storage divided by C inputs).
Note that the system boundaries for the storage factor do not encompass the entire life-cycle of fossil-based C
consumed in the United States insofar as emissions of CC>2 from waste combustion are accounted for separately in the
Inventory and are discussed in the Incineration of Waste section of the Energy chapter.
The following sections provide details on the calculation steps, assumptions, and data sources employed in
estimating and classifying the C in each product and waste shown in Table A-62. Summing the C stored and dividing it by
total C outputs yields the overall storage factor, as shown in the following equation for 2012:
Overall Storage Factor = C Stored / (C Stored + C Emitted) =
136.7 Tg CO2Eq. / (136.7 + 60.3) Tg CO2 Eq. = 70%
Table A-62: C Stored and Emitted by Products from Feedstocks in 2012 (Tg Clh Eq.)
C Stored C Emitted
Product/Waste Type (Tg C02 Eq.) (Tg C02 Eq.)
Industrial Releases
TRI Releases
Industrial VOCs
Non-combustion CO
Hazardous Waste Incin.
Energy Recovery
Products
Plastics
Synthetic Rubber
Antifreeze and deicers
Abraded tire rubber
Food additives
Silicones
Synthetic Fiber
Pesticides
Soaps, shampoos, detergents
Solvent VOCs
Total
0.1
0.1
-
-
-
-
136.7
118.4
10.6
-
-
-
0.5
6.9
0.2
-
-
136.7
5.2
1.0
3.4
0.5
0.4
41.3
13.8
-
-
0.7
0.3
0.8
-
-
0.2
6.5
5.2
60.3
- Not applicable
Note: Totals may not sum due to independent rounding.
The three categories of C accounted for in the table are industrial releases, energy recovery, and products. Each
is discussed below.
Industrial Releases
Industrial releases include toxic chemicals reported through the Toxics Release Inventory, industrial emissions of
volatile organic compounds (VOCs), CO emissions (other than those related to fuel combustion), and emissions from
hazardous waste incineration.
28 Overall, there was fairly close agreement between inputs and outputs: for the entire 1990 through 2012 time series, inputs exceeded
outputs by a time-weighted average of 0.2 percent. During the period 1990 through 2000, carbon inputs exceeded carbon outputs (i.e.,
the sum of carbon stored and carbon emitted) by a time-weighted average of 10 percent. For those years, the assumption was made that
the "missing" carbon was lost through fates leading to emissions. This discrepancy shifted during the period from 2001 through 2012, in
which carbon outputs exceeded carbon inputs by a time-weighted average of 12 percent.
A-101
-------
TRI Releases
Fossil-derived C is found in many toxic substances released by industrial facilities. The Toxics Release
Inventory (TRI), maintained by EPA, tracks these releases by chemical and environmental release medium (i.e., land, air,
or water) on a biennial basis (EPA 2000b). By examining the C contents and receiving media for the top 35 toxic
chemicals released, which account for 90 percent of the total mass of chemicals, the quantity of C stored and emitted in the
form of toxic releases can be estimated.
The TRI specifies releases by chemical, so C contents were assigned to each chemical based on molecular
formula. The TRI also classifies releases by disposal location as either off-site or on-site. The on-site releases are further
subdivided into air emissions, surface water discharges, underground injection, and releases to land; the latter is further
broken down to disposal in a RCRA Subtitle C (i.e., hazardous waste) landfill or to "Other On-Site Land Disposal."29 The
C released in each disposal location is provided in Table A-63.
Each on-site classification was assigned a storage factor. A 100 percent storage factor was applied to disposition
of C to underground injection and to disposal to RCRA-permitted landfills, while the other disposition categories were
assumed to result in an ultimate fate of emission as CC>2 (i.e., a storage factor of zero was applied to these categories.) The
release allocation is not reported for off-site releases; therefore, the approach was to develop a C-weighted average storage
factor for the on-site C and apply it to the off-site releases.
For the remaining 10 percent of the TRI releases, the weights of all chemicals were added and an average C
content value, based upon the top 35 chemicals' C contents, was applied. The storage and emission allocation for the
remaining 10 percent of the TRI releases was carried out in the same fashion as for the 35 major chemicals.
Data on TRI releases for the full 1990 through 2012 time series were not readily available. Since this category is
small (less than 1 Tg C emitted and stored), the 1998 value was applied for the entire time series.
Table A-63:1998 TRI Releases by Disposal Location [Gg Clh Eq.l
Carbon Stored Carbon Emitted
Disposal Location (Gg C02 Eq.) (Gg C02 Eq.)
Air Emissions - 924.0
Surface Water Discharges - 6.7
Underground Injection 89.4
RCRA Subtitle C Landfill Disposal 1.4
Other On-Site Land Releases - 15.9
Off-site Releases 6.4 36.0
Total 97.2 982.6
- Not applicable
Note: Totals may not sum due to independent rounding.
Volatile Organic Compound Emissions from Industrial Processes and Solvent Evaporation Emissions
Data on annual non-methane volatile organic compound (NMVOC) emissions were obtained from preliminary
data (EPA 2013a), and disaggregated based on EPA (2003), which, in its final iteration, will be published on the National
Emission Inventory (NEI) Air Pollutant Emission Trends web site. The 1990-2013 Trends data include information on
NMVOC emissions by end-use category; some of these fall into the heading of "industrial releases" in Table A-63 above,
and others are related to "product use"; for ease of discussion, both are covered here. The end-use categories that
represent "Industrial NMVOC Emissions" include some chemical and allied products, certain petroleum related industries,
and other industrial processes. NMVOC emissions from solvent utilization (product use) were considered to be a result of
non-energy use of petrochemical feedstocks. These categories were used to distinguish non-energy uses from energy uses;
other categories where VOCs could be emitted due to combustion of fossil fuels were excluded to avoid double counting.
Data for 2012 are not yet available, so they have been set equal to 2011 values.
Because solvent evaporation and industrial NMVOC emission data are provided in tons of total NMVOCs,
assumptions were made concerning the average C content of the NMVOCs for each category of emissions. The
29 Only the top 9 chemicals had their land releases separated into RCRA Landfills and Other Land Disposal. For the remaining
chemicals, it was assumed that the ratio of disposal in these two categories was equal to the carbon-weighted average of the land disposal
fate of the top 9 chemicals (i.e., 8 percent attributed to RCRA Landfills and 92 percent in the "Other" category).
A-102 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
assumptions for calculating the C fraction of industrial and solvent utilization emissions were made separately and differ
significantly. For industrial NMVOC emissions, a C content of 85 percent was assumed. This value was chosen to reflect
the C content of an average volatile organic compound based on the list of the most abundant NMVOCs provided in the
Trends Report. The list contains only pure hydrocarbons, including saturated alkanes (C contents ranging from 80 to 85
percent based upon C number), alkenes (C contents approximately 85 percent), and some aromatics (C contents
approximately 90 percent, depending upon substitution).
An EPA solvent evaporation emissions dataset (Tooly 2001) was used to estimate the C content of solvent
emissions. The dataset identifies solvent emissions by compound or compound category for six different solvent end-use
categories: degreasing, graphic arts, dry cleaning, surface coating, other industrial processes, and non-industrial processes.
The percent C of each compound identified in the dataset was calculated based on the molecular formula of the individual
compound (e.g., the C content of methylene chloride is 14 percent; the C content of toluene is 91 percent). For solvent
emissions that are identified in the EPA dataset only by chemical category (e.g., butanediol derivatives) a single individual
compound was selected to represent each category, and the C content of the category was estimated based on the C content
of the representative compound. The overall C content of the solvent evaporation emissions for 1998, estimated to be 56
percent, is assumed to be constant across the entire time series.
The results of the industrial and solvent NMVOC emissions analysis are provided in Table A-64 for 1990
through 2012. Industrial NMVOC emissions in 2012 were 3.4 Tg CO2 Eq. and solvent evaporation emissions in 2012
were 5.2 Tg CC^Eq.
Table A-64: Industrial and Solvent NMVOC Emissions
1990
1995
2000
2005
2006
2007
2008
2009
2010
2011
2012
Industrial NMVOCs"
NMVOCs ('000 Short Tons)
Carbon Content (%)
Carbon Emitted (Tg C02 Eq.)
1,279
85%
3.6
1,358
85%
3.8
802
85%
2.3
824
85%
2.3
878
85%
2.5
933
85%
2.6
987
85%
2.8
752
85%
2.1
972
85%
2.7
1,192
85%
3.4
1,192
85%
3.4
Solvent Evaporation11
Solvents ('000 Short Tons)
Carbon Content (%)
Carbon Emitted (Tg C02 Eq.)
5,750
56%
10.8
6,183
56%
11.6
4,832
56%
9.0
4,245
56%
7.9
3,930
56%
7.4
3,614
56%
6.8
3,298
56%
6.2
3,129
56%
5.9
2,959
56%
5.5
2,790
56%
5.2
2,790
56%
5.2
a Includes emissions from chemical and allied products, petroleum and related industries, and other industrial processes categories.
b Includes solvent usage and solvent evaporation emissions from degreasing, graphic arts, dry cleaning, surface coating, other industrial processes, and non-
industrial processes.
Non-Combustion Carbon Monoxide Emissions
Carbon monoxide (CO) emissions data were also obtained from the NEI preliminary data (EPA 2013a), and
disaggregated based on EPA (2003). There are three categories of CO emissions in the report that are classified as
process-related emissions not related to fuel combustion. These include chemical and allied products manufacturing,
metals processing, and other industrial processes. Some of these CO emissions are accounted for in the Industrial
Processes section of this report, and are therefore not accounted for in this section. These include total C emissions from
the primary aluminum, titanium dioxide, iron and steel, and ferroalloys production processes. The total C (CO and CO2)
emissions from oil and gas production, petroleum refining, and asphalt manufacturing are also accounted for elsewhere in
this Inventory. Biogenic emissions (e.g., pulp and paper process emissions) are accounted for in the Land Use, Land-Use
Change and Forestry chapter and excluded from calculation of CO emissions in this section. Those CO emissions that are
not accounted for elsewhere are considered to be by-products of non-fuel use of feedstocks, and are thus included in the
calculation of the petrochemical feedstocks storage factor. Table A-65 lists the CO emissions that remain after taking into
account the exclusions listed above. As data for 2012 are not yet available, they have been set equal to 2011 values.
Table A-65: Non-Combustion Carbon Monoxide Emissions
1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 2012
Thousand short tons CO 489 481 623 461 443 426 408 461 405 348 348
Carbon Emitted (Tg C02 Eq.) 0.7 0.7 0.9 0.7 0.6 0.6 0.6 0.7 0.6 0.5 0.5
Includes emissions from chemical and allied products, petroleum and related industries, metals processing, and other industrial processes categories.
A-103
-------
Hazardous Waste Incineration
Hazardous wastes are defined by the EPA under the Resource Conservation and Recovery Act (RCRA).30
Industrial wastes, such as rejected products, spent reagents, reaction by-products, and sludges from wastewater or air
pollution control, are federally regulated as hazardous wastes if they are found to be ignitable, corrosive, reactive, or toxic
according to standardized tests or studies conducted by the EPA.
Hazardous wastes must be treated prior to disposal according to the federal regulations established under the
authority of RCRA. Combustion is one of the most common techniques for hazardous waste treatment, particularly for
those wastes that are primarily organic in composition or contain primarily organic contaminants. Generally speaking,
combustion devices fall into two categories: incinerators that burn waste solely for the purpose of waste management, and
boilers and industrial furnaces (BIFs) that burn waste in part to recover energy from the waste. More than half of the
hazardous waste combusted in the United States is burned in BIFs; because these processes are included in the energy
recovery calculations described below, they are not included as part of hazardous waste incineration.
EPA's Office of Solid Waste requires biennial reporting of hazardous waste management activities, and these
reports provide estimates of the amount of hazardous waste burned for incineration or energy recovery. EPA stores this
information in its Resource Conservation and Recovery Act (RCRA) Information system (EPA 2013b), formerly reported
in its Biennial Reporting System (BRS) database (EPA 2000a, 2009). Combusted hazardous wastes are identified based
on EPA-defined management system types M041 through M049 (incineration). Combusted quantities are grouped into
four representative waste form categories based on the form codes reported in the BRS: aqueous liquids, organic liquids
and sludges, organic solids, and inorganic solids. To relate hazardous waste quantities to C emissions, "fuel equivalent"
factors were derived for hazardous waste by assuming that the hazardous wastes are simple mixtures of a common fuel,
water, and noncombustible ash. For liquids and sludges, crude oil is used as the fuel equivalent and coal is used to
represent solids.
Fuel equivalent factors were multiplied by the tons of waste incinerated to obtain the tons of fuel equivalent.
Multiplying the tons of fuel equivalent by the C content factors (discussed in the Estimating the Carbon Content from
Fossil Fuel Combustion Annex) yields tons of C emitted. Implied C content is calculated by dividing the tons of C
emitted by the associated tons of waste incinerated.
Waste quantity data for hazardous wastes were obtained from EPA's RCRA Information/BRS database for
reporting years 1989, 1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005, 2007, 2009, and 2011 (EPA 2000a, 2009, 2013b).
Combusted waste quantities were obtained from Form GM (Generation and Management) for wastes burned on site and
Form WR (Wastes Received) for waste received from off-site for combustion. For each of the waste types, assumptions
were developed on average waste composition (see Table A-66). Regulations require incinerators to achieve at least 99.99
percent destruction of organics; this formed the basis for assuming the fraction of C oxidized. Emissions from hazardous
waste incineration in 2012 were 0.4 Tg CC>2 Eq. Table A-67 lists the CC>2 emissions from hazardous waste incineration.
Table A-66: Assumed Composition of Combusted Hazardous Waste by Weight [Percent!
Waste Type Water (%) Noncombustibles (%) Fuel Equivalent (%)
Aqueous Waste 90 5 5
Organic Liquids and Sludges 40 20 40
Organic Solids 20 40 40
Inorganic Solids 20 70 10
Table A-67: C02 Emitted from Hazardous Waste Incineration [Tg GO? Eq.l
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
C Emissions 1.1 1.7 1.0 0.6 0.6 0.6 0.6 0.6 0.5 0.4 0.4 0.3 0.3 0.4 0.4
Energy Recovery
The amount of feedstocks combusted for energy recovery was estimated from data included in EIA's
Manufacturers Energy Consumption Survey (MEGS) for 1991, 1994, 1998, 2002, and 2006 (EIA 1994, 1997, 2001, 2005,
2010, 2013b). Some fraction of the fossil C exiting refineries and designated for use for feedstock purposes actually ends
up being combusted for energy recovery (despite the designation of feedstocks as a "non-energy" use) because the
chemical reactions in which fuel feedstocks are used are not 100 percent efficient. These chemical reactions may generate
30 [42 U.S.C. §6924, SDWA §3004]
A-104 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
unreacted raw material feedstocks or generate by-products that have a high energy content. The chemical industry and
many downstream industries are energy-intensive and often have boilers or other energy recovery units on-site, and thus
these unreacted feedstocks or by-products are often combusted for energy recovery. Also, as noted above in the section on
hazardous waste incineration, regulations provide a strong incentive—and in some cases require—burning of organic
wastes generated from chemical production processes.
Information available from the MECS include data on the consumption for energy recovery of "other" fuels in
the petroleum and coal products, chemicals, primary metals, nonmetallic minerals, and other manufacturing sectors. These
"other" fuels include refinery still gas; waste gas; waste oils, tars, and related materials; petroleum coke, coke oven and
blast furnace gases; scrap tires; liquor or black liquor; woodchips and bark; and other uncharacterized fuels. Fuel use of
petroleum coke is included separately in the fuel use data provided annually by EIA, and energy recovery of coke oven gas
and blast furnace gas (i.e., by-products of the iron and steel production process) is addressed in the Iron and Steel
production section in the Industrial Processes chapter. Consumption of refinery still gas in the refinery sector is also
included separately in the fuel use data from EIA. The combustion of scrap tires in cement kilns, lime kilns, and electric
arc furnaces is accounted for in the Waste Incineration chapter; data from the Rubber Manufacturers Association (RMA
2009a) were used to difference out energy recovery from scrap tires in these industries. Consumption of net steam,
assumed to be generated from fossil fuel combustion, is also included separately in the fuel use data from EIA. Therefore,
these categories of "other" fuels are addressed elsewhere in the Inventory and not considered as part of the petrochemical
feedstocks energy recovery analysis. Liquor or black liquor and woodchips and bark are assumed to be biogenic fuels, in
accordance with IPCC (2006), and therefore are not included in the Inventory. The remaining categories of fuels,
including waste gas; waste oils, tars, and related materials; and other uncharacterized fuels are assumed to be
petrochemical feedstocks burned for energy recovery (see Table A-68). The conversion factors listed in the Estimating
Emissions from Fossil Fuel Combustion Annex were used to convert the Btu values for each fuel feedstock to Tg CO2.
Petrochemical feedstocks combusted for energy recovery corresponded to 42.5 Tg CO2 Eq. in 1991, 35.1 Tg CC>2 Eq. in
1994, 58.0 Tg CO2Eq. in 1998, 70.6 Tg CO2 Eq. in 2002, 74.8 Tg CO2 Eq. in 2006, and 41.3 Tg CO2 Eq. in 2010. Values
for petrochemical feedstocks burned for energy recovery for years between 1991 and 1994, between 1994 and 1998,
between 1998 and 2002, between 2002 and 2006, and between 2007 and 2010 have been estimated by linear interpolation.
The value for 1990 is assumed to be the same as the value for 1991, and 2011 and 2012 are assumed to be the same as the
value for 2010 (Table A-69).
Table A-68: Summary of 2010 MECS Data for Other Fuels Used in Manufacturing/Energy Recovery [Trillion Btul
Refinery Still
Subsector and Industry NAICS CODE Waste Gas* Waste Oils/Tars» Gas° Net Steam" Other Fuels'
Printing and Related Support
Petroleum and Coal Products
Chemicals
Plastics and Rubber Products
Nonmetallic Mineral Products
Primary Metals
Fabricated Metal Products
Machinery
Computer and Electronic Products
Electrical Equip., Appliances, Components
Transportation Equipment
Furniture and Related Products
Miscellaneous
Total (Trillion Btu)
Average C Content (Tg/QBtu)
Fraction Oxidized
Total C (Tg)
Total C (Tg) (ex. still gas from refining)
323
324
325
326
327
331
332
333
334
335
336
337
339
0
0
376
0
1
0
0
0
0
0
2
0
0
379
18.14
1
6.88
6.88
0
6
7
0
7
0
0
0
0
0
0
0
0
20
20.62
1
0.41
0.41
0
1349
0
0
0
0
0
0
0
0
0
0
0
1349
17.51
1
23.62
-
0
153
266
0
0
12
0
0
0
0
0
0
0
432
0
0
0.00
-
0
54
110
0
5
34
0
1
0
0
3
0
0
205
19.37
1
3.98
3.98
- Not applicable
a C content: Waste Gas is assumed to be same as naphtha <401 deg. F
b C content: Waste Oils/Tars is assumed to be same as asphalt/road oil
c Refinery "still gas" fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate
d Net steam fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate
e C content: "Other" is assumed to be the same as petrochemical feedstocks
A-105
-------
Table A-69: Carbon Emitted from Fuels Burned for Energy Recovery [Tg Clh Eq.l
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
C Emissions 42.5 40.8 64.3 67.4 70.6 71.6 72.7 73.7 74.8 66.4 58.1 49.7 41.3 41.3 41.3
Products
More C is found in products than in industrial releases or energy recovery. The principal types of products are
plastics; synthetic rubber; synthetic fiber; C black; pesticides; soaps, detergents, and cleansers; food additives; antifreeze
and deicers (glycols); silicones; and solvents. Solvent evaporation was discussed previously along with industrial releases
of NMVOCs; the other product types are discussed below.
Plastics
Data on annual production of plastics through 2005 were taken from the American Plastics Council (APC), as
published in Chemical & Engineering News and on the APC and Society of Plastics Industry (SPI) websites, and through
direct communication with the APC (APC 2000, 2001, 2003 through 2006; SPI 2000; Eldredge-Roebuck 2000). Data for
2006 through 2012 were taken directly or derived from the American Chemistry Council (ACC 2007 through 2010
supplemented by Vallianos 2011, 2012, 2013). In 2009, the American Chemistry Council consolidated the resin
categories for which it reports plastics production. Production numbers in the original categories were provided via
personal correspondence for 2009, 2011, and 2012 (Vallianos 2011, 2012, 2013). Production figures for the consolidated
resin categories in 2010 were linearly interpolated from 2009 and 2011 data. Production was organized by resin type (see
Table A-70) and by year.
Several of the resin categories included production from Canada and/or Mexico, in addition to the U.S. values for
part of the time series. The production data for the affected resins and years were corrected using an economic adjustment
factor, based on the percent of North American production value in this industry sector accounted for by the United States.
A C content was then assigned for each resin. These C contents were based on molecular formulae and are listed in Table
A-71 and Table A-72. In cases where the resin type is generic, referring to a group of chemicals and not a single polymer
(e.g., phenolic resins, other styrenic resins), a representative compound was chosen. For other resins, a weighted C
content of 68 percent was assumed (i.e., it was assumed that these resins had the same content as those for which a
representative compound could be assigned).
There were no emissive uses of plastics identified, so 100 percent of the C was considered stored in products. As
noted in the chapter, an estimate of emissions related to the combustion of these plastics in the municipal solid waste
stream can be found in the Incineration of Waste section of the Energy chapter; those emissions are not incorporated in the
mass balance for feedstocks (described in this annex) to avoid double-counting.
A-106 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-70:2012 Plastic Resin Production tTg dry weight! and C Stored tTg Clh Eq.l
2012 Production" Carbon Stored
Resin Type (Tg dry weight) (Tg C02 Eq.)
Epoxy
Urea
Melamine
Phenolic
Low-Density Polyethylene (LDPE)
Linear Low-Density Polyethylene (LLDPE)
High Density Polyethylene (HOPE)
Polypropylene (PP)
Acrylonitrile-butadiene-styrene(ABS)
Other Styrenicsc
Polystyrene (PS)
Nylon
Polyvinyl chloride (PVC)b
Thermoplastic Polyester
All Other (including Polyester (unsaturated))
Total
0.3
0.5
0.5
1.4
2.9
5.7
7.5
6.7
0.4
0.5
2.3
0.5
6.5
3.5
5.7
44.8
0.8
0.6
0.5
3.9
9.1
17.8
23.5
21.1
1.3
1.7
7.8
1.2
9.1
8.0
14.5
120.9
a Production estimates provided by the American Chemistry Council include Canadian production for Urea, Melamine, Phenolic, LDPE, LLDPE, HOPE, PP, ABS,
SAN, Other Styrenics, PS, Nylon, PVC, and Thermoplastic Polyester, and Mexican production for PP, ABS, SAN, Other Styrenics, Nylon, and Thermoplastic
Polyester. Values have been adjusted to account just for U.S. production.
b Includes copolymers
c Includes Styrene-acrylonitrile (SAN)
Note: Totals may not sum due to independent rounding.
Table A-71: Assigned c Contents of Plastic Resins [% by weight!
Resin Type
C Content Source of C Content Assumption
Epoxy
Polyester (Unsaturated)
Urea
Melamine
Phenolic
Low-Density Polyethylene (LDPE)
Linear Low-Density Polyethylene (LLDPE)
High Density Polyethylene (HOPE)
Polypropylene (PP)
Acrylonitrile-Butadiene-Styrene(ABS)
Styrene-Acrylonitrile (SAN)
Other Styrenics
Polystyrene (PS)
Nylon
Polyvinyl Chloride (PVC)
Thermoplastic Polyester
All Other
76% Typical epoxy resin made from epichlorhydrin and bisphenol A
63% Poly (ethylene terephthalate) (PET)
34% 50% carbamal, 50% N-(hydroxymethyl) urea *
29% Trimethylol melamine *
77% Phenol
86% Polyethylene
86% Polyethylene
86% Polyethylene
86% Polypropylene
85% 50% styrene, 25% acrylonitrile, 25% butadiene
80% 50% styrene, 50% acrylonitrile
92% Polystyrene
92% Polystyrene
65% Average of nylon resins (see Table A-72)
38% Polyvinyl chloride
63% Polyethylene terephthalate
69% Weighted average of other resin production
•Does not include alcoholic hydrogens.
A-107
-------
Table A-72: Major Nylon Resins and their C Contents (% by weight)
Resin C Content
Nylon 6 64%
Nylon 6,6 64%
Nylon 4 52%
Nylon 6,10 68%
Nylon 6,11 69%
Nylon 6,12 70%
Nylon 11 72%
Synthetic Rubber
Data on synthetic rubber in tires were derived from data on the scrap tire market and the composition of scrap
tires from the Rubber Manufacturers' Association (RMA). The market information is presented in the report U.S. Scrap
Tire Management Summary: 2005-2009 (RMA 2011), while the tire composition information is from the "Scrap Tires,
Facts and Figures" section of the organization's website (RMA 2009). Data on synthetic rubber in other products (durable
goods, nondurable goods, and containers and packaging) were obtained from EPA's Municipal Solid Waste in the United
States reports (1996 through 2003a, 2005, 2007b, 2008, 2009a, 201 la, 2013c; 2014) and detailed unpublished backup data
for some years not shown in the Characterization of Municipal Solid Waste in the United States reports (Schneider 2007).
The abraded rubber from scrap passenger tires was assumed to be 2.5 Ibs per scrap tire, while the abraded rubber from
scrap commercial tires was assumed to be 10 Ibs per scrap tire. Data on abraded rubber weight were obtained by
calculating the average weight difference between new and scrap tires (RMA 20011). RMA data were not yet available
for 2010 through 2012, so they were set equal to 2009 values.
A C content for synthetic rubber (90 percent for tire synthetic rubber and 85 percent for non-tire synthetic
rubber) was assigned based on the weighted average of C contents (based on molecular formula) by elastomer type
consumed in 1998, 2001, and 2002 (see Table A-73). The 1998 consumption data were obtained from the International
Institute of Synthetic Rubber Producers (IISRP) press release "Synthetic Rubber Use Growth to Continue Through 2004,
Says IISRP and RMA" (IISRP 2000). The 2001 and 2002 consumption data were obtained from the IISRP press release,
"IISRP Forecasts Moderate Growth in North America to 2007" (IISRP 2003).
The rubber in tires that is abraded during use (the difference between new tire and scrap tire rubber weight) was
considered to be 100 percent emitted. Other than abraded rubber, there were no emissive uses of scrap tire and non-tire
rubber identified, so 100 percent of the non-abraded amount was assumed stored. Emissions related to the combustion of
rubber in scrap tires and consumer goods can be found in the Incineration of Waste section of the Energy chapter.
Table A-73:2002 Rubber Consumption tGgl and C Content [%1
Elastomer Type
SBR Solid
Polybutadiene
Ethylene Propylene
Polychloroprene
NBR Solid
Polyisoprene
Others
Weighted Average
Total
2002 Consumption (Gg)*
768
583
301
54
84
58
367
-
2,215
C Content
91%
89%
86%
59%
77%
88%
88%
90%
-
* Includes consumption in Canada.
- Not applicable
Note: Totals may not sum due to independent rounding.
Synthetic Fibers
Annual synthetic fiber production data were obtained from the Fiber Economics Bureau, as published in
Chemical & Engineering News (FEE 2001, 2003, 2005, 2007, 2009, 2010, 2011, 2012, 2013). These data are organized
by year and fiber type. For each fiber, a C content was assigned based on molecular formula (see Table A-74). For
polyester, the C content for poly (ethylene terephthalate) (PET) was used as a representative compound. For nylon, the
average C content of nylon 6 and nylon 6.6 was used, since these are the most widely produced nylon fibers. Cellulosic
fibers, such as acetate and rayon, have been omitted from the synthetic fibers' C accounting displayed here because much
A-108 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
of their C is of biogenic origin and carbon fluxes from biogenic compounds are accounted for in the Land Use, Land-Use
Change and Forestry chapter. These fibers account for only 4 percent of overall fiber production by weight.
There were no emissive uses of fibers identified, so 100 percent of the C was considered stored. Note that
emissions related to the combustion of textiles in municipal solid waste are accounted for under the Incineration of Waste
section of the Energy chapter.
Table A-74:2012 Fiber Production (Tg), G Content (%), and G Stored (Tg C0? Eq.)
Fiber Type
Polyester
Nylon
Olefin
Acrylic
Total
Production
(Tg)
1.2
0.6
1.0
+
2.8
C Content
63%
64%
86%
68%
-
C Stored
(TgC02Eq.)
2.8
1.3
3.2
0.1
7.3
+ Less than 0.05 Tg
- Not applicable
Note: Totals may not sum due to independent rounding
Pesticides
Pesticide consumption data were obtained from the 1994/1995, 1996/1997, 1998/1999, 2000/2001, 2006/2007
Pesticides Industry Sales and Usage Market Estimates (EPA 1998, 1999, 2002, 2004, 20lib) reports. The most recent
data available were for 2007, so it was assumed that the 2008 through 2012 consumption was equal to that of 2007.
Active ingredient compound names and consumption weights were available for the top 25 agriculturally-used pesticides
and top 10 pesticides used in the home and garden and the industry/commercial/government categories. The report
provides a range of consumption for each active ingredient; the midpoint was used to represent actual consumption. Each
of these compounds was assigned a C content value based on molecular formula. If the compound contained aromatic
rings substituted with chlorine or other halogens, then the compound was considered persistent and the C in the compound
was assumed to be stored. All other pesticides were assumed to release their C to the atmosphere. Over one-third of 2007
total pesticide active ingredient consumption was not specified by chemical type in the Sales and Usage report (EPA
201 Ib). This unspecified portion of the active ingredient consumption was treated as a single chemical and assigned a C
content and a storage factor based on the weighted average of the known chemicals' values.
Table A-75: Active Ingredient Consumption in Pesticides [Million lbs.1 and C Emitted and Stored tTg Clh Eq.) in 2007
Active Ingredient C Emitted C Stored
Pesticide Use* (Million Ibs.) (TgC02Eq.) (Tg CCfe Eq.)
Agricultural Uses
Non-Agricultural Uses
Home & Garden
Industry/Gov't/Commercial
Other
Total
473.5
76.8
30.3
46.5
337.7
888.0
0.1
+
+
+
0.1
0.2
0.1
+
+
+
0.1
0.2
- Less than 0.05 Tg C02 Eq.
•2007 estimates (EPA 2011b).
Note: Totals may not sum due to independent rounding.
Soaps, Shampoos, and Detergents
Cleansers—soaps, shampoos, and detergents—are among the major consumer products that may contain fossil
C. All of the C in cleansers was assumed to be fossil-derived, and, as cleansers eventually biodegrade, all of the C was
assumed to be emitted. The first step in estimating C flows was to characterize the "ingredients" in a sample of cleansers.
For this analysis, cleansers were limited to the following personal household cleaning products: bar soap, shampoo,
laundry detergent (liquid and granular), dishwasher detergent, and dishwashing liquid. Data on the annual consumption of
household personal cleansers were obtained from the U.S. Census Bureau 1992, 1997, 2002, and 2007 Economic Census
(U.S. Bureau of the Census 1994, 1999, 2004, 2009). Consumption values for 1990 and 1991 were assumed to be the
same as the 1992 value; consumption was interpolated between 1992 and 1997, 1997 and 2002, and 2002 and 2007;
consumption for 2008 through 2012 was assumed to equal the 2007 value. Cleanser consumption values were adjusted by
import and export data to develop US consumption estimates.
A-109
-------
Chemical formulae were used to determine C contents (as percentages) of the ingredients in the cleansers. Each
product's overall C content was then derived from the composition and contents of its ingredients. From these values the
mean C content for cleansers was calculated to be 21.9 percent.
The Census Bureau presents consumption data in terms of quantity (in units of million gallons or million pounds)
and/or terms of value (thousands of dollars) for eight specific categories, such as "household liquid laundry detergents,
heavy duty" and "household dry alkaline automatic dishwashing detergents." Additionally, the report provides dollar
values for the total consumption of "soaps, detergents, etc.—dry" and "soaps, detergents, etc.—liquid." The categories for
which both quantity and value data are available is a subset of total production. Those categories that presented both
quantity and value data were used to derive pounds per dollar and gallons per dollar conversion rates, and they were
extrapolated (based on the Census Bureau estimate of total value) to estimate the total quantity of dry and liquid31 cleanser
categories, respectively.
Next, the total tonnage of cleansers was calculated (wet and dry combined) for 1997. Multiplying the mean C
content (21.9 percent) by this value yielded an estimate of 4.6 Tg CO2 Eq. in cleansers for 1997. For all subsequent years,
it was assumed that the ratio of value of shipments to total carbon content remained constant. For 1998 through 2012,
value of shipments was adjusted to 1997 dollars using the producer price index for soap and other detergent manufacturing
(Bureau of Labor Statistics 2013). The ratio of value of shipments to carbon content was then applied to arrive at total
carbon content of cleansers. For 1992, 2002, and 2007 the estimates are 3.6 Tg CO2 Eq., 5.1 Tg CO2 Eq., 7.6 Tg CO2 Eq.,
respectively. Estimates for other years are based on these values as described above, and are shown in Table A-76.
Table A-76: C Emitted from Utilization of Soaps, Shampoos, and Detergents [Tg C0? Eq.l
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
C Emissions 3.6 4.2 4.5 4.8 5.1 5.7 6.2 6.7 7.0 7.6 7.1 6.7 6.7 6.5 6.1
Antifreeze and Deicers
Glycol compounds, including ethylene glycol, propylene glycol, diethylene glycol, and triethylene glycol, are
used as antifreeze in motor vehicles, deicing fluids for commercial aircraft, and other similar uses. These glycol
compounds are assumed to ultimately enter wastewater treatment plants where they are degraded by the wastewater
treatment process to CO2 or to otherwise biodegrade to CO2. Glycols are water soluble and degrade rapidly in the
environment (Howard 1993).
Annual production data for each glycol compound used as antifreeze and deicers were obtained from the Guide
to the Business of Chemistry (ACC 2012). Import and export data were used to adjust annual production data to annual
consumption data. The percentage of the annual consumption of each glycol compound used for antifreeze and deicing
applications was estimated from Chemical Profiles data published on The Innovation Group website32 and from similar
data published in the Chemical Market Reporter, which became ICIS Chemical Business in 2005. Production data for
propylene glycol, diethylene glycol, and triethylene glycol are no longer reported in the Guide to the Business of
Chemistry, so data from ICIS Chemical Business on total demand was used with import and export data to estimate
production of these chemicals.
The glycol compounds consumed in antifreeze and deicing applications is assumed to be 100 percent emitted as
CO2. Emissions of CO2 from utilization of antifreeze and deicers are summarized in Table A-77.
Table A-77: C Emitted from Utilization of Antifreeze and Deicers [Tg GO? Eq.l
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
C Emissions 1.2 1.4 1.5 1.3 1.3 1.3 1.4 1.2 1.3 1.2 1.0 0.9 0.8 0.7 0.7
Food/AcW/f/Ves
Petrochemical feedstocks are used to manufacture synthetic food additives, including preservatives, flavoring
agents, and processing agents. These compounds include glycerin, propylene glycol, benzoic acid, and other compounds.
These compounds are incorporated into food products, and are assumed to ultimately enter wastewater treatment plants
31 A density of 1.05 g/mL—slightly denser than water—was assumed for liquid cleansers.
32 See
See < http://www.icis.com/home/default.aspx>
A-110 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
where they are degraded by the wastewater treatment processes to CC>2 or to otherwise biodegrade to CC>2. Certain food
additives, e.g., glycerin, are manufactured both from petrochemical feedstocks and from biogenic feedstocks. Food
additives that are derived from biogenic feedstocks are accounted for in the Land Use, Land-Use Change and Forestry
chapter.
Annual production data for food additive compounds were obtained from the Guide to the Business of Chemistry
(ACC 2012). Import and export data were used to adjust annual production data to annual consumption data. The
percentage of the annual consumption of food additive compounds was estimated from Chemical Profiles data published
on The Innovation Group website34 and from similar data published in the Chemical Market Reporter, which became ICIS
Chemical Business in 2005.35 Production data for several food additive compounds are no longer reported in the Guide to
the Business of Chemistry, so data from ICIS Chemical Business on total demand was used with import and export data to
estimate production of these chemicals. The consumption of synthetic food additives is assumed to be 100 percent emitted
as CC>2. Emissions of CC>2 from utilization of synthetic food additives are summarized in Table A-78.
Table A-78: C Emitted from Utilization of Food Additives [Tg C0? Eq.l
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
C Emissions 0.6 0.7 0.7 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8
S/7/cones
Silicone compounds (e.g., polymethyl siloxane) are used as sealants and in manufactured products. Silicone
compounds are manufactured from petrochemical feedstocks including methyl chloride. It is assumed that petrochemical
feedstocks used to manufacture silicones are incorporated into the silicone products and not emitted as CC>2 in the
manufacturing process. It is also assumed that the C contained in the silicone products is stored, and not emitted as CC>2.
Annual production data for each silicone manufacturing compound were obtained from the Guide to the Business
of Chemistry (ACC 2012). Import and export data were used to adjust annual production data to annual consumption data.
The percentage of the annual consumption of each silicone manufacturing compound was estimated from Chemical
Profiles data published on The Innovation Group website and from similar data published in the Chemical Market
Reporter, which became ICIS Chemical Business in 2005 . The consumption of silicone manufacturing compounds is
assumed to be 100 percent stored, and not emitted as CC>2. Storage of silicone manufacturing compounds is summarized
in Table A-79.
Table A-79: C Stored in Silicone Products [Tg C0? Eq.l
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
C Storage 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the estimates of the feedstocks C storage factor and the quantity of C emitted from feedstocks in 2012. The
Tier 2 analysis was performed to allow the specification of probability density functions for key variables, within a
computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of
uncertainty were not available directly from the information sources for the activity variables; thus, uncertainty estimates
were determined using assumptions based on source category knowledge. Uncertainty estimates for production data (the
majority of the variables) were assumed to exhibit a normal distribution with a relative error of ±20 percent in the
underlying EIA estimates, plus an additional ±15 percent to account for uncertainty in the assignment of imports and
exports. An additional 10 percent (for a total of ±45 percent) was applied to the production of other oils (>401 deg. F) to
reflect the additional uncertainty in the assignment of part of the production quantity to industrial processes. A relatively
narrow uniform distribution ±1 percent to ±15 percent, depending on the fuel type) was applied to each C coefficient.
The Monte Carlo analysis produced a storage factor distribution with a mean of 67 percent, a standard deviation
of 4.2 percent, and the 95 percent confidence interval of 56 percent and 72 percent. This compares to the calculated
Inventory estimate of 70 percent. The analysis produced a C emission distribution with a mean of 65.4 Tg CC>2 Eq.,
34 See
35 See
36 Ibid.
A-111
-------
standard deviation of 15.5 Tg CC>2 Eq., and 95 percent confidence limits of 45.8 and 104.0 Tg CC>2 Eq. This compares
with a calculated Inventory estimate of 59.2 Tg CC>2 Eq.
The apparently tight confidence limits for the storage factor and C storage probably understate uncertainty, as a
result of the way this initial analysis was structured. As discussed above, the storage factor for feedstocks is based on an
analysis of six fates that result in long-term storage (e.g., plastics production), and eleven that result in emissions (e.g.,
volatile organic compound emissions). Rather than modeling the total uncertainty around all 17 of these fate processes,
the current analysis addresses only the storage fates, and assumes that all C that is not stored is emitted. As the production
statistics that drive the storage factors are relatively well-characterized, this approach yields a result that is probably biased
toward understating uncertainty.
As far as specific sources of uncertainty, there are several cross-cutting factors that pervade the characterization
of C flows for feedstocks. The aggregate storage factor for petrochemical feedstocks (industrial other coal, natural gas for
non-fertilizer uses, LPG, pentanes plus, naphthas, other oils, still gas, special naphtha) is based on assuming that the
ultimate fates of all of these fuel types—in terms of storage and emissions—are similar. In addition, there are
uncertainties associated with the simplifying assumptions made for each end use category C estimate. Generally, the
estimate for a product is subject to one or more of the following uncertainties:
• The value used for estimating the C content has been assumed or assigned based upon a representative
compound.
• The split between C storage and emission has been assumed based on an examination of the environmental fate
of the products in each end use category.
• Environmental fates leading to emissions are assumed to operate rapidly, i.e., emissions are assumed to occur
within one year of when the fossil C enters the non-energy mass balance. Some of the pathways that lead to
emissions as CC>2 may actually take place on a time-scale of several years or decades. By attributing the
emissions to the year in which the C enters the mass balance (i.e., the year in which it leaves refineries as a non-
energy fuel use and thus starts being tracked by EIA), this approach has the effect of "front-end loading" the
emission profile.
Another cross-cutting source of uncertainty is that for several sources the amount of C stored or emitted was
calculated based on data for only a single year. This specific year may not be representative of storage for the entire
Inventory period. Sources of uncertainty associated with specific elements of the analysis are discussed below.
Import and export data for petrochemical feedstocks were obtained from EIA, the National Petroleum Refiners
Association, and the U.S. BoC for the major categories of petrochemical feedstocks (EIA 2001, NPRA 2001, and U.S.
BoC 2006). The complexity of the organic chemical industry, with multiple feedstocks, intermediates, and subtle
differences in nomenclature, makes it difficult to ensure that the adjustments to the EIA data for imports and exports is
accurate and the approach used here may underestimate or overestimate net exports of C.
Oxidation factors have been applied to non-energy uses of petrochemical feedstocks in the same manner as for
energy uses. However, for those fuels where IPCC storage factors are used, this "oxidation factor" may be inherent in the
storage factor applied when calculating emissions from non-energy consumption, which would result in a double-counting
of the unoxidized C. Oxidation factors are small corrections, on the order of 1 percent, and therefore application of
oxidation factors to non-energy uses may result in a slight underestimation of C emissions from non-energy uses.
The major uncertainty in using the TRI data is the possibility of double counting emissions that are already
accounted for in the NMVOC data (see above) and in the storage and emission assumptions used. The approach for
predicting environmental fate simplifies some complex processes, and the balance between storage and emissions is very
sensitive to the assumptions on fate. Extrapolating from known to unknown characteristics also introduces uncertainty.
The two extrapolations with the greatest uncertainty are: 1) that the release media and fate of the off-site releases were
assumed to be the same as for on-site releases, and 2) that the C content of the least frequent 10 percent of TRI releases
was assumed to be the same as for the chemicals comprising 90 percent of the releases. However, the contribution of
these chemicals to the overall estimate is small. The off-site releases only account for 3 percent of the total releases, by
weight, and, by definition, the less frequent compounds only account for 10 percent of the total releases.
The principal sources of uncertainty in estimating CO2 emissions from solvent evaporation and industrial
NMVOC emissions are in the estimates of (a) total emissions and (b) their C content. Solvent evaporation and industrial
NMVOC emissions reported by EPA are based on a number of data sources and emission factors, and may underestimate
or overestimate emissions. The C content for solvent evaporation emissions is calculated directly from the specific solvent
compounds identified by EPA as being emitted, and is thought to have relatively low uncertainty. The C content for
A-112 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
industrial emissions has more uncertainty, however, as it is calculated from the average C content of an average volatile
organic compound based on the list of the most abundant measured NMVOCs provided in EPA (2002a).
Uncertainty in the hazardous waste combustion analysis is introduced by the assumptions about the composition
of combusted hazardous wastes, including the characterization that hazardous wastes are similar to mixtures of water,
noncombustibles, and fuel equivalent materials. Another limitation is the assumption that all of the C that enters
hazardous waste combustion is emitted—some small fraction is likely to be sequestered in combustion ash—but given that
the destruction and removal efficiency for hazardous organics is required to meet or exceed 99.99 percent, this is a very
minor source of uncertainty. C emission estimates from hazardous waste should be considered central value estimates that
are likely to be accurate to within +50 percent.
The amount of feedstocks combusted for energy recovery was estimated from data included in the Manufacturers
Energy Consumption Surveys (MEGS) for 1991, 1994, 1998, 2002, and 2006 (EIA 1994, 1997, 2001, 2005, 2010; 2013b).
MECS is a comprehensive survey that is conducted every four years and intended to represent U.S. industry as a whole,
but because EIA does not receive data from all manufacturers (i.e., it is a sample rather than a census), EIA must
extrapolate from the sample. Also, the "other" fuels are identified in the MECS data in broad categories, including
refinery still gas; waste gas; waste oils, tars, and related materials; petroleum coke, coke oven and blast furnace gases; and
other uncharacterized fuels. Moreover, the industries using these "other" fuels are also identified only in broad categories,
including the petroleum and coal products, chemicals, primary metals, nonmetallic minerals, and other manufacturing
sectors. The "other" fuel consumption data are reported in BTUs (energy units) and there is uncertainty concerning the
selection of a specific conversion factor for each broad "other" fuel category to convert energy units to mass units. Taken
as a whole, the estimate of energy recovery emissions probably introduces more uncertainty than any other element of the
non-energy analysis.
Uncertainty in the C storage estimate for plastics arises primarily from four factors. First, production of some
plastic resins is not tracked directly and must be estimated based on other market data. Second, the raw data on
production for several resins include Canadian and/or Mexican production and may overestimate the amount of plastic
produced from U.S. fuel feedstocks; this analysis includes adjustments to "back out" the Canadian and Mexican values,
but these adjustments are approximate. Third, the assumed C content values are estimates for representative compounds,
and thus do not account for the many formulations of resins available. This uncertainty is greater for resin categories that
are generic (e.g., phenolics, other styrenics, nylon) than for resins with more specific formulations (e.g., polypropylene,
polyethylene). Fourth, the assumption that all of the C contained in plastics is stored ignores certain end uses (e.g.,
adhesives and coatings) where the resin may be released to the atmosphere; however, these end-uses are likely to be small
relative to use in plastics.
The quantity of C stored in synthetic rubber only accounts for the C stored in scrap tire synthetic rubber. The
value does not take into account the rubber stored in other durable goods, clothing, footwear, and other non-durable goods,
or containers and packaging. This adds uncertainty to the total mass balance of C stored. There are also uncertainties as to
the assignment of C content values; however, they are much smaller than in the case of plastics. There are probably fewer
variations in rubber formulations than in plastics, and the range of potential C content values is much narrower. Lastly,
assuming that all of the C contained in rubber is stored ignores the possibility of volatilization or degradation during
product lifetimes. However, the proportion of the total C that is released to the atmosphere during use is probably
negligible.
A small degree of uncertainty arises from the assignment of C content values in textiles; however, the magnitude
of this uncertainty is less than that for plastics or rubber. Although there is considerable variation in final textile products,
the stock fiber formulations are standardized and proscribed explicitly by the Federal Trade Commission.
For pesticides, the largest source of uncertainty involves the assumption that an active ingredient's C is either 0
percent stored or 100 percent stored. This split is a generalization of chemical behavior, based upon active-ingredient
molecular structure, and not on compound-specific environmental data. The mechanism by which a compound is bound
or released from soils is very complicated and can be affected by many variables, including the type of crop, temperature,
application method, and harvesting practice. Another smaller source of uncertainty arises from the C content values
applied to the unaccounted for portion of active ingredient. C contents vary widely among pesticides, from 7 to 72
percent, and the remaining pesticides may have a chemical make-up that is very different from the 32 pesticides that have
been examined. Additionally, pesticide consumption data were only available for 1987, 1993, 1995, 1997, 1999, and 2001
and 2007; the majority of the time series data were interpolated or held constant at the latest (2007) value. Another source
of uncertainty is that only the "active" ingredients of pesticides are considered in the calculations; the "inactive"
ingredients may also be derived from petrochemical feedstocks.
A-113
-------
It is important to note that development of this uncertainty analysis is a multi-year process. The current
feedstocks analysis examines NEU fuels that end in storage fates Thus only C stored in pesticides, plastics, synthetic
fibers, synthetic rubbers, silicones, and TRI releases to underground injection and Subtitle C landfills is accounted for in
the uncertainty estimate above. In the future this analysis will be expanded to include the uncertainty surrounding emitted
fates in addition to the storage fates. Estimates of variable uncertainty will also be refined where possible to include fewer
assumptions. With these major changes in future Inventories, the uncertainty estimate is expected to change, and likely
increase. An increase in the uncertainty estimate in the coming years will not indicate that the Inventory calculations have
become less certain, but rather that the methods for estimating uncertainty have become more comprehensive; thus,
potential future changes in the results of this analysis will reflect a change in the uncertainty analysis, not a change in the
Inventory quality.
Asphalt and Road Oil
Asphalt is one of the principal non-energy uses of fossil fuels. The term "asphalt" generally refers to a mixture
of asphalt cement and a rock material aggregate, a volatile petroleum distillate, or water. For the purposes of this analysis,
"asphalt" is used interchangeably with asphalt cement, a residue of crude oil. Though minor amounts of C are emitted
during production, asphalt has an overall C storage factor of almost 100 percent, as discussed below.
Paving is the primary application of asphalt cement, comprising 86 percent of production. The three types of
asphalt paving produced in the United States are hot mix asphalt (HMA), cut-backs, and emulsified asphalt. HMA, which
makes up 90 percent of total asphalt paving (EPA 2001), contains asphalt cement mixed with an aggregate of rock
materials. Cut-back asphalt is composed of asphalt cement thinned with a volatile petroleum distillate (e.g., naphtha).
Emulsified asphalt contains only asphalt cement and water. Roofing products are the other significant end use of asphalt
cement, accounting for approximately 14 percent of U. S. production (Kelly 2000). No data were available on the fate of C
in asphalt roofing; it was assumed that it has the same fate as C in asphalt paving applications.
Methodology and Data Sources
A C storage factor was calculated for each type of asphalt paving. The fraction of C emitted by each asphalt type
was multiplied by consumption data for asphalt paving (EPA 2001) to estimate a weighted average C storage factor for
asphalt as a whole.
The fraction of C emitted by HMA was determined by first calculating the organic emissions (volatile organic
compounds [VOCs], carbon monoxide [CO], polycyclic aromatic hydrocarbons [PAHs], hazardous air pollutants [HAPs],
and phenol) from HMA paving, using emission factors reported in EPA (2001) and total HMA production.37 The next
step was to estimate the C content of the organic emissions. This calculation was based on the C content of CO and
phenol, and an assumption of 85 percent C content for PAHs and HAPs. The C content of asphalt paving is a function of
(1) the proportion of asphalt cement in asphalt paving, assumed to be 8 percent asphalt cement content based on EPA
(2001), and (2) the proportion of C in asphalt cement. For the latter factor, all paving types were characterized as having a
mass fraction of 85 percent C in asphalt cement, based on the assumption that asphalt is primarily composed of saturated
paraffinic hydrocarbons. By combining these estimates, the result is that over 99.56 percent of the C in asphalt cement
was retained (i.e., stored), and less than 0.44 percent was emitted.
Cut-back asphalt is produced in three forms: rapid, medium, and slow cure. The production processes for all
three forms emit C primarily from the volatile petroleum distillate used in the process as a diluent to thin the asphalt
cement so that it can be applied more readily (EPA 2001).
A mass balance on C losses from asphalt was constructed by first estimating the amount of carbon emitted as
VOCs. Values for medium cure asphalt are used to represent all cut-back asphalt. The average weight of distillates used in
medium cure cut-back asphalt (35 percent) is multiplied by the loss rate (as emissions of VOCs) of 70 percent from the
Emissions Inventory Guidebook to arrive at an estimate that 25 percent of the diluent is emitted (Environment Canada
2006). Next, the fraction of C in the asphalt/ diluent mix that is emitted was estimated, assuming 85 percent C content;
this yields an overall storage factor of 93.5 percent for cut-back asphalt.
One caveat associated with this calculation is that it is possible that the carbon flows for asphalt and diluent
(volatile petroleum distillate) are accounted for separately in the EIA statistics on fossil fuel flows, and thus the mass
The emission factors are expressed as a function of asphalt paving tonnage (i.e., including the rock aggregate as well as the asphalt
cement).
A-114 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
balance calculation may need to re-map the system boundaries to correctly account for carbon flows. EPA plans to re-
evaluate this calculation in the future.
It was assumed that there was no loss of C from emulsified asphalt (i.e., the storage factor is 100 percent) based
on personal communication with an expert from Akzo Nobel Coatings, Inc. (James 2000).
Data on asphalt and road oil consumption and C content factors were supplied by EIA. Hot mix asphalt
production and emissions factors, and the asphalt cement content of HMA were obtained from "Hot Mix Asphalt Plants
Emissions Assessment Report" from EPA's AP-42 (EPA 2001) publication. The consumption data for cut-back and
emulsified asphalts were taken from a Moulthrop, et al. study used as guidance for estimating air pollutant emissions from
paving processes (EIIP 2001). "Asphalt Paving Operation" AP-42 (EPA 2001) provided the emissions source information
used in the calculation of the C storage factor for cut-back asphalt. The storage factor for emulsified asphalt was provided
by Alan James of Akzo Nobel Coatings, Inc. (James 2000).
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the estimates of the asphalt C storage factor and the quantity of C stored in asphalt in 2012. The Tier 2
analysis was performed to allow the specification of probability density functions for key variables, within a
computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of
uncertainty were not available directly from the information sources for the activity variables; thus, uncertainty estimates
were determined using assumptions based on source category knowledge. Uncertainty estimates for asphalt production
were assumed to be ±20 percent, while the asphalt property variables were assumed to have narrower distributions. A
narrow uniform distribution, with maximum 5 percent uncertainty (± 5 percent) around the mean, was applied to the C
content coefficient.
The Monte Carlo analysis produced a tight distribution of storage factor values, with the 95 percent confidence
interval of 99.1 percent and 99.8 percent, with the mean value of 99.6 percent. This compares to the storage factor value
used in the Inventory of 99.5 percent. The analysis produced a C emission distribution with a mean of 0.30 Tg CC>2 Eq.,
standard deviation of 0.13 and 95 percent confidence limits of 0.12 Tg CC>2 Eq. and 0.61 Tg CC>2 Eq. This compares to an
Inventory calculated estimate of 0.27 Tg CC^Eq.
The principal source of uncertainty is that the available data are from short-term studies of emissions associated
with the production and application of asphalt. As a practical matter, the cement in asphalt deteriorates over time,
contributing to the need for periodic re-paving. Whether this deterioration is due to physical erosion of the cement and
continued storage of C in a refractory form or physicochemical degradation and eventual release of CC>2 is uncertain.
Long-term studies may reveal higher lifetime emissions rates associated with degradation.
Many of the values used in the analysis are also uncertain and are based on estimates and professional judgment.
For example, the asphalt cement input for hot mix asphalt was based on expert advice indicating that the range is
variable—from about 3 to 5 percent—with actual content based on climate and geographical factors (Connolly 2000).
Over this range, the effect on the calculated C storage factor is minimal (on the order of 0.1 percent). Similarly, changes
in the assumed C content of asphalt cement would have only a minor effect.
The consumption figures for cut-back and emulsified asphalts are based on information reported for 1994. More
recent trends indicate a decrease in cut-back use due to high VOC emission levels and a related increase in emulsified
asphalt use as a substitute. This change in trend would indicate an overestimate of emissions from asphalt.
Future improvements to this uncertainty analysis, and to the overall estimation of a storage factor for asphalt,
include characterizing the long-term fate of asphalt.
Lubricants
Lubricants are used in industrial and transportation applications. They can be subdivided into oils and greases,
which differ in terms of physical characteristics (e.g., viscosity), commercial applications, and environmental fate.
According to EIA (2013a), the C content from U.S. production of lubricants in 2012 was approximately 5.6 Tg C. Based
on apportioning oils and greases to various environmental fates, and characterizing those fates as resulting in either long-
term storage or emissions, the overall C storage factor was estimated to be 9.2 percent; thus, emissions in 2012 were about
5.1 Tg C, or 18.6 Tg CO2 Eq. EIA data were not available for 2012, so it was set equal to the 2011 value.
A-115
-------
Methodology and Data Sources
For each lubricant category, a storage factor was derived by identifying disposal fates and applying assumptions
as to the disposition of the C for each practice. An overall lubricant C storage factor was calculated by taking a
production-weighted average of the oil and grease storage factors.
Oils
Regulation of used oil in the United States has changed dramatically over the past 20 years.38 The effect of these
regulations and policies has been to restrict landfilling and dumping, and to encourage collection of used oil. The
economics of the petroleum industry have generally not favored re-refining—instead, most of the used oil that has been
collected has been combusted.
Table A-80 provides an estimated allocation of the fates of lubricant oils (Rinehart 2000), along with an estimate
of the proportion of C stored in each fate. The ultimate fate of the majority of oils (about 84 percent) is combustion, either
during initial use or after collection as used oil. Combustion results in 99 percent oxidation to CC>2 (EIIP 1999), with
correspondingly little long-term storage of C in the form of ash. Dumping onto the ground or into storm sewers, primarily
by "do-it-yourselfers" who change their own oil, is another fate that results in conversion to CC>2 given that the releases are
generally small and most of the oil is biodegraded (based on the observation that land farming—application to soil—is one
of the most frequently used methods for degrading refinery wastes). In the landfill environment, which tends to be
anaerobic within municipal landfills, it is assumed that 90 percent of the oil persists in an undegraded form, based on
analogy with the persistence of petroleum in native petroleum-bearing strata, which is also anaerobic. Re-refining adds a
recycling loop to the fate of oil. Re-refined oil was assumed to have a storage factor equal to the weighted average for the
other fates (i.e., after re-refining, the oil would have the same probability of combustion, landfilling, or dumping as virgin
oil), that is, it was assumed that about 97 percent of the C in re-refined oil is ultimately oxidized. Because of the
dominance of fates that result in eventual release as CC>2, only about 3 percent of the C in oil lubricants goes into long-
term storage.
Table A-80: Commercial and Environmental Fate of Oil Lubricants (Percent)
Fate of Oil
Combusted During Use
Not Combusted During Use
Combusted as Used Oil"
Dumped on the ground or in storm sewers
Landfilled
Re-refined into lube oil base stock and other products
Weighted Average
Portion of Total Oil
20%
80%
64%
6%
2%
8%
C Stored
0.2%
2.7%
0.6%
1.8%
0.2%
2.9%
* (e.g., in boilers or space heaters)
- Not applicable
Greases
Table A-81 provides analogous estimates for lubricant greases. Unlike oils, grease is generally not combusted
during use, and combustion for energy recovery and re-refining is thought to be negligible. Although little is known about
the fate of waste grease, it was assumed that 90 percent of the non-combusted portion is landfilled, and the remainder is
dumped onto the ground or storm sewers. Because much of the waste grease will be in containers that render it relatively
inaccessible to biodegradation, and because greases contain longer chain paraffins, which are more persistent than oils, it
was assumed that 90 percent and 50 percent of the C in landfilled and dumped grease, respectively, would be stored. The
overall storage factor is 82 percent for grease.
For example, the U.S. EPA "RCRA (Resource Conservation and Recovery Act) On-line" web site ()
has over 50 entries on used oil regulation and policy for 1994 through 2000.
A-116 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-81: Commercial and Environmental Fate of Grease Lubricants [Percent!
Fate of Grease Portion of Total Grease C Stored
Combusted During Use 5% 0.1%
Not Combusted During Use 95% 81.7%
Landfilled 86% 77.0%
Dumped on the ground or in storm sewers 10% 4.8%
Weighted Average - 81.8%
- Not applicable
Having derived separate storage factors for oil and grease, the last step was to estimate the weighted average for
lubricants as a whole. No data were found apportioning the mass of lubricants into these two categories, but the U.S.
Census Bureau does maintain records of the value of production of lubricating oils and lubricating greases. These were
retrieved from the relevant industry series summaries from the 1997 Economic Census (U.S. Bureau of the Census 1999).
Assuming that the mass of lubricants can be allocated according to the proportion of value of production (92 percent oil, 8
percent grease), applying these weights to the storage factors for oils and greases (3 percent and 82 percent) yields an
overall storage factor of 9.2 percent.
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the estimates of the lubricants weighted average C storage factor and the quantity of C emitted from
lubricants in 2012. The Tier 2 analysis was performed to allow the specification of probability density functions for key
variables, within a computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or
expert judgments of uncertainty were not available directly from the information sources for the activity variables; thus,
uncertainty estimates were determined using assumptions based on source category knowledge. Uncertainty estimates for
oil and grease variables were assumed to have a moderate variance, in triangular or uniform distribution. Uncertainty
estimates for lubricants production were assumed to be rather high (±20 percent). A narrow uniform distribution, with 6
percent uncertainty (± 6 percent) around the mean, was applied to the lubricant C content coefficient.
The Monte Carlo analysis produced a storage factor distribution with the 95 percent confidence interval of 4
percent and 17 percent around a mean value of 10 percent. This compares to the calculated Inventory estimate of 9
percent. The analysis produced a C emission distribution approximating a normal curve with a mean of 16.9 Tg CC>2 Eq.,
standard deviation of 1.5 and 95 percent confidence limits of 14.2 Tg CC>2 Eq. and 19.8 Tg CC>2 Eq. This compares to an
Inventory calculated estimate of 17.1 Tg CC^Eq.
The principal sources of uncertainty for the disposition of lubricants are the estimates of the commercial use,
post-use, and environmental fate of lubricants, which, as noted above, are largely based on assumptions and judgment.
There is no comprehensive system to track used oil and greases, which makes it difficult to develop a verifiable estimate
of the commercial fates of oil and grease. The environmental fate estimates for percent of C stored are less uncertain, but
also introduce uncertainty in the estimate.
The assumption that the mass of oil and grease can be divided according to their value also introduces
uncertainty. Given the large difference between the storage factors for oil and grease, changes in their share of total
lubricant production have a large effect on the weighted storage factor.
Future improvements to the analysis of uncertainty surrounding the lubricants C storage factor and C stored
include further refinement of the uncertainty estimates for the individual activity variables.
Waxes
Waxes are organic substances that are solid at ambient temperature, but whose viscosity decreases as temperature
increases. Most commercial waxes are produced from petroleum refining, though "mineral" waxes derived from animals,
plants, and lignite [coal] are also used. An analysis of wax end uses in the United States, and the fate of C in these uses,
suggests that about 42 percent of C in waxes is emitted, and 58 percent is stored.
Methodology and Data Sources
The National Petroleum Refiners Association (NPRA) considers the exact amount of wax consumed each year
by end use to be proprietary (Maguire 2004). In general, about thirty percent of the wax consumed each year is used in
packaging materials, though this percentage has declined in recent years. The next highest wax end use, and fastest
growing end use, is candles, followed by construction materials and firelogs. Table A-82 categorizes some of the wax end
A-117
-------
uses, which the NPRA generally classifies into cosmetics, plastics, tires and rubber, hot melt (adhesives), chemically
modified wax substances, and other miscellaneous wax uses (NPRA 2002).
Table A-82: Emissive and Non-emissive [Storage! Fates of Waxes: Uses by Fate and Percent of Total Mass
Use
Packaging
Non-packaging
Candles
Construction Materials
Firelogs
Cosmetics
Plastics
Tires/Rubber
Hot Melts
Chemically Modified
Other
Total
Emissive
6%
36%
18%
4%
7%
1%
1%
1%
1%
0%
2%
42%
Non-emissive
24%
34%
2%
14%
+
2%
2%
1%
1%
1%
9%
58%
+ Does not exceed 0.5 percent
A C storage factor for each wax end use was estimated and then summed across all end uses to provide an overall
C storage factor for wax. Because no specific data on C contents of wax used in each end use were available, all wax
products are assumed to have the same C content. Table A-83 categorizes wax end uses identified by the NPRA, and lists
each end use's estimated C storage factor.
Table A-83: Wax End-Uses by Fate, Percent of Total Mass, Percent G Stored, and Percent of Total G Mass Stored
Use
Packaging
Non-Packaging
Candles
Construction Materials
Firelogs
Cosmetics
Plastics
Tires/Rubber
Hot Melts
Chemically Modified
Other
Percent of Total Percent of C Percent of Total C
Wax Mass Stored Mass Stored
30%
20%
18%
7%
3%
3%
3%
3%
1%
12%
79%
10%
79%
1%
79%
79%
47%
50%
79%
79%
24%
2%
14%
+
2%
2%
1%
1%
1%
9%
Total 100% NA 58%
+ Does not exceed 0.5 percent
Source, mass percentages: NPRA 2002. Estimates of percent stored are based on professional judgment, ICF International.
Note: Totals may not sum due to independent rounding.
Emissive wax end-uses include candles, firelogs (synthetic fireplace logs), hotmelts (adhesives), matches, and
explosives. At about 20 percent, candles consume the greatest portion of wax among emissive end uses. As candles
combust during use, they release emissions to the atmosphere. For the purposes of the Inventory, it is assumed that 90
percent of C contained in candles is emitted as CC>2. In firelogs, petroleum wax is used as a binder and as a fuel, and is
combusted during product use, likely resulting in the emission of nearly all C contained in the product. Similarly, C
contained in hotmelts is assumed to be emitted as CC>2 as heat is applied to these products during use. It is estimated that
50 percent of the C contained in hot melts is stored. Together, candles, firelogs, and hotmelts constitute approximately 30
percent of annual wax production (NPRA 2002).
All of the wax utilized in the production of packaging, cosmetics, plastics, tires and rubber, and other products is
assumed to remain in the product (i.e., it is assumed that there are no emissions of CC>2 from wax during the production of
the product). Wax is used in many different packaging materials including wrappers, cartons, papers, paperboard, and
corrugated products (NPRA 2002). Davie (1993) and Davie et al. (1995) suggest that wax coatings in packaging products
degrade rapidly in an aerobic environment, producing CC>2; however, because packaging products ultimately enter landfills
typically having an anaerobic environment, most of the C from this end use is assumed to be stored in the landfill.
A-118 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
In construction materials, petroleum wax is used as a water repellent on wood-based composite boards, such as
particle board (IGI 2002). Wax used for this end-use should follow the life-cycle of the harvested wood used in product,
which is classified into one of 21 categories, evaluated by life-cycle, and ultimately assumed to either be disposed of in
landfills or be combusted (EPA 2003).
The fate of wax used for packaging, in construction materials, and for most remaining end uses is ultimately to
enter the municipal solid waste (MSW) stream, where it is either combusted or sent to landfill for disposal. Most of the C
contained in these wax products will be stored. It is assumed that approximately 21 percent of the C contained in these
products will be emitted through combustion or at landfill. With the exception of tires and rubber, these end-uses are
assigned a C storage factor of 79 percent.
Waxes used in tires and rubber follow the life cycle of the tire and rubber products. Used tires are ultimately
recycled, landfilled, or combusted. The life-cycle of tires is addressed elsewhere in this annex as part of the discussion of
rubber products derived from petrochemical feedstocks. For the purposes of the estimation of the C storage factor for
waxes, wax contained in tires and rubber products is assigned a C storage factor of 47 percent.
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the estimates of the wax C storage factor and the quantity of C emitted from wax in 2011. A Tier 2 analysis
was performed to allow the specification of probability density functions for key variables, within a computational
structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of uncertainty
were not available directly from the information sources for the activity variables; thus, uncertainty estimates were
determined using assumptions based on source category knowledge. Uncertainty estimates for wax variables were
assumed to have a moderate variance, in normal, uniform, or triangular distribution; uniform distributions were applied to
total consumption of waxes and the C content coefficients.
The Monte Carlo analysis produced a storage factor distribution, whose 95 percent confidence interval values fell
within the range of 49 percent and 71 percent, around the mean value of about 60 percent. This compares to the calculated
Inventory estimate of 58 percent. The analysis produced an emission distribution, with the 95 percent confidence interval
values of 0.3 Tg CCh Eq. and 0.8 Tg CC>2 Eq., with a mean value of 0.5 Tg CC>2 Eq. This compares with a calculated
Inventory estimate of 0.5 Tg CCh Eq., which falls within the range of 95 percent confidence limits established by this
quantitative uncertainty analysis. Uncertainty associated with the wax storage factor is considerable due to several
assumptions pertaining to wax imports/exports, consumption, and fates.
Miscellaneous Products
Miscellaneous products are defined by the U.S. Energy Information Administration as: "all finished [petroleum]
products not classified elsewhere, e.g., petrolatum; lube refining by-products (e.g., aromatic extracts and tars); absorption
oils; ram-jet fuel; petroleum rocket fuel; synthetic natural gas feedstocks; and specialty oils."
Methodology and Data Sources
Data are not available concerning the distribution of each of the above-listed subcategories within the
"miscellaneous products" category. However, based on the anticipated disposition of the products in each subcategory, it is
assumed that all of the C content of miscellaneous products is emitted rather than stored. Petrolatum and specialty oils
(which include greases) are likely to end up in solid waste or wastewater streams rather than in durable products, and
would be emitted through waste treatment. Absorption oil is used in natural gas processing and is not a feedstock for
manufacture of durable products Jet fuel and rocket fuel are assumed to be combusted in use, and synthetic natural gas
feedstocks are assumed to be converted to synthetic natural gas that is also combusted in use. Lube refining by-products
could potentially be used as feedstocks for manufacture of durable goods, but such by-products are more likely to be used
in emissive uses. Lube refining by-products and absorption oils are liquids and are precluded from disposal in landfills.
Because no sequestering end uses of any of the miscellaneous products subcategories have been identified, a zero percent
storage factor is assigned to miscellaneous products. According to EIA (2013a), the C content of miscellaneous petroleum
products in 2012 was approximately 20.3 Tg C/QBtu. EIA data were not available for 2012, so it was set equal to the
2011 value. One hundred percent of the C content is assumed to be emitted to the atmosphere, where it is oxidized to CC>2.
Uncertainty
A separate uncertainty analysis was not conducted for miscellaneous products, though this category was included
in the uncertainty analysis of other non-energy uses discussed in the following section.
A-119
-------
Other Non-Energy Uses
The remaining fuel types use storage factors that are not based on U.S.-specific analysis. For industrial coking
coal and distillate fuel oil, storage factors were taken from IPCC (2006), which in turn draws from Marland and Rotty
(1984). These factors are 0.1 and 0.5, respectively.
IPCC does not provide guidance on storage factors for the remaining fuel types (petroleum coke, miscellaneous
products, and other petroleum), and assumptions were made based on the potential fate of C in the respective NEUs.
Specifically, the storage factor for petroleum coke is 0.3, based on information from Huurman (2006) indicating that
petroleum coke is used in the Netherlands for production of pigments, with 30% being stored long-term. EIA (2013a)
defines "miscellaneous products" as "all finished products not classified elsewhere (e.g., petrolatum, lube refining by-
products (aromatic extracts and tars), absorption oils, ram-jet fuel, petroleum rocket fuels, synthetic natural gas feedstocks,
and specialty oils)." All of these uses are emissive, and therefore the storage factor for miscellaneous products is set at
zero. The "other petroleum" category is reported by U.S. territories and accounts mostly for the same products as
miscellaneous products, but probably also includes some asphalt, known to be non-emissive. The exact amount of asphalt
or any of the other miscellaneous products is confidential business information, but based on judgment the storage factor
for this category was estimated at 0.1.
For all these fuel types, the overall methodology simply involves multiplying C content by a storage factor,
yielding an estimate of the mass of C stored. To provide a complete analysis of uncertainty for the entire NEU
subcategory, the uncertainty around the estimate of "other" NEUs was characterized, as discussed below.
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the weighted average of the remaining fuels' C storage factors and the total quantity of C emitted from these
other fuels in 2012. A Tier 2 analysis was performed to allow the specification of probability density functions for key
variables, within a computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or
expert judgments of uncertainty were not available directly from the information sources for some of the activity variables;
thus, uncertainty estimates were determined using assumptions based on source category knowledge. A uniform
distribution was applied to coking coal consumption, while the remaining consumption inputs were assumed to be
normally distributed. The C content coefficients were assumed to have a uniform distribution; the greatest uncertainty
range of 10 percent (±10%) around the inventory value, was applied to coking coal and miscellaneous products. C
coefficients for distillate fuel oil ranged from 18.5 to 21.1 Tg C/QBtu. The fuel-specific storage factors were assigned
wide triangular distributions indicating greater uncertainty.
The Monte Carlo analysis produced a storage factor distribution with 95 percent confidence limits of 8 percent
and 47 percent, with a mean of 25 percent. This compares to the Inventory calculation of weighted average (across the
various fuels) storage factor of about 8 percent. The analysis produced an emission distribution, with the 95 percent
confidence limit of 18.8 Tg CC>2 Eq. and 35.0 Tg CC>2 Eq., and a mean of 27.2 Tg CCh Eq. This compares with the
Inventory estimate of 33.3 Tg CCh Eq., which falls closer to the upper boundary of the 95 percent confidence limit. The
uncertainty analysis results are driven primarily by the very broad uncertainty inputs for the storage factors.
References
ACC (2013) "U.S. Resin Production & Sales: 2012 vs. 2011," American Chemistry Council. Available online at:
ACC (2012) "Guide to the Business of Chemistry, 2012, " American Chemistry Council.
ACC (2011) "Guide to the Business of Chemistry, 2011 " American Chemistry Council.
ACC (2010) "Guide to the Business of Chemistry, 2010 " American Chemistry Council.
ACC (2007-2011) "PIPS Year-End Resin Statistics: Production, Sales and Captive Use,"
http://www.americancheniistry.com/Jobs/EconomicStatistics/Plastics-Statistics/Production-and-Sales-Data-by-
Resin.pdf
APC (2003-2006) "APC Year-End Statistics," http://www.americanplasticscouncil.org/s_apc/docs/1700/1678.pdf
APC (2001) as cited in ACS (2001) "Production: slow gains in output of chemicals and products lagged behind U.S.
economy as a whole" Chemical & Engineering News.
A-120 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
APC (2000) Facts and Figures, Chemical & Engineering News, June 26, 2000.
Bank of Canada (2013) Financial Markets Department Year Average of Exchange Rates. Available online at:
Bank of Canada (2012) Financial Markets Department Year Average of Exchange Rates. Available online at:
http://www.bankofcanada.ca/stats/assets/pdf/nraa-2011 .pdf>
Bank of Canada (2009) Financial Markets Department Year Average of Exchange Rates. Available online at:
http://www.bankofcanada.ca/pdf/nraa09.pdf
Bureau of Labor Statistics (2013) Producer Price Index Industry Data: Soap and Other Detergent Manufacturing.
Available online at: http://data.bls.gov:8080/PDQ/outside.jsp?survey=pc
Davie, IN., J.P. Winter, and R.P. Varoney (1995) "Decomposition of Coated Papers from a Quick Service Restaurant."
Technical Association for Pulp and Paper Industry Journal. Vol 78 (5): 127-130.
Davie, IN. (1993) "Compostability of Petroleum Wax-based Coatings." Technical Association for Pulp and Paper
Industry Journal. Vol 76 (2): 167-170.
EIA (2013a) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, February 2013, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2013/2)
EIA (2013b) EIA Manufacturing Consumption of Energy (MECS) 2010, U.S. Department of Energy, Energy Information
Administration, Washington, DC.
EIA (2010) El A Manufacturing Consumption of Energy (MECS) 2006, U.S. Department of Energy, Energy Information
Administration, Washington, DC.EIA (2005) EIA Manufacturing Consumption of Energy (MECS) 2002, U.S.
Department of Energy, Energy Information Administration, Washington, DC.
EIA (2001) El A Manufacturing Consumption of Energy (MECS) 1998, U.S. Department of Energy, Energy Information
Administration, Washington, DC.
EIA (1997) El A Manufacturing Consumption of Energy (MECS) 1994, U.S. Department of Energy, Energy Information
Administration, Washington, DC.
EIA (1994) El A Manufacturing Consumption of Energy (MECS) 1991, U.S. Department of Energy, Energy Information
Administration, Washington, DC.
Eldredge-Roebuck (2000) Personal communication between Joe Casola, ICF Consulting and Brandt Eldredge-Roebuck,
American Plastics Council, 11 July 2000.
EIIP (2001) "Area Sources" Asphalt Paving, Emissions Inventory Improvement Program: State and Territorial Air
Pollution Program Administrators/Association of Local Air Pollution Control Officials and US EPA, EIIP Document
Series Vol. Ill, Ch. 17. (STAPPA/ALAPCO/EPA), Washington DC, January 2001.
http: //www. epa. go v/ttn/chief/eiip/techreport/volumeO 3 /index. html.
EIIP (1999) Methods for Estimating Greenhouse Gas Emissions from Combustion of Fossil Fuels. Emissions Inventory
Improvement Program: State and Territorial Air Pollution Program Administrators/Association of Local Air Pollution
Control Officials and U.S. Environmental Protection Agency, EIIP Document Series Volume VIII, Chapter 1,
STAPPA/ALAPCO/EPA, Washington, DC. August 2000. Available online at
. 28 August 2000.
Environment Canada (2006). Emissions Inventory Guidebook vl.3. Criteria Air Contaminants Division: Quebec, Canada.
Available online at: http://www.eea.europa.eu/publications/EMEPCORINAIR5/B461 lvsl.3.pdf.
EPA (2013a). "1970 - 2013 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards, December 2013. Available
online at .
EPA (2013b). Resource Conservation and Recovery Act (RCRA) Info, Biennial Report, GM Form (Section 2- Onsite
Management) and WR Form.
EPA (1996-2003a, 2005, 2007b, 2008, 2009a, 201 la, 2013c; 2014) Municipal Solid Waste in the United States: Facts and
Figures. Office of Solid Waste and Emergency Response, U.S. Environmental Protection Agency, Washington,
DC. Available online at .
A-121
-------
EPA (201 Ib) EPA's Pesticides Industry Sales and Usage, 2006 and 2007 Market Estimates
http://www.epa.gov/oppbeadl/pestsales/. Accessed January 2012.
EPA (2009) Biennial Reporting System (BRS) Database. U.S. Environmental Protection Agency, Envirofacts Warehouse.
Washington, DC. Available online at .
EPA (2006) Air Emissions Trends - Continued Progress Through 2005. U.S. Environmental Protection Agency,
Washington DC. December 19, 2006.
EPA (2004) EPA's Pesticides Industry Sales and Usage, 2000 and 2001 Market Estimates
http://www.epa.gov/oppbeadl/pestsales/. Accessed September 2006.
EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and the
Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
EPA (2002) EPA's Pesticides Industry Sales and Usage, 1998 and 1999 Market Estimates, table 3.6.
(http://www.epa.gov/oppbeadl/pestsales/99pestsales/market_estimatesl999.pdf). Accessed July 2003.
EPA (2001) AP 42, Volume I, Fifth Edition. Chapter 11: Mineral Products Industry. Available online at
http ://www. epa. gov/ttn/chief/ap42/ch 11 /index.html
EPA (2000a) Biennial Reporting System (BRS). U.S. Environmental Protection Agency, Envirofacts Warehouse.
Washington, DC. Available online at .
EPA (2000b) Toxics Release Inventory, 1998. U.S. Environmental Protection Agency, Office of Environmental
Information, Office of Information Analysis and Access, Washington, DC. Available online at
.
EPA (1999) EPA's Pesticides Industry Sales and Usage, 1996-1997 Market Estimates and
http://www.epa.go v/oppbeadl/pestsales/97pestsales/market_estimates 1997.pdf.
EPA (1998) EPA's Pesticides Industry Sales and Usage, 1994-1995 Market Estimates
http://www.epa.go v/oppbeadl/pestsales/95pestsales/market_estimates 1995.pdf.
FEB (2013) Fiber Economics Bureau, as cited in C&EN (2013) Lackluster Year for Chemical OUtput: Production stayed
flat or dipped in most world regions in 2012. Chemical &Engineering News, American Chemical Society, 1 July.
Available online at http://www.cen-online.org.
FEB (2012) Fiber Economics Bureau, as cited in C&EN (2012) Too Quiet After the Storm: After a rebound in 2010,
chemical production hardly grew in 2011. Chemical &Engineering News, American Chemical Society, 2 July.
Available online at http://www.cen-online.org.
FEB (2011) Fiber Economics Bureau, as cited in C&EN (2011) Output Ramps up in all Regions. Chemical & Engineering
News, American Chemical Society, 4 July. Available online at http://www.cen-online.org.
FEB (2010) Fiber Economics Bureau, as cited in C&EN (2010) Output Declines in U.S., Europe. Chemical &
Engineering News, American Chemical Society, 6 July. Available online at http://www.cen-online.org.
FEB (2009) Fiber Economics Bureau, as cited in C&EN (2009) Chemical Output Slipped In Most Regions. Chemical &
Engineering News, American Chemical Society, 6 July. Available online at http://www.cen-online.org.
FEB (2007) Fiber Economics Bureau, as cited in C&EN (2007) Gains in Chemical Output Continue. Chemical &
Engineering News, American Chemical Society. July 2, 2007. Available online at http://www.cen-online.org.
FEB (2005) Fiber Economics Bureau, as cited in C&EN (2005) Production: Growth in Most Regions. Chemical &
Engineering News, American Chemical Society, 11 July. Available online at http://www.cen-online.org.
FEB (2003) Fiber Economics Bureau, as cited in C&EN (2003) Production Inches Up in Most Countries. Chemical &
Engineering News, American Chemical Society, 7 July. Available online at http://www.cen-online.org.
FEB (2001) Fiber Economics Bureau, as cited in ACS (2001) Production: slow gains in output of chemicals and products
lagged behind U.S. economy as a whole Chemical & Engineering News, American Chemical Society, 25 June.
Available online at http://pubs.acs.org/cen
Financial Planning Association (2006) Canada/US Cross-Border Tools: US/Canada Exchange Rates. Available online at:
http://www.fpanet.org/global/planners/US_Canada_ex_rates.cfm. Accessed August 16, 2006.
A-122 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Gosselin, Smith, and Hodge (1984), Clinical Toxicology of Commercial Products. Fifth Edition, Williams & Wilkins,
Baltimore.
Huurman, J.W.F. (2006) Recalculation of Dutch Stationary Greenhouse Gas Emissions Based on sectoral Energy
Statistics 1990-2002. Statistics Netherlands, Voorburg, The Netherlands.
IGI (2002) 100 Industry Applications. The International Group Inc. Available online at:
http://www.igiwax.com/100_apps.html Toronto, Ontario.
IISRP (2003) "IISRP Forecasts Moderate Growth in North America to 2007" International Institute of Synthetic Rubber
Producers, Inc. New Release; available online at: .
IISRP (2000) Synthetic Rubber Use Growth to Continue Through 2004, Says IISRP andRMA. International Institute of
Synthetic Rubber Producers press release.
INEGI (2006) Produccion bruta total de las unidades economicas manufactureras por Subsector, Rama, Subrama y Clase
de actividad. http://www.megi.gob.mx/est/contenidos/espanol/proyectos/censos/ce2004/tb_manufacturas.asp.
Accessed August 15.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, eds.; Institute for Global Environmental
Strategies (IGES). Hayama, Kanagawa, Japan.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
James, A. (2000) Personal communication between Suzanne Bratis of ICF International and Alan James of Akzo Nobel
Coatings, Inc. July 2000. (Tel: 614-294-3361).
Kelly (2000) Personal communication between Tom Smith, ICF Consulting and Peter Kelly, Asphalt Roofing
Manufacturers Association, August 2000.
Maguire (2004) Personal communication with J. Maguire, National Petrochemicals and Refiners Association. August -
September 2004.
Marland, G., and R.M. Rotty (1984) Carbon dioxide emissions from fossil fuels: A procedure for estimation and results for
1950-1982, Tellus 36b:232-261.
NPRA (2002) North American Wax - A Report Card
Rinehart, T. (2000) Personal communication between Thomas Rinehart of U.S. Environmental Protection Agency, Office
of Solid Waste, and Randall Freed of ICF International. July 2000. (Tel: 703 -308-4309).
RMA (2011) U.S. Scrap Tire Management Summary: 2005-2009. Rubber Manufacturers Association, Washington, DC.
October 2011, updated September 2013.
RMA (2009) "Scrap Tire Markets: Facts and Figures - Scrap Tire Characteristics." Available online at:
http://www.rma.org/scrap_tires/scrap_tire_markets/scrap_tire_characteristics/ Accessed 17 September 2009.
Schneider, S. (2007) E-mail between Shelly Schneider of Franklin Associates (a division of ERG) and Sarah Shapiro of
ICF International, January 10, 2007.
SPI (2000) The Society of the Plastics Industry Website, http://www.plasticsindustry.org/industry/stat3.htm, Accessed 28
June 2000.
UNFCCC (2006) Updated UNFCCC reporting guidelines on annual inventories following incorporation of the provisions
of decision 14/CP.ll. Subsidiary Body for Scientific and Technological Advice (SBSTA), Twenty-fifth session,
Nairobi, Kenya. (FCCC/SBSTA/2006/9). August 18, 2006.
U.S. Bureau of the Census (1994, 1999, 2004, 2009) 1992, 1997, 2002 and 2007 Economic Census. Available online at:
http://factfinder.census.gov/servlet/DatasetMainPageServlet?_program=ECN&_submenuId=&_lang=en&_ts=
U.S. International Trade Commission (1990-2013) "Interactive Tariff and Trade Data Web: Quick Query." Available
online at: http://dataweb.usitc.gov/AccessedNovember 2013.
A-123
-------
Vallianos, Jean (2013) Personal communication between Sarah Biggar of ICF International and Jean Vallianos of the
American Chemistry Council, November 8, 2013.
Vallianos, Jean (2012) Personal communication between Ben Eskin of ICF International and Jean Vallianos of the
American Chemistry Council, September 14, 2012.
Vallianos, Jean (2011) Personal communication between Joe Indvik of ICF International and Jean Vallianos of the
American Chemistry Council, January 4, 2011.
A-124 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
ANNEX 3 Methodological Descriptions for Additional
Source or Sink Categories
3.1. Methodology for Estimating Emissions of Chk N20, and Indirect Greenhouse
Gases from Stationary Combustion
Estimates of ChU and N20 Emissions
Energy consumption from stationary combustion activities was grouped by sector: industrial, commercial,
residential, electric power, and U.S. territories. For CH4 and N2(D from industrial, commercial, residential, and U.S.
territories, estimates were based upon consumption of coal, gas, oil, and wood. Energy consumption and wood
consumption data for the United States were obtained from EIA' s Monthly Energy Review, February 2014 and Published
Supplemental Tables on Petroleum Product detail (EIA 2014). Because the United States does not include territories in its
national energy statistics, fuel consumption data for territories were collected separately from the EIA's International
Energy Statistics (EIA 2013a).39 Fuel consumption for the industrial sector was adjusted to subtract out construction and
agricultural use, which is reported under mobile sources.40 Construction and agricultural fuel use was obtained from EPA
(2011). The energy consumption data by sector were then adjusted from higher to lower heating values by multiplying by
0.9 for natural gas and wood and by 0.95 for coal and petroleum fuel. This is a simplified convention used by the
International Energy Agency. Table A-84 provides annual energy consumption data for the years 1990 through 2012.
In this inventory, the emission estimation methodology for the electric power sector was revised from Tier 1 to
Tier 2 as fuel consumption by technology-type for the electricity generation sector was obtained from the Acid Rain
Program Dataset (EPA 2013). This combustion technology-and fuel-use data was available by facility from 1996 to 2012.
Since there was a difference between the EPA (2013) and EIA (2014) total energy consumption estimates, the remainder
between total energy consumption using EPA (2013) and EIA (2014) was apportioned to each combustion technology
type and fuel combination using a ratio of energy consumption by technology type from 1996 to 2012.
Energy consumption estimates were not available from 1990 to 1995 in the EPA (2013) dataset, and as a result,
consumption was calculated using total electric power consumption from EIA (2014) and the ratio of combustion
technology and fuel types from EPA 2013. The consumption estimates from 1990 to 1995 were estimated by applying the
1996 consumption ratio by combustion technology type to the total EIA consumption for each year from 1990 to 1995.
Lastly, there were significant differences between wood biomass consumption in the electric power sector
between the EPA (2013) and EIA (2014) datasets. The difference in wood biomass consumption in the electric power
sector was distributed to the residential, commercial, and industrial sectors according to their percent share of wood
biomass energy consumption calculated from EIA (2014).
Step 2: Determine the Amount of ChU and N20 Emitted
Activity data for industrial, commercial, residential, and U.S. territories and fuel type for each of these sectors
were then multiplied by default Tier 1 emission factors to obtain emission estimates. Emission factors for the residential,
commercial, and industrial sectors were taken from the 2006IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC 2006). These N2O emission factors by fuel type (consistent across sectors) were also assumed for U.S. territories.
The CH4 emission factors by fuel type for U.S. territories were estimated based on the emission factor for the primary
sector in which each fuel was combusted. Table A-85 provides emission factors used for each sector and fuel type. For
the electric power sector, emissions were estimated by multiplying fossil fuel and wood consumption by technology- and
fuel-specific Tier 2 IPCC emission factors shown in Table A-86. Emission factors were used from the 2006 IPCC
Guidelines as the factors presented in this IPCC guidance were taken directly from U.S. EPA publications on emissions
rates for combustion sources.
U.S. territories data also include combustion from mobile activities because data to allocate territories' energy use were unavailable.
For this reason, CFLi and N2O emissions from combustion by U.S. Territories are only included in the stationary combustion totals.
Though emissions from construction and farm use occur due to both stationary and mobile sources, detailed data was not available to
determine the magnitude from each. Currently, these emissions are assumed to be predominantly from mobile sources.
A-125
-------
Estimates of NOx, CO, and NMVOC Emissions
Emissions estimates for NOX, CO, and NMVOCs were obtained from data published on the National Emission
Inventory (NET) Air Pollutant Emission Trends web site (EPA 2013a, b, EPA 2009), and disaggregated based on EPA
(2003). .
For indirect greenhouse gases, the major source categories included coal, fuel oil, natural gas, wood, other fuels
(i.e., bagasse, liquefied petroleum gases, coke, coke oven gas, and others), and stationary internal combustion, which
includes emissions from internal combustion engines not used in transportation. EPA periodically estimates emissions of
NOX, CO, and NMVOCs by sector and fuel type using a "bottom-up" estimating procedure. In other words, the emissions
were calculated either for individual sources (e.g., industrial boilers) or for many sources combined, using basic activity
data (e.g., fuel consumption or deliveries, etc.) as indicators of emissions. The national activity data used to calculate the
individual categories were obtained from various sources. Depending upon the category, these activity data may include
fuel consumption or deliveries of fuel, tons of refuse burned, raw material processed, etc. Activity data were used in
conjunction with emission factors that relate the quantity of emissions to the activity.
The basic calculation procedure for most source categories presented in EPA (2003) and EPA (2009) is
represented by the following equation:
Ep,s = As x EFP,S x (l - Cp.s/100)
where,
E = Emissions
p = Pollutant
s = Source category
A = Activity level
EF = Emission factor
C = Percent control efficiency
The EPA currently derives the overall emission control efficiency of a category from a variety of sources,
including published reports, the 1985 National Acid Precipitation and Assessment Program (NAPAP) emissions inventory,
and other EPA databases. The U.S. approach for estimating emissions of NOX, CO, and NMVOCs from stationary
combustion as described above is similar to the methodology recommended by the IPCC (IPCC/UNEP/OECD/iEA 1997).
A-126 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-84: Fuel Consumption by Stationary Combustion for Calculating CHa and M Emissions tTBtul
Fuel/End-Use Sector
Coal
Residential
Commercial
Industrial
Electric Power
U.S. Territories
Petroleum
Residential
Commercial
Industrial
Electric Power
U.S. Territories
Natural Gas
Residential
Commercial
Industrial
Electric Power
U.S. Territories
Wood
Residential
Commercial
Industrial
Electric Power
U.S. Territories
1990
19,610
31
124
1,640
17,807
7
6,226
1,375
891
2,788
797
375
17,266
4,491
2,682
7,716
2,376
NE
2,216
614
70
1,526
7
NE
1995
20,888
17
117
1,527
19,217
10
5,652
1,261
694
2,375
860
462
19,337
4,954
3,096
8,723
2,564
NE
2,370
547
76
1,739
8
NE
1996
21,328
17
122
1,455
19,724
10
6,150
1,397
718
2,718
883
435
20,233
5,354
3,226
9,020
2,632
NE
2,437
571
80
1,779
8
NE
1997
21,879
16
129
1,458
20,266
10
6,194
1,334
655
2,660
1,100
445
20,131
5,093
3,285
9,033
2,720
NE
2,371
455
78
1,831
8
NE
1998
22,224
12
93
1,471
20,637
11
5,885
1,207
609
2,220
1,403
445
19,840
4,646
3,083
8,826
3,285
NE
2,184
404
68
1,704
8
NE
1999
22,159
14
103
1,373
20,659
10
6,205
1,342
614
2,328
1,459
461
19,778
4,835
3,115
8,425
3,403
NE
2,214
414
71
1,720
9
NE
2000
23,080
11
92
1,349
21,618
10
6,160
1,427
694
2,298
1,269
472
20,919
5,105
3,252
8,656
3,894
13
2,262
444
76
1,731
11
NE
2001
22,391
12
97
1,358
20,920
4
6,638
1,463
719
2,545
1,279
632
20,224
4,889
3,097
7,949
4,266
23
2,006
393
71
1,533
9
NE
2002
22,343
12
90
1,244
20,987
11
6,016
1,359
645
2,381
1,074
557
20,908
4,995
3,212
8,086
4,591
23
1,995
409
74
1,503
9
NE
2003
22,576
12
82
1,249
21,199
34
6,399
1,466
762
2,505
1,043
622
20,894
5,209
3,261
7,845
4,551
27
2,002
434
78
1,480
10
NE
2004
22,636
11
103
1,262
21,228
32
6,586
1,475
767
2,684
1,007
654
21,152
4,981
3,201
7,914
5,032
25
2,121
442
76
1,592
11
NE
2005
22,949
8
97
1,219
21,591
33
6,504
1,369
716
2,792
1,004
623
20,938
4,946
3,073
7,330
5,565
24
2,137
468
76
1,581
11
NE
2006
22,458
6
65
1,189
21,161
37
6,212
1,205
678
3,118
590
621
20,626
4,476
2,902
7,323
5,899
26
2,099
413
71
1,599
17
NE
2007
22,710
8
70
1,131
21,465
37
6,079
1,225
681
3,004
618
552
22,019
4,835
3,085
7,521
6,550
27
2,089
456
76
1,534
23
NE
2008
22,225
NE
81
1,081
21,026
37
5,268
1,183
650
2,456
488
492
22,286
5,010
3,228
7,571
6,447
29
2,059
508
79
1,446
27
NE
2009
19,670
NE
73
877
18,682
37
4,692
1,139
675
1,959
383
536
21,952
4,883
3,187
7,125
6,730
27
1,931
545
79
1,284
23
NE
2010
20,697
NE
70
952
19,639
37
4,865
1,119
651
2,068
412
614
22,913
4,878
3,165
7,683
7,159
28
1,981
482
78
1,395
25
NE
2011
18,989
NE
62
866
18,024
37
4,454
1,032
635
1,907
266
614
23,115
4,805
3,216
7,783
7,194
27
2,010
489
75
1,422
24
NE
2012
6,525
NE
44
785
5,659
37
6,611
950
517
1,795
2,736
614
31,423
4,242
2,960
8,201
15,994
27
2,001
417
62
1,318
204
NE
NE (Not Estimated)
Note: Totals may not sum due to independent rounding.
A-127
-------
Table A-85: CHa and N20 Emission Factors by Fuel Type and Sector Ig/GJl"
Fuel/End-Use Sector
Coal
Residential
Commercial
Industrial
Electric Power
U.S. Territories
Petroleum
Residential
Commercial
Industrial
Electric Power
U.S. Territories
Natural Gas
Residential
Commercial
Industrial
Electric Power
U.S. Territories
Wood
Residential
Commercial
Industrial
Electric Power
U.S. Territories
CH4
300
10
10
1
1
10
10
3
3
5
5
5
1
1
1
300
300
30
30
NA
N20
1.5
1.5
1.5
1.5
1.5
0.6
0.6
0.6
0.6
0.6
0.1
0.1
0.1
0.1
0.1
4.0
4.0
4.0
4.0
NA
NA (Not Applicable)
Table A-86: CHa and M Emission Factors by Technology Type and Fuel Type for the Electric Power Sectorlg/GJl"
Technology
Liquid Fuels
Residual Fuel Oil/Shale Oil Boilers
Gas/Diesel Oil Boilers
Large Diesel Oil Engines >600 hp (447kW)
Solid Fuels
Pulverized Bituminous Combination Boilers
Bituminous Spreader Stoker Boilers
Bituminous Fluidized Bed Combustor
Bituminous Cyclone Furnace
Lignite Atmospheric Fluidized Bed
Natural Gas
Boilers
Gas-Fired Gas Turbines >3MW
Large Duel-Fuel Engines
Combined Cycle
Peat
Peat Fluidized Bed Combustion
Biomass
Wood/Wood Waste Boilers
Wood Recovery Boilers
Configuration
Normal Firing
Tangential Firing
Normal Firing
Tangential Firing
Dry Bottom, wall fired
Dry Bottom, tangentially fired
Wet bottom
With and without re-injection
Circulating Bed
Bubbling Bed
Circulating Bed
Bubbling Bed
CH4
0.8
0.8
0.9
0.9
4
0.7
0.7
0.9
1
1
1
0.2
NA
1
4
258
1
3
3
11
1
N20
0.3
0.3
0.4
0.4
NA
0.5
1.4
1.4
0.7
61
61
0.6
71
1
1
NA
3
7
3
7
1
41 GJ (Gigajoule) = 109 joules. One joule = 9.486X10'4 Btu
42 GJ (Gigajoule) = 109 joules. One joule = 9.486xlO'4 Btu
A-128 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-87: NIL Emissions from Stationary Combustion [Ggl
Sector/Fuel Type
Electric Power
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
Internal Combustion
Industrial
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
Internal Combustion
Commercial
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
Residential
Coal"
Fuel Oilb
Natural Gas"
Wood
Other Fuels3
Total
1990
6,045
5,119
200
513
NA
NA
213
2,559
530
240
877
NA
119
792
671
36
88
181
NA
366
749
NA
NA
NA
42
707
10,023
1995
5,792
5,061
87
510
NA
NA
134
2,650
541
224
999
NA
111
774
607
35
94
210
NA
269
813
NA
NA
NA
44
769
9,862
1996
5,581
5,079
107
248
5
NA
142
2,667
490
203
900
NA
109
965
734
30
86
224
NA
394
726
NA
NA
NA
27
699
9,708
1997
5,683
5,118
131
277
6
NA
150
2,613
487
196
880
NA
103
948
538
32
88
229
NA
190
699
NA
NA
NA
27
671
9,534
1998
5,638
4,932
202
329
24
NA
149
2,570
475
190
869
NA
104
932
510
34
73
220
NA
184
651
NA
NA
NA
27
624
9,369
1999
5,183
4,437
179
393
33
NA
141
2,283
475
190
706
NA
100
813
483
23
54
156
NA
249
441
NA
NA
NA
25
416
8,390
2000
4,829
4,130
147
376
36
NA
140
2,278
484
166
710
NA
109
809
507
21
52
161
NA
273
439
NA
NA
NA
21
417
8,053
2001
4,454
3,802
149
325
37
NA
140
2,296
518
153
711
NA
116
798
428
21
52
165
NA
189
446
NA
NA
NA
22
424
7,623
2002
4,265
3,634
142
310
36
NA
143
1,699
384
114
526
NA
86
591
438
19
50
157
NA
212
422
NA
NA
NA
21
402
6,825
2003
3,929
3,348
131
286
33
NA
132
1,636
369
109
506
NA
82
568
455
19
49
156
NA
230
421
NA
NA
NA
21
400
6,442
2004
3,594
3,062
120
261
30
NA
121
1,573
355
105
487
NA
79
546
473
19
49
156
NA
249
419
NA
NA
NA
21
399
6,059
2005
3,434
2,926
114
250
29
NA
115
1,506
340
101
466
NA
76
523
490
19
49
155
NA
267
417
NA
NA
NA
20
397
5,847
2006
3,213
2,738
107
234
27
NA
108
1,390
314
93
430
NA
70
483
471
18
46
145
NA
263
390
NA
NA
NA
19
371
5,464
2007
2,993
2,550
100
218
25
NA
100
1,274
288
85
394
NA
64
443
452
17
42
134
NA
258
362
NA
NA
NA
18
345
5,081
2008
2,772
2,362
92
202
23
NA
93
1,158
261
77
359
NA
58
403
433
15
39
124
NA
254
335
NA
NA
NA
16
318
4,698
2009
2,451
2,088
82
178
21
NA
82
1,131
255
76
350
NA
57
393
453
15
39
122
NA
276
330
NA
NA
NA
16
314
4,365
2010
2,129
1,814
71
155
18
NA
71
1,105
249
74
342
NA
56
384
472
15
38
121
NA
299
325
NA
NA
NA
16
309
4,031
2011
1,807
1,540
60
131
15
NA
61
1,078
243
72
334
NA
54
375
582
15
38
119
NA
410
321
NA
NA
NA
16
305
3,787
2012
1,557
1,327
52
113
13
NA
52
1,078
243
72
334
NA
54
375
582
15
38
119
NA
410
321
NA
NA
NA
16
305
3,538
NA (Not Applicable)
a "Other Fuels" include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2009, 201 Ob, 2013).
b Residential coal, fuel oil, and natural gas emissions are included in the "Other Fuels" category (EPA 2003, 2009, 201 Ob, 2013).
Note: Totals may not sum due to independent rounding.
Table A-88: CO Emissions from Stationary Combustion tGgl
Sector/Fuel Type
Electric Power
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
Internal Combustion
Industrial
Coal
1990
329
213
18
46
NA
NA
52
797
95
1995
337
227
9
49
NA
NA
52
958
88
1996
369
228
11
72
NA
7
52
1,078
100
1997
384
233
13
76
NA
8
54
1,055
99
1998
410
220
17
88
NA
30
55
1,044
96
1999
450
187
36
151
NA
24
52
1,100
114
2000
439
221
27
96
NA
31
63
1,106
118
2001
439
220
28
92
NA
32
67
1,137
125
2002
594
298
38
125
NA
44
91
1,150
127
2003
590
296
37
124
NA
43
90
1,114
123
2004
586
293
37
123
NA
43
89
1,079
119
2005
581
291
37
122
NA
43
89
1,042
115
2006
606
304
38
127
NA
44
93
965
106
2007
631
316
40
132
NA
46
96
888
98
2008
655
328
41
137
NA
48
100
811
89
2009
670
336
42
141
NA
49
102
820
90
2010
685
343
43
144
NA
50
105
829
91
2011
700
351
44
147
NA
51
107
839
92
2012
700
351
44
147
NA
51
107
839
92
A-129
-------
Fuel Oil
Natural gas
Wood
Other Fuels3
Internal Combustion
Commercial
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
Residential
Coalb
Fuel Oilb
Natural Gasb
Wood
Other Fuels3
Total
67
205
NA
253
177
205
13
16
40
NA
136
3,668
NA
NA
NA
3,430
238
5,000
64
313
NA
270
222
211
14
17
49
NA
132
3,877
NA
NA
NA
3,629
248
5,383
49
307
NA
316
305
122
13
17
58
NA
34
2,364
NA
NA
NA
2,132
231
3,933
47
307
NA
302
299
126
13
18
59
NA
36
2,362
NA
NA
NA
2,133
229
3,927
46
305
NA
303
294
122
14
15
57
NA
36
2,353
NA
NA
NA
2,133
220
3,928
54
350
NA
286
296
151
16
17
81
NA
36
3,323
NA
NA
NA
3,094
229
5,024
48
355
NA
300
285
151
14
17
83
NA
36
2,644
NA
NA
NA
2,416
228
4,340
45
366
NA
321
279
154
13
17
84
NA
38
2,648
NA
NA
NA
2,424
224
4,377
46
370
NA
325
282
177
15
20
97
NA
44
3,044
NA
NA
NA
2,787
257
4,965
44
359
NA
315
274
173
15
19
95
NA
43
2,981
NA
NA
NA
2,730
252
4,859
43
347
NA
305
265
169
15
19
93
NA
42
2,919
NA
NA
NA
2,672
246
4,752
41
335
NA
295
256
166
14
19
91
NA
41
2,855
NA
NA
NA
2,614
241
4,644
38
311
NA
273
237
156
14
18
86
NA
39
2,689
NA
NA
NA
2,462
227
4,416
35
286
NA
251
218
146
13
16
80
NA
37
2,523
NA
NA
NA
2,310
213
4,188
32
261
NA
229
199
137
12
15
75
NA
34
2,356
NA
NA
NA
2,157
199
3,959
33
264
NA
232
201
140
12
16
77
NA
35
2,406
NA
NA
NA
2,202
203
4,036
33
267
NA
235
204
142
12
16
78
NA
36
2,455
NA
NA
NA
2,248
207
4,112
33
270
NA
237
206
145
13
16
80
NA
36
2,504
NA
NA
NA
2,293
211
4,188
33
270
NA
237
206
145
13
16
80
NA
36
2,504
NA
NA
NA
2,293
211
4,188
NA (Not Applicable)
a "Other Fuels" include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2009, 201 Ob).
b Residential coal, fuel oil, and natural gas emissions are included in the "Other Fuels" category (EPA 2003, 2009,
Note: Totals may not sum due to independent rounding.
201 Ob).
Table A-89: NMVOC Emissions from Stationary Combustion tGgl
Sector/Fuel Type
Electric Power
Coal
Cii«i nil
ruei Ull
Natural gas
Wood
Other Fuels3
Internal Combustion
Industrial
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
Internal Combustion
Commercial
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
1990
43
24
2
NA
NA
11
165
7
11
52
NA
46
49
18
1
3
7
NA
8
1995
40
26
0
L
2
NA
NA
9
187
5
11
66
NA
45
60
21
1
3
10
NA
8
1996
44
25
0
0
7
NA
+
9
163
6
8
54
NA
33
63
22
1
3
13
NA
5
1997
47
26
7
NA
+
10
160
6
7
54
NA
31
62
22
1
3
13
NA
5
1998
51
26
9
NA
1
10
159
6
7
53
NA
31
61
21
1
3
12
NA
5
1999
49
25
9
NA
2
10
156
9
10
52
NA
26
60
25
1
3
11
NA
10
2000
56
27
12
NA
2
11
157
9
9
53
NA
27
58
28
1
4
14
NA
9
2001
55
26
12
NA
2
10
159
10
9
54
NA
29
57
29
1
4
14
NA
10
2002
45
21
10
NA
1
9
138
9
7
47
NA
25
49
61
1
6
23
NA
31
2003
44
21
10
NA
1
8
132
9
7
45
NA
24
47
53
1
5
18
NA
29
2004
44
21
0
0
10
NA
1
8
126
8
7
43
NA
23
45
45
1
3
14
NA
27
2005
44
21
0
0
10
NA
1
8
120
8
6
41
NA
22
43
33
1
2
9
NA
22
2006
42
20
0
0
9
NA
1
8
112
7
6
38
NA
21
40
34
1
2
8
NA
24
2007
41
19
0
0
9
NA
1
8
104
7
6
35
NA
19
37
35
+
2
7
NA
26
2008
39
19
0
0
9
NA
1
7
97
6
5
33
NA
18
35
36
+
2
6
NA
28
2009
38
18
0
0
8
NA
1
7
97
6
5
33
NA
18
35
38
+
2
7
NA
30
2010
37
18
0
0
8
NA
1
7
98
6
5
33
NA
18
35
40
+
2
7
NA
31
2011
36
17
0
0
8
NA
1
7
98
6
5
33
NA
18
35
45
+
2
7
NA
35
2012
36
17
0
0
8
NA
1
7
98
6
5
33
NA
18
35
45
+
2
7
NA
35
A-130 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Residential
Coal"
Fuel Oilb
Natural Gas"
Wood
Other Fuels3
Total
686
NA
NA
NA
651
35
912
725
NA
NA
NA
688
37
973
789
NA
NA
NA
756
33
1,018
788
NA
NA
NA
756
32
1,017
786
NA
NA
NA
756
30
1,017
815
NA
NA
NA
794
21
1,045
837
NA
NA
NA
809
27
1,077
836
NA
NA
NA
809
27
1,080
1,341
NA
NA
NA
1,297
43
1,585
1,067
NA
NA
NA
1,032
35
1,296
792
NA
NA
NA
767
26
1,008
518
NA
NA
NA
501
17
715
465
NA
NA
NA
450
15
653
411
NA
NA
NA
398
13
591
357
NA
NA
NA
346
12
530
379
NA
NA
NA
367
12
553
401
NA
NA
NA
388
13
576
423
NA
NA
NA
409
14
602
423
NA
NA
NA
409
14
602
NA (Not Applicable)
+ Does not exceed 1 Gg.
a "Other Fuels" include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2009, 201 Ob).
b Residential coal, fuel oil, and natural gas emissions are included in the "Other Fuels" category (EPA 2003, 2009, 201 Ob).
Note: Totals may not sum due to independent rounding.
A-131
-------
3.2. Methodology for Estimating Emissions of Chk N20, and Indirect Greenhouse
Gases from Mobile Combustion and Methodology for and Supplemental
Information on Transportation-Related GHG Emissions
Estimating C02 Emissions by Transportation Mode
Transportation-related CC>2 emissions, as presented in the Carbon Dioxide Emissions from Fossil Fuel
Combustion section of the Energy chapter, were calculated using the methodology described in Annex 2.1. This section
provides additional information on the data sources and approach used for each transportation fuel type. As noted in
Annex 2.1, CC>2 emissions estimates for the transportation sector were calculated directly for on-road diesel fuel and motor
gasoline based on data sources for individual modes of transportation (considered a "bottom up" approach). For most
other fuel and energy types (aviation gasoline, residual fuel oil, natural gas, LPG, and electricity), CC>2 emissions were
calculated based on transportation sector-wide fuel consumption estimates from the Energy Information Administration
(EIA 2014 and EIA 2013a) and apportioned to individual modes (considered a "top down" approach). CC>2 emissions from
commercial jet fuel use are obtained directly from the Federal Aviation Administration (FAA 2014), while CCh emissions
from other aircraft jet fuel consumption is determined using a top down approach.
Based on interagency discussions between EPA, EIA, and FHWA beginning in 2005, it was agreed that use of
"bottom up" data would be more accurate for diesel fuel and motor gasoline, based on the availability of reliable
transportation-specific data sources. A "bottom up" diesel calculation was first implemented in the 1990-2005 Inventory,
and a bottom-up gasoline calculation was introduced in the 1990-2006 Inventory for the calculation of emissions from on-
road vehicles. Estimated motor gasoline and diesel consumption data for on-road vehicles by vehicle type come from
FHWA's Highway Statistics, Table VM-1 (FHWA 1996 through 2014)43, and are based on federal and state fuel tax
records. These fuel consumption estimates were then combined with estimates of fuel shares by vehicle type from DOE's
Transportation Energy Data Book (DOE 1993 through 2013) to develop an estimate of fuel consumption for each vehicle
type (i.e., passenger cars, light-duty trucks, buses, medium- and heavy-duty trucks, motorcycles). The on-road gas and
diesel fuel consumption estimates by vehicle type were then adjusted for each year so that the sum of gasoline and diesel
fuel consumption across all vehicle categories matched the fuel consumption estimates in Highway Statistics ' Tables MF-
21 and MF-27 (FHWA 1996 through 2014). Table MF-21 provided fuel consumption estimates for the most current
Inventory year; Table MF-27 provided fuel consumption estimates for years 1990-2011. This resulted in a final estimate of
motor gasoline and diesel fuel use by vehicle type, consistent with the FHWA total for on-road motor gasoline and diesel
fuel use.
Estimates of diesel fuel consumption from rail were taken from the Association of American Railroads (AAR
2008 through 2013) for Class I railroads, the American Public Transportation Association (APTA 2007 through 2013 and
APTA 2006) and Gaffney (2007) for commuter rail, the Upper Great Plains Transportation Institute (Benson 2002 through
2004) and Whorton (2006 through 2012) for Class II and III railroads, and DOE's Transportation Energy Data Book
(DOE 1993 through 2013) for passenger rail. Estimates of diesel from ships and boats were taken from EIA's Fuel Oil
and Kerosene Sales (1991 through 2013).
Since EIA's total fuel consumption estimate for each fuel type is considered to be accurate at the national level,
adjustments needed to be made in the estimates for other sectors to equal the EIA total. In the case of motor gasoline,
estimates of fuel use by recreational boats come from EPA's NONROAD Model (EPA 2013b), and these estimates along
with those from other sectors (e.g., commercial sector, industrial sector) were adjusted. Similarly, to ensure consistency
with EIA's total diesel estimate for all sectors, the diesel consumption totals for the residential, commercial, and industrial
sectors were adjusted downward proportionately.
43 In 2011 FHWA changed its methods for estimating vehicle miles traveled (VMT) and related data. These methodological changes
included how vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. These changes were
first incorporated for the 2010 Inventory and apply to the 2007-12 time period. This resulted in large changes in VMT and fuel
consumption data by vehicle class, thus leading to a shift in emissions among on-road vehicle classes. For example, the category
"Passenger Cars" has been replaced by "Light-duty Vehicles-Short Wheelbase" and "Other 2 axle-4 Tire Vehicles" has been replaced by
"Light-duty Vehicles, Long Wheelbase." This change in vehicle classification has moved some smaller trucks and sport utility vehicles
from the light truck category to the passenger vehicle category in this emission inventory. These changes are reflected in a large drop in
light-truck emissions between 2006 and 2007.
A-132 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
As noted above, for fuels other than motor gasoline and diesel, EIA' s transportation sector total was apportioned
to specific transportation sources. For jet fuel, estimates come from: FAA (2014) for domestic and international
commercial aircraft, and DESC (2013) for domestic and international military aircraft. General aviation jet fuel
consumption is calculated as the difference between total jet fuel consumption as reported by EIA and the total
consumption from commercial and military jet fuel consumption. Commercial jet fuel CO 2 estimates are obtained directly
from the Federal Aviation Administration (FAA 2014), while CC>2 emissions from domestic military and general aviation
jet fuel consumption is determined using a top down approach. Domestic commercial jet fuels CC>2 from FAA is
subtracted from total domestic jet fuel CC>2 emissions, and this remaining value is apportioned among domestic military
and domestic general aviation based on their relative proportion of energy consumption. Estimates for biofuels, including
ethanol and biodiesel were discussed separately in Chapter 3.2 under the methodology for Estimating CO2 from Fossil
Combustion, and in Chapter 3.10 Wood Biomass and Ethanol Consumption and were not apportioned to specific
transportation sources. Consumption estimates for biofuels were calculated based on data from the Energy Information
Administration (EIA 2014).
Table A-90 displays estimated fuel consumption by fuel and vehicle type. Table A-91 displays estimated energy
consumption by fuel and vehicle type. The values in both of these tables correspond to the figures used to calculate CC>2
emissions from transportation. Except as noted above, they are estimated based on EIA transportation sector energy
estimates by fuel type, with activity data used to apportion consumption to the various modes of transport. For motor
gasoline, the figures do not include ethanol blended with gasoline; although ethanol is included in FHWA's totals for
reported motor gasoline use. Ethanol is a biofuel and in order for consistency with IPCC methodological guidance and
UNFCCC reporting obligations, net carbon fluxes in biogenic carbon reservoirs in croplands are accounted for in the
estimates for Land Use, Land-Use Change and Forestry chapter, not in Energy chapter totals. Ethanol and biodiesel
consumption estimates are shown separately in Table A-92.
A-133
-------
Table A-90. Fuel Consumption by Fuel and Vehicle Type [million gallons unless otherwise specified!
Fuel/Vehicle Type
Motor Gasoline1'
Passenger Cars
Light-Duty Trucks
Motorcycles
Buses
Medium- and Heavy-
Duty Trucks
Recreational Boats'
Distillate Fuel Oil
(Diesel Fuel)
Passenger Cars
Light-Duty Trucks
Buses
Medium- and Heavy-
Duty Trucks
Recreational Boats
Ships and Other Boats
Rail
Jet Fuel'1
Commercial Aircraft
General Aviation
Aircraft
Military Aircraft
Aviation Gasoline'1
General Aviation
Aircraft
Residual Fuel Oil'1 e
Ships and Other Boats
1990
110,441
69,763
34,698
194
39
4,350
1,397
25,631
771
1,119
781
18,574
190
735
3,461
19,186
11,569
4,034
3,583
374
374
2,006
2,006
1995
118,217
67,948
44,369
200
42
4,072
1,585
31,605
765
1,452
851
23,241
228
1,204
3,864
17,991
12,136
3,361
2,495
329
329
2,587
2,587
2000
129,102
72,860
50,774
210
44
4,096
1,119
39,241
356
1,961
997
30,180
266
1,377
4,106
20,002
14,672
3,163
2,167
302
302
2,963
2,963
2001
130,582
73,466
51 ,251
194
40
3,990
1,641
39,058
357
2,029
906
30,125
274
1,248
4,119
19,454
13,121
3,975
2,359
291
291
1,066
1,066
2002
133,257
74,911
52,442
191
38
4,038
1,637
40,348
364
2,133
860
31,418
282
1,202
4,089
19,004
12,774
4,119
2,110
281
281
1,522
1,522
2003
133,900
72,623
55,951
185
36
3,479
1,626
41,177
412
2,652
930
31 ,540
289
1,178
4,176
18,389
12,943
3,323
2,123
251
251
662
662
2004
135,708
72,223
58,118
195
50
3,510
1,612
42,668
419
2,822
1,316
32,599
297
807
4,407
19,147
13,147
3,815
2,185
260
260
1,245
1,245
2005
134,659
74,600
54,274
184
41
3,962
1,599
44,659
414
2,518
1,030
35,160
305
785
4,446
19,420
13,976
3,583
1,860
294
294
1,713
1,713
2006
132,947
71 ,647
55,460
212
42
4,008
1,577
45,848
403
2,610
1,049
36,079
313
729
4,665
18,695
14,426
2,590
1,679
278
278
2,046
2,046
2007=
132,546
89,795
35,401
478
81
5,233
1,557
46,432
402
1,326
1,547
37,496
321
800
4,539
18,407
14,708
2,043
1,656
263
263
2,579
2,579
2008
127,528
86,376
33,723
496
86
5,322
1,524
44,032
362
1,182
1,477
35,692
329
773
4,216
17,749
13,400
2,682
1,667
235
235
1,812
1,812
2009
127,322
86,019
34,311
479
89
4,918
1,506
39,940
354
1,181
1,374
32,384
337
775
3,535
15,809
12,588
1,787
1,434
221
221
1,241
1,241
2010
126,922
85,745
34,267
424
87
4,918
1,481
41,571
368
1,228
1,370
33,719
345
733
3,807
15,537
11,931
2,322
1,283
225
225
1,818
1,818
2011
124,430
85,301
32,638
414
85
4,525
1,468
42,536
401
1,283
1,479
34,015
353
1,005
3,999
15,036
12,067
1,895
1,074
225
225
1,723
1,723
2012
124,081
85,241
32,325
473
91
4,493
1,459
42,445
405
1,284
1,585
34,140
361
746
3,923
14,705
11,932
1,659
1,114
209
209
1,410
1,410
Natural Gasd (trillion
cubic feet)
Passenger Cars
Light-Duty Trucks
Buses
0.7
0.7
0.7
0.6
0.7
0.6
0.6
0.6
0.6
0.6
0.7
0.7
0.7
0.7
0.8
Pipelines
LPG"
Buses
Light-Duty Trucks
Medium- and Heavy-
Duty Trucks
Electricity.'
Rail
0.7
265
-
106
159
4,751
4,751
0.7
206
1.6
98
106
4,975
4,975
0.6
138
1.5
88
49
5,382
5,382
0.6
159
0.3
108
51
5,724
5,724
0.7
166
0.6
117
49
5,517
5,517
0.6
207
0.7
144
62
6,810
6,810
0.6
222
0.7
167
55
7,224
7,224
0.6
327
1.0
247
79
7,506
7,506
0.6
320
1.0
229
89
7,358
7,358
0.6
257
-
185
72
8,173
8,173
0.7
468
-
340
128
7,700
7,700
0.7
331
-
228
103
7,781
7,781
0.7
348
-
243
106
7,712
7,712
0.7
390
-
274
117
7,672
7,672
0.7
401
-
281
120
7,320
7,320
a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007-2012 time period. These methodological changes include how on-road vehicles are classified, moving from a system based
on body-type to one that is based on wheelbase. This resulted in large changes in fuel consumption data by vehicle class between 2006 and 2007.
A-134 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
b Figures do not include ethanol blended in motor gasoline. Net carbon fluxes associated with ethanol are accounted for in the Land Use, Land-Use Change and Forestry chapter.
c Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline estimates.
d Estimated based on EIA transportation sector energy estimates by fuel type, with bottom-up activity data used for apportionment to modes.
e Fluctuations in reported fuel consumption may reflect data collection problems.
'Million Kilowatt-hours
+ Less than 0.05 million gallons or 0.05 trillion cubic feet
- Unreportedorzero
A-135
-------
Table A-91: Energy Consumption by Fuel and Vehicle Type tThtul
Fuel/Vehicle Type
Motor Gasoline1'
Passenger Cars
Light-Duty Trucks
Motorcycles
Buses
Medium- and Heavy-
Duty Trucks
Recreational Boats'
Distillate Fuel Oil
(Diesel Fuel)
Passenger Cars
Light-Duty Trucks
Buses
Medium- and Heavy-
Duty Trucks
Recreational Boats
Ships and Other
Boats
Rail
Jet Fuel'1
Commercial Aircraft
General Aviation
Aircraft
Military Aircraft
Aviation Gasoline'1
General Aviation
Aircraft
Residual Fuel Oil" °
Ships and Other
Boats
Natural Gas'1
Passenger Cars
Light-Duty Trucks
Buses
Pipelines
LPG«
Buses
Light-Duty Trucks
Medium- and Heavy-
Duty Trucks
Electricity11
Rail
Total
1990
13,813
8,725
4,340
24
5
544
175
3,555
107
155
108
2,576
26
102
480
2,590
1,562
545
484
45
45
300
300
680
-
-
-
680
23
-
9
14
16
16
21,022
1995
14,679
8,437
5,509
25
5
506
197
4,383
106
201
118
3,223
32
167
536
2,429
1,638
454
337
40
40
387
387
724
2
-
1
721
18
-
8
9
17
17
22,676
2000
16,015
9,038
6,298
26
5
508
139
5,442
49
272
138
4,186
37
191
569
2,700
1,981
427
293
36
36
443
443
672
-
-
8
664
12
-
8
4
18
18
25,339
2001
16,198
9,113
6,358
24
5
495
204
5,417
50
281
126
4,178
38
173
571
2,626
1,771
537
318
35
35
159
159
658
-
-
9
649
14
-
9
4
20
20
25,127
2002
16,524
9,289
6,503
24
5
501
203
5,596
51
296
119
4,357
39
167
567
2,565
1,725
556
285
34
34
228
228
699
-
-
12
687
14
-
10
4
19
19
25,679
2003
16,600
9,004
6,937
23
4
431
202
5,711
57
368
129
4,374
40
163
579
2,482
1,747
449
287
30
30
99
99
627
-
-
14
614
18
-
12
5
23
23
25,592
2004
16,850
8,968
7,216
24
6
436
200
5,918
58
391
183
4,521
41
112
611
2,585
1,775
515
295
31
31
186
186
602
-
-
16
586
19
-
14
5
25
25
26,216
2005
16,730
9,268
6,743
23
5
492
199
6,194
57
349
143
4,876
42
109
617
2,622
1,887
484
251
35
35
256
256
624
-
-
16
608
28
-
21
7
26
26
26,515
2006
16,517
8,901
6,890
26
5
498
196
6,359
56
362
145
5,004
43
101
647
2,524
1,948
350
227
33
33
306
306
625
-
-
16
609
27
-
20
8
25
25
26,417
2007=
16,470
11,158
4,399
59
10
650
193
6,440
56
184
215
5,200
45
111
630
2,485
1,986
276
224
32
32
386
386
663
-
-
19
645
22
-
16
6
28
28
26,526
2008
15,844
10,731
4,190
62
11
661
189
6,107
50
164
205
4,950
46
107
585
2,396
1,809
362
225
28
28
271
271
692
-
-
21
672
40
-
29
11
26
26
25,405
2009
15,818
10,687
4,263
60
11
611
187
5,539
49
164
191
4,491
47
107
490
2,134
1,699
241
194
27
27
186
186
715
-
-
22
693
28
-
19
9
27
27
24,474
2010
15,769
10,653
4,257
53
11
611
184
5,765
51
170
190
4,676
48
102
528
2,097
1,611
314
173
27
27
272
272
719
-
-
20
699
29
-
21
9
26
26
24,705
2011
15,459
10,598
4,055
51
11
562
182
5,899
56
178
205
4,718
49
139
555
2,030
1,629
256
145
27
27
258
258
734
-
-
20
713
33
-
23
10
26
26
24,466
2012
15,419
10,592
4,017
59
11
558
181
5,887
56
178
220
4,735
50
103
544
1,985
1,611
224
150
25
25
211
211
777
-
-
20
757
34
-
24
10
25
25
24,362
a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007-2012 time period. These methodological changes include how on-road vehicles are classified, moving from a system based
on body-type to one that is based on wheelbase. This resulted in large changes in fuel consumption data by vehicle class between 2006 and 2007.
A-136 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
b Figures do not include ethanol blended in motor gasoline. Net carbon fluxes associated with ethanol are accounted for in the Land Use, Land-Use Change and Forestry chapter.
c Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline estimates.
d Estimated based on EIA transportation sector energy estimates, with bottom-up data used for apportionment to modes.
e Fluctuations in reported fuel consumption may reflect data collection problems. Residual fuel oil for ships and other boats data is based on ElA's February 2013 Monthly Energy Review data.
- Unreportedorzero
Table A-92. Biofuel Consumption by Fuel Type [million gallons!
Fuel Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Ethanol 712.6 1,327 1,591 1,661 1,977 2,690 3,377 3,862 5,210 6,567 9,269 10,543 12,282 12,326 12,316
Biodiesel NA NA NA 10 16 14 27 91 261 354 304 322 260 886 895
NA (Not Available)
A-137
-------
Estimates of ChU and N20 Emissions
Mobile source emissions of greenhouse gases other than CC>2 are reported by transport mode (e.g., road, rail,
aviation, and waterborne), vehicle type, and fuel type. Emissions estimates of CH4 and N2O were derived using a
methodology similar to that outlined in the Revised 1996IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997).
Activity data were obtained from a number of U.S. government agencies and other publications. Depending on
the category, these basic activity data included fuel consumption and vehicle miles traveled (VMT). These estimates were
then multiplied by emission factors, expressed as grams per unit of fuel consumed or per vehicle mile.
Methodology for On-Road Gasoline and Diesel Vehicles
Step 1: Determine Vehicle Miles Traveled by Vehicle Type, Fuel Type, and Model Year
VMT by vehicle type (e.g., passenger cars, light-duty trucks, medium- and heavy-duty trucks,44 buses, and
motorcycles) were obtained from the Federal Highway Administration's (FHWA) Highway Statistics (FHWA 1996
through 2014).4^ As these vehicle categories are not fuel-specific, VMT for each vehicle type was disaggregated by fuel
type (gasoline, diesel) so that the appropriate emission factors could be applied. VMT from Highway Statistics Table VM-
1 (FHWA 1996 through 2014) was allocated to fuel types (gasoline, diesel, other) using historical estimates of fuel shares
reported in the Appendix to the Transportation Energy Data Book (DOE 1993 through 2013). These fuel shares are drawn
from various sources, including the Vehicle Inventory and Use Survey, the National Vehicle Population Profile, and the
American Public Transportation Association. The fuel shares were first adjusted proportionately so that the gasoline and
diesel shares for each vehicle type summed to 100 percent in order to develop an interim estimate of VMT for each
vehicle/fuel type category that summed to the total national VMT estimate. VMT for alternative fuel vehicles (AFVs) was
calculated separately, and the methodology is explained in the following section on AFVs. Estimates of VMT from AFVs
were then subtracted from the appropriate total VMT estimates to develop the final VMT estimates by vehicle/fuel type
category.4'' The resulting national VMT estimates for gasoline and diesel on-road vehicles are presented in Table A- 93
and Table A- 94, respectively.
Total VMT for each on-road category (i.e., gasoline passenger cars, light-duty gasoline trucks, heavy-duty
gasoline vehicles, diesel passenger cars, light-duty diesel trucks, medium- and heavy-duty diesel vehicles, and
motorcycles) were distributed across 31 model years shown for 2012 in Table A- 97. Distributions for 1990-2012 are
presented in the Inventory Docket 47. This distribution was derived by weighting the appropriate age distribution of the
U.S. vehicle fleet according to vehicle registrations by the average annual age-specific vehicle mileage accumulation of
U.S. vehicles. Age distribution values were obtained from EPA's MOBILE6 model for all years before 1999 (EPA 2000)
and EPA's MOVES model for years 2009 forward (EPA 2013c).48 Age-specific vehicle mileage accumulation was
obtained from EPA's MOBILE6 model (EPA 2000).
Step 2: Allocate VMT Data to Control Technology Type
VMT by vehicle type for each model year was distributed across various control technologies as shown in Table A- 101
through Table A- 104. The categories "EPA Tier 0" and "EPA Tier 1" were used instead of the early three-way catalyst
and advanced three-way catalyst categories, respectively, as defined in the Revised 1996 IPCC Guidelines. EPA Tier 0,
EPA Tier 1, EPA Tier 2, and LEV refer to U.S. emission regulations, rather than control technologies; however, each does
Medium- and heavy-duty trucks correspond to FHWA's reporting categories of single-unit tracks and combination trucks. Single-unit tracks
are defined as single frame trucks that have 2-axles and at least 6 tires or a gross vehicle weight rating (GVWR) exceeding 10,000 Ibs.
In 2011 FHWA changed its methods for estimated vehicle miles traveled (VMT) and related data. These methodological changes included
how vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. These changes were first incorporated
for the 2010 Inventory and apply to the 2007-12 time period. This resulted in large changes in VMT data by vehicle class, thus leading to a shift
in emissions among on-road vehicle classes. For example, the category "Passenger Cars" has been replaced by "Light-duty Vehicles-Short
Wheelbase" and "Other 2 axle-4 Tire Vehicles" has been replaced by "Light-duty Vehicles, Long Wheelbase." This change in vehicle
classification has moved some smaller trucks and sport utility vehicles from the light truck category to the passenger vehicle category in this
emission inventory. These changes are reflected in a large drop in light-truck emissions between 2006 and 2007.
In Inventories through 2002, gasoline-electric hybrid vehicles were considered part of an "alternative fuel and advanced technology" category.
However, vehicles are now only separated into gasoline, diesel, or alternative fuel categories, and gas-electric hybrids are now considered within
the gasoline vehicle category.
4' Available on CD by request.
AR
Age distributions were held constant for the period 1990-1998, and reflect a 25-year vehicle age span. EPA (2010) provides a variable age
distribution and 31-year vehicle age span beginning in year 1999.
A-138 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
correspond to particular combinations of control technologies and engine design. EPA Tier 2 and its predecessors EPA
Tier 1 and Tier 0 apply to vehicles equipped with three-way catalysts. The introduction of "early three-way catalysts," and
"advanced three-way catalysts," as described in the Revised 1996IPCC Guidelines, roughly correspond to the introduction
of EPA Tier 0 and EPA Tier 1 regulations (EPA 1998).49 EPA Tier 2 regulations affect vehicles produced starting in
2004 and are responsible for a noticeable decrease in N2O emissions compared EPA Tier 1 emissions technology (EPA
1999b).
Control technology assignments for light and heavy-duty conventional fuel vehicles for model years 1972 (when
regulations began to take effect) through 1995 were estimated in EPA (1998). Assignments for 1998 through 2007 were
determined using confidential engine family sales data submitted to EPA (EPA 2007b). Vehicle classes and emission
standard tiers to which each engine family was certified were taken from annual certification test results and data (EPA
2007a). This information was used to determine the fraction of sales of each class of vehicle that met EPA Tier 0, EPA
Tier 1, Tier 2, and LEV standards. Assignments for 1996 and 1997 were estimated based on the fact that EPA Tier 1
standards for light-duty vehicles were fully phased in by 1996. Tier 2 began initial phase-in by 2004.
Step 3: Determine ChU and N20 Emission Factors by Vehicle, Fuel, and Control Technology Type
Emission factors for gasoline and diesel on-road vehicles utilizing Tier 2 and Low Emission Vehicle (LEV)
technologies were developed by ICF (2006b); all other gasoline and diesel on-road vehicle emissions factors were
developed by ICF (2004).These factors were based on EPA,CARB and Environment Canada laboratory test results of
different vehicle and control technology types. The EPA, CARB and Environment Canada tests were designed following
the Federal Test Procedure (FTP), which covers three separate driving segments, since vehicles emit varying amounts of
GHGs depending on the driving segment. These driving segments are: (1) a transient driving cycle that includes cold start
and running emissions, (2) a cycle that represents running emissions only, and (3) a transient driving cycle that includes
hot start and running emissions. For each test run, a bag was affixed to the tailpipe of the vehicle and the exhaust was
collected; the content of this bag was later analyzed to determine quantities of gases present. The emission characteristics
of segment 2 was used to define running emissions, and subtracted from the total FTP emissions to determine start
emissions. These were then recombined based upon MOBILE6.2's ratio of start to running emissions for each vehicle
class to approximate average driving characteristics.
Step 4: Determine the Amount of ChU and N20 Emitted by Vehicle, Fuel, and Control Technology Type
Emissions of CLL and N2O were then calculated by multiplying total VMT by vehicle, fuel, and control
technology type by the emission factors developed in Step 3.
Methodology for Alternative Fuel Vehicles (AFVs)
Step 1: Determine Vehicle Miles Traveled by Vehicle and Fuel Type
VMT for alternative fuel and advanced technology vehicles were calculated from "VMT Projections for
Alternative Fueled and Advanced Technology Vehicles through 2025" (Browning 2003). Alternative Fuels include
Compressed Natural Gas (CNG), Liquid Natural Gas (LNG), Liquefied Petroleum Gas (LPG), Ethanol, Methanol, and
Electric Vehicles (battery powered). Most of the vehicles that use these fuels run on an Internal Combustion Engine (ICE)
powered by the alternative fuel, although many of the vehicles can run on either the alternative fuel or gasoline (or diesel),
or some combination.50 The data obtained include vehicle fuel use and total number of vehicles in use from 1992 through
2007. Because AFVs run on different fuel types, their fuel use characteristics are not directly comparable. Accordingly,
fuel economy for each vehicle type is expressed in gasoline equivalent terms, i.e., how much gasoline contains the
equivalent amount of energy as the alternative fuel. Energy economy ratios (the ratio of the gasoline equivalent fuel
economy of a given technology to that of conventional gasoline or diesel vehicles) were taken from full fuel cycle studies
done for the California Air Resources Board (Unnasch and Browning, Kassoy 2001). These ratios were used to estimate
fuel economy in miles per gasoline gallon equivalent for each alternative fuel and vehicle type. Energy use per fuel type
was then divided among the various weight categories and vehicle technologies that use that fuel. Total VMT per vehicle
type for each calendar year was then determined by dividing the energy usage by the fuel economy. Note that for AFVs
capable of running on both/either traditional and alternative fuels, the VMT given reflects only those miles driven that
For further description, see "Definitions of Emission Control Technologies and Standards" section of this annex below.
Fuel types used in combination depend on the vehicle class. For light-duty vehicles, gasoline is generally blended with ethanol and diesel is
blended with biodiesel; dual-fuel vehicles can run on gasoline or an alternative fuel - either natural gas or LPG - but not at the same time, while
flex-fuel vehicles are designed to run on E85 (85% ethanol) or gasoline, or any mixture of the two in between. Heavy-duty vehicles are more
likely to run on diesel fuel, natural gas, or LPG,.
A-139
-------
were powered by the alternative fuel, as explained in Browning (2003). VMT estimates for AFVs by vehicle category
(passenger car, light-duty truck, heavy-duty vehicles) are shown in Table A- 95, while more detailed estimates of VMT by
control technology are shown in Table A- 96.
Step 2: Determine ChU and N20 Emission Factors by Vehicle and Alternative Fuel Type
CH4 and N2O emission factors for alternative fuel vehicles (AFVs) are calculated according to studies by
Argonne National Laboratory (2006) and Lipman & Delucchi (2002), and are reported in ICF (2006a). In these studies,
N2O and CFU emissions for AFVs were expressed as a multiplier corresponding to conventional vehicle counterpart
emissions. Emission estimates in these studies represent the current AFV fleet and were compared against Tier 1
emissions from light-duty gasoline vehicles to develop new multipliers. Alternative fuel heavy-duty vehicles were
compared against gasoline heavy-duty vehicles as most alternative fuel heavy-duty vehicles use catalytic after treatment
and perform more like gasoline vehicles than diesel vehicles. These emission factors are shown in Table A- 106.
Step 3: Determine the Amount of ChU and N20 Emitted by Vehicle and Fuel Type
Emissions of CFU and N2O were calculated by multiplying total VMT for each vehicle and fuel type (Step 1) by
the appropriate emission factors (Step 2).
Methodology for Non-Road Mobile Sources
CFLi and N2O emissions from non-road mobile sources were estimated by applying emission factors to the
amount of fuel consumed by mode and vehicle type.
Activity data for non-road vehicles include annual fuel consumption statistics by transportation mode and fuel
type, as shown in Table A- 100. Consumption data for ships and other boats (i.e., vessel bunkering) were obtained from
DHS (2008) and EIA (1991 through 2013) for distillate fuel, and DHS (2008) and EIA (2013a) for residual fuel; marine
transport fuel consumption data for U.S. territories (EIA 2008b) were added to domestic consumption, and this total was
reduced by the amount of fuel used for international bunkers. Gasoline consumption by recreational boats was obtained
from EPA's NONROAD model (EPA 2013b). Annual diesel consumption for Class I rail was obtained from the
Association of American Railroads (AAR 2008 through 2013), diesel consumption from commuter rail was obtained from
APTA (2007 through 2013) and Gaffney (2007), and consumption by Class II and III rail was provided by Benson (2002
through 2004) and Whorton (2006 through 2012). Diesel consumption by commuter and intercity rail was obtained from
DOE (1993 through 2013). Data on the consumption of jet fuel and aviation gasoline in aircraft were obtained from EIA
(2014) and FAA (2014), as described in Annex 2.1: Methodology for Estimating Emissions of CC>2 from Fossil Fuel
Combustion, and were reduced by the amount allocated to international bunker fuels (DESC 2013 and FAA 2014).
Pipeline fuel consumption was obtained from EIA (2007 through 2012) (note: pipelines are a transportation source but are
stationary, not mobile, sources). Data on fuel consumption by all non-transportation mobile sources were obtained from
EPA's NONROAD model (EPA 2013b) and from FHWA (1996 through 2014) for gasoline consumption for trucks used
off-road.
Emissions of CtLt and N2O from non-road mobile sources were calculated by multiplying U.S. default emission
factors in the Revised 1996 IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997) by activity data for each source type (see
Table A- 107).
Estimates of NOX, CO, and NMVOC Emissions
The emission estimates of NOX, CO, and NMVOCs from mobile combustion (transportation) were obtained from
preliminary data (EPA 2010), which, in final iteration, will be published on the EPA's National Emission Inventory (NEI)
Air Pollutant Emission Trends web site. This EPA report provides emission estimates for these gases by fuel type using a
procedure whereby emissions were calculated using basic activity data, such as amount of fuel delivered or miles traveled,
as indicators of emissions. Table A- 108 through Table A- 110 provides complete emission estimates for 1990 through
2012.
See International Bunker Fuels section of the Energy Chapter.
S9
Diesel consumption from Class II and Class III railroad were unavailable for 2012. Values are proxied from 2010, which is the last year the
data was available.
"Non-transportation mobile sources" are defined as any vehicle or equipment not used on the traditional road system, but excluding aircraft,
rail and watercraft. This category includes snowmobiles, golf carts, riding lawn mowers, agricultural equipment, and trucks used for off-road
purposes, among others.
A-140 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 93: Vehicle Miles Traveled for Gasoline On-Road Vehicles HO9 Miles)
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007"
2008
2009
2010
2011
2012
Passenger
Cars
1,391.2
1,341.7
1,354.8
1,356.5
1,387.5
1,420.6
1,454.7
1,488.5
1,536.6
1,559.1
1,591.5
1,619.3
1,649.2
1,662.6
1,690.2
1,698.8
1,681.0
2,092.8
2,013.6
2,004.6
2,014.6
2,034.6
2,051.4
Light-Duty
Trucks
554.3
627.2
682.9
720.5
738.8
762.5
788.0
820.8
836.8
867.4
886.7
904.9
925.8
943.0
984.2
997.8
1,037.5
561.5
579.5
591.1
595.7
576.9
573.4
Heavy-Duty
Vehicles
25.4
25.0
24.8
24.5
25.0
24.7
24.0
23.6
23.6
23.8
23.6
23.2
23.1
23.5
23.9
24.2
24.4
33.7
34.5
32.1
31.9
29.7
29.9
Motorcycles
9.6
9.2
9.6
9.9
10.2
9.8
9.9
10.1
10.3
10.6
10.5
9.6
9.6
9.6
10.1
10.5
12.0
21.4
20.8
20.8
18.5
18.5
21.3
Source: Derived from FHWA (1996 through 2014).
a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007-2012 time period. These methodological changes include how
on-road vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. This resulted in large changes in VMT data by
vehicle class between 2006 and 2007
Table A- 94: Vehicle Miles Traveled for Diesel Dn-Road Vehicles (10 Miles)
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Passenger
Cars
16.9
16.3
16.5
17.9
18.3
17.3
14.7
13.5
12.4
9.4
8.0
8.1
8.3
8.3
8.4
8.4
8.3
10.3
9.9
9.8
9.9
10.0
10.1
Light-Duty
Trucks
19.7
21.6
23.4
24.7
25.3
26.9
27.8
29.0
30.5
32.6
35.2
37.0
38.9
39.7
41.3
41.9
43.5
23.4
24.2
24.7
24.9
24.2
24.1
Heavy-Duty
Vehicles'1
125.5
129.3
133.5
140.3
150.5
158.7
164.3
173.4
178.4
185.3
188.0
191.1
196.4
199.1
201.6
202.6
201.0
280.2
286.4
265.8
264.3
247.5
249.0
Source: Derived from FHWA (1996 through 2014).
a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007-2012 time period. These methodological changes include how
on-road vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. This resulted in large changes in VMT data by
vehicle class between 2006 and 2007.
b Heavy-Duty Vehicles includes Medium-Duty Trucks, Heavy-Duty Trucks, and Buses
A-141
-------
Table A-95: Vehicle Miles Traveled for Alternative Fuel On-Read Vehicles (10g Miles)
Passenger
Year Cars Light-Duty Trucks Heavy-Duty Vehicles3
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
0.2
0.2
0.3
0.3
0.3
0.4
0.5
0.6
0.6
0.6
0.8
0.9
1.0
1.2
1.3
1.2
1.3
1.3
1.3
1.2
1.3
1.6
1.8
0.7
0.6
0.6
0.6
0.5
0.6
0.7
0.9
1.0
1.0
1.2
1.2
1.3
1.4
1.6
1.4
1.5
1.6
1.8
1.8
2.1
3.1
3.6
1.1
1.0
0.9
1.3
1.2
1.2
1.2
1.3
1.4
1.3
1.5
1.8
2.0
2.1
2.2
2.7
3.9
4.8
4.6
4.8
4.1
4.2
4.2
Source: Derived from Browning (2003).
a Heavy Duty-Vehicles includes medium-duty trucks, heavy-duty trucks, and buses.
A-142 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-96: Detailed Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles HO8 Miles)
Vehicle Type
Passenger Cars
Methanol-Flex Fuel ICE
Ethanol-Flex Fuel ICE
CNG ICE
CNG Bi-fuel
LPG ICE
LPG Bi-fuel
Biodiesel (BD20)
NEVs
Electric Vehicle
Light-Duty Trucks
Ethanol-Flex Fuel ICE
CNG ICE
CNG Bi-fuel
LPG ICE
LPG Bi-fuel
Biodiesel (BD20)
Electric Vehicle
Medium-Duty Trucks
CNG Bi-fuel
LPG ICE
LPG Bi-fuel
Biodiesel (BD20)
Heavy-Duty Trucks
Neat Methanol ICE
Neat Ethanol ICE
CNG ICE
LPG ICE
LPG Bi-fuel
LNG
Biodiesel (BD20)
Buses
Neat Methanol ICE
Neat Ethanol ICE
CNG ICE
LPG ICE
LNG
Biodiesel (BD20)
Electric
Total VMT
1990
206.3
+
+
10.6
28.2
20.6
146.9
+
+
+
660.7
+
10.9
24.2
56.9
568.7
+
+
508.0
2.3
24.3
481.4
+
523.9
3.0
+
12.7
36.3
471.9
+
+
41.4
1.9
0.1
11.2
28.2
+
+
+
1,940.3
1995
400.6
40.9
2.2
28.0
75.1
40.3
201.7
+
5.2
7.2
606.8
1.3
29.6
71.0
48.5
449.4
+
7.1
458.4
20.1
20.0
418.3
+
627.0
7.8
2.0
32.2
46.3
531.9
6.9
+
80.5
3.7
2.2
37.5
30.9
4.3
+
2.0
2,173.4
2000
788.1
13.2
120.4
68.9
202.9
41.9
197.6
8.2
62.4
72.6
1,162.0
122.6
145.9
280.1
58.4
511.9
8.2
35.0
629.6
117.0
29.7
475.9
7.0
712.3
0.0
0.1
83.7
48.3
529.7
22.2
28.3
111.9
0.0
0.0
53.4
35.6
13.3
4.5
5.1
3,403.9
2001
922.0
10.1
147.9
78.1
236.6
45.0
224.8
8.3
88.4
82.8
1,235.1
150.1
145.7
280.1
64.4
522.9
8.4
63.4
862.5
203.2
41.9
609.7
7.6
820.5
0.0
0.0
149.0
57.1
558.0
26.9
29.5
133.1
0.0
0.0
65.0
36.9
21.0
4.7
5.6
3,973.1
2002
1,042.4
7.8
189.1
83.0
267.2
48.8
237.9
13.4
98.8
96.3
1,344.2
179.1
153.4
301.2
68.2
557.1
17.1
68.2
977.7
228.2
48.3
671.8
29.4
845.2
0.0
0.0
146.3
60.9
548.5
28.6
60.9
140.3
0.0
0.0
65.5
36.4
22.3
9.9
6.3
4,349.9
2003
1,172.9
6.4
271.2
85.0
283.5
43.1
221.9
13.6
114.4
133.8
1,447.5
279.1
158.0
313.4
64.9
541.8
16.9
73.4
899.7
245.3
43.9
585.0
25.6
1,041.2
0.0
0.0
183.6
73.4
650.2
56.8
77.1
139.0
0.0
0.0
64.3
34.1
23.6
10.5
6.6
4,700.4
2004
1,273.0
3.6
311.5
112.1
274.1
37.4
199.4
62.3
124.8
147.8
1,567.3
353.7
162.2
330.0
61.5
525.6
55.9
78.3
856.6
241.9
40.7
535.7
38.2
1,093.9
0.0
0.0
187.7
70.8
626.7
108.1
100.6
229.3
0.0
0.0
145.2
38.9
25.7
13.1
6.4
5,020.2
2005=
1,243.1
0.0
391.7
62.6
187.6
40.7
207.1
125.1
104.2
124.2
1,401.0
420.8
65.5
171.4
60.1
513.2
109.4
60.1
802.6
146.7
40.5
517.2
98.2
1,644.2
0.0
0.0
408.0
69.0
499.2
115.9
552.2
250.8
0.0
0.0
160.0
30.3
28.6
15.6
16.2
5,341.7
2006
1,296.4
0.0
412.8
62.5
193.8
37.3
186.4
186.4
99.2
118.1
1,545.6
532.4
65.5
178.6
60.1
486.1
163.8
58.4
628.8
112.5
33.8
326.5
156.0
2,867.9
0.0
0.0
491.8
81.9
530.0
123.2
1,641.1
406.5
0.0
0.0
165.1
28.9
30.0
165.1
17.3
6,745.2
2007
1,295.4
0.0
448.6
58.4
180.0
24.6
121.4
246.1
99.4
116.9
1,648.5
702.4
64.7
175.4
43.8
383.7
219.2
58.0
693.1
154.4
31.3
298.2
209.1
3,627.7
0.0
0.0
557.2
78.4
522.5
127.5
2,342.2
468.7
0.0
0.0
179.8
28.9
33.5
209.1
17.1
7,733.3
2008
1,268.7
0.0
459.9
53.0
170.9
25.9
112.0
235.8
99.1
112.0
1,779.6
864.0
63.0
169.6
43.8
361.4
219.0
57.5
716.3
188.3
35.0
289.0
204.0
3,416.3
0.0
0.0
647.6
76.9
516.8
129.5
2,045.4
481.7
0.0
0.0
202.5
28.7
37.6
194.0
18.4
7,662.6
2009
1,242.1
0.0
488.4
47.3
159.7
14.2
88.7
236.6
97.1
110.0
1,806.9
1,021.1
58.2
157.5
33.0
259.7
219.8
55.8
629.8
178.8
31.0
215.2
204.8
3,639.5
0.0
0.0
843.7
74.7
513.4
131.9
2,075.8
500.4
0.0
0.0
220.2
28.4
37.8
194.8
18.5
7,818.6
2010
1,252.2
0.0
546.0
50.4
163.2
11.9
83.1
189.9
97.7
109.8
2,096.3
1 ,363.4
60.7
161.6
30.3
239.9
176.5
61.9
578.6
187.6
29.9
211.7
149.4
3,083.2
0.0
0.0
900.0
74.4
515.2
136.6
1 ,456.9
493.8
0.0
0.0
238.0
28.4
38.1
170.0
18.5
7,503.9
2011
1,604.1
0.0
569.3
53.4
166.0
11.9
83.0
187.4
97.5
296.5
3,113.9
2,378.8
66.1
165.2
30.2
238.9
170.7
61.8
578.7
198.9
30.5
212.6
136.7
3,107.6
0.0
0.0
959.6
75.0
515.0
139.8
1,418.3
493.6
0.0
0.0
254.7
28.3
38.2
152.9
18.7
8,897.8
2012
1,849.4
0.0
725.3
54.8
172.7
11.9
83.4
185.8
98.1
374.3
3,602.1
2,864.4
72.0
171.6
28.8
234.8
166.1
62.1
573.3
206.1
30.0
212.3
124.9
3,141.5
0.0
0.0
1,011.7
72.0
507.2
143.3
1 ,407.2
513.2
0.0
0.0
273.2
27.7
38.5
151.6
21.3
9,679.5
A-143
-------
Source: Derived from Browning (2003).
Note: Throughout the rest of this Inventory, medium-duty trucks are grouped with heavy-duty trucks; they are reported separately here because these two categories may run on a slightly different range of fuel types.
a In 2011, EIA changed its reporting methodology for 2005-2010 data. EIA provided more detail on alternative fuel vehicle use by vehicle class. The fuel use breakdown by vehicle class had previously been based on
estimates of the distribution of fuel use by vehicle class. The new data from EIA allowed actual data to be used for fuel use, and resulted in greater share of heavy-duty AFV VMT estimated for 2005-2010. The source of this
data is the U.S. Energy Information Administration, Office of Energy Consumption and Efficiency Statistics and the DOE/GSA Federal Automotive Statistical Tool (FAST).
+ Less than 0.05 million vehicle miles traveled
Table A- 97: Age Distribution by Vehicle/Fuel Type for On-Road Vehicles," 2012
Vehicle Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Total
LDGV
7.7%
7.0%
6.2%
5.1%
5.7%
6.3%
6.3%
6.1%
5.8%
5.6%
5.7%
5.7%
5.5%
4.5%
3.5%
2.9%
2.4%
1.9%
1.5%
1.1%
0.8%
0.6%
0.5%
0.4%
0.4%
0.3%
0.2%
0.2%
0.1%
0.1%
0.1%
100.0%
LDGT
6.2%
6.0%
5.4%
3.8%
4.7%
6.8%
6.8%
7.0%
6.7%
6.1%
5.6%
5.2%
4.8%
4.0%
3.5%
2.8%
2.5%
2.1%
1.8%
1.5%
1.1%
0.9%
0.9%
0.8%
0.7%
0.6%
0.5%
0.4%
0.3%
0.2%
0.2%
100.0%
HDGV
6.0%
5.6%
5.2%
5.0%
5.4%
5.5%
5.5%
5.3%
4.2%
3.3%
2.9%
3.3%
3.6%
3.6%
2.9%
2.2%
2.4%
2.8%
2.7%
2.0%
1.5%
1.5%
1.8%
2.5%
2.3%
2.0%
2.6%
2.0%
1.2%
1.8%
1.4%
100.0%
LDDV
10.1%
9.3%
8.1%
6.8%
7.5%
8.3%
8.3%
8.0%
7.6%
7.4%
7.6%
7.5%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.6%
0.7%
0.8%
1.5%
100.0%
LDDT
6.3%
6.1%
5.5%
3.8%
4.8%
6.2%
5.8%
6.0%
8.6%
6.1%
6.4%
6.5%
5.3%
5.6%
1.3%
3.2%
2.1%
2.0%
1.7%
1.2%
0.9%
0.7%
0.6%
0.5%
0.4%
0.3%
0.4%
0.4%
0.4%
0.3%
0.4%
100.0%
Source: EPA (201 3c).
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles),
diesel trucks), HDDV (heavy-duty diesel vehicles), and MC (motorcycles).
HDDV
6.2%
5.8%
5.4%
5.3%
5.8%
5.9%
7.1%
6.6%
5.2%
4.0%
3.6%
3.9%
4.5%
4.7%
3.6%
2.8%
2.6%
2.8%
2.4%
1.8%
1.2%
1.1%
1.3%
1.4%
1.2%
0.9%
0.9%
0.7%
0.4%
0.4%
0.3%
100.0%
MC
11.0%
9.0%
6.8%
4.8%
9.0%
8.1%
7.7%
6.7%
5.7%
4.8%
4.3%
3.6%
2.9%
2.2%
1.9%
1.8%
1.6%
1.2%
1.4%
1.1%
0.9%
0.7%
0.6%
0.4%
0.4%
0.4%
0.3%
0.3%
0.2%
0.2%
0.2%
100.0%
LDGT (light-duty gasoline trucks), HDGV (heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty
A-144 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
b According to EPA's MOVES model, sales of diesel passenger cars 12-26 years of age was very small compared to total passenger car sales, so the calculated fraction of these vehicles was stored as zero.
Table A-98: Annual Average Vehicle Mileage Accumulation per Vehicle3 [milesl
Vehicle Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
LDGV
14,910
14,174
13,475
12,810
12,178
11,577
11,006
10,463
9,947
9,456
8,989
8,546
8,124
7,723
7,342
6,980
6,636
6,308
5,997
5,701
5,420
5,152
4,898
4,656
4,427
4,427
4,427
4,427
4,427
4,427
4,427
LDGT
19,906
18,707
17,559
16,462
15,413
14,411
13,454
12,541
11,671
10,843
10,055
9,306
8,597
7,925
7,290
6,690
6,127
5,598
5,103
4,642
4,214
3,818
3,455
3,123
2,822
2,822
2,822
2,822
2,822
2,822
2,822
HDGV
20,218
18,935
17,100
16,611
15,560
14,576
13,655
12,793
11,987
11,231
10,524
9,863
9,243
8,662
8,028
7,610
7,133
6,687
6,269
5,877
5,510
5,166
4,844
4,542
4,259
4,259
4,259
4,259
4,259
4,259
4,259
LDDV
14,910
14,174
13,475
12,810
12,178
11,577
11,006
10,463
9,947
9,456
8,989
8,546
8,124
7,723
7,342
6,980
6,636
6,308
5,997
5,701
5,420
5,152
4,898
4,656
4,427
4,427
4,427
4,427
4,427
4,427
4,427
LDDT
26,371
24,137
22,095
20,228
18,521
16,960
15,533
14,227
13,032
11,939
10,939
10,024
9,186
8,420
7,718
7,075
6,487
5,948
5,454
5,002
4,588
4,209
3,861
3,542
3,250
3,250
3,250
3,250
3,250
3,250
3,250
HDDV
28,787
26,304
24,038
21,968
20,078
18,351
16,775
15,334
14,019
12,817
11,719
10,716
9,799
8,962
8,196
7,497
6,857
6,273
5,739
5,250
4,804
4,396
4,023
3,681
3,369
3,369
3,369
3,369
3,369
3,369
3,369
MC»
4,786
4,475
4,164
3,853
3,543
3,232
2,921
2,611
2,300
1,989
1,678
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
Source: EPA (2000).
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV (heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty
diesel trucks), HDDV (heavy-duty diesel vehicles), and MC (motorcycles).
b Because of a lack of data, all motorcycles over 12 years old are considered to have the same emissions and travel characteristics, and therefore are presented in aggregate.
A-145
-------
Table A- 99: VMT Distribution by Vehicle Age and Vehicle/Fuel Type,a 2012
Vehicle Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Total
LDGV
11.10%
9.64%
8.03%
6.36%
6.71%
7.02%
6.72%
6.16%
5.55%
5.14%
4.99%
4.70%
4.29%
3.32%
2.46%
1.93%
1.51%
1.15%
0.88%
0.61%
0.43%
0.31%
0.26%
0.20%
0.15%
0.11%
0.09%
0.07%
0.05%
0.03%
0.03%
100.00%
LDGT
10.47%
9.52%
8.14%
5.30%
6.15%
8.40%
7.81%
7.50%
6.61%
5.65%
4.80%
4.15%
3.48%
2.71%
2.15%
1.62%
1.29%
0.98%
0.80%
0.58%
0.40%
0.29%
0.25%
0.21%
0.17%
0.14%
0.12%
0.11%
0.08%
0.05%
0.05%
100.00%
HDGV
10.91%
9.55%
7.93%
7.40%
7.56%
7.17%
6.65%
6.10%
4.48%
3.30%
2.70%
2.93%
2.96%
2.76%
2.08%
1.50%
1.53%
1.65%
1.53%
1.04%
0.75%
0.70%
0.77%
1.00%
0.88%
0.77%
0.98%
0.75%
0.44%
0.68%
0.55%
100.00%
LDDV"
13.33%
11.58%
9.65%
7.64%
8.05%
8.43%
8.07%
7.39%
6.67%
6.17%
5.99%
5.64%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.25%
0.28%
0.31%
0.58%
100.00%
LDDT
11.90%
10.54%
8.79%
5.59%
6.34%
7.53%
6.50%
6.14%
8.07%
5.25%
5.06%
4.70%
3.52%
3.39%
0.74%
1.62%
0.99%
0.84%
0.65%
0.45%
0.28%
0.20%
0.17%
0.13%
0.09%
0.07%
0.10%
0.09%
0.09%
0.08%
0.10%
100.00%
HDDV
12.17%
10.46%
8.90%
7.91%
7.93%
7.41%
8.10%
6.95%
4.99%
3.49%
2.86%
2.83%
3.05%
2.87%
2.03%
1.43%
1.22%
1.22%
0.95%
0.64%
0.40%
0.35%
0.37%
0.36%
0.28%
0.22%
0.22%
0.16%
0.10%
0.09%
0.07%
100.00%
MC
17.67%
13.48%
9.51%
6.17%
10.67%
8.80%
7.53%
5.90%
4.41%
3.22%
2.42%
1.67%
1.34%
1.01%
0.86%
0.82%
0.72%
0.54%
0.63%
0.51%
0.43%
0.34%
0.27%
0.20%
0.17%
0.17%
0.14%
0.12%
0.11%
0.08%
0.09%
100.00%
Note: Estimated by weighting data in Table A- 97 by data in Table A- 98.
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV (heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty
diesel trucks), HDDV (heavy-duty diesel vehicles), and MC (motorcycles).
b According to EPA's MOVES model, sales of diesel passenger cars 12-26 years of age was very small compared to total passenger car sales, so the calculated fraction of these vehicles was stored as zero.
A-146 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-100: Fuel Consumption for Off-Road Sources by Fuel Type [million gallons!
Vehicle Type/Year
Aircraft3
Aviation Gasoline
Jet Fuel
Commercial
Aviation
Ships and Other
Boats
Diesel
Gasoline
Residual
Construction/
Mining Equipment11
Diesel
Gasoline
Agricultural
Equipment0
Diesel
Gasoline
Rail
Diesel
Other"
Diesel
Gasoline
Total
1990
19,560
374
19,186
11,569
4,507
1,043 1
1,403 1
2,061
4,160 1
3,674 1
486 1
3,134 1
2,321
813 1
3,461
3,461
5,916
1,423 1
4,493
40,738
1995
18,320
329
17,991
12,136
5,789
1,546 1
1,597 1
2,646 1
4,835
4,387 1
448 1
3,698
2,772 1
927 1
3,864
3,864 1
6,525
1,720
4,805
43,031
2000
20,304
302
20,002
14,672
6,431
1,750
1,653
3,028
5,439
5,095
344
3,875
3,222
652
4,106
4,106
6,826
2,016
4,810
46,980
2001
19,745
291
19,454
13,121
4,416
1,630
1,655
1,131
5,897
5,241
657
4,107
3,305
802
4,119
4,119
7,657
2,079
5,578
45,941
2002
19,284
281
19,004
12,774
4,834
1,592
1,654
1,588
6,067
5,386
681
4,220
3,388
832
4,089
4,089
7,840
2,144
5,696
46,334
2003
18,640
251
18,389
12,943
4,089
1,711
1,648
730
6,248
5,532
716
4,324
3,471
853
4,176
4,176
8,049
2,210
5,840
45,528
2004
19,407
260
19,147
13,147
4,300
1,347
1,640
1,313
6,428
5,678
751
4,648
3,554
1,094
4,407
4,407
8,263
2,275
5,988
47,453
2005
19,714
294
19,420
13,976
4,881
1,470
1,630
1,781
6,520
5,823
697
4,715
3,637
1,078
4,446
4,446
8,281
2,340
5,941
48,558
2006
18,973
278
18,695
14,426
5,143
1,409
1,620
2,115
6,656
5,968
688
4,948
3,719
1,229
4,665
4,665
8,396
2,405
5,991
48,781
2007
18,670
263
18,407
14,708
5,746
1,489
1,610
2,647
6,684
6,113
571
4,862
3,801
1,061
4,539
4,539
8,256
2,471
5,785
48,755
2008
17,984
235
17,749
13,400
4,950
1,470
1,600
1,880
6,835
6,258
577
4,517
3,883
634
4,216
4,216
8,387
2,536
5,851
46,888
2009
16,030
221
15,809
12,588
4,379
1,480
1,591
1,308
6,960
6,403
558
4,641
3,965
676
3,535
3,535
8,482
2,601
5,881
44,027
2010
15,762
225
15,537
11,931
4,910
1,446
1,578
1,886
7,204
6,547
656
4,739
4,046
692
3,807
3,807
8,830
2,666
6,164
45,252
2011
15,262
225
15,036
12,067
5,084
1,727
1,567
1,791
7,307
6,693
614
4,928
4,129
799
3,999
3,999
8,795
2,731
6,063
45,375
2012
14,914
209
14,705
11,932
4,510
1,475
1,557
1,477
7,473
6,839
634
5,086
4,211
875
3,923
3,923
8,730
2,797
5,933
44,636
Sources: MR (2008 through 2013), APIA (2007 through 2013), BEA (1991 through 2013), Benson (2002 through 2004), DHS (2008), DOC (1991 through 2013), DESC (2013), DOE (1993 through 2013), DOT (1991 through 2013),
EIA (2002), EIA (2007b), EIA (2008), EIA (2007 through 2013), EIA (1991 through 2013), EPA (2013b), FAA (2014), Gaffney (2007), and Whorton (2006 through 2012).
a For aircraft, this is aviation gasoline. For all other categories, this is motor gasoline.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d"0ther" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption
from trucks that are used off-road for commercial/industrial purposes.
A-147
-------
Table A-101: Control Technology Assignments for Gasoline Passenger Cars (Percent of VHT1
Model Years
Non-catalyst
Oxidation
EPA Tier 0
EPA Tier 1
LEV
Sources: EPA (1998), EPA (2007a), and EPA (2007b).
Note: Detailed descriptions of emissions control technologies are provided in the following section of this annex.
- Not applicable.
Table A-102: Control Technology Assignments for Gasoline Light-Duty Trucks [Percent of VHTla
Model Years
Non-catalyst
Oxidation
EPA Tier 0
EPA Tier 1
LEV
EPA Tier 2
1973-1974
1975
1976-1977
1978-1979
1980
1981
1982
1983
1984-1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009-12
100%
20% 80%
15% 85%
10% 90%
5% 88% 7%
15% 85%
14% 86%
12% 88%
100%
60% 40%
20% 80%
1% 97%
0.5% 96.5%
<1% 87%
<1% 67%
44%
3%
1%
<1%
<1%
.
.
.
.
-
-
-
-
-
2%
3%
13%
33%
56%
97%
99%
87%
41%
38%
18%
4%
2%
-
-
-
-
-
-
-
-
-
-
-
-
13%
59%
62%
82%
96%
98%
100%
EPA Tier 2
1973-1974
1975
1976
1977-1978
1979-1980
1981
1982
1983
1984
1985
1986
1987-1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008-2012
100% ....
30% 70%
20% 80%
25% 75%
20% 80%
95% 5%
90% 10%
80% 20%
70% 30%
60% 40%
50% 50%
5% 95%
60% 40%
20% 80%
100%
100%
80% 20%
57% 43%
65% 35%
1% 99%
10% 90%
<1% 53%
72%
38%
25%
14%
-
-
.
47%
28%
62%
75%
86%
100%
Sources: EPA (1998), EPA (2007a), and EPA (2007b).
a Detailed descriptions of emissions control technologies are provided in the following section of this annex.
b The proportion of LEVs as a whole has decreased since 2001, as carmakers have been able to achieve greater emission reductions with certain types of LEVs,
such as ULEVs. Because ULEVs emit about half the emissions of LEVs, a carmaker can reduce the total number of LEVs they need to build to meet a specified
emission average for all of their vehicles in a given model year.
- Not applicable.
A-148 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-103: Control Technology Assignments for Gasoline Heavy-Duty Vehicles [Percent of VHT]a
Model Years Uncontrolled Non-catalyst Oxidation EPA Tier 0 EPA Tier 1 LEV" EPA Tier 2
<1981
1982-1984
1985-1986
1987
1988-1989
1990-1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008-2012
100% ....
95% - 5%
95% 5%
70% 15% 15%
60% 25% 15%
45% 30% 25%
25% 10% 65%
10% 5% 85%
96%
78%
54%
64%
69%
65%
5%
.
.
.
-
-
.
.
-
4%
22%
46%
36%
31%
30%
37%
23%
20%
10%
0%
-
-
-
-
-
-
-
-
-
5%
59%
77%
80%
90%
100%
Sources: EPA (1998), EPA (2007a), and EPA (2007b).
a Detailed descriptions of emissions control technologies are provided in the following section of this annex.
b The proportion of LEVs as a whole has decreased since 2000, as carmakers have been able to achieve greater emission reductions with certain types of LEVs,
such as ULEVs. Because ULEVs emit about half the emissions of LEVs, a manufacturer can reduce the total number of LEVs they need to build to meet a
specified emission average for all of their vehicles in a given model year.
- Not applicable.
Table A-104: Control Technology Assignments for Diesel On-Road Vehicles and Motorcycles
Vehicle Type/Control Technology Model Years
Diesel Passenger Cars and Light-Duty Trucks
Uncontrolled 1960-1982
Moderate control 1983-1995
Advanced control 1996-2012
Diesel Medium- and Heavy-Duty Trucks and Buses
Uncontrolled 1960-1990
Moderate control 1991-2003
Advanced control 2004-2006
Aftertreatment 2007-2012
Motorcycles
Uncontrolled 1960-1995
Non-catalyst controls 1996-2012
Source: EPA (1998) and Browning (2005)
Note: Detailed descriptions of emissions control technologies are provided in the following section of this annex.
Table A-105: Emission Factors for CHa and H?0 for On-Road Vehicles
Vehicle Type/Control Technology N20 CH4
(g/mi) (g/mi)
Gasoline Passenger Cars
EPA Tier 2 0.0036 0.0173
Low Emission Vehicles 0.0150 0.0105
EPA Tier 1= 0.0429 0.0271
EPA Tier Oa 0.0647 0.0704
Oxidation Catalyst 0.0504 0.1355
Non-Catalyst Control 0.0197 0.1696
Uncontrolled 0.0197 0.1780
Gasoline Light-Duty Trucks
EPA Tier 2 0.0066 0.0163
Low Emission Vehicles 0.0157 0.0148
EPA Tier 1= 0.0871 0.0452
EPA Tier 0= 0.1056 0.0776
Oxidation Catalyst 0.0639 0.1516
Non-Catalyst Control 0.0218 0.1908
Uncontrolled 0.0220 0.2024
A-149
-------
Gasoline Heavy-Duty Vehicles
EPA Tier 2 0.0134 0.0333
Low Emission Vehicles 0.0320 0.0303
EPA Tier 1= 0.1750 0.0655
EPA Tier 0= 0.2135 0.2630
Oxidation Catalyst 0.1317 0.2356
Non-Catalyst Control 0.0473 0.4181
Uncontrolled 0.0497 0.4604
Diesel Passenger Cars
Advanced 0.0010 0.0005
Moderate 0.0010 0.0005
Uncontrolled 0.0012 0.0006
Diesel Light-Duty Trucks
Advanced 0.0015 0.0010
Moderate 0.0014 0.0009
Uncontrolled 0.0017 0.0011
Diesel Medium- and Heavy-Duty
Trucks and Buses
Aftertreatment 0.0048 0.0051
Advanced 0.0048 0.0051
Moderate 0.0048 0.0051
Uncontrolled 0.0048 0.0051
Motorcycles
Non-Catalyst Control 0.0069 0.0672
Uncontrolled 0.0087 0.0899
Source: ICF (2006b and 2004).
a The categories "EPA Tier 0° and "EPA Tier 1" were substituted for the early three-way catalyst and advanced three-way catalyst categories, respectively, as
defined in the Revised 1996IPCC Guidelines. Detailed descriptions of emissions control technologies are provided at the end of this annex.
Table A-106: Emission Factors for Clh and IhO for Alternative Fuel Vehicles (g/mi)
N20 CH4
Light Duty Vehicles
Methanol
CNG
LPG
Ethanol
Biodiesel (BD20)
Medium- and Heavy-Duty Trucks
Methanol
CNG
LNG
LPG
Ethanol
Biodiesel (BD20)
Buses
Methanol
CNG
Ethanol
Biodiesel (BD20)
0.067
0.050
0.067
0.067
0.001
0.175
0.175
0.175
0.175
0.175
0.005
0.175
0.175
0.175
0.005
0.018
0.737
0.037
0.055
0.0005
0.066
1.966
1.966
0.066
0.197
0.005
0.066
1.966
0.197
0.005
Source: Developed by ICF (2006a) using ANL (2006) and Lipman and Delucchi (2002).
Table A-107: Emission Factors for Clh and M Emissions from Non-Road Mobile Combustion (g/kg fuel)
Vehicle Type/Fuel Type N^O CH4
Ships and Boats
Residual 0.16 0.03
Gasoline 0.08 0.23
Diesel 0.14 0.02
Rail
Diesel 0.08 0.25
Agricultural Equipment"
Gasoline 0.08 0.45
Diesel 0.08 0.45
Construction/Mining Equipment0
Gasoline 0.08 0.18
A-150 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Diesel
Other Non-Road
All "Other" Categories'
Aircraft
Jet Fuel
Aviation Gasoline
0.08
0.08
0.10
0.04
0.18
0.18
0.00
2.64
Source: IPCC/UNEP/OECD/IEA (1997) and ICF (2009).
a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
c "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment,
commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
d Emissions of ChU from jet fuels have been zeroed out across the time series. Recent research indicates that modern aircraft jet engines are typically net
consumers of methane (Santoni et al, 2011). Methane is emitted at low power and idle operation, but at higher power modes aircraft engines consumer
methane. Over the range of engine operating modes, aircraft engines are net consumers of methane on average. Based on this data, methane emissions
factors for jet aircraft were changed to zero in this year's Inventory to reflect the latest emissions testing data.
A-151
-------
Table A-108: NIL Emissions from Mobile Combustion tGgl
Fuel Type/Vehicle Type
Gasoline On-Road
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty Trucks and
Buses
Motorcycles
Diesel On-Road
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty Trucks and
Buses
Alternative Fuel On-Roada
Non-Road
Ships and Boats
Rail
Aircraftb
Agricultural Equipment0
Construction/Mining Equipment11
Other6
Total
1990
5,746
3,847 1
1,364
515 1
20
2,956
39 1
20
2,897 1
IE 1
2,160
402 1
338
25 1
437 1
641 1
318
10,862
1995
4,560
2,752 1
1,325 1
469 1
14
3,493
19 1
12
3,462 1
IE 1
2,483
488 1
433 1
31
478 1
697 1
357
10,536
2000
3,812
2,084
1,303
411
13
3,803
7
6
3,791
IE
2,584
506
451
40
484
697
407
10,199
2001
3,715
2,027
1,285
390
14
3,338
6
5
3,326
IE
2,643
544
485
39
480
690
406
9,696
2002
4,917
2,683
1,700
516
18
4,418
8
7
4,403
IE
3,095
640
571
46
560
804
474
12,430
2003
4,600
2,510
1,591
483
17
4,134
7
7
4,119
IE
2,969
614
548
44
537
771
454
11,703
2004
4,284
2,337
1,481
450
16
3,849
7
6
3,836
IE
2,844
588
525
43
514
739
435
10,977
2005
3,967
2,164
1,372
416
15
3,564
6
6
3,552
IE
2,719
562
502
41
492
706
416
10,250
2006
3,800
2,073
1,314
399
14
3,414
6
6
3,403
IE
2,479
513
458
37
448
644
379
9,694
2007
3,633
1,982
1,257
381
13
3,265
6
5
3,254
IE
2,240
463
414
33
405
582
343
9,138
2008
3,295
1,798
1,139
346
12
2,960
5
5
2,950
IE
2,225
460
411
33
402
578
341
8,481
2009
2,962
1,616
1,024
311
11
2,661
5
4
2,652
IE
2,186
452
403
33
395
568
335
7,809
2010
2,722
1,485
941
286
10
2,446
4
4
2,438
IE
2,139
443
395
32
387
556
327
7,307
2011
2,749
1,500
951
288
10
2,470
4
4
2,461
IE
1,995
413
368
30
361
518
305
7,214
2012
2,564
1,399
887
269
9
2,303
4
4
2,295
IE
1,865
386
344
28
337
484
285
6,732
a NOx emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTD cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
e"0ther" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are
used off-road for commercial/industrial purposes.
Note: Totals may not sum due to independent rounding.
IE= Included Elsewhere
Table A-109: CO Emissions from Mobile Combustion tGgl
Fuel Type/Vehicle Type
Gasoline On-Road
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty
Trucks and Buses
Motorcycles
Diesel On-Road
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty
Trucks and Buses
Alternative Fuel On-Roada
Non-Road
Ships and Boats
Rail
Aircraftb
Agricultural Equipment0
1990
98,328
60,757
29,237
8,093 1
240 1
1,696
35 |
1995
74,673
42,065
27,048
5,404 1
155 1
1,424
1,391
E|
21,533
1,781
93 1
224 1
628 |
2000
60,657
32,867
24,532
3,104
154
1,088
7
6
1,075
IE
21,814
1,825
90
245
626
2001
56,716
31 ,600
22,574
2,411
131
869
6
5
858
IE
22,266
1,831
90
233
621
2002
45,690
25,456
18,186
1,942
106
700
5
4
691
IE
20,187
1,660
81
212
563
2003
43,102
24,014
17,155
1,832
100
661
4
4
652
IE
19,976
1,643
81
209
557
2004
40,513
22,572
16,125
1,722
94
621
4
4
613
IE
19,766
1,626
80
207
551
2005
37,925
21,130
15,095
1,612
88
581
4
4
574
IE
19,556
1,609
79
205
546
2006
35,485
19,771
14,124
1,509
82
544
4
3
537
IE
18,181
1,496
73
191
507
2007
33,046
18,412
13,153
1,405
77
506
3
3
500
IE
16,807
1,382
68
176
469
2008
29,418
16,390
11,709
1,251
68
451
3
3
445
IE
16,134
1,327
65
169
450
2009
24,554
13,680
9,773
1,044
57
376
3
2
371
IE
14,290
1,175
58
150
399
2010
25,294
14,093
10,068
1,075
59
388
3
2
383
IE
13,786
1,134
56
145
385
2011
23,793
13,256
9,470
1,011
55
365
2
2
360
IE
13,329
1,096
54
140
372
201?
23,793
13,256
9,470
1,011
55
365
2
2
360
IE
13,329
1,096
54
140
372
A-152 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Construction/Mining
Equipment11
Other
Total
1,090
15,805
119,360
1,132
17,676
97,630
1,047
17,981
83,559
1,041
18,449
79,851
944
16,726
66,577
934
16,552
63,739
924
16,377
60,900
914
16,203
58,062
850
15,064
54,211
786
13,926
50,359
754
13,368
46,003
668
11,840
39,219
645
11,423
39,468
623
11,044
37,486
623
11,044
37,486
a NOx emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTD cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
e"0ther" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are
used off-road for commercial/industrial purposes.
'Criteria Air Pollutant emissions for 2012 were unavailable. Values from 2011 are used as proxy estimates.
Note: Totals may not sum due to independent rounding.
IE = Included Elsewhere
Table A-110: NMVOCs Emissions from Mobile Combustion tGgl
Fuel Type/Vehicle Type
Gasoline On-Road
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty
Trucks and Buses
Motorcycles
Diesel On-Road
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty
Trucks and Buses
Alternative Fuel On-Roada
Non-Road
Ships and Boats
Rail
Aircraftb
Agricultural Equipment0
Construction/Mining
Equipment11
Other^
Total
1990
8,110
5,120 1
2,374 1
575 1
42 1
406
13
377 1
IE
2,415
608
33
28
85
149 1
1,512
10,932
1995
5,819
3,394
2,019
382
24
304
'
286
IE
2,622
739
36
28
30
152
1,580
8,745
2000
4,615
2,610
1,750
232
23
216
3
4
209
IE
2,398
744
35
24
76
130
1,390
7,230
2001
4,285
2,393
1,664
206
22
207
3
4
201
IE
2,379
730
35
19
72
125
1,397
6,872
2002
3,437
1,919
1,335
165
18
166
2
3
161
IE
2,774
851
41
22
84
146
1,629
6,377
2003
3,274
1,828
1,271
157
17
158
2
3
153
IE
2,708
831
40
22
82
142
1,590
6,140
2004
3,111
1,737
1,208
150
16
151
2
3
146
IE
2,642
811
39
21
80
139
1,552
5,903
2005
2,948
1,646
1,145
142
15
143
2
3
138
IE
2,576
791
38
21
78
135
1,513
5,667
2006
2,970
1,659
1,153
143
15
144
2
3
139
IE
2,469
757
37
20
75
130
1,450
5,583
2007
2,993
1,671
1,162
144
15
145
2
3
140
IE
2,361
724
35
19
72
124
1,387
5,498
2008
2,623
1,465
1,019
126
13
127
2
2
123
IE
2,309
708
34
19
70
121
1,356
5,059
2009
2,383
1,331
926
115
12
115
2
2
112
IE
2,153
661
32
17
66
113
1,264
4,652
2010
2,393
1,336
929
115
12
116
2
2
112
IE
2,087
640
31
17
64
110
1,225
4,596
2011
2,076
1,159
806
100
11
100
1
2
97
IE
1,941
596
29
16
59
102
1,140
4,118
2012
1,967
1,098
764
95
10
95
1
2
92
IE
1,863
572
28
15
57
98
1,094
3,925
a NOx emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTD cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
e"0ther" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are
used off-road for commercial/industrial purposes.
'Note: Totals may not sum due to independent rounding.
IE = Included Elsewhere
A-153
-------
Definitions of Emission Control Technologies and Standards
The N2O and CH4 emission factors used depend on the emission standards in place and the corresponding level
of control technology for each vehicle type. Table A- 101 through Table A- 104 show the years in which these
technologies or standards were in place and the penetration level for each vehicle type. These categories are defined below
and were compiled from EPA (1993, 1994a, 1994b, 1998, 1999a) and IPCC/UNEP/OECD/TEA (1997).
Uncontrolled
Vehicles manufactured prior to the implementation of pollution control technologies are designated as
uncontrolled. Gasoline passenger cars and light-duty trucks (pre-1973), gasoline heavy-duty vehicles (pre-1984), diesel
vehicles (pre-1983), and motorcycles (pre-1996) are assumed to have no control technologies in place.
Gasoline Emission Controls
Below are the control technologies and emissions standards applicable to gasoline vehicles.
Non-catalyst
These emission controls were common in gasoline passenger cars and light-duty gasoline trucks during model
years (1973-1974) but phased out thereafter, in heavy-duty gasoline vehicles beginning in the mid-1980s, and in
motorcycles beginning in 1996. This technology reduces hydrocarbon (HC) and carbon monoxide (CO) emissions
through adjustments to ignition timing and air-fuel ratio, air injection into the exhaust manifold, and exhaust gas
recirculation (EGR) valves, which also helps meet vehicle NOX standards.
Oxidation Catalyst
This control technology designation represents the introduction of the catalytic converter, and was the most
common technology in gasoline passenger cars and light-duty gasoline trucks made from 1975 to 1980 (cars) and 1975 to
1985 (trucks). This technology was also used in some heavy-duty gasoline vehicles between 1982 and 1997. The two-way
catalytic converter oxidizes HC and CO, significantly reducing emissions over 80 percent beyond non-catalyst-system
capacity. One reason unleaded gasoline was introduced in 1975 was due to the fact that oxidation catalysts cannot
function properly with leaded gasoline.
EPA TierO
This emission standard from the Clean Air Act was met through the implementation of early "three-way"
catalysts, therefore this technology was used in gasoline passenger cars and light-duty gasoline trucks sold beginning in
the early 1980s, and remained common until 1994. This more sophisticated emission control system improves the
efficiency of the catalyst by converting CO and HC to CO2 and H2O, reducing NOX to nitrogen and oxygen, and using an
on-board diagnostic computer and oxygen sensor. In addition, this type of catalyst includes a fuel metering system
(carburetor or fuel injection) with electronic "trim" (also known as a "closed-loop system"). New cars with three-way
catalysts met the Clean Air Act's amended standards (enacted in 1977) of reducing HC to 0.41 g/mile by 1980, CO to 3.4
g/mile by 1981 andNOxto 1.0 g/mile by 1981.
EPA Tier 1
This emission standard created through the 1990 amendments to the Clean Air Act limited passenger car NOX
emissions to 0.4 g/mi, and HC emissions to 0.25 g/mi. These bounds respectively amounted to a 60 and 40 percent
reduction from the EPA Tier 0 standard set in 1981. For light-duty trucks, this standard set emissions at 0.4 to 1.1 g/mi for
NOX, and 0.25 to 0.39 g/mi for HCs, depending on the weight of the truck. Emission reductions were met through the use
of more advanced emission control systems, and applied to light-duty gasoline vehicles beginning in 1994. These
advanced emission control systems included advanced three-way catalysts, electronically controlled fuel injection and
ignition timing, EGR, and air injection.
EPA Tier 2
This emission standard was specified in the 1990 amendments to the Clean Air Act, limiting passenger car NOX
emissions to 0.07 g/mi on average and aligning emissions standards for passenger cars and light-duty trucks.
Manufacturers can meet this average emission level by producing vehicles in 11 emission "Bins", the three highest of
A-154 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
which expire in 2006. These new emission levels represent a 77 to 95 percent reduction in emissions from the EPA Tier 1
standard set in 1994. Emission reductions were met through the use of more advanced emission control systems and lower
sulfur fuels and are applied to vehicles beginning in 2004. These advanced emission control systems include improved
combustion, advanced three-way catalysts, electronically controlled fuel injection and ignition timing, EGR, and air
injection.
Low Emission Vehicles (LEV)
This emission standard requires a much higher emission control level than the Tier 1 standard. Applied to light-
duty gasoline passenger cars and trucks beginning in small numbers in the mid-1990s, LEV includes multi-port fuel
injection with adaptive learning, an advanced computer diagnostics systems and advanced and close coupled catalysts with
secondary air injection. LEVs as defined here include transitional low-emission vehicles (TLEVs), low emission vehicles,
ultra-low emission vehicles (ULEVs) and super ultra-low emission vehicles (SULEVs). In this analysis, all categories of
LEVs are treated the same due to the fact that there are very limited CLU or N2O emission factor data for LEVs to
distinguish among the different types of vehicles. Zero emission vehicles (ZEVs) are incorporated into the alternative fuel
and advanced technology vehicle assessments.
Diesel Emission Controls
Below are the three levels of emissions control for diesel vehicles.
Moderate control
Improved injection timing technology and combustion system design for light- and heavy-duty diesel vehicles
(generally in place in model years 1983 to 1995) are considered moderate control technologies. These controls were
implemented to meet emission standards for diesel trucks and buses adopted by the EPA in 1985 to be met in 1991 and
1994.
Advanced control
EGR and modern electronic control of the fuel injection system are designated as advanced control technologies.
These technologies provide diesel vehicles with the level of emission control necessary to comply with standards in place
from 1996 through 2006.
Aftertreatment
Use of diesel particulate filters (DPFs), oxidation catalysts and NOX absorbers or selective catalytic reduction
(SCR) systems are designated as aftertreatment control. These technologies provide diesel vehicles with a level of
emission control necessary to comply with standards in place from 2007 on.
Supplemental Information on GHG Emissions from Transportation and Other Mobile Sources
This section of this Annex includes supplemental information on the contribution of transportation and other
mobile sources to U.S. greenhouse gas emissions. In the main body of the Inventory report, emission estimates are
generally presented by greenhouse gas, with separate discussions of the methodologies used to estimate CCh, N2(D, CLLi,
and HFC emissions. Although the inventory is not required to provide detail beyond what is contained in the body of this
report, the IPCC allows presentation of additional data and detail on emission sources. The purpose of this sub-annex,
within the annex that details the calculation methods and data used for non-CC>2 calculations, is to provide all
transportation estimates presented throughout the report in one place.
This section of this Annex reports total greenhouse gas emissions from transportation and other (non-
transportation) mobile sources in CCh equivalents, with information on the contribution by greenhouse gas and by mode,
vehicle type, and fuel type. In order to calculate these figures, additional analyses were conducted to develop estimates of
CC>2 from non-transportation mobile sources (e.g., agricultural equipment, construction/mining equipment, recreational
vehicles), and to provide more detailed breakdowns of emissions by source.
Estimation of C02 from Non-Transportation Mobile Sources
The estimates of N2(D and CLL from fuel combustion presented in the Energy chapter of the inventory include
both transportation sources and other mobile sources. Other mobile sources include construction/mining equipment,
agricultural equipment, vehicles used off-road, and other sources that have utility associated with their movement but do
not have a primary purpose of transporting people or goods (e.g., snowmobiles, riding lawnmowers, etc.). Estimates of
CC>2 from non-transportation mobile sources, based on EIA fuel consumption estimates, are included in the agricultural,
A-155
-------
industrial, and commercial sectors. In order to provide comparable information on transportation and mobile sources,
Table A- 111 provides estimates of CC>2 from these other mobile sources, developed from EPA's NONROAD model and
FHWA's Highway Statistics. These other mobile source estimates were developed using the same fuel consumption data
utilized in developing the N2O and CH4 estimates.
A-156 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-111: C02 Emissions from Non-Transportation Mobile Sources tTg Clh Eq.l
Fuel Type/Vehicle Type
Agricultural Equipment3
Construction/Mining Equipment11
Other Sources0
Total
1990
31.0
42.0
54.5
127.6
1995
36.6
48.9
59.8
145.4
2000
38.8
55.3
62.8
156.9
2001
41.0
59.5
70.2
170.7
2002
42.1
61.2
72.0
175.3
2003
43.1
63.0
73.9
180.0
2004
46.1
64.9
76.0
187.0
2005
46.8
65.9
76.2
188.9
2006
49.0
67.3
77.6
193.9
2007
48.4
67.8
76.7
193.0
2008
45.4
69.3
111
192.4
2009
46.7
70.6
78.6
195.9
2010
47.6
73.0
81.8
202.4
2011
49.4
74.1
81.6
205.1
2012
51.0
75.8
81.1
207.8
a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
c "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial equipment, and industrial equipment, as well as fuel
consumption from trucks that are used off-road for commercial/industrial purposes.
Estimation of HFC Emissions from Transportation Sources
In addition to CCh, N2O and CH4 emissions, transportation sources also result in emissions of HFCs. HFCs are emitted to the atmosphere during equipment
manufacture and operation (as a result of component failure, leaks, and purges), as well as at servicing and disposal events. There are three categories of transportation-
related HFC emissions; Mobile AC represents the emissions from air conditioning units in passenger cars and light-duty trucks, Comfort Cooling represents the emissions
from air conditioning units in passenger trains and buses, and Refrigerated Transport represents the emissions from units used to cool freight during transportation.
Table A- 112 below presents these HFC emissions. Table A- 113 presents all transportation and mobile source greenhouse gas emissions, including HFC
emissions.
Table A-112: HFC Emissions from Transportation Sources
Vehicle Type
Mobile AC
Passenger Cars
Light-Duty Trucks
Comfort Cooling for Trains and Buses
School and Tour Buses
Transit Buses
Rail
Refrigerated Transport
Medium- and Heavy-Duty Trucks
Rail
Ships and Other Boats
Total
1990 1995
17.2
10.1
7.1
+
+
+
+
2.3
1.7
0.5
+
19.6
2000
48.7
25.6
23.1
0.1
0.1
+
+
9.6
7.4
2.0
0.1
58.4
2001
53.7
27.9
25.8
0.2
0.1
+
+
10.5
8.1
2.2
0.1
64.3
2002
56.9
29.3
27.6
0.2
0.2
+
+
11.2
8.7
2.4
0.2
68.3
2003
59.4
30.0
29.4
0.2
0.2
+
+
12.0
10.0
1.9
+
71.6
2004
62.0
30.8
31.2
0.2
0.2
+
+
12.8
10.7
2.1
+
75.0
2005
64.3
31.4
32.8
0.3
0.2
+
+
13.2
11.1
2.1
+
77.8
2006
66.3
32.2
34.2
0.3
0.3
+
+
13.6
11.4
2.2
+
80.2
2007
68.0
32.6
35.4
0.3
0.3
+
+
13.8
11.5
2.2
+
82.1
2008
69.4
32.9
36.4
0.4
0.3
+
+
13.8
11.6
2.2
+
83.6
2009
69.2
32.1
37.0
0.4
0.4
+
+
13.9
11.6
2.2
+
83.5
2010
66.9
30.4
36.5
0.4
0.4
+
+
13.9
11.6
2.3
+
81.3
2011
62.5
27.6
34.9
0.4
0.4
+
+
14.0
11.7
2.3
+
76.9
2012
58.5
25.2
33.3
0.4
0.4
+
+
14.0
11.7
2.3
+
72.9
Note: Totals may not sum due to independent rounding.
+ Less than 0.05 Tg C02 Eq.
-Unreportedorzero
A-157
-------
Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by Mode/Vehicle Type/Fuel
Type
Table A- 113 presents estimates of greenhouse gas emissions from an expanded analysis including all
transportation and additional mobile sources, as well as emissions from electricity generation by the consuming category,
in CC>2 equivalents. In total, transportation and non-transportation mobile sources emitted 2,050.7 Tg CC>2 Eq. in 2012, an
increase of 22 percent from 1990. Transportation sources account for 1,841.0 Tg CC>2 Eq. while non-transportation mobile
sources account for 209.8 Tg CC>2 Eq. These estimates include HFC emissions for mobile AC, comfort cooling for trains
and buses, and refrigerated transport. These estimates were generated using the estimates of CC>2 emissions from
transportation sources reported in the Carbon Dioxide Emissions from Fossil Fuel Combustion section, and CFU emissions
and N2O emissions reported in the Mobile Combustion section of the Energy chapter; information on FfFCs from mobile
air conditioners, comfort cooling for trains and buses, and refrigerated transportation from Chapter 4; and estimates of CC>2
emitted from non-transportation mobile sources reported in Table A- 109 above.
Although all emissions reported here are based on estimates reported throughout this inventory, some additional
calculations were performed in order to provide a detailed breakdown of emissions by mode and vehicle category. In the
case of N2O and CtLt, additional calculations were performed to develop emissions estimates by type of aircraft and type
of heavy-duty vehicle (i.e., medium- and heavy-duty trucks or buses) to match the level of detail for CC>2 emissions. N2(D
estimates for jet fuel and aviation gasoline and CFU estimates for aviation gasoline were developed for individual aircraft
types by multiplying the emissions estimates for aircraft for each fuel type (jet fuel and aviation gasoline) by the portion of
fuel used by each aircraft type (from FAA 2014). Emissions of CFLi from jet fuels are no longer considered to be emitted
across the time series from aircraft gas turbine engines burning jet fuel A at higher power settings.54 Recent research
indicates that modern aircraft jet engines are typically net consumers of methane (Santoni et al, 2011). Methane is emitted
at low power and idle operation, but at higher power modes aircraft engines consumer methane. Over the range of engine
operating modes, aircraft engines are net consumers of methane on average. Based on this data, methane emissions factors
for jet aircraft were reported as zero to reflect the latest emissions testing data.
Similarly, N2O and CtLt estimates were developed for medium- and heavy-duty trucks and buses by multiplying
the emission estimates for heavy-duty vehicles for each fuel type (gasoline, diesel) from the Mobile Combustion section in
the Energy chapter, by the portion of fuel used by each vehicle type (from DOE 1993 through 2013). Otherwise, the table
and figure are drawn directly from emission estimates presented elsewhere in the inventory, and are dependent on the
methodologies presented in Annex 2.1 (for CCh), Chapter 4, and Annex 3.8 (for HFCs), and earlier in this Annex (for CFU
andN2O).
Transportation sources include on-road vehicles, aircraft, boats and ships, rail, and pipelines (note: pipelines are a
transportation source but are stationary, not mobile sources). In addition, transportation-related greenhouse gas emissions
also include FIFC released from mobile air conditioners and refrigerated transportation, and the release of CO2 from
lubricants (such as motor oil) used in transportation. Together, transportation sources were responsible for 1,841.0 Tg
CO2Eq. in 2012.
On-road vehicles were responsible for about 76 percent of all transportation and non-transportation mobile GHG
emissions in 2012. Although passenger cars make up the largest component of on-road vehicle greenhouse gas emissions,
light-duty and medium- and heavy-duty trucks have been the primary sources of growth in on-road vehicle emissions.
Between 1990 and 2012, greenhouse gas emissions from passenger cars increased by 21 percent, while emissions from
light-duty trucks increased by one percent. 55 Meanwhile, greenhouse gas emissions from medium- and heavy-duty trucks
increased 75 percent between 1990 and 2012, reflecting the increased volume of total freight movement and an increasing
share transported by trucks.
Recommended Best Practice for Quantifying Speciated Organic Gas Emissions from Aircraft Equipped with Turbofan, Turbojet and
Turboprop Engines," EPA-420-R-09-901, May 27, 2009 (see http://www.epa.gov/otaq/regs/nonroad/aviation/420r09901.pdf>
In 2011 FHWA changed how they defined vehicle types for the purposes of reporting VMT for the years 2007-2010. The old approach to
vehicle classification was based on body type and split passenger vehicles into "Passenger Cars" and "Other 2 Axle 4-Tire Vehicles". The new
approach is a vehicle classification system based on wheelbase. Vehicles with a wheelbase less than or equal to 121 inches are counted as
"Light-duty Vehicles -Short Wheelbase". Passenger vehicles with a Wheelbase greater than 121 inches are counted as "Light-duty Vehicles -
Long Wheelbase". This change in vehicle classification has moved some smaller trucks and sport utility vehicles from the light truck category to
the passenger vehicle category in this emission inventory. These changes are reflected in a large drop in light-truck emissions between 2006 and
2007.
A-158 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Greenhouse gas emissions from aircraft decreased 23 percent between 1990 and 2012. Emissions from military
aircraft decreased 65 percent between 1990 and 2012. Commercial aircraft emissions rose 27 percent between 1990 and
2007 then dropped 19 percent from 2007 to 2012, a change of approximately 3.1 percent between 1990 and 2012.
Non-transportation mobile sources, such as construction/mining equipment, agricultural equipment, and
industrial/commercial equipment, emitted approximately 209.8 Tg CC>2 Eq. in 2012. Together, these sources emitted more
greenhouse gases than ships and boats, and rail combined. Emissions from non-transportation mobile sources increased
rapidly, growing approximately 63 percent between 1990 and 2012. CH4 and N2(D emissions from these sources are
included in the "Mobile Combustion" section and CC>2 emissions are included in the relevant economic sectors.
Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by Gas
Table A- 114 presents estimates of greenhouse gas emissions from transportation and other mobile sources
broken down by greenhouse gas. As this table shows, CC>2 accounts for the vast majority of transportation greenhouse gas
emissions (approximately 96 percent in 2012). Emissions of CCh from transportation and mobile sources increased by
323.1 Tg CC>2 Eq. between 1990 and 2012. In contrast, the combined emissions of CH4 and N2O decreased by 30.4 Tg
CC>2 Eq. over the same period, due largely to the introduction of control technologies designed to reduce criteria pollutant
emissions.56 Meanwhile, HFC emissions from mobile air conditioners and refrigerated transport increased from virtually
no emissions in 1990 to 72.9 Tg CCh Eq. in 2012 as these chemicals were phased in as substitutes for ozone depleting
substances. It should be noted, however, that the ozone depleting substances that HFCs replaced are also powerful
greenhouse gases, but are not included in national greenhouse gas inventories due to their mandated phase out.
Greenhouse Gas Emissions from Freight and Passenger Transportation
Table A- 115 and Table A- 116 present greenhouse gas estimates from transportation, broken down into the
passenger and freight categories. Passenger modes include light-duty vehicles, buses, passenger rail, aircraft (general
aviation and commercial aircraft), recreational boats, and mobile air conditioners, and are illustrated in Table A- 115.
Freight modes include medium- and heavy-duty trucks, freight rail, refrigerated transport, waterborne freight vessels,
pipelines, and commercial aircraft and are illustrated in Table A- 116. Commercial aircraft do carry some freight, in
addition to passengers, and for this Inventory, the emissions have been split between passenger and freight transportation.
(In previous Inventories, all commercial aircraft emissions were considered passenger transportation.) The amount of
commercial aircraft emissions to allocate to the passenger and freight categories was calculated using BTS data on freight
shipped by commercial aircraft, and the total number of passengers enplaned. Each passenger was considered to weigh an
average of 150 pounds, with a luggage weight of 50 pounds. The total freight weight and total passenger weight carried
were used to determine percent shares which were used to split the total commercial aircraft emissions estimates. The
remaining transportation and mobile emissions were from sources not considered to be either freight or passenger modes
(e.g., construction/mining and agricultural equipment, lubricants).
The estimates in these tables are derived from the estimates presented in Table A- 113. In addition, estimates of
fuel consumption from DOE (1993 through 2013) were used to allocate rail emissions between passenger and freight
categories.
In 2012, passenger transportation modes emitted 1,296.OTg CC>2 Eq., while freight transportation modes emitted
524.5 Tg CC>2 Eq. Between 1990 and 2012, the percentage growth of greenhouse gas emissions from freight sources was
49 percent, while emissions from passenger sources grew by 12 percent. This difference in growth is due largely to the
rapid increase in emissions associated with medium- and heavy-duty trucks.
The decline in CFC emissions is not captured in the official transportation estimates.
A-159
-------
Table A-113: Total U.S. Greenhouse Gas Emissions from Transportation and Mobile Sources [Tg Clh Eq.l
Mode /Vehicle Type/
Fuel Type
Transportation Total3
On-Road Vehicles
Passenger Cars
Gasoline
Diesel
AFVs
MFCs from Mobile AC
Light-Duty Trucks
Gasoline
Diesel
AFVs
MFCs from Mobile AC
Medium- and Heavy-
Duty Trucks
Gasoline
Diesel
AFVs
MFCs from
Refrigerated
Transport
Buses
Gasoline
Diesel
AFVs
MFCs from Comfort
Cooling
Motorcycles
Gasoline
Aircraft
General Aviation
Aircraft
Jet Fuel
Aviation Gasoline
Commercial Aircraft
Jet Fuel
Military Aircraft
Jet Fuel
Ships and Boats'1
Gasoline
Distillate Fuel
Residual Fuel
MFCs from Refrigerated
Transport
Rail
Distillate Fuel
Electricity
1990
1,556.3
1,235.2
657.4
649.4
7.9
+
+
336.6
324.5
11.5
0.6
+
231.1
39.5
190.7
0.9
+
8.4
0.4
8.0
+
+
1.8
1.8
189.2
43.0
39.8
3.2
110.9
110.9
35.3
35.3
45.1
12.6
9.6
22.9
+
39.0
35.8
3.1
1995
1700.4
1371.9
646.0
627.9
7.9
0.1
10.1
437.1
414.6
14.9
0.5
7.1
277.8
36.8
238.6
0.6
1.7
9.2
0.4
8.7
0.1
0.0
1.8
1.8
176.7
35.8
33.0
2.8
116.4
116.4
24.5
24.5
58.6
14.1
14.9
29.5
0.0
43.7
40.0
3.1
2000
1933.6
1577.8
696.6
667.3
3.7
0.0
25.6
513.5
469.8
20.1
0.5
23.1
354.6
37.0
309.9
0.3
7.4
11.2
0.4
10.2
0.5
0.1
1.9
1.9
199.4
35.9
33.4
2.6
140.7
140.7
22.9
22.9
61.0
10.0
17.1
33.8
0.1
48.1
42.5
3.5
2001
1920.2
1589.5
703.2
671.6
3.7
0.0
27.9
520.4
473.2
20.8
0.6
25.8
353.8
36.1
309.3
0.4
8.1
10.3
0.4
9.3
0.5
0.2
1.7
1.7
194.0
43.7
41.2
2.5
125.8
125.8
24.5
24.5
42.7
14.6
15.8
12.2
0.1
48.6
42.6
3.7
2002
1961.0
1628.4
716.9
683.9
3.7
0.0
29.3
531.6
481.4
21.9
0.7
27.6
368.1
36.5
322.6
0.4
8.7
10.0
0.3
8.8
0.6
0.2
1.7
1.7
189.4
45.1
42.7
2.4
122.5
122.5
21.9
21.9
47.6
14.6
15.4
17.4
0.2
48.2
42.3
3.5
2003
1953.7
1641.1
695.6
661.3
4.2
0.0
30.0
567.1
509.7
27.2
0.8
29.4
365.9
31.6
323.8
0.5
10.0
10.8
0.3
9.5
0.7
0.2
1.7
1.7
183.1
36.9
34.7
2.1
124.1
124.1
22.2
22.2
37.3
14.4
15.3
7.6
0.0
49.5
43.2
4.3
2004
2002.0
1677.5
693.1
658.0
4.3
0.0
30.8
589.9
528.9
29.0
0.9
31.2
377.7
31.9
334.7
0.4
10.7
15.0
0.5
13.5
0.9
0.2
1.8
1.8
190.7
41.9
39.7
2.2
126.0
126.0
22.7
22.7
40.1
14.4
11.5
14.2
0.0
52.3
45.6
4.6
2005
2022.0
1687.7
712.6
676.9
4.2
0.0
31.4
553.1
493.0
25.9
1.3
32.8
408.4
35.8
361.0
0.5
11.1
12.1
0.4
10.6
0.9
0.2
1.7
1.7
193.7
40.1
37.6
2.5
134.0
134.0
19.5
19.5
45.2
14.2
11.4
19.6
0.0
53.0
46.0
4.8
2006
2019.8
1687.7
688.0
651.6
4.1
0.0
32.2
566.9
504.7
26.8
1.2
34.2
418.6
36.3
370.4
0.6
11.4
12.3
0.4
10.8
0.8
0.3
1.9
1.9
186.4
30.1
27.7
2.4
138.3
138.3
18.0
18.0
48.4
14.1
10.9
23.4
0.0
55.1
48.3
4.6
2007
2031.5
1694.1
855.4
818.6
4.1
0.0
32.6
371.7
321.7
13.6
1.0
35.4
444.7
47.6
385.0
0.5
11.5
18.0
0.7
15.9
1.0
0.3
4.3
4.3
183.4
24.4
22.2
2.2
141.0
141.0
18.0
18.0
55.2
14.0
11.7
29.5
0.0
54.4
47.0
5.1
2008
1939.9
1621.6
817.9
781.2
3.7
0.0
32.9
354.8
304.4
12.1
1.8
36.4
427.0
48.1
366.5
0.8
11.6
17.4
0.8
15.2
1.1
0.4
4.5
4.5
176.7
30.5
28.5
2.0
128.5
128.5
17.7
17.7
45.9
13.6
11.5
20.7
0.0
50.7
43.6
4.7
2009
1866.9
1581.4
811.5
775.7
3.6
0.0
32.1
359.9
309.5
12.1
1.2
37.0
389.2
44.3
332.6
0.7
11.6
16.5
0.8
14.1
1.2
0.4
4.3
4.3
157.5
21.2
19.4
1.9
120.7
120.7
15.6
15.6
39.3
13.5
11.6
14.2
0.0
43.4
36.6
4.5
2010
1880.9
1587.9
805.8
771.7
3.8
0.0
30.4
359.1
308.6
12.6
1.3
36.5
402.9
44.2
346.3
0.7
11.6
16.3
0.8
14.1
1.1
0.4
3.8
3.8
154.8
26.7
24.8
1.9
114.4
114.4
13.7
13.7
45.3
13.3
11.3
20.8
0.0
46.3
39.4
4.5
2011
1856.4
1564.6
798.0
766.2
4.1
0.0
27.6
343.1
293.5
13.2
1.5
34.9
402.4
40.7
349.3
0.8
11.7
17.5
0.8
15.2
1.1
0.4
3.7
3.7
149.9
22.5
20.6
1.9
115.7
115.7
11.7
11.7
47.0
13.1
14.2
19.7
0.0
48.0
41.4
4.3
2012
1841.0
1558.4
793.8
764.4
4.2
0.0
25.2
338.4
290.3
13.2
1.5
33.3
403.4
40.3
350.6
0.8
11.7
18.6
0.8
16.3
1.1
0.4
4.3
4.3
146.5
19.9
18.2
1.8
114.4
114.4
12.2
12.2
40.8
13.1
11.6
16.1
0.0
46.9
40.6
3.9
Percent
Change
1990-2012
18%
26%
21%
18%
-47%
421%
NA
1%
-11%
15%
164%
NA
75%
2%
84%
-12%
NA
122%
131%
103%
40484%
NA
141%
141%
-23%
-54%
-54%
-44%
3%
3%
-65%
-65%
-10%
4%
20%
-30%
NA
20%
13%
27%
A-160 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Other Emissions from
Rail Electricity Use
HFCs from Comfort
Cooling
HFCs from Refrigerated
Transport
Pipelines'
Natural Gas
Other Transportation
Lubricants
Non-Transportation
Mobile Total
Agricultural Equipment'1
Gasoline
Diesel
Construction/ Mining
Equipment6
Gasoline
Diesel
Other Equipment'
Gasoline
Diesel
Transportation and Non-
Transportation Mobile
Total
0.1
+
+
36.0
36.0
11.8
11.8
128.8
31.4
7.3
24.1
42.4
4.4
38.0
55.0
40.3
14.7
1,685.1
0.1
0.0
0.5
38.2
38.2
11.3
11.3
146.8
37.0
8.3
28.7
49.4
4.0
45.4
60.4
42.6
17.8
1847.1
+
0.0
2.0
35.2
35.2
12.1
12.1
158.3
39.2
5.8
33.4
55.8
3.1
52.7
63.4
42.5
20.9
2091.9
+
0.0
2.2
34.4
34.4
11.1
11.1
172.3
41.4
7.1
34.3
60.1
5.8
54.2
70.9
49.3
21.5
2092.5
+
0.0
2.4
36.4
36.4
10.9
10.9
177.0
42.5
7.4
35.1
61.8
6.1
55.7
72.7
50.5
22.2
2137.9
+
0.0
1.9
32.5
32.5
10.1
10.1
181.7
43.6
7.6
36.0
63.6
6.4
57.2
74.5
51.7
22.9
2135.4
0.1
0.0
2.1
31.1
31.1
10.2
10.2
188.7
46.6
9.8
36.8
65.4
6.7
58.8
76.7
53.1
23.5
2190.7
0.1
0.0
2.1
32.2
32.2
10.2
10.2
190.7
47.3
9.6
37.7
66.5
6.2
60.3
76.9
52.7
24.2
2212.7
0.1
0.0
2.2
32.3
32.3
9.9
9.9
195.8
49.6
11.0
38.6
67.9
6.2
61.8
78.3
53.4
24.9
2215.6
0.1
0.0
2.2
34.2
34.2
10.2
10.2
194.8
49.0
9.6
39.4
68.4
5.1
63.3
77.4
51.8
25.6
2226.3
+
0.0
2.2
35.6
35.6
9.5
9.5
194.2
45.9
5.7
40.3
69.9
5.2
64.8
78.4
52.2
26.2
2134.2
+
0.0
2.2
36.7
36.7
8.5
8.5
197.7
47.2
6.1
41.1
71.2
5.0
66.3
79.3
52.4
26.9
2064.7
+
0.0
2.3
37.1
37.1
9.5
9.5
204.3
48.2
6.2
41.9
73.6
5.9
67.8
82.5
54.9
27.6
2085.2
+
0.0
2.3
37.8
37.8
9.0
9.0
207.0
50.0
7.2
42.8
74.8
5.5
69.3
82.3
54.0
28.3
2063.4
+
0.0
2.3
40.1
40.1
8.3
8.3
209.8
51.5
7.8
43.7
76.4
5.7
70.8
81.8
52.9
28.9
2050.7
-31%
NA
NA
11%
11%
-30%
-30%
63%
64%
7%
81%
80%
30%
86%
49%
31%
97%
22%
* Not including emissions from international bunker fuels.
b Fluctuations in emission estimates reflect data collection problems.
c Includes only C02 from natural gas used to power natural gas pipelines; does not include emissions from electricity use or non-C02 gases.
d Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
e Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
f "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that
are used off-road for commercial/industrial purposes.
+ Less than 0.05 Tg C02 Eq.
-Unreportedorzero
NA = Not Applicable, as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated.
Table A-114: Transportation and Mobile Source Emissions by Gas [Tg C0? Eq.l
C02
N20
CH4
HFC
Total
1990
1,636.4
44.0
4.6
+
1,685.0
1995
1,769.3
54.0
4.2
19.6
1,847.0
2000
1,977.1
53.1
3.3
58.4
2,091.9
2001
1 ,974.6
50.4
3.2
64.3
2,092.5
2002
2,020.3
46.4
2.8
68.3
2,137.9
2003
2,018.2
42.8
2.6
71.6
2,135.3
2004
2,072.9
40.2
2.5
75.0
2,190.6
2005
2,095.6
36.9
2.4
77.8
2,212.6
2006
2,099.3
33.8
2.3
80.2
2,215.5
2007
2,112.9
29.0
2.1
82.1
2,226.2
2008
2,023.1
25.5
1.9
83.6
2,134.1
2009
1 ,956.6
22.7
1.8
83.5
2,064.6
2010
1,981.4
20.7
1.8
81.3
2,085.2
2011
1 ,966.3
18.5
1.7
76.9
2,063.4
Percent Change
2012 1990-2012
1 ,959.5
16.5
1.7
72.9
2,050.7
20%
-62%
-63%
N/A
22%
-Unreportedorzero
NA = Not Applicable, as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated.
A-161
-------
Figure A-4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type,1990 to 2012 (Tg G02 Eq.)
^alm MaC i<= 5aj-c=
l3. la .and13= -=
• '•ted JT- and -teavy-D JC.- T-jc
-------
Note: Data from DOE (1993 through 2013) were used to disaggregate emissions from rail and buses. Emissions from MFCs have been included in these estimates.
Table A-116: Greenhouse Gas Emissions from Domestic Freight Transportation [Tg Clh Eq.l
By Mode
Trucking
Freight Rail
Ships and Other Boats
Pipelines3
Commercial Aircraft
Total
1990
231.1
34.5
30.6
36.0
19.2
351.5
1995
277.8
39.1
42.2
38.2
20.1
417.4
2000
354.6
42.8
48.3
35.2
24.4
505.3
2001
353.8
43.1
25.4
34.4
21.8
478.5
2002
368.1
43.1
30.1
36.4
21.2
499.0
2003
365.9
43.7
19.9
32.5
21.5
483.4
2004
377.7
46.2
22.7
31.1
21.1
498.8
2005
408.4
46.7
27.9
32.2
21.4
536.5
2006
418.6
49.0
31.1
32.3
21.8
552.8
2007
444.7
47.8
37.9
34.2
20.5
585.0
2008
427.0
44.4
28.9
35.6
18.0
553.9
2009
389.2
37.2
22.4
36.7
16.7
502.2
2010
402.9
40.0
28.5
37.1
16.3
524.8
2011
402.4
42.0
30.3
37.8
16.1
528.5
% Change
2012 1990-2012
403.4
41.2
24.0
40.1
15.8
524.5
75%
19%
-22%
11%
-18%
49%
a Pipelines reflect C02 emissions from natural gas powered pipelines transporting natural gas
Note: Data from DOE (1993 through 2013) were used to disaggregate emissions from rail and buses. Emissions from MFCs have been included in these estimates.
A-163
-------
3.3. Methodology for Estimating Emissions from Commercial Aircraft Jet Fuel
Consumption
IPCC Tier 3B Method: Commercial aircraft jet fuel burn and carbon dioxide (CCh) emissions estimates were
developed by the U.S. Federal Aviation Administration (FAA) using radar-informed data from the FAA Enhanced Traffic
Management System (ETMS) for 2000 through 2012 as modeled with the Aviation Environmental Design Tool (AEDT).
This bottom-up approach is built from modeling dynamic aircraft performance for each flight occurring within an
individual calendar year. The analysis incorporates data on the aircraft type, date, flight identifier, departure time, arrival
time, departure airport, arrival airport, ground delay at each airport, and real-world flight trajectories. To generate results
for a given flight within AEDT, the radar-informed aircraft data is correlated with engine and aircraft performance data to
calculate fuel burn and exhaust emissions. Information on exhaust emissions for in-production aircraft engines comes
from the International Civil Aviation Organization (ICAO) Aircraft Engine Emissions Databank (EDB). This bottom-up
approach is in accordance with the Tier 3B method from the 2006 IPCC Guidelines for National Greenhouse Gas
Inventories.
International Bunkers: The IPCC guidelines define international aviation (International Bunkers) as emissions
from flights that depart from one country and arrive in a different country. Bunker fuel emissions estimates for
commercial aircraft were developed for this report for 2000 through 2012 using the same radar-informed data modeled
with AEDT. Since this process builds estimates from flight-specific information, the emissions estimates for commercial
aircraft can include emissions associated with the U.S. territories (i.e., American Samoa, Guam, Puerto Rico, U.S. Virgin
Islands, Wake Island, and other U.S. Pacific Islands). However, to allow for the alignment of emissions estimates for
commercial aircraft with other data that is provided without the U.S. territories, this annex includes emissions estimates
for commercial aircraft both with and without the U.S. territories included.
Time Series and Analysis Update: The FAA incrementally improves the consistency, robustness, and fidelity
of the CO2 emissions modeling for commercial aircraft, which is the basis of the TierSB inventories presented in this
report. While the FAA does not anticipate significant changes to the AEDT model in the future, recommended
improvements are limited by budget and time constraints, as well as data availability. For instance, previous reports
included reported annual CC>2 emission estimates for 2000 through 2005 that were modeled using the FAA's System for
assessing Aviation's Global Emissions (SAGE). That tool and its capabilities were significantly improved after it was
incorporated and evolved into AEDT. For this report, the AEDT model was used to generate annual CO2 emission
estimates for 2000, 2005, 2010, 2011 and 2012 only. The reported annual CO2 emissions values for 2001 through 2004
were estimated from the previously reported SAGE data. Likewise, CC>2 emissions values for 2006 through 2009 were
estimated by interpolation to preserve trends from past reports.
The radar-informed method is not possible for 1990 through 1999 because radar data sets are not available for
years prior to 2000. Instead, the FAA applied a TierSB methodology by developing Official Airline Guide (OAG)
schedule-informed estimates modeled with AEDT and great circle trajectories for 1990, 2000 and 2010. The ratios
between the OAG schedule-informed and the radar-informed inventories for the years 2000 and 2010 were applied to the
1990 OAG scheduled-informed inventory to generate the best possible CO2 inventory estimate for commercial aircraft in
1990. The resultant 1990 CO2 inventory served as the reference for generating the additional 1991-1999 emissions
estimates, which were established using previously available trends.
Notes on Revised 1990 COi Emissions Inventory for Commercial Aircraft: In 2013, the 1990 inventory was
revised to achieve time series consistency. The observed change in 1990 emissions when compared to previous GHG
inventory reports (EPA GHG Sources and Sinks Report circa 2000, 2002, 2007, 2010) was purely due to using a
TierSB methodology, and not reflective of revised industry performance and should not be used to infer or evaluate such
performance.
To achieve time series consistency, the 1990 jet fuel burn was modeled with the latest AEDT version using great
circle trajectories and OAG schedule information. There are uncertainties associated with the modeled 1990 data that do
not exist for the modeled 2000 to 2012 data. Radar-based data is not available for 1990. The OAG schedule information
generally includes fewer carriers than radar information, and this will result in a different fleet mix, and in turn, different
CO2 emissions than would be quantified using a radar-based data set. For this reason, the FAA adjusted the OAG-
informed schedule for 1990 with a ratio based on radar-informed information. In addition, radar trajectories are also
generally longer than great circle trajectories. The revised 1990 CO2 emissions inventory now reflects only commercial
aircraft jet fuel consumption, while previous reports may have aggregated jet fuel sales data from non-commercial aircraft
into this category. Thus, it would be inappropriate to compare 1990 to future years for other than qualitative purposes.
A-164 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The revised 1990 commercial aircraft CC>2 emissions inventory is approximately 3 percent lower than the 2012
CC>2 emissions inventory. It is important to note that the distance flown increased 41percent over this twenty-three-year
period and that fuel burn and aviation activity trends over the past two decades indicate significant improvements in
commercial aviation's ability to provide increased service levels while using less fuel.57
Methane Emissions: Contributions of methane (CH/i) emissions from commercial aircraft are reported as zero.
Years of scientific measurement campaigns conducted at the exhaust exit plane of commercial aircraft gas turbine engines
have repeatedly indicated that CH4 emissions are consumed over the full mission flight envelope (Aircraft Emissions of
Methane and Nitrous Oxide during the Alternative Aviation Fuel Experiment, Santoni et al., Environ. Sci. Techno!., 2011,
45, 7075-7082). As a result, the U.S. Environmental Protection Agency published that "...methane is no longer
considered to be an emission from aircraft gas turbine engines burning Jet A at higher power settings and is, in fact,
consumed in net at these higher powers. In accordance with the following statements in the 2006 IPCC Guidelines
(IPCC 2006), the FAA does not calculate CH4 emissions for either the domestic or international bunker commercial
aircraft jet fuel emissions inventories. "Methane (CH^) may be emitted by gas turbines during idle and by older
technology engines, but recent data suggest that little or no CH4 is emitted by modern engines. " "Current scientific
understanding does not allow other gases (e.g., A^O and CT/^) to be included in calculation of cruise emissions. " (IPCC
1999).
Results: The graph and table below, four jet fuel burn values are reported for each calendar year. These values
are comprised of domestic and international fuel burn totals for the US 50 States and the US 50 States + Territories. Data
are presented for domestic defined as jet fuel burn from any commercial aircraft flight departing and landing in the US 50
States and for the US 50 States + Territories. The data presented as international is respective of the two different
domestic definitions, and represents flights departing from the specified domestic area and landing anywhere in the world
outside of that area.
Note that the graph and table present less fuel burn for the international US 50 States + Territories than for the
international US 50 States. This is because the flights between the 50 states and US Territories are "international" when
only the 50 states are defined as domestic, but they are "domestic" for the US 50 States + Territories definition.
Additional information on the AEDT modeling process is available at:
http://www.faa.gov/about/office_org/headquarters_offices/apl/research/models/
58 Recommended Best Practice for Quantifying Speciated Organic Gas Emissions from Aircraft Equipped with Turbofan,
Turbojet and Turboprop Engines, EPA-420-R-09-901, May 27,2009, http://www.epa.gov/otaq/aviation.htm.
A-165
-------
Figure A-5:Gommerical Aviation Fuel Burnforthe United States andTerrirtories
5.00E+10
4.50E+10
4.00E+10
3.50E+10 H
12 3.00E+10
2.50E+10
0) 2.00E+10
j_
1.50E+10
l.OOE+10 -
5.00E+09
O.OOE+00
3
CQ
L
1 1
| J
O
L 1
O
O
O
1
O
L
*
^1
Q
n
i
0
r 1
n
L I
X
Q
•
1 1
Q
\ j
Q
L
A
•
Q
7\
Q
*
^1
I
A
L I
*
"
VVVVVVVVVV V "P "V V 'V "P "V V "V 'V "V *P V
OStates+Territories (Domestic) DStates+Territories (International) States (Domestic) OStates (International)
Note: Hollow markers are estimates from data generated by prior tools and methods.
1990 is estimated using non-radar methods.
Table A- Ml: Commercial Aviation Fuel Burn for the United States and Territories
Year
iqqn
i yy\j
1995
1996
1997
1998
1999
onnn
£.\J\J\J
onn-i
L.\l\l 1
9(1(19
L\J\)L
2003
Region
Domestic US 50 States and US Territories
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
Domestic US 50 States and US Territories
Domestic US 50 States and US Territories
Domestic US 50 States and US Territories
Domestic US 50 States and US Territories
Domestic US 50 States and US Territories
Domestic US 50 States and US Territories
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
Domestic US 50 States and US Territories
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
Domestic US 50 States and US Territories
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
Domestic US 50 States and US Territories
Distance Flown Fuel Burn Fuel Burn
(M Gallon) (Tbtu)
4,057,195,988
599,486,893
3,984,482,217
617,671,849
N/A
N/A
N/A
N/A
N/A
5,994,679,944
1,309,565,963
5,891,481,028
1,331,784,289
5,360,977,447
1,171,130,679
5,268,687,772
1,191,000,288
5,219,345,344
1,140,190,481
5,129,493,877
1,159,535,153
5,288,138,079
11,568
3,155
11,287
3,228
12,136
12,492
12,937
12,601
13,726
14,672
6,040
14,349
6,117
13,121
5,402
12,832
5,470
12,774
5,259
12,493
5,326
12,942
1,562
426
1,524
436
1,638
1,686
1,747
1,701
1,853
1,981
815
1,937
826
1,771
729
1,732
739
1,725
710
1,687
719
1,747
Fuel Burn (Kg)
34,820,800,463
9,497,397,919
33,972,832,399
9,714,974,766
36,528,990,675
37,600,624,534
38,940,896,854
37,930,582,643
41,314,843,250
44,161,841,348
18,181,535,058
43,191,000,202
18,412,169,613
39,493,457,147
16,259,550,186
38,625,244,409
16,465,804,174
38,450,076,259
15,829,987,794
37,604,800,905
16,030,792,741
38,956,861,262
C02 (Tg)
109.9
30.0
107.2
30.7
115.2
118.6
122.9
119.7
130.3
139.3
57.4
136.3
58.1
124.6
51.3
121.9
51.9
121.3
49.9
118.6
50.6
122.9
A-166 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
9(1(14
tUUt
2005
9006
£. \J\J\J
0(1(17
L.\l\l 1
9(i(io
i.UUO
2009
0(1-1(1
i.U 1 U
9(111
i.U 1 1
9(119
L\) 1 £
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
Domestic US 50 States and US Territories
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
Domestic US 50 States and US Territories
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
Domestic US 50 States and US Territories
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
Domestic US 50 States and US Territories
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
Domestic US 50 States and US Territories
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
Domestic US 50 States and US Territories
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
Domestic US 50 States and US Territories
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
Domestic US 50 States and US Territories
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
Domestic US 50 States and US Territories
International US 50 States and US Territories
Domestic US 50 States
International US 50 States
1,155,218,577
5,197,102,340
1,174,818,219
5,371,498,689
1,173,429,093
5,279,027,890
1,193,337,698
6,476,007,697
1,373,543,928
6,370,544,998
1,397,051,323
5,894,323,482
1,287,642,623
5,792,852,211
1,309,488,994
6,009,247,818
1,312,748,383
5,905,798,114
1,335,020,703
5,475,092,456
1,196,059,638
5,380,838,282
1,216,352,196
5,143,268,671
1,123,571,175
5,054,726,871
1,142,633,881
5,652,264,576
1,474,839,733
5,554,043,585
1,497,606,695
5,767,378,664
1,576,982,962
5,673,689,481
1,596,797,398
5,735,605,432
1,619,012,587
5,636,910,529
1,637,917,110
5,328
12,658
5,396
13,146
5,412
12,857
5,481
13,976
5,858
13,654
5,936
14,426
5,939
14,109
6,015
14,707
6,055
14,384
6,132
13,400
5,517
13,105
5,587
12,588
5,182
12,311
5,248
11,931
6,044
11,667
6,113
12,067
6,496
11,823
6,554
11,932
6,464
11,672
6,507
719
1,709
728
1,775
731
1,736
740
1,887
791
1,843
801
1,948
802
1,905
812
1,986
817
1,942
828
1,809
745
1,769
754
1,699
700
1,662
709
1,611
816
1,575
825
1,629
877
1,596
885
1,611
873
1,576
879
16,038,632,384
38,100,444,893
16,242,084,008
39,570,965,441
16,291,460,535
38,701,048,784
16,498,119,309
42,067,562,737
17,633,508,081
41,098,359,387
17,868,972,965
43,422,531,461
17,877,159,421
42,467,943,091
18,103,932,940
44,269,160,525
18,225,718,619
43,295,960,105
18,456,913,646
40,334,124,033
16,605,654,741
39,447,430,318
16,816,299,099
37,889,631,668
15,599,251,424
37,056,676,966
15,797,129,457
35,912,723,830
18,192,953,916
35,116,863,245
18,398,996,825
36,321,170,730
19,551,631,939
35,588,754,827
19,727,043,614
35,915,745,616
19,457,378,739
35,132,961,140
19,587,140,347
50.6
120.2
51.2
124.8
51.4
122.1
52.1
132.7
55.6
129.7
56.4
137.0
56.4
134.0
57.1
139.7
57.5
136.6
58.2
127.3
52.4
124.5
53.1
119.5
49.2
116.9
49.8
113.3
57.4
110.8
58.0
114.6
61.7
112.3
62.2
113.3
61.4
110.8
61.8
'Estimates for these years were derived from previously reported tools and methods
A-167
-------
3.4. Methodology for Estimating ChU Emissions from Coal Mining
The methodology for estimating CH4 emissions from coal mining consists of two steps. The first step is to
estimate emissions from underground mines. There are two sources of underground mine emissions: ventilation systems
and degasification systems. These emissions are estimated on a mine-by-mine basis and then are summed to determine
total emissions. The second step of the analysis involves estimating CH4 emissions from surface mines and post-mining
activities. In contrast to the methodology for underground mines, which uses mine-specific data, the methodology for
estimating emissions from surface mines and post-mining activities consists of multiplying basin-specific coal production
by basin-specific gas content and an emission factor.
Step 1: Estimate ChU Liberated and ChU Emitted from Underground Mines
Underground mines generate CH4 from ventilation systems and from degasification systems. Some mines
recover and use the generated CH4, thereby reducing emissions to the atmosphere. Total CH4 emitted from underground
mines equals the CH4 liberated from ventilation systems, plus the CH4 liberated from degasification systems, minus CH4
recovered and used.
Step 1.1: Estimate CM Liberated from Ventilation Systems
All coal mines with detectable CH4 emissions59 use ventilation systems to ensure that CH4 levels remain within
safe concentrations. Many coal mines do not have detectable levels of CH4, while others emit several million cubic feet
per day (MMCFD) from their ventilation systems. On a quarterly basis, the U.S. Mine Safety and Health Administration
(MSHA) measures CH4 emissions levels at underground mines. MSHA maintains a database of measurement data from
all underground mines with detectable levels of CH4 in their ventilation air (MSHA 2013). Based on the four quarterly
measurements, MSHA estimates average daily CH4 liberated at each of the underground mines with detectable emissions.
For the years 1990 through 1996, 1998 through 2006, and 2008 through 2012, MSHA emissions data were
obtained for a large but incomplete subset of all mines with detectable emissions. This subset includes mines emitting at
least 0.1 MMCFD for some years and at least 0.5 MMCFD for other years, as shown in Table A-118. Well over 90
percent of all ventilation emissions were concentrated in these subsets of approximately 125-150 mines. For 1997 and
2007, the complete MSHA databases for all 586 mines (in 1997) and 730 mines (in 2007) with detectable CH4 emissions
were obtained. These mines were assumed to account for 100 percent of CH4 liberated from underground mines. Using
the complete database from 1997, the proportion of total emissions accounted for by mines emitting less than 0.1 MMCFD
or 0.5 MMCFD was estimated (see Table A-118). The proportion was then applied to the years 1990 through 2006 to
account for the less than 5 percent of ventilation emissions coming from mines without MSHA data.
For 1990 through 1999, average daily CH4 emissions were multiplied by the number of days in the year (i.e., coal
mine assumed in operation for all four quarters) to determine the annual emissions for each mine. For 2000 through 2011,
MSHA provided quarterly emissions. The average daily CH4 emissions were multiplied by the number of days
corresponding to the number of quarters the mine vent was operating. For example, if the mine vent was operational in
one out of the four quarters, the average daily CH4 emissions were multiplied by 92 days. Total ventilation emissions for a
particular year were estimated by summing emissions from individual mines.
Since 2009, two coal mines have destroyed a portion of their CH4 emissions from ventilation systems using
thermal oxidation technology. The amount of methane destroyed through these two projects was determined through
publicly-available emission reduction project information (CAR 2013).
59 MSHA records coal mine methane readings with concentrations of greater than 50 ppm (parts per million) methane. Readings below
this threshold are considered non-detectable.
A-168 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-118: Mine-Specific Data Used to Estimate Ventilation Emissions
Year Individual Mine Data Used
1990 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
1991 1990 Emissions Factors Used Instead of Mine-Specific Data
1992 1990 Emissions Factors Used Instead of Mine-Specific Data
1993 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
1994 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
1995 All Mines Emitting at Least 0.5 MMCFD (Assumed to Account for 94.1% of Total)*
1996 All Mines Emitting at Least 0.5 MMCFD (Assumed to Account for 94.1% of Total)*
1997 All Mines with Detectable Emissions (Assumed to Account for 100% of Total)
1998 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
1999 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2000 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2001 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2002 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2003 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2004 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2005 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2006 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2007 All Mines with Detectable Emissions (Assumed to Account for 100% of Total)
2008 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)**
2009 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)**
2010 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)**
2011 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)**
2012 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)**
* Factor derived from a complete set of individual mine data collected for 1997.
** Factor derived from a complete set of individual mine data collected for 2007.
Step 1.2: Estimate Cfa Liberated from Degasification Systems
Coal mines use several different types of degasification systems to remove CH4, including vertical wells and
horizontal boreholes to recover CH4 prior to mining of the coal seam. Gob wells and cross-measure boreholes recover
CH4 from the overburden (i.e., gob area) after mining of the seam (primarily in longwall mines).
MSHA collects information about the presence and type of degasification systems in some mines, but does not
collect quantitative data on the amount of CH4 liberated. Thus, degasification emissions were estimated on a mine-by-
mine basis based on other sources of available data. For those mines that sell CH4 recovered from degasification systems
to a pipeline, gas sales records were used to estimate CH4 liberated from degasification systems (see Step 1.3). For those
mines venting CFLt from degasification wells, data reported by mines to EPA's Greenhouse Gas Reporting Program
(GHGRP) were used. Since 2011, EPA's GHGRP has required underground coal mines liberating greater than 36,500,000
actual cubic feet of CFU per year (about 14,700 metric tons CC>2 Eq.) to report their emissions directly to EPA.
Step 1.3: Estimate CM Recovered from Degasification Systems and Utilized (Emissions Avoided)
In 2012, sixteen active coal mines had CFU recovery and use projects, of which fourteen mines sold the
recovered CFU to a pipeline. One of the mines that sold gas to a pipeline also used CFU to fuel a thermal coal dryer. One
mine used recovered CFU for electrical power generation, and another mine used recovered CFU to heat mine ventilation
air. For mines that utilize CFU on-site, either the GHGRP (EPA 2013) or project-specific information (CAR 2013) was
used to estimate CFLi liberated from degasification systems.
In order to calculate emissions avoided from pipeline sales, information was needed regarding the amount of gas
recovered and the number of years in advance of mining that wells were drilled. Alabama and West Virginia state
agencies provided gas sales data (GSA 2013; WVGES 2013), which were used to estimate emissions avoided for these
projects. Additionally, coal mine operators provided information on eligible pre-drainage wells drilled in advance of
mining (JWR 2010, 2013). Emissions avoided were attributed to the year in which the coal seam was mined. For
example, if a coal mine recovered and sold CFU using a vertical well drilled five years in advance of mining, the emissions
avoided associated with those gas sales (cumulative production) were attributed to the well at the time it was mined
through (e.g., five years of gas production). The coal mine operators with the largest CtLt recovery and use projects
provided this information (Consol 2013; JWR 2010, 2013), which was then used to estimate emissions avoided for a
particular year.
A-169
-------
Step 2: Estimate ChU Emitted from Surface Mines and Post-Mining Activities
Mine-specific data were not available for estimating CH4 emissions from surface coal mines or for post-mining
activities. For surface mines, basin-specific coal production was multiplied by basin-specific gas contents and a 150
percent emission factor (to account for CFLi from over- and under-burden) to estimate CFLi emissions. This emission factor
was revised downward in 2012 from 200 percent based on more recent studies (King 1994; Saghafi 2013). The 150
percent emission factor was applied to all inventory years since 1990, retroactively. For post-mining activities, basin-
specific coal production was multiplied by basin-specific gas contents and a 32.5 percent emission factor accounting for
CFLi desorption during coal transportation and storage (Greedy 1993).
Step 2.1: Define the Geographic Resolution of the Analysis and Collect Coal Production Data
The first step in estimating CFU emissions from surface mining and post-mining activities was to define the
geographic resolution of the analysis and to collect coal production data at that level of resolution. The analysis was
conducted by coal basin as defined in Table A-l 19, which presents coal basin definitions by basin and by state.
The Energy Information Administration's (EIA) Annual Coal Report (2013) includes state- and county-specific
underground and surface coal production by year. To calculate production by basin, the state level data were grouped into
coal basins using the basin definitions listed in Table A-l 19. For two states —West Virginia and Kentucky—county-level
production data was used for the basin assignments because coal production occurred in geologically distinct coal basins
within these states. Table A-120 presents the coal production data aggregated by basin.
Step 2.2: Estimate Emissions Factors for Each Emissions Type
Emission factors for surface-mined coal were developed from the in situ CFLi content of the surface coal in each
basin. Based on analyses conducted in Canada and Australia on coals similar to those present in the U.S. (King 1994;
Saghafi 2013), the surface mining emission factor used was conservatively estimated to be 150 percent of the in situ CFLi
content of the basin. Furthermore, the post-mining emission factors used were estimated to be 25 to 40 percent of the
average in situ CFLi content in the basin. For this analysis, the post-mining emission factor was determined to be 32.5
percent of the in situ CFU content in the basin. Table A-121 presents the average in situ content for each basin, along with
the resulting emission factor estimates.
Step 2.3: Estimate CH< Emitted
The total amount of CFU emitted from surface mines and post-mining activities was calculated by multiplying the
coal production in each basin by the appropriate emission factors.
Table A-l 19 lists each of the major coal mine basins in the U.S. and the states in which they are located. As
shown in Figure A-6, several coal basins span several states. Table A-120 shows annual underground, surface, and total
coal production (in short tons) for each coal basin. Table A-121 shows the surface, post-surface, and post-underground
emission factors used for estimating CFU emissions for each of the categories. Table A-122 presents annual estimates of
CFLi emissions for ventilation and degasification systems, and CFU used and emitted by underground coal mines. Table A-
123 presents annual estimates of total CFU emissions from underground, post-underground, surface, and post-surface
activities. Table A-124 provides the total net CFU emissions by state.
Table A-119: Coal Basin Definitions by Basin and by State
Basin
Northern Appalachian Basin
Central Appalachian Basin
Warrior Basin
Illinois Basin
South West and Rockies Basin
North Great Plains Basin
West Interior Basin
Northwest Basin
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Illinois
States
Maryland, Ohio, Pennsylvania, West Virginia North
Kentucky East, Tennessee, Virginia, West Virginia South
Alabama, Mississippi
Illinois, Indiana, Kentucky West
Arizona, California, Colorado, New Mexico, Utah
Montana, North Dakota, Wyoming
Arkansas, Iowa, Kansas, Louisiana, Missouri, Oklahoma, Texas
Alaska, Washington
Basin
Warrior Basin
Northwest Basin
South West and Rockies Basin
West Interior Basin
South West and Rockies Basin
South West and Rockies Basin
Illinois Basin
A-170 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Indiana
Iowa
Kansas
Kentucky East
Kentucky West
Louisiana
Maryland
Mississippi
Missouri
Montana
New Mexico
North Dakota
Ohio
Oklahoma
Pennsylvania.
Tennessee
Texas
Utah
Virginia
Washington
West Virginia South
West Virginia North
Wyoming
Illinois Basin
West Interior Basin
West Interior Basin
Central Appalachian Basin
Illinois Basin
West Interior Basin
Northern Appalachian Basin
Warrior Basin
West Interior Basin
North Great Plains Basin
South West and Rockies Basin
North Great Plains Basin
Northern Appalachian Basin
West Interior Basin
Northern Appalachian Basin
Central Appalachian Basin
West Interior Basin
South West and Rockies Basin
Central Appalachian Basin
Northwest Basin
Central Appalachian Basin
Northern Appalachian Basin
North Great Plains Basin
Figure A-6: Locations of U.S Goal Basins
Coalbed Methane Fields, Lower 48 States j;l /
*Coos Bay
< Field
•i Powddr River
' "f Big Hofn"4r»-HSsin
WM River Basin. Basin <5»Bas|1
Wyoming ' >*
, n f ^
\ Michigan '
' *«*f ,-r-*^ ( ¥
-.- - ^^^ \ Annn lsi-J-ii?tri •> \
Coalbed Methane Fields
Coal Basins, Regions & Field
Mile
• "' V
Kalr^V 0 100200300400
1 J
N
A
(iia
Source Energy Information Administration based on data from USGS and various published studies
Updated: April 8. 2009
A-171
-------
Table A-120: Annual Goal Production (Thousand Short Tons)
Basin
Underground
Coal
Production
N. Appalachia
Cent. Appalachia
Warrior
Illinois
S. West/Rockies
N. Great Plains
West Interior
Northwest
Surface Coal
Production
N. Appalachia
Cent. Appalachia
Warrior
Illinois
S. West/Rockies
N. Great Plains
West Interior
Northwest
Total Coal
Production
N. Appalachia
Cent. Appalachia
Warrior
Illinois
S. West/Rockies
N. Great Plains
West Interior
Northwest
1990
423,556 1
103,865
198,412
17,531
69,167
32,754
1,722 1
105
+
602,753 1
60,761
94,343
11,413
72,000
43,863
249,356
64,310
6,707 •
1,026,309
164,626
292,755
28,944
141,167
76,617
251,078
64,415
6,707 •
2005
368,611 1
111,151
123,083
13,295
59,180
60,865
572 1
465
+ •
762,191 1
28,873
112,222
11,599
33,702
42,756
474,056
52,263
6,720 •
1,130,802 1
140,024
235,305
24,894
92,882
103,621
474,628
52,728
6,720 •
2008
357,074
105,228
114,998
12,281
64,609
55,781
3,669
508
+
813,321
30,413
118,962
11,172
34,266
34,283
538,387
44,361
1,477
1,170,395
135,641
233,960
23,453
98,875
90,064
542,056
44,869
1,477
2009
332,061
99,629
98,689
11,505
67,186
50,416
4,248
388
+
740,175
26,552
97,778
10,731
34,837
32,167
496,290
39,960
1,860
1,072,236
126,181
196,467
22,236
102,023
82,583
500,538
40,348
1,860
2010
337,155
103,109
96,354
12,513
72,178
44,368
8,208
425
+
764,709
26,082
89,788
11,406
32,911
28,889
507,995
46,136
2,151
1,101,864
129,191
186,142
23,919
105,089
73,257
516,203
46,561
2,151
2011
345,607
105,752
94,034
10,879
81,089
45,139
8,179
535
+
754,871
26,382
90,778
10,939
34,943
31,432
502,734
55,514
2,149
1,100,478
132,134
184,812
21,818
116,032
76,571
510,913
56,049
2,149
2012
342,387
103,408
78,067
12,570
92,500
45,052
10,345
445
+
672,748
21,411
69,721
9,705
34,771
30,475
455,320
49,293
2,052
1,015,135
124,819
147,788
22,275
127,271
75,527
465,665
49,738
2,052
Source for 1990-2012 data: EIA (1990 through 2012), Annual Coal Report.
spreadsheet for the 2012 Annual Coal Report.
Note: Totals may not sum due to independent rounding.
U.S. Department of Energy, Washington, DC, Tablet Source for 2012 data:
A-172 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-121: Coal Underground, Surface, and Post-Mining Clh Emission Factors [ft3 per Short Tonl
Surface Average Underground Average Surface Mine Post-Mining Post Mining
Basin in situ Content In situ Content Factors Surface Factors Underground
Northern Appalachia
Central Appalachia (WV)
Central Appalachia (VA)
Central Appalachia (E KY)
Warrior
Illinois
Rockies (Piceance Basin)
Rockies (Uinta Basin)
Rockies (San Juan Basin)
Rockies (Green River Basin)
Rockies (Raton Basin)
N. Great Plains (WY, MT)
N. Great Plains (ND)
West Interior (Forest City, Cherokee Basins)
West Interior (Arkoma Basin)
West Interior (Gulf Coast Basin)
Northwest (AK)
Northwest (WA)
59.5
24.9
24.9
24.9
30.7
34.3
33.1
16.0
7.3
33.1
33.1
20.0
5.6
34.3
74.5
11.0
16.0
16.0
138.4
136.8
399.1
61.4
266.7
64.3
196.4
99.4
104.8
247.2
127.9
15.8
15.8
64.3
331.2
127.9
160.0
47.3
119.0
49.8
49.8
49.8
61.4
68.6
66.2
32.0
14.6
66.2
66.2
40.0
11.2
68.6
149.0
22.0
32.0
32.0
19.3
8.1
8.1
8.1
10.0
11.1
10.8
5.2
2.4
10.8
10.8
6.5
1.8
11.1
24.2
3.6
1.8
5.2
45.0
44.5
129.7
20.0
86.7
20.9
63.8
32.3
34.1
80.3
41.6
5.1
5.1
20.9
107.6
41.6
52.0
15.4
Sources: 1986 USBM Circular 9067, Results of the Direct Method Determination of the Gas Contents of U. S. Coal Basins, 1983 U.S. DOE Report (DOE/METC/83-76),
Methane Recovery from Coalbeds: A Potential Energy Source, 1986-88 Gas Research Institute Topical Reports, A Geologic Assessment of Natural Gas from Coal Seams;
Surface Mines Emissions Assessment, U.S. EPA Draft Report, November 2005.
Table A-122: Underground Coal Mining Clh Emissions [Billion Cubic Feetl
Activity 1990 2005
2008 2009 2010 2011
2012
Ventilation Output
Adjustment Factor for Mine Data*
Adjusted Ventilation Output
Degasification System Liberated
Total Underground Liberated
Recovered & Used
Total
112
97.8%
114
54
168
(14)
154
75
1 97.8%
77
48 1
124
(37)
87
100
99.0%
101
49
150
(40)
110
114
99.0%
115
49
163
(40)
123
117
99%
118
58
177
(49)
128
97
99%
98
48
147
(42)
104
90
99%
91
45
137
(38)
98
* Refer to Table A-118.
Note: Totals may not sum due to independent rounding
Table A-123: Total Coal Mining CH* Emissions [Billion Cubic Feetl
Activity
Underground Mining
Surface Mining
Post-Mining (Underground)
Post-Mining (Surface)
Total
1990
154
22
19
5
200
2005
87
25
16
5
132
2008
110
27
15
6
157
2009
123
24
14
5
166
2010
128
24
14
5
171
2011
104
24
14
5
148
2012
98
21
14
5
138
Note: Totals may not sum due to independent rounding.
Table A-124: Total Coal Mining CH* Emissions by State [Million Cubic Feetl
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maryland
1990
32,097
50
151 1
5 1
1
10,187
10,180
2,232
24
45
10,018
64
474 |
2005
15,789
42
161 1
+ 1
13,441
6,488
3,303 1
11 1
6,898
84 1
361 |
2008
20,992
43
107
237
12,871
7,568
5,047
14
9,986
77
263
2009
22,119
54
100
119
13,999
7,231
5,763
12
12,035
73
219
2010
21,377
63
103
130
16,470
8,622
5,938
8
12,303
79
238
2011
18,530
63
108
348
11,187
7,579
6,203
2
10,592
168
263
2012
18,129
60
100
391
9,305
9,763
7,374
1
7,993
80
197
A-173
-------
Mississippi
Missouri
Montana
New Mexico
North Dakota
Ohio
Oklahoma
Pennsylvania
Tennessee
Texas
Utah
Virginia
Washington
West Virginia
Wyoming
Total
-
166 1
1,373 1
363 1
299
4,406
226
21,864
276 1
1,119 1
3,587
46,041
146
48,335
6,671 •
200,399
199
3 1
1,468
2,926
306 1
3,120 1
825
17,904
115 1
922 1
4,787
8,649
154
29,745
14,745 •
132,481
159
15
1,629
3,411
303
3,686
932
20,684
86
783
5,524
9,223
-
36,421
16,959
157,112
193
28
1,417
3,836
306
4,443
624
22,939
69
704
5,449
8,042
-
40,452
15,627
165,854
224
29
1,495
3,956
296
3,614
436
23,372
67
823
5,628
9,061
-
40,638
16,032
171,000
154
29
1,445
4,187
289
3,909
360
17,708
60
922
3,651
8,526
-
35,709
15,916
147,908
165
26
1,160
2,148
281
3,389
499
17,773
35
887
3,624
6,516
-
33,608
14,507
138,012
Zero Cubic Feet
+ Does not exceed 0.5 Million Cubic Feet
Note: The emission estimates provided above are inclusive of emissions from underground mines, surface mines and post-
mining activities. The following states have neither underground nor surface mining and thus report no emissions as a
result of coal mining: Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Maine, Massachusetts, Michigan,
Minnesota, Nebraska, Nevada, New Hampshire, New Jersey, New York, North Carolina, Oregon, Rhode Island, South
Carolina, South Dakota, Vermont, and Wisconsin.
A-174 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
3.5. Methodology for Estimating ChU and C02 Emissions from Natural Gas Systems
As described in the main body text on Natural Gas Systems, the GHG Inventory methodology involves the
calculation of CH4 and CC>2 emissions for over 100 emissions sources, and then the summation of emissions for each
natural gas sector stage.
Stepl: Calculate Potential Methane
Potential Methane Factors
The primary basis for potential CH4 factors and emission factors for non-combustion-related CC>2 emissions from
the U.S. natural gas industry is a detailed study by the Gas Research Institute and EPA (EPA/GRI 1996). The EPA/GRI
study developed over 80 CH4 emission factors to characterize emissions from the various components within the operating
stages of the U.S. natural gas system. Since the time of this study, practices and technologies have changed. While this
study still represents best available data in many cases, using these emission factors alone to represent actual emissions
without adjusting for emissions controls would in many cases overestimate emissions. For this reason, "potential
methane" are calculated using the data, and then more recent data on voluntary and regulatory emission reduction
activities (step 3) is deducted to calculate actual emissions. See main body text on Natural Gas Systems for more
information.
For certain CFU emissions sources, new data and information allows for net emissions to be calculated directly:
gas well completions and workovers with hydraulic fracturing, liquids unloading, condensate storage tanks, and
centrifugal compressors. For these sources, EPA developed emissions factors that directly reflect the use of control
technologies. For gas well completions and workovers with hydraulic fracturing, separate emissions estimates were
developed for hydraulically fractured completions and workovers that vent, flared hydraulic fracturing completions and
workovers, hydraulic fracturing completions and workovers with RECs, and hydraulic fracturing completions and
workovers with RECs that flare. For liquids unloading, separate emissions estimates were developed for wells with
plunger lifts, and wells without plunger lifts. Likewise, for condensate tanks, emissions estimates were developed for tanks
with and without control devices. Finally, for centrifugal compressors, separate emissions estimates were developed for
compressors with wet and dry seals.
For potential CFU factors and emission factors used in the Inventory, see Table A-125 to Table A-130. Methane
compositions from GTI 2001 are adjusted year to year using gross production for National Energy Modeling System
(NEMS) oil and gas supply module regions from the EIA. These adjusted region-specific annual CFL; compositions are
presented in Table A-131 (for general sources), Table A-132 (for gas wells without hydraulic fracturing), and Table A-133
(for gas wells with hydraulic fracturing). Therefore, emission factors may vary from year to year due to slight changes in
the CH4 composition between each NEMS oil and gas supply module region.
1990-2012 Inventory updates to potential emission factors and emission factors
The current Inventory includes an update to emission factors for gas well completions and workovers with
hydraulic fracturing. Technology- specific national emission factors were developed based on 2011 and 2012 GHGRP
data. The emission factors used for gas well completions and workovers with hydraulic fracturing are not potential factors,
but are factors for actual emissions because control technologies are taken into account through the use of separate
emission factors for each of the aforementioned categories. The updated factors are included in Table A-125.
Activity Data
Activity data were taken from the following sources: IDrillinglnfo, Inc (Drillinglnfo 2014); American Gas
Association (AGA 1991-1998); Bureau of Ocean Energy Management, Regulation and Enforcement (previous Minerals
and Management Service) (BOEMRE 201 la, 201 Ib, 201 Ic, 201 Id); Monthly Energy Review (EIA 2012f, 2012g, 2012h,
201 la, 201 Ib, 201 Ic, 201 Id); Natural Gas Liquids Reserves Report (EIA 2005); Natural Gas Monthly (EIA 2012c, 2012d,
2012e, 2013a, 2013b, 2013c); the Natural Gas STAR Program annual emissions savings (EPA 2012a, 2013c); Oil and Gas
Journal (OGJ 1997-2013); Pipeline and Hazardous Materials Safety Administration (PHMSA 2013); Federal Energy
Regulatory Commission (FERC 2011); GHGRP data for natural gas systems (40 CFR 98, subpart W); and other Energy
Information Administration publications (EIA 2001, 2004, 2010, 2011, 2012i, 2014). Data for estimating emissions from
hydrocarbon production tanks were incorporated (EPA 1999). Coalbed CH4 well activity factors were taken from the
Wyoming Oil and Gas Conservation Commission (Wyoming 2013) and the Alabama State Oil and Gas Board (Alabama
2013). Activity data are presented in Table A-125 through Table A-130.
A-175
-------
For many sources, recent direct activity data were not available. For these sources, either 2011 data was used as
proxy for 2012 data or a set of industry activity data drivers was developed and was used to update activity data. Drivers
include statistics on gas production, number of wells, system throughput, miles of various kinds of pipe, and other
statistics that characterize the changes in the U.S. natural gas system infrastructure and operations. For example, recent
data on various types of field separation equipment in the production stage (i.e., heaters, separators, and dehydrators) were
unavailable. EPA determined that each of these types of field separation equipment relate to the number of non-associated
gas wells. Using the number of each type of field separation equipment estimated by GRI/EPA in 1992, and the number
of non-associated gas wells in 1992, EPA developed a factor that is used to estimate the number of each type of field
separation equipment throughout the time series. The key activity drivers are presented in Table A-134.
EPA used DI Desktop, a production database maintained by Drillinglnfo, Inc. (Drillinglnfo) (Drillinglnfo 2014),
covering U.S. oil and natural gas wells to populate activity data for associated gas wells, non-associated gas wells, gas
wells with hydraulic fracturing, and completions with hydraulic fracturing. EPA queried DI Desktop for relevant data on
an individual well basis—including location, natural gas and liquids (i.e., oil and condensate) production by year, drill
type (e.g., horizontal or vertical), and date of completion or first production. Associated gas wells were identified as any
well within DI Desktop that EPA classified as producing "oil" or "oil and gas" based on the production type description
and that had non-zero natural gas and liquids production. Non-associated gas wells were identified as any well that met
either of the following criteria: (1) classified as "oil" or "oil and gas" producing and had zero liquids production, but non-
zero natural gas production; or (2) classified as "gas" producing and had non-zero gas production. Both oil and condensate
are included in the liquids production data in DI Desktop; therefore, the count of associated gas wells may include wells
that produce gas and condensate only. Gas wells with hydraulic fracturing were assumed to be the subset of the non-
associated gas wells that were horizontally drilled and/or located in an unconventional formation (i.e., shale, tight sands, or
coalbed). Unconventional formations were identified based on well basin, reservoir, and field data reported in DI Desktop
referenced against a formation type crosswalk developed by EIA (EIA 2012a).
For 1990 through 2010, gas well completions with hydraulic fracturing were identified as a subset of the gas
wells with hydraulic fracturing that had a date of completion or first production in the specified year. To calculate
workovers for 1990 through 2010, EPA applied a refracture rate of 1 percent (i.e. 1 percent of all wells with hydraulic
fracturing are assumed to be refractured in a given year) to the total counts of wells with hydraulic fracturing from the
Drillinglnfo data. For 2011 and 2012, EPA used GHGRP data for the total number of well completions and workovers.
The GHGRP data represents a subset of the national completions and workovers, due to the reporting threshold, and
therefore using this data without scaling it up to national level results in an underestimate. However, because EPA's
GHGRP counts of completions and workovers were higher than national counts of completions and workovers, obtained
using DIDesktop data, EPA directly used the GHGPR data for completions and workovers for 2011 and 2012.
The methodological update for gas well completions and workovers with hydraulic fracturing required updated
activity data on RECs use and flaring for use with the new emission factors. EPA calculated the percentage of gas well
completions and workovers with hydraulic fracturing in the each of the four categories using 2011 Subpart W data. EPA
assumed 0 percent RECs use from 1990 through 2000, used GHGRP RECs percentage for 2011 and 2012, and then used
linear interpolation between the 2000 and 2011 percentages. For flaring, EPA used an assumption of 10 percent (the
average of the percent of completions and workovers that were flared in 2011 and 2012 GHGRP data) flaring from 1990-
2010 to recognize that some flaring has occurred over that time period. For 2011 and 2012, EPA used the GHGRP data on
flaring.
Step 2: Compile Reductions Data
The emissions calculated in Step 1 above represent expected emissions from an activity in the absence of
emissions controls (with the exceptions of gas well completions and workovers with hydraulic fracturing, liquids
unloading, centrifugal compressors, and condensate tanks, as noted above), and do not take into account any use of
technologies or practices that reduce emissions. To take into account use of such technologies, data were collected on
voluntary and regulatory reductions. Voluntary reductions included in the Inventory were those reported to Gas STAR for
activities such as replacing a high bleed pneumatic device with a low bleed device and replacing wet seals with dry seals at
reciprocating compressors. Regulatory actions reducing emissions include NESHAP regulations for dehydrator vents and
condensate tanks.
Voluntary reductions. Industry partners report CH4 emission reductions by project to the Natural Gas STAR
Program. The reductions from the implementation of specific technologies and practices (e.g., vapor recovery units,
centrifugal compressors are calculated by the reporting partners using actual measurement data or equipment-specific
emission factors. Natural Gas STAR Partners do not report reductions when they are required due to regulation.
Therefore, the Inventory assumes there is no overlap between the reductions reported through Natural Gas STAR and
reductions due to state regulations. The reductions undergo quality assurance and quality control checks to identify
A-176 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
errors, inconsistencies, or irregular data before being incorporated into the Inventory. In general, the Inventory uses
aggregated Gas STAR reductions by natural gas system stage (i.e., production, processing, transmission and storage, and
distribution). However, aggregate emissions reductions data by Gas STAR technology are provided for several sources, as
shown in Table A-135 of the Annex. For those sources, EPA has also used data on potential emissions, and the Gas STAR
data on reductions, to calculate net emissions, as shown in Table A-141 of the Annex. Many of the activities reported to
Gas Star are cross-cutting and apply to more than one emissions source and therefore cannot be assigned to one emissions
source, but instead are included in the "other" category. For Inventory sources with emission factors that already take into
account the use of control technologies (i.e., gas well completions and workovers with hydraulic fracturing, liquids
unloading, and condensate storage tanks) Natural Gas STAR reported reductions for those activities are not incorporated
into the Inventory, as this would double count reductions. CFU emission reductions from the Natural Gas STAR Program
are summarized in Table A-135.
Federal regulations. The 1990 Clean Air Act (CAA) sets limits on the amount of hazardous air pollutants
(HAPs) that can be emitted in the United States. The NESFIAP regulations set the standards to limit emissions of FIAPs.
The emission sources are required to use the Maximum Achievable Control Technology (MACT), giving the operators
flexibility to choose the type of control measure(s) to implement. In regards to the oil and natural gas industry, the
NESFIAP regulation addresses HAPs from the oil and natural gas production sectors and the natural gas transmission and
storage sectors of the industry. Though the regulation deals specifically with HAPs reductions, methane emissions are also
incidentally reduced.
The NESFIAP regulation requires that glycol dehydration unit vents and storage tanks that have HAP emissions
and exceed a gas throughput and liquids throughput threshold, respectively, be connected to a closed loop emission control
system that reduces emissions by 95 percent. Also, gas processing plants exceeding the threshold natural gas throughput
limit are required to routinely implement Feak Detection and Repair (FDAR) programs. The emissions reductions
achieved as a result of NESFIAP regulations were calculated using data provided in the Federal Register Background
Information Document (BID) for this regulation. The BID provides the levels of control measures in place before the
enactment of regulation. The emissions reductions were estimated by analyzing the portion of the industry without control
measures already in place that would be impacted by the regulation. CH4 emission reductions from federal regulations,
such as NESHAP, are summarized in Table A-136. In addition to the NESHAP applicable to natural gas, future
Inventories will reflect the 2012 NSPS for oil and gas. By separating gas well completions and workovers with hydraulic
fracturing into four categories and developing control technology-specific methane emission factors for each category,
EPA is implicitly accounting for NSPS reductions from hydraulically fractured gas wells. The rule also has VOC
reduction requirements for compressors, storage vessels, pneumatic controllers, and equipment leaks at processing plants,
which will also impact CtLt emissions in future Inventories.
Step 3: Calculate Net Emissions
For CH4, the reductions described above in Step 2 are summed and deducted from the potential CH4 emissions
calculated in Step 1. These net emissions are reported in the Natural Gas Systems inventory text.
The same procedure for estimating CH4 emissions holds true for estimating non-energy related CC>2 emissions,
except the emission estimates are not adjusted for reductions due to the Natural Gas STAR program or regulations.
Produced natural gas is composed of primarily CFU, but as shown in Table A-142, the natural gas contains, in
some cases, as much as 8 percent CC>2. The same vented and fugitive natural gas that led to CFU emissions also contains a
certain volume of CC>2. Accordingly, the CC>2 emissions for each sector can be estimated using the same activity data for
these vented and fugitive sources. The emission factors used to estimate CH4 were also used to calculate non-combustion
CC>2 emissions. The Gas Technology Institute's (GTI, formerly GRF) Unconventional Natural Gas and Gas Composition
Databases (GTI 2001) were used to adapt the CH4 emission factors into non-combustion related CC>2 emission factors.
Additional information about CC>2 content in transmission quality natural gas was obtained from numerous U.S.
transmission companies to help further develop the non-combustion CC>2 emission factors. For the CC>2 content used to
develop CC>2 emission factors from CH4 potential factors, see Table A-142. The detailed source emission estimates for
CH4 and CC>2 from the production sector are presented in Table A-137 and Table A-137, respectively.
In the processing sector, the CC>2 content of the natural gas remains the same as the CC>2 content in the
production sector for the equipment upstream of the acid gas removal unit because produced natural gas is usually only
minimally treated after being produced and then transported to natural gas processing plants via gathering pipelines. The
CQz content in gas for the remaining equipment that is downstream of the acid gas removal is the same as in pipeline
quality gas. The EPA/GRI study estimates the average CFU content of natural gas in the processing sector to be 87 percent
CH4. Consequently, the processing sector CC>2 emission factors were developed using CFU emission factors, proportioned
A-177
-------
to reflect the CC>2 content of either produced natural gas or pipeline quality gas using the same methodology as the
production sector. The detailed source emission estimates for CH4 and CC>2 from the processing sector are presented in
Table A-138 and Table A-144, respectively.
For the transmission sector, CC>2 content in natural gas transmission pipelines was estimated for the top 20
transmission pipeline companies in the United States (separate analyses identified the top 20 companies based on gas
throughput and total pipeline miles). The weighted average CC>2 content in the transmission pipeline quality gas in both
cases—total gas throughput and total miles of pipeline—was estimated to be about 1 percent. To estimate the CC>2
emissions for the transmission sector, the CH4 emission factors were proportioned from the 93.4 percent CH4 reported in
EPA/GRI (1996) to reflect the 1 percent CC>2 content found in transmission quality natural gas. The detailed source
emissions estimates for CH4 and CC>2 for the transmission sector are presented in Table A-139 and Table A-145,
respectively.
The natural gas in the distribution sector of the system has the same characteristics as the natural gas in the
transmission sector. The CH4 content (93.4 percent) and CC>2 content (1 percent) are identical to transmission segment
contents due to the absence of any further treatment between sector boundaries. Thus, the CH4 emissions factors were
converted to CC>2 emission factors using the same methodology as discussed for the transmission sector. The detailed
source emission estimates for CH4 and CC>2 for the distribution sector are presented in Table A-140 and Table A-146,
respectively.
Three exceptions to this methodology are CC>2 emissions from flares, CC>2 from acid gas removal units, and CC>2
from condensate tanks. In the case of flare emissions, a direct CC>2 emission factor from EIA (1996) was used. This
emission factor was applied to the portion of offshore gas that is not vented and all of the gas reported as vented and flared
onshore by EIA, including associated gas. The amount of CC>2 emissions from an acid gas unit in a processing plant is
equal to the difference in CC>2 concentrations between produced natural gas and pipeline quality gas applied to the
throughput of the plant. This methodology was applied to the national gas throughput using national average CC>2
concentrations in produced gas (3.45 percent) and transmission quality gas (1 percent). Data were unavailable to use
annual values for CC>2 concentration. For condensate tanks, a series of E&P Tank (EPA 1999) simulations provide the
total CC>2 vented per barrel of condensate throughput from fixed roof tank flash gas for condensate gravities of API 45
degree and higher. The ratios of emissions to throughput were used to estimate the CC>2 emission factor for condensate
passing through fixed roof tanks.
Table A-125 through Table A-130 display the 2012 activity data, CFU emission factors, and calculated potential
CFLi emissions for each stage.
The tables provide references for emission factors and activity data in footnotes (i.e., lettered footnotes). The
tables also provide information on which method was used for supplying activity data for 2012 (i.e., numbered footnotes).
A-178 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-125:2012 Data and Calculated Clh Potential Emissions tMgl for the Natural Gas Production Stage, by HEMS Region
Activity
2072 EPA Inventory Values
Activity Data
Emission Factor (Potential)33
Calculated
Potential (Mg)bb
North East
Gas Wells
NE - Associated Gas Wellscc-dd
NE - Non-associated Gas Wells (less wells with
hydraulic fracturing)
NE - Gas Wells with Hydraulic Fracturing
Field Separation Equipment
Heaters
Separators
Dehydrators
Meters/Piping
Gathering Compressors
Small Reciprocating Compressors
Large Reciprocating Compressors
Large Reciprocating Stations
Pipeline Leaks
Drilling, Well Completion, and Well Workover
Gas Well Completions without Hydraulic
Fracturing66
Gas Well Workovers without Hydraulic Fracturing
Gas Well Completions and Workovers with
Hydraulic Fracturing
Well Drilling
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without Control Devices
Condensate Tanks with Control Devices
Compressor Exhaust Vented
Gas Engines
Liquids Unloading
Liquids Unloading (with plunger lifts)
Liquids Unloading (without plunger lifts)
Slowdowns
Vessel Blowdown
Pipeline Blowdown
Compressor Blowdown
Compressor Starts
Upsets
Pressure Relief Valves
Mishaps
Midcontinent
Gas Wells
MC - Associated Gas Wells1*.'"'
MC - Non-associated Gas Wells (less wells with
hydraulic fracturing)
MC - Gas Wells with Hydraulic Fracturing
Field Separation Equipment
Heaters
Separators
38,770
wells3'1
NA
112,607
46,367
318
112,872
22,164
7,910
159
24
3
77,956
268
4,898
2,985
384
6,359
77,261
795
6,487,241
7,280,854
1
1
-
6,924
17,906
135,353
77,956
159
159
354,796
19,489
^m
27,470
77,896
30,156
43,869
47,003
wells3'1
wells3'1
heatersb'2
separators'"'2
dehydratorsb'2
meters0'2
compressors b'2
compressors b'2
stations'"'2
miles0'2
completions/yrd'2
workovers/yr3.1
completions/yr0
workovers/yr°
wells'.1
controllers b'2
active pumps b'2
MMscf/yr"'2
MMscf/yr"'2
MMbbl/yrh'1
MMbbl/yrh'1
MMHPhrb>2
venting wells3J'23J'2
venting wells3J>2
vessels'"'2
miles (gathering)0'2
compressors'"'2
compressors'"'2
PRV'2
miles0'2
^^m
wells3'1
wells3'1
wells3'1
heaters'"'2
separators'"'2
7.67
7.54
15.38
0.97
23.53
9.75
289.63
16,447.52
8,920.47
57.50
791
2,647
2,751
373
268
1,073
298
21.87
4.37
0.26
268,185
141,646
84
334
4,082
9,133
36.78
724
^H
NA
7.45
8.35
14.90
0.94
scfd/well"
scfd/wellb
scfd/heaterb
scfd/separatorb
scfd/dehydratorb
scfd/meterb
scfd/compressorb
scfd/compressorb
scfd/stationb
scfd/mileb
set/completion1"
scf/workoverb
See Table A-126P
scf/wells
scfd/deviceb
scfd/pumpb
scf/MMscf"
scf/MMscf"
scf/bbl'.ff
scf/bbF
scf/HPhr"
scfy/venting well^s
scfy/venting well^s
scfy/vesselb
scfy/mile1"
scfy/compressorb
scfy/compressorb
scfy/PRV"
scf/mileb
•
scfd/wellb
scfd/wellb
scfd/heaterb
scfd/separatorb
NA
6,071.08
2,457.18
34.37
771.31
3,665.46
542.00
323.68
2,774.99
188.13
31,508.62
4.08
249.74
68,559.0
336.92
202,695.69
1,499.03
134,073.18
41,805.34
421.22
84.24
35,764.16
48,849.27
219.96
501.85
12.50
27.96
251.32
271.64
4,080.19
1,770.61
4,595.66
311.30
A-179
-------
Dehydrators
Meters/Piping
Gathering Compressors
Small Reciprocating Compressors
Large Reciprocating Compressors
Large Reciprocating Stations
Pipeline Leaks
Drilling, Well Completion, and Well Workover
Gas Well Completions without Hydraulic
Fracturing66
Gas Well Workovers without Hydraulic
Fracturing
Gas Well Completions and Workovers with
Hydraulic Fracturing
Well Drilling
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without Control Devices
Condensate Tanks with Control Devices
Compressor Exhaust Vented
Gas Engines
Liquids Unloading
Liquids Unloading (with plunger lifts)
Liquids Unloading (without plunger lifts)
Slowdowns
Vessel Blowdown
Pipeline Blowdown
Compressor Blowdown
Compressor Starts
Upsets
Pressure Relief Valves
Mishaps
Rocky Mountain
Gas Wells
RM - Associated Gas Wellscc'dd
RM - Non-associated Gas Wells (less wells with
hydraulic fracturing)
RM - Gas Wells with Hydraulic Fracturing
Field Separation Equipment
Heaters
Separators
Dehydrators
Meters/Piping
Gathering Compressors
Small Reciprocating Compressors
Large Reciprocating Compressors
Large Reciprocating Stations
Pipeline Leaks
Drilling, Well Completion, and Well Workover
Gas Well Completions without Hydraulic
Fracturing66
Gas Well Workovers without Hydraulic Fracturing
Gas Well Completions and Workovers with
Hydraulic Fracturing
Well Drilling
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
15,064
143,186
12,210
24
3
81,359
182
3,388
1,139
143
4,322
167,589
15,343
4,409,271
4,948,676
19
19
19,041
2,516
4,469
105,936
81,359
12,210
12,210
241,149
20,340
^^m
32,598
9,665
73,755
38,040
41,627
11,630
97,399
9,260
32
4
107,797
141
420
2,334
275
3,337
122,127
14,849
dehydratorsb<2
meters0'2
compressors b>2
compressors b>2
stations b>2
miles0'2
completions/yrd'2
workovers/yra>1
completions/yr°
workovers/yr°
wells'.1
controllers b>2
active pumps b>2
MMscf/yrb>2
MMscf/yrb>2
MMbbl/yrh>1
MMbbl/yrh>1
MMHPhrb>2
venting wells aJ'2
venting wells aJ<2
vessels'"'2
miles (gathering)0'2
compressors b'2
compressors b'2
PRVb'2
miles0'2
^^H
wells3'1
wells a>1
wells a>1
heaters b'2
separators b'2
dehydratorsb'2
meters0'2
compressors b'2
compressors b'2
stations b'2
miles0'2
completions/yrd'2
workovers/yra'1
completions/yr°
workovers/yr°
wells''1
controllers b'2
active pumps b'2
95.54
9.45
280.71
15,941
8,646
55.72
768
2,572
2,666
362
260
1,040
288.9
302.75
60.55
0.25
1,140,052
190,179
82
324
3,957
8,852
36
701
NA
35.05
40.72
56.73
120
89.58
52.01
263.20
14,947
8,107
52.25
705
2,360
2,500
339
244
scfd/dehydratorb
scfd/meterb
scfd/compressorb
scfd/compressorb
scfd/stationb
scfd/mileb
scf/completionb
scf/workoverb
See Table A-126P
scf/wells
scfd/deviceb
scfd/pumpb
scf/MMscf
scf/MMscf>
scf/bbl'."
scf/bbl1'"
scf/HPhrb
scfy/venting well^g
scfy/venting welM
scfy/vesselb
scfy/mileb
scfy/compressorb
scfy/compressorb
scfy/PRV6
scf/mileb
scfd/wellb
scfd/wellb
scfd/heaterb
scfd/separatorb
scfd/dehydratorb
scfd/meterb
scfd/compressorb
scfd/compressorb
scfd/stationb
scfd/mileb
scf/completionb
scf/workoverb
See Table A-126P
scf/wells
scfd/deviceb
scfd/pumpb
10,117.92
9,509.40
24,094.82
2,689.55
182.34
31,871.63
2.70
167.84
26,118.0
221.95
426,133.39
28,044.95
88,321.70
27,539.58
107,874.48
21,574.90
92,278.23
55,244.84
16,369.26
166.85
507.64
930.47
2,081.60
165.56
274.77
^^m
-
2,381.29
21,115.06
15,171.70
35,099.35
7,324.14
35,608.50
17,133.02
3,362.38
227.95
39,594.37
1.91
19.11
53,087.0
160.66
291,165.83
25,447.90
A-180 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without Control Devices
Condensate Tanks with Control Devices
Compressor Exhaust Vented
Gas Engines
Liquids Unloading
Liquids Unloading (with plunger lifts)
Liquids Unloading (without plunger lifts)
Slowdowns
Vessel Blowdown
Pipeline Blowdown
Compressor Blowdown
Compressor Starts
Upsets
Pressure Relief Valves
Mishaps
Produced Water from Coal Bed Methane
Powder River
South West
Gas Wells
SW - Associated Gas Wellscc'dd
SW - Non-associated Gas Wells (less wells with
hydraulic fracturing)
SW - Gas Wells with Hydraulic Fracturing
Field Separation Equipment
Heaters
Separators
Dehydrators
Meters/Piping
Gathering Compressors
Small Reciprocating Compressors
Large Reciprocating Compressors
Large Reciprocating Stations
Pipeline Leaks
Drilling, Well Completion, and Well Workover
Gas Well Completions without Hydraulic
Fracturing66
Gas Well Workovers without Hydraulic Fracturing
Gas Well Completions and Workovers with
Hydraulic Fracturing
Well Drilling
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without Control Devices
Condensate Tanks with Control Devices
Compressor Exhaust Vented
Gas Engines
Liquids Unloading
Liquids Unloading (with plunger lifts)
Liquids Unloading (without plunger lifts)
Slowdowns
Vessel Blowdown
Pipeline Blowdown
3,404,114
3,820,555
5
5
14,701
10,741
1,267
91,296
107,797
9,260
9,260
186,176
26,949
20,602,022,700
^^^H
155,119
13,860
27,627
11,243
23,316
5,784
55,885
5,642
16
2
60,832
70
603
649
116
1,660
55,095
2,531
1,692,957
1,900,064
10
10
7,311
1,379
8,078
MMscf/yrb>2
MMscf/yrb>2
MMbbl/yrh>1
MMbbl/yrh>1
MMHPhrb>2
venting wells aJ<2
venting wells aJ'2
vessels^2
miles (gathering) c<2
compressors b'2
compressors b<2
PRVb,2
miles c,2
gallons produced
water k<1
^m
wells3'1
wells3'1
wells a>1
heaters b'2
separators b'2
dehydratorsb'2
meters0'2
compressors b'2
compressors b'2
stations b'2
miles0'2
completions/yrd'2
workovers/yra'1
completions/yr°
workovers/yr°
wells'.1
controllers b'2
active pumps b'2
MMscf/yr"'2
MMscf/yr"'2
MMbbl/yrh>1
MMbbl/yrh>1
MMHPhr"'2
venting wells aJ>2
venting wells aJ'2
975 scf/MMscf
270.9 scf/MMscf
21.87 scf/bbP.f
4.37 scf/bbP>ff
0.24 scf/HPhrb
119,523 scfy/venting welP«
1,998,082 scfy/venting welP«
77 scfy/vesselb
304 scfy/mileb
3,710 scfy/compressorb
8,300 scfy/compressorb
33 scfy/PRy
658 scf/mileb
Gg/gallon water
2.3E-09 drainage k
NA
37.24 scfd/wellb
37.24 scfd/wellb
58.97 scfd/heaterb
125 scfd/separatorb
93.11 scfd/dehydratorb
54.06 scfd/meterb
274 scfd/compressorb
15,536 scfd/compressorb
8,426 scfd/stationb
54.31 scfd/mileb
749 scf/completionb
2,507 scf/workoverb
See Table A-126P
2,598 scf/wells
353 scfd/deviceb
253 scfd/pumpb
1,014 scf/MMscf
282 scf/MMscf
303 scf/bbP>ff
60.55 scf/bbP>ff
0.25 scf/HPhrb
2,856 scfy/venting welP«
77,899 scfy/venting welP«
40,343 vesselsb'2
60,832 miles (gathering)0'2
80 scfy/vesselb
316 scfy/mileb
63,934.16
19,935.30
1,895.47
379.09
66,798.21
24,726.01
48,758.03
134.82
630.64
661.63
1,480.15
119.84
341.34
47,244.27
3,628.27
7,232.19
4,661.03
20,435.18
3,786.17
21,236.97
10,851.59
1,747.50
118.47
23,225.26
1.01
29.12
15,601.0
83.05
136,534.04
4,508.22
33,050.39
10,305.44
55,395.00
11,079.00
34,530.94
75.85
12,119.67
61.93
369.92
A-181
-------
Compressor Blowdown
Compressor Starts
Upsets
Pressure Relief Valves
Mishaps
Gas Wells
WC - Associated Gas Wellscc-dd
WC - Non-associated Gas Wells (less wells with
hydraulic fracturing)
WC - Gas Wells with Hydraulic Fracturing
Field Separation Equipment
Heaters
Separators
Dehydrators
Meters/Piping
Gathering Compressors
Small Reciprocating Compressors
Large Reciprocating Compressors
Large Reciprocating Stations
Pipeline Leaks
Drilling, Well Completion, and Well Workover
Gas Well Completions without Hydraulic
Fracturing66
Gas Well Workovers without Hydraulic Fracturing
Gas Well Completions and Workovers with
Hydraulic Fracturing
Well Drilling
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without Control Devices
Condensate Tanks with Control Devices
Compressor Exhaust Vented
Gas Engines
Liquids Unloading
Liquids Unloading (with plunger lifts)
Liquids Unloading (without plunger lifts)
Slowdowns
Vessel Blowdown
Pipeline Blowdown
Compressor Blowdown
Compressor Starts
Upsets
Pressure Relief Valves
Mishaps
Gulf Coast
Gas Wells
GC - Associated Gas Wellscc-dd
GC - Non-associated Gas Wells (less wells with
hydraulic fracturing)
GC - Gas Wells with Hydraulic Fracturing
Field Separation Equipment
Heaters
Separators
Dehydrators
Meters/Piping
Gathering Compressors
Small Reciprocating Compressors
Large Reciprocating Compressors
5,642
5,642
92,590
15,208
29,726
1,999
95
2,094
1,529
292
3,994
2,431
8
1
16,712
4
87
2
1
84
2,098
1,422
85,450
95,903
10
10
369
159
142
3,915
16,712
2,431
2,431
4,673
4,178
39,709
27,024
49,862
17,222
50,591
10,719
90,288
6,228
32
compressors b<2
compressors b>2
PRVb>2
miles0'2
wells3'1
wells3'1
wells3'1
heaters b'2
separators b'2
dehydratorsb'2
meters0'2
compressors b'2
compressors b'2
stations b'2
miles0'2
completions/yrd'2
workovers/yr3'1
completions/yr0
workovers/yr°
wells'.1
controllers b'2
active pumps b'2
MMscf/yrb'2
MMscf/yrb'2
MMbbl/yrh'1
MMbbl/yrh'1
MMHPhr"'2
wells 3J'2
wells 3J'2
vessels'"'2
miles (gathering)0'2
compressors b'2
compressors b'2
PRVb>2
miles0'2
^m
wells3'1
wells3'1
wells3'1
heaters b'2
separators b'2
dehydratorsb'2
meters0'2
compressors b'2
compressors b'2
3,856
8,627
35
684
NA
42.49
42.49
67.29
142
106
61.68
312
17,728
9,615
61.97
855
2,861
2,965
402
289
1,157
321
21.87
4.37
0.28
317,292
279,351
90.94
360
4,400
9,844
40
780
^H
NA
7.96
7.96
64.60
136.57
102.00
59.21
300
17,019
scfy/compressorb
scfy/compressorb
scfy/PRy>
scf/mileb
••
scfd/wellb
scfd/wellb
scfd/heaterb
scfd/separatorb
scfd/dehydratorb
scfd/meterb
scfd/compressorb
scfd/compressorb
scfd/stationb
scfd/mileb
scf/completionb
scf/workoverb
See Table A-126P
scf/wells
scfd/deviceb
scfd/pumpb
scf/MMscf
scf/MMscf
scf/bbl''ff
scf/bbF
scf/HPhrb
scfy/venting welM
scfy/venting welM
scfy/vesselb
scfy/mileb
scfy/compressorb
scfy/compressorb
scfy/PRy>
scf/mileb
scfd/wellb
scfd/wellb
scfd/heaterb
scfd/separatorb
scfd/dehydratorb
scfd/meterb
scfd/compressorb
scfd/compressorb
419.06
937.49
61.95
200.22
^•H
-
597.13
28.38
990.60
1,528.80
218.06
1,732.08
5,335.45
997.03
67.59
7,280.76
0.06
4.79
123.0
4.78
5,933.29
2,890.21
1,903.54
593.54
4,212.16
842.43
1,988.81
971.66
764.00
6.86
115.96
206.04
460.94
3.57
62.77
-
1,511.56
2,789.34
7,821.18
48,571.24
7,686.19
37,584.36
13,120.51
3,828.46
A-182 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Large Reciprocating Stations
Pipeline Leaks
Drilling, Well Completion, and Well Workover
Gas Well Completions without Hydraulic
Fracturing66
Gas Well Workovers without Hydraulic Fracturing
Gas Well Completions and Workovers with
Hydraulic Fracturing
Well Drilling
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without Control Devices
Condensate Tanks with Control Devices
Compressor Exhaust Vented
Gas Engines
Liquids Unloading
Liquids Unloading (with plunger lifts)
Liquids Unloading (without plunger lifts)
Slowdowns
Vessel Blowdown
Pipeline Blowdown
Compressor Blowdown
Compressor Starts
Upsets
Pressure Relief Valves
Mishaps
Produced Water from Coal Bed Methane Wells
Black Warrior
Offshore Platforms
Shallow water Gas Platforms (GoM and Pacific)
Deepwater Gas Platforms (GoM and Pacific)
4
100,479
stations'"'2
miles0'2
130 completions/yrd'2
1,176 workovers/yra'1
2,357 completions/yr0
279 workovers/yr°
3,076 wells'.1
53,436
2,537
3,137,482
3,521,304
43
43
13,549
1,784
5,445
controllersb'2
active pumpsb'2
MMscf/yr"'2
MMscf/yr"'2
MMbbl/yrh'1
MMbbl/yrh'1
MMHPhr"'2
venting wells aJ'2
venting wells aJ'2
9,230
59.49
820
2,746
2,846
386
278
1,110
308
21.87
4.37
0.27
scfd/stationb
scfd/mileb
scf/completionb
scf/workoverb
See Table A-126P
scf/well9
scfd/deviceb
scfd/pumpb
scf/MMscf
scf/MMscf
scf/bbF
scf/bbl''ff
scf/HPhrb
78,533 vesselsb'2
100,479 miles (gathering)0'2
6,228
6,228
171,593
25,120
5,517
compressors"
compressors
b,2
61,758 scfy/venting wellJ>99
265,120 scfy/venting welP'99
87 scfy/vesselb
346 scfy/mileb
4,224 scfy/compressorb
9,450 scfy/compressorb
PRVb'2
miles0'2
38
749
wells1'1 2.33E-03
scfy/PRV6
scf/mileb
Gg/well1
shallow water gas
1,973 platformsm'3
deepwater gas
41 platforms m>3
19,178 scfd/platform"
79,452 scfd/platform"
259.55
42,022.30
2.05
62.18
53,643.0
168.61
145,057.07
4,951.09
67,094.63
20,920.77
17,901.69
3,580.34
70,100.25
2,121.99
27,803.30
132.05
669.31
506.67
1,133.51
125.77
362.28
12,779.20
266,066.46
22,950.41
Regulatory Reductions (Gg)
Voluntary Reductions (Gg)
Total Reductions (Gg)
Total Potential Emissions (Gg)
Total Net Emissions (Gg)
(99.2)
(1,635.7)
(1,734.8)
3,726.6
1,991.8
= DI Desktop (2014)
b.EPA/GRI (1996), Methane Emissions from the Natural Gas Industry
c ICF (1996), Estimation of Activity Factors for the Natural Gas Exploration and Production Industry in the U.S.
dAPI/ICF memo (1997)
6 EPA NSPS Technical Support Document (2012)
f EIA Monthly Energy Review
a Radian (1992), Global Emissions of Methane Sources
h EIA US Crude Oil, Natural Gas, and Natural Gas Liquids Reserves Annual Report
'EP&P/API Tank Calc runs
J API/ANGA (2012), Characterizing Pivotal Sources of Methane Emissions from Natural Gas Production - Summary and Analysis of API and ANGA Survey
Responses
k Wyoming Oil and Gas Conservation Commission (2013)
1 Alabama State Oil and Gas Board (2013)
m Bureau of Ocean Energy Management, Regulation and Enforcement (2011)
" MMS (2000), 2000 Gulfwide Offshore Activity Data System
0 2012 GHGRP - Subpart W data
p Emissions for hydraulic fracturing completions and workovers are split into 4 categories and the same emission factors (shown in Table A-2) are used for all
NEMS regions. For more details, refer to EPA memo "Updating GHG Inventory Estimate for Hydraulically Fractured." The factors for hydraulically fractured
completions and workovers in Table A-2 represent actual emissions and can be used to calculate emissions directly
i Emissions for hydraulic fracturing completions and workovers are calculated together.
A-183
-------
aa Emission factors listed in this table are for potential emissions (unless otherwise indicated in a footnote). For many of these sources, emission reductions are
subtracted from potential emissions to calculate net emissions. For this reason, emission factors presented in these tables cannot be used to directly estimate net
emissions from these sources. See detailed explanation of methodology above.
bb Totals may not sum due to independent rounding.
cc Emissions from oil wells that produce associated gas are estimated in the Petroleum Systems model. In the Natural Gas Systems model, the oil wells counts are
used as a driver only.
ddNA= not applicable (i.e., this data is not applicable for the Natural Gas Systems model).
"Emission factors for condensate tanks represent actual emissions and can be used to calculate emissions directly.
99 Emission factors for liquids unloading represent actual emissions and can be used to calculate emissions directly.
1 Activity data for 2012 available from source.
2 Ratios relating other factors for which activity data are available.
3 2011 activity data are used to determine some or all of the 2012 activity.
Table A-126:2012 National Activity Data and Emission Factors, and Emissions (Mg), by category for Hydraulically Fractured
Gas Well Completions and Workovers
Activity
Hydraulic Fracturing Completions and Workovers
that vent
Flared Hydraulic Fracturing Completions and
Workovers
Hydraulic Fracturing Completions and Workovers
with RECs
Hydraulic Fracturing Completions and Workovers
with RECs that flare
2072 EPA Inventory Values
Activity Data
4,688
775
3,386
1,815
completions and
workovers/yeara
completions and
workovers/yeara
completions and
workovers/yeara
completions and
workovers/yeara
Emission Factor
41
5
3
6
Mg/comp or
workoverb
Mg/comp or
workoverb
Mg/comp or
workoverb
Mg/comp or
workoverb
Emissions
(Mg)-
192,208
3,875
10,158
10,890
' 2012 GHGRP - Subpart W data. The GHGRP data represents a subset of national completions and workovers, due to the reporting threshold. Please see the
section on "Activity Data" above for more information and the Planned Improvements section of the Inventory report.
b Emissions for hydraulic fracturing completions and workovers are split into 4 categories and the same emission factors are used for all NEMS regions. For more
details, refer to EPA memo "Updating GHG Inventory Estimate for Hydraulically Fractured."
aa Totals may not sum due to independent rounding.
Table A-127: U.S. Activity Data for Hydraulic Fracturing Completions and Worhovers split by 4 categories
Activity 1990 1995 2000 2005 2011"
2012=
Hydraulic Fracturing Completions and
Workovers that vent
Flared Hydraulic Fracturing Completions and
Workovers
Hydraulic Fracturing Completions and
Workovers with RECs
Hydraulic Fracturing Completions and
Workovers with RECs that flare
5,345
591
4,852
536
Total
5,936
5,388
8,257
= 2011 and 2012 GHGRP - Subpart W data
A-184 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-128:2012 Data and CHa Emissions tMgl for the Natural Gas Processing Stage
Activity
Normal Fugitives
Plants
Reciprocating Compressors
Centrifugal Compressors (wet seals)
Centrifugal Compressors (dry seals)
Compressor Exhaust
Gas Engines
Gas Turbines
AGR Vents
Kimray Pumps
Dehydrator Vents
Pneumatic Devices
Compressor Exhaust
BlowdownsA/enting
2072 EPA Inventory Values
Activity Data
606
5,624
658
248
40,403
47,907
307
1,463,675
13,186,262
606
606
Regulatory Reductions (Gg)
Voluntary Reductions (Gg)
Total Reductions (Gg)
Total Potential Emissions (Gg)
Total Net Emissions (Gg)
plants3'1
compressors0'2
compressors'1'2
compressors'1'2
MMHPrir".2
MMHPhr"'2
AGR units".2
MMscf/yr".2
MMscf/yr".2
gas plants3'1
gas plants3'1
Calculated
Potential
Emission Factor (Potential)™ Emissions (Mg)
7,906
11,196
51,370
25,189
0.24
0.01
6,083
178
122
164,721
4,060
scfd/planf
scfd/compressorb
scfd/compressord
scfd/compressord
scf/HPhrb
scf/HPhrb
scfd/AGRb
scf/MMscf
scf/MMscf>
scfy/planf?
Mscfy/planf?
33,680.5
442,633.5
237,724.1
43,936.6
186,760.5
5,259.3
13,134.2
5,010.8
30,869.7
1,922.6
47,386.5
(16.3)
(140.4)
(156.8)
1,048.3
891.5
a Oil and Gas Journal
b EPA/GRI (1996), Methane Emissions from the Natural Gas Industry
c ICF (2008), Natural Gas Model Activity Factor Basis Change
d ICF (2010), Emissions from Centrifugal Compressors
aa Emission factors listed in this table are for potential emissions (unless otherwise indicated in a footnote). For many of these sources, emission
reductions are subtracted from potential emissions to calculate net emissions. For this reason, emission factors presented in these tables cannot be used
to directly estimate net emissions from these sources. See detailed explanation of methodology above.
1 Activity data for 2012 available from source.
2 Ratios relating other factors for which activity data are available.
A-185
-------
Table A-129:2012 Data and CHa Emissions tMgl for the Natural Gas Transmission Stage
Activity
Fug/fives
Pipeline Leaks
Compressor Stations (Transmission)
Station
Reciprocating Compressor
Centrifugal Compressor (wet seals)
Centrifugal Compressor (dry seals)
Compressor Stations (Storage)
Station
Reciprocating Compressor
Centrifugal Compressor (wet seals)
Centrifugal Compressor (dry seals)
Wells (Storage)
M&R (Trans. Co. Interconnect)
M& R (Farm Taps + Direct Sales)
Vented and Combusted
Dehydrator vents (Transmission)
Dehydrator vents (Storage)
Compressor Exhaust
Engines (Transmission)
Turbines (Transmission)
Engines (Storage)
Turbines (Storage)
Generators (Engines)
Generators (Turbines)
Pneumatic Devices Transmission +
Storage
Pneumatic Devices Transmission
Pneumatic Devices Storage
Routine Maintenance/Upsets
Pipeline venting
Station Venting Transmission + Storage
Station Venting Transmission
Station Venting Storage
LNG Storage
LNG Stations
LNG Reciprocating Compressors
LNG Centrifugal Compressors
LNG Compressor Exhaust
LNG Engines
LNG Turbines
LNG Station Venting
LNG Import Terminals
LNG Stations
LNG Reciprocating Compressors
LNG Centrifugal Compressors
LNG Compressor Exhaust
LNG Engines
LNG Turbines
LNG Station Venting
2072 EPA Inventory Values
Activity Data
303,126
1,799
7,235
659
66
344
1,012
70
29
16,042
2,698
80,009
1,146,991
1,782,492
50,908
12,147
4,387
1,541
2,491
29
70,827
13,542
303,126
1,799
344
70
270
64
579
113
70
8
37
7
774
178
8
Regulatory Reductions (Gg)
Voluntary Reductions (Gg)
miles3'1
stations0'2
compressors °'2
compressors'1'2
compressors'1'2
stations6'2
compressors6'2
compressors'1'2
compressors'1'2
wells b>2
stations0'2
stations0'2
MMscf/yearb'2
MMscf/yearb'2
MMHPhr"'2
MMHPhr"'2
MMHPhr"'2
MMHPhr"'2
MMHPhr"'2
MMHPhr"'2
devices''2
devices6'2
miles3'1
compressor
stations c-2
compressor
stations e'2
stations f'9'3
compressors
f,g,3
compressors
f,g,3
MMHPhrf.8'3
MMHPhrf.9'3
stations f'9'3
stations f'9'3
compressors f*3
compressors f*3
MMHPhrf.9'3
MMHPhrf.9'3
stations f'9'3
Emission Factor (Potential)33
1.55
8,778
15,205
50,222
32,208
21,507
21,116
45,441
31,989
115
3,984
31
93.72
117
0.24
0.01
0.24
0.01
0.24
0.01
162,197
162,197
31.65
4,359
4,359
21,507
21,116
30,573
0.24
0.01
4,359
21,507
21,116
30,573
0.24
0.01
4,359
Scfd/ mileb
Scfd/stationb
Scfd/ compressor11
Scfd/ compressor11
Scfd/ compressor11
Scfd/ stationb
Scfd/ compressor11
Scfd/ compressor11
Scfd/ compressor11
Scfd/wellb
scfd/stationb
scfd/stationb
scf/MMscf1
scf/MMscf1
scf/HPhrb
scf/HPhrb
scf/HPhrb
scf/HPhrb
scf/HPhrb
scf/HPhrb
Scfy/deviceb
Scfy/deviceb
Mscfy/mileb
Mscfy/stationb
Mscfy/stationb
scfd/stationb
scfd/compb
scfd/compb
scf/HPhrb
scf/HPhrb
Mscfy/stationb
scfd/stationb
scfd/compressorb
scfd/compressorb
scf/HPhrb
scf/HPhrb
Mscfy/stationb
Calculated
Potential
Emissions
(Mg)
3,311.4
111,037.1
773,294.3
232,825.7
14,971.9
52,013.2
150,224.7
22,346.7
6,531.7
12,912.2
75,573.6
17,548.7
2,070.4
4,022.9
235,315.3
1,333.5
20,277.1
169.2
11,515.2
3.2
221,256.9
42,304.0
184,779.2
151,065.9
28,882.0
-
10,622.8
40,146.5
13,766.0
2,677.7
12.4
5,898.6
1,164.2
5,551.8
1,418.5
3,576.6
19.5
646.4
(0.0)
(390.1)
A-186 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Total Reductions (Gg) (390.1)
Total Potential Emissions (Gg) 2,461.1
Total Net Emissions (Gg) 2,071.0
' Pipeline and Hazardous Materials Safety Administration (PHMSA), Office of Pipeline Safety (OPS) (2013)
b EPA/GRI (1996), Methane Emissions from the Natural Gas Industry
c ICF (2008), Natural Gas Model Activity Factor Basis Change
d ICF (2010), Emissions from Centrifugal Compressors
e ICF (1997), Additional Changes to Activity Factors for Portions of the Gas Industry
f ICF (1996), Estimation of Activity Factors for the Natural Gas Exploration and Production Industry in the U.S.
a EIA (2004), US LNG Markets and Uses
1 Activity data for 2012 available from source.
2 Ratios relating other factors for which activity data are available.
3 2011 activity data are used to determine some or all of the 2012 activity (to be updated).
aa Emission factors listed in this table are for potential emissions (unless otherwise indicated in a footnote). For many of these sources, emission
reductions are subtracted from potential emissions to calculate net emissions. For this reason, emission factors presented in these tables
cannot be used to directly estimate net emissions from these sources. See detailed explanation of methodology above.
A-187
-------
Table A-130:2012 Data and Clh Emissions tMgl for the Natural Gas Distribution Stage
Activity
Pipeline Leaks
Mains— Cast Iron
Mains— Unprotected steel
Mains— Protected steel
Mains— Plastic
Services— Unprotected steel
Services Protected steel
Services— Plastic
Services— Copper
Meter/Regulator (City Gates)
M&R >300
M&R 100-300
M&R<100
Reg >300
R-Vault >300
Reg 100-300
R-Vault 100-300
Reg 40-1 00
R-Vault 40-1 00
Reg <40
Customer Meters
Residential
Commercial/Industry
Routine Maintenance
Pressure Relief Valve Releases
Pipeline Blowdown
Upsete
Mishaps (Dig-ins)
2072 EPA Inventory Values
Calculated
Activity Data Emission Factor (Potential)™ Potential
Emissions (Mg)
32,418
63,727
487,225
661,100
3,916,353
14,951,473
45,147,410
1,009,255
3,465
12,644
6,758
3,788
2,225
11,459
5,148
34,387
30,494
14,581
35,693,769
4,481,003
1,244,470
1,156,453
1,156,453
Regulatory Reductions (Gg)
Voluntary Reductions (Gg)
Total Reductions (Gg)
Total Potential Emissions (Gg)
Total Net Emissions (Gg)
miles3'1
miles3'1
miles3'1
miles3'1
services3'1
services3'1
services3'1
services3'1
stations'1'2
stations'1'2
stations'1'2
stations'1'2
stations'1'2
stations'1'2
stations'1'2
stations'1'2
stations'1'2
stations'1'2
outdoor
meters b'2
meters b'2
mile main3'1
miles b'2
miles b'2
239
110
3.07
9.91
1.70
0.18
0.01
0.25
180
95.60
4.31
162
1.30
40.50
0.18
1.04
0.09
0.13
143
47.90
0.05
0.10
1.59
Mscf/mile-yrb
Mscf/mile-yrb
Mscf/mile-yrb
Mscf/mile-yrc
Mscf/serviceb
Mscf/serviceb
Mscf/serviceb
Mscf/serviceb
scfh/stationb
scfh/stationb
scfh/stationb
scfh/stationb
scfh/stationb
scfh/stationb
scfh/stationb
scfh/stationb
scfh/stationb
scfh/stationb
scfy/meterb
scfy/meterb
Mscf/mileb
Mscfy/mileb
Mscfy/mileb
149,037.3
135,245.1
28,779.7
126,181.9
128,287.1
50,824.7
8,085.1
4,943.6
105,101.3
203,935.5
4,914.3
103,468.3
487.9
78,300.9
156.3
6,033.8
445.0
327.2
98,493.2
4,134.0
1,198.4
2,271.9
35,414.5
(0.0)
(44.8)
(44.8)
1,276.1
1,231.3
= Pipeline and Hazardous Materials Safety Administration (PHMSA), Office of Pipeline Safety (OPS) (2012)
b EPA/GRI (1996), Methane Emissions from the Natural Gas Industry
c ICF (2005), Plastic Pipe Emission Factors
d ICF (2008), Natural Gas Model Activity Factor Basis Change
aa Emission factors listed in this table are for potential emissions (unless otherwise indicated in a footnote). For many of these sources, emission
reductions are subtracted from potential emissions to calculate net emissions. For this reason, emission factors presented in these tables cannot
be used to directly estimate net emissions from these sources. See detailed explanation of methodology above.
1 Activity data for 2012 available from source.
2 Ratios relating other factors for which activity data are available.
A-188 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-131: U.S. Production Sector CHa Content in Natural Gas by HEMS Region [General Sources!
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
North East Midcontinent
84.0%
83.8%
83.5%
82.9%
82.0%
81.5%
81.2%
80.3%
81.0%
80.5%
80.8%
80.3%
80.4%
76.4%
80.4%
80.1%
79.5%
85.8%
86.0%
85.1%
84.3%
85.2%
85.2%
78.3%
78.7%
79.1%
79.9%
80.7%
81.6%
82.6%
82.5%
82.5%
82.5%
82.5%
82.5%
82.5%
82.6%
82.7%
82.7%
83.0%
82.7%
82.7%
82.7%
82.8%
82.6%
82.6%
U.S. Region
Rocky
Mountain South West West Coast Gulf Coast Lower 48 States
67.4%
69.3%
71.2%
73.4%
75.5%
77.6%
80.5%
80.4%
80.5%
80.4%
80.2%
79.5%
79.3%
79.1%
79.0%
79.0%
79.0%
77.5%
77.7%
77.5%
77.4%
77.5%
77.5%
64.4%
67.1%
74.4%
76.1%
77.4%
79.0%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
75.3%
78.1%
80.8%
83.6%
86.4%
89.1%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
79.8%
80.1%
82.7%
84.1%
85.6%
87.2%
88.7%
88.6%
88.6%
88.7%
88.7%
88.7%
88.6%
88.6%
88.6%
88.6%
88.6%
88.6%
88.5%
88.5%
88.3%
88.2%
88.2%
n/a
n/a
n/a
n/a
n/a
n/a
84.2%
84.1%
84.2%
84.2%
84.0%
83.8%
83.5%
83.2%
83.4%
83.4%
83.5%
83.9%
83.9%
83.6%
83.4%
83.3%
83.3%
Table A-132: U.S. Production Sector CH* Content in Natural Gas by HEMS Region (Gas Wells Without Hydraulic Fracturingl
U.S. Region
Rocky
Year North East Midcontinent Mountain South West West Coast Gulf Coast Lower 48 States
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
84.0%
83.8%
83.5%
82.9%
82.0%
81.5%
81.2%
80.5%
81.2%
80.7%
81.0%
80.4%
80.5%
76.5%
80.5%
80.3%
79.6%
85.6%
85.6%
84.7%
83.8%
85.0%
85.0%
78.3%
78.7%
79.1%
79.9%
80.7%
81.6%
82.5%
82.5%
82.5%
82.5%
82.5%
82.5%
82.5%
82.6%
82.6%
82.7%
83.0%
82.7%
82.7%
82.7%
82.8%
82.6%
82.6%
67.4%
69.3%
71.2%
73.4%
75.5%
77.6%
79.6%
79.5%
79.6%
79.5%
79.2%
78.3%
78.1%
77.9%
77.8%
77.7%
77.7%
75.8%
76.0%
75.8%
75.7%
75.8%
75.8%
64.4%
67.1%
74.4%
76.1%
77.4%
79.0%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
75.3%
78.1%
80.8%
83.6%
86.4%
89.1%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
79.8%
80.1%
82.7%
84.1%
85.6%
87.2%
88.6%
88.6%
88.6%
88.7%
88.7%
88.6%
88.6%
88.6%
88.6%
88.6%
88.6%
88.6%
88.5%
88.5%
88.3%
88.2%
88.2%
n/a
n/a
n/a
n/a
n/a
n/a
84.0%
83.9%
84.0%
83.9%
83.8%
83.5%
83.2%
82.9%
83.1%
83.1%
83.1%
83.5%
83.5%
83.2%
82.9%
82.8%
82.8%
Table A-133: U.S. Production Sector CHa Content in Natural Gas by HEMS Region (Gas Wells With Hydraulic Fracturingl
U.S. Region
Rocky
Year North East Midcontinent Mountain South West West Coast Gulf Coast Lower 48 States
1990
1991
1992
84.0%
83.8%
83.5%
78.3%
78.7%
79.1%
67.4%
69.3%
71.2%
64.4%
67.1%
74.4%
75.3%
78.1%
80.8%
79.8%
80.1%
82.7%
n/a
n/a
n/a
A-189
-------
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
82.9%
82.0%
81.5%
83.2%
83.1%
83.1%
83.1%
83.0%
83.0%
83.0%
83.1%
83.0%
83.0%
83.0%
83.5%
84.1%
84.1%
84.3%
83.6%
83.6%
79.9%
80.7%
81.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
92.6%
73.4%
75.5%
77.6%
74.4%
74.9%
75.5%
75.4%
76.4%
78.9%
80.5%
81.4%
81.7%
82.0%
82.3%
86.5%
86.2%
86.8%
86.8%
87.9%
88.1%
76.1%
77.4%
79.0%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
80.5%
83.6%
86.4%
89.1%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
91.9%
84.1%
85.6%
87.2%
88.7%
88.6%
88.6%
88.7%
88.7%
88.7%
88.6%
88.6%
88.6%
88.6%
88.6%
88.6%
88.5%
88.5%
88.3%
88.2%
88.2%
n/a
n/a
n/a
82.1%
82.1%
82.3%
82.0%
82.5%
83.6%
84.4%
84.9%
85.2%
85.3%
85.5%
88.7%
88.4%
88.7%
89.0%
89.4%
89.5%
A-190 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-134: Key Activity Data Drivers
Variable
Units
1990
1995
2000
2005
2011
2012
Transmission Pipelines Length
miles
291,925
296,947
298,957
300,468
304,954
303,126
Wells
NE—Associated Gas Wells3-1 # wells 25,835
NE—Non-associated Gas Wells3-1 # wells 64,134
MC—Associated Gas Wells3-1 #wells 37,568
MC—Non-associated Gas Wells3-1 # wells 65,317
RM—Associated Gas Wells3-1 #wells 18,565
RM—Non-associated Gas Wells3-1 #wells 26,036
SW-Associated Gas Wells3-1 #wells 233,236
SW—Non-associated Gas Wells3-1 # wells 22,146
WC-Associated Gas Wells3-1 # wells 17,130
WC—Non-associated Gas Wells3-1 #wells 2,041
GC—Associated Gas Wells3-1 #wells 101,151
GC—Non-associated Gas Wells3-1 #wells 36,692
Platforms33
Gulf of Mexico and Pacific DCS Off-shore
Platforms"-2 # platforms 3,941
GoM and Pacific DCS Deep Water
23,534
90,350
32,508
69,934
19,852
31,574
175,589
22,653
14,035
1,929
69,484
38,279
3,978
24,522
103,760
28,605
75,000
19,332
42,324
156,865
25,472
15,775
2,029
50,089
43,419
4,016
29,536
129,765
27,496
89,817
21,032
66,975
148,294
33,600
18,765
2,030
41,391
57,415
3,909
37,115
156,158
27,926
108,559
29,243
84,349
154,144
41,380
29,318
2,152
38,319
76,828
3,432
aa Number of platforms include both oil and gas platforms
3 Dl Desktop (2014)
b Bureau of Ocean Energy Management, Regulation and Enforcement (2011)
c Oil and Gas Journal
d Pipeline and Hazardous Materials Safety Administration (PHMSA), Office of Pipeline Safety (OPS) (2013)
1 Activity data for 2012 available from source.
2 2011 activity data are used to determine some or all of the 2012 activity (to be updated).
38,770
158,974
27,470
108,052
32,598
83,420
155,119
41,487
29,726
2,094
39,709
76,886
3,432
Platforms "-2
Gas Plants' 1
Distribution Services
Steel— Unprotected d>1
Steel— Protected d>1
Plastic i-1
Copper d-1
Distribution Mains
Cast Iron d>1
Steel— Unprotected d>1
Steel— Protected d-1
Plastic d-1
# platforms
# gas plants
# of services
# of services
# of services
# of services
# of services
miles
miles
miles
miles
miles
17
761
47,883,083
7,633,526
19,781,581
18,879,865
1,588,111
944,157
58,292
108,941
465,538
311,386
23
675
54,644,033
6,151,653
21,002,455
26,044,545
1,445,380
1,001,706
50,625
94,058
503,288
353,735
38
585
56,761,042
5,675,520
17,855,560
31,795,871
1,434,091
1,048,485
44,750
82,800
471,510
449,425
59
566
61,832,574
5,507,356
16,529,118
38,549,089
1,247,011
1,162,560
39,645
72,458
490,156
560,301
70
606
64,731,838
4,140,616
15,267,357
44,269,061
1,054,804
1,231,045
33,586
64,092
488,265
645,102
70
606
65,024,491
3,916,353
14,951,473
45,147,410
1,009,255
1,244,470
32,418
63,727
487,225
661,100
A-191
-------
Table A-135: CHa Reductions Derived from the Natural Gas STAR Program tCgl
Process
1990
1995
2000
2005
2011
2012
Production
Pipeline Leaks
Pneumatic Device Vents
Chemical Injection Pumps
Gas Engines
Compressor Starts
Other Production
Processing
Fugitives Reciprocating Compressors
Gas Engines
AGR Vents
Dehydrator Vents
Other Processing
Transmission and Storage
Fugitives Reciprocating Compressors
Engines
Pneumatic Device Vents (Transmission)
Pipeline Vents
Other Transmission
Distribution
Fugitives Cast Iron
Mishaps (Dig-ins)
Other Distribution
(9.0) (86.4)
(0.0) (0.0)
(5.4)
(0.0)
-
(3.5)
(1.5)
-
(1.3)
(0.2)
(25.8)
(13.8)
(0.0)
(46.8)
(21.8)
_•
(21.8)
(107.7)
(0.6)
(12.5)
(5.4)1
(36.3)
(52.8)
(19.7)
(0.0)
_
(19.7)
(302.5)
1"
(U.I) •
(130.9)
(42.8)
(0.1)
(0.2)
(42.6)
(264.0)
.
(49.3)1
(8.9)
(33.3) •
(172.5)
(29.9)
(0.1)1
_
(29.9) 1
(579.0) (1,603.0)
(2.4)
(226.4)
(0.0)
(97.9)
(0.2)
(251.9)
(155.5)
(1.1)
(2.1)
(152.2)
(506.8)
(0.2)
(83.2)
(10.5)
(124.9)
(288.1)
(48.4)
(0.1)
(0.3)
-
(806.7)
(2.1)
(137.2)
(0.5)
(656.5)
(140.4)
(6.1)
(9.3)
(125.0)
(355.0)
(0.2)
(121.6)
(13.0)
(58.9)
(161.3)
(58.1)
(0.1)
(4.7)
(48.0) (53.3)
(1,635.7)
-
(873.1)
(2.8)
(139.9)
(0.5)
(619.3)
(140.4)
(6.1)
(9.3)
(125.0)
(390.1)
(0.7)
(123.9)
(14.1)
(100.1)
(151.2)
(44.8)
(0.1)
(0.7)
(44.0)
Total
(10.5)
(235.6)
(639.3)
(1,289.6)
(2,156.5)
(2,211.0)
These reductions will not match the Natural Gas STAR program reductions. These numbers are adjusted for reductions prior to the 1992 base year, and do not
include a "sunsetting" period. Totals may not sum due to independent rounding.
This table presents aggregate Gas STAR reduction data for each natural gas system stage, and also presents reductions for select technologies for which
disaggregated Gas STAR data can be matched to an Inventory source category. In general, the Inventory uses aggregated Gas STAR reductions by natural gas
system stage (i.e., production, processing, transmission and storage, and distribution). In some cases, emissions reductions reported to Gas STAR have been
matched to potential emissions calculated in the Inventory, to provide a net emissions number for specific emissions sources. This table presents sources for
which Gas STAR reductions can be matched to Inventory emissions sources. Net emissions values for these sources are presented in Table A-141. Some
reported Gas STAR reduction activities are cross-cutting and cover multiple Inventory sources. It is not possible to attribute those reductions to specific Inventory
source categories, and they are included in the "Other" category.
Table A-136: CH* Reductions Derived from Regulations (Gg)
Process
Production
Dehydrator vents (NESHAP)
Condensate tanks (NESHAP)
Processing
Dehydrator vents (NESHAP)
Transmission and Storage
Distribution
Total
1990
NA
NA
NA
(0.0)
(0.0)
NA
NA
(0.0)
1995
NA
NA
NA
(0.0)
(0.0)
NA
NA
(0.0)
2000
(45.5)
(23.8)
(21. 7) •
(12.9)
(12.9)
NA
NA
(58.4)
2005
(62.7)
(30.8)
(31.9)
(12.1)
(12.1) •
NA
NA
(74.8)
2011
(99.0)
(38.7)
(60.3)
(15.5)
(15.5)
NA
NA
(114.6)
2012
(99.2)
(38.9)
(60.3)
(16.3)
(16.3)
NA
NA
(115.5)
NA Not applicable
Note: Totals may not sum due to independent rounding.
Table A-137: National CH* Potential Emission Estimates from the Natural Gas Production Stage, and Reductions from the
Natural Gas STAR Program and Regulations tCgl
Activity
1990
1995
2000
2005
2011
2012
Normal Fugitives
Associated Gas Wells IE
Non-Associated Gas Wells (less wells
with hydraulic fracturing) 10.8
Gas Wells with Hydraulic Fracturing 8.5
Field Separation Equipment
Heaters 12.9
I
12.71
12.1 •
15.9
IE
18.3
35.5
33.5
IE
18.3
35.4
33.3
A-192 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Activity
Separators
Dehydrators
Meters/ Piping
1990
40.9
14.4
46.2
1995B
50.1
17.2
54.1
2000
60.7
20.0
63.4
2005 2011
84.0
107.1
2012
106.7
32.8
106.2
Gathering Compressors
Small Reciprocating Compressors 33.2
Large Reciprocating Compressors 7.31
Large Reciprocating Stations 0.5
Pipeline Leaks 89.1
Vented and Combusted
Drilling, Well Completion, and Well
Workover
Gas Well Completions without
Hydraulic Fracturing 0.0
Gas Well Workovers without
Hydraulic Fracturing 0.3
Hydraulic Fracturing Completions
and Workovers that vent 219.1
Flared Hydraulic Fracturing
Completions and Workovers 3.0
Hydraulic Fracturing Completions
and Workovers with RECs
Hydraulic Fracturing Completions
and Workovers with RECs that
flare
Well Drilling 0.7
Produced Water from Coal Bed
Methane Wells
Powder River 0.0
Black Warrior 2.7
Normal Operations
Pneumatic Device Vents 529.6
Chemical Injection Pumps 30.0
Kimray Pumps 165.9
Dehydrator Vents 51.7
Condensate Tank Vents
Condensate Tanks without Control
Device 77.7
Condensate Tanks with Control
Device 15.5
Compressor Exhaust Vented
Gas Engines 117.6
Liquids Unloading
Liquids Unloading (with plunger lifts)
Liquids Unloading (without plunger lifts) 893.0
Slowdowns
Vessel Blowdown 0.31
Pipeline Blowdown 1.4l
Compressor Blowdown 1.31
Compressor Starts 2.9
Upsets
Pressure Relief Valves 0.3
Mishaps 0.8
Offshore
Offshore Water Gas Platforms (Gulf of
39.1
8.91
0.6
105.7
0.0
0.4
198.9
2.7
0.5
1.5
6.3
642.7
35.8
206.0
64.2
58.1
11.61
139.0
16.5
912.5
0.4
ii
3.4
0.4
0.9
45.2
10.0
0.7
118.8
0.0
0.4
304.8
4.1
1.0
31.4
6.8
745.8
42.1
238.0
74.2
67.5
135
162.0
37.8
831.3
0.4|
1.9
1.7
3.9
O.J
1.0
58.1
11.7
0.8
146.3
0.0
0.5
415.3
7.3
5.4
3.6
1.6
50.0
9.9
973.5
55.5
307.6
95.9
99.3
19.9
213.9
72.2
678.5
0.6
2.3
2.2
5.0
0.6
1.3
71.3
15.4
1.0
175.5
0.0
0.5
191.8
6.9
11.6
7.8
1.0
47.2
12.7
1,209.0
67.8
387.1
120.7
187.7
37.5
266.8
118.8
154.4
0.7
2.8
2.8
6.2
0.7
1.5
70.9
15.4
1.0
175.5
0.0
0.5
192.2
3.9
10.2
10.9
1.0
47.2
12.8
1,207.5
67.3
388.4
121.1
187.7
37.5
265.7
118.9
154.7
0.7
2.8
2.7
6.1
0.7
1.5
Mexico & Pacific)
Deepwater Gas Platforms (Gulf of
Mexico & Pacific)
290.5
5.2
307.3
7.4
Regulatory Reductions - -H
Voluntary Reductions
Total Reductions
Total Potential Emissions
Total Net Emissions
(9.0)
(9.0)
2,673.4
2,664.5
(86.4)
(86.4)
2,936.2
2,849.8
323.7
12.8
(45.5)
(302.5)
(348.0)
3,274.5
2,926.5
20.4
(62.7)
(579.0)
(641.6)
3,847.6
3,206.0
1 23.0
(99.0)
(1,603.0)
(1,702.0)
3,729.8
2,027.7
266.1
23.0
(99.2)
(1,635.7)
(1,734.8)
3,726.6
1,991.8
Note 1: Totals may not sum due to independent rounding.
A-193
-------
IE: Included Elsewhere. These emissions are included in the Petroleum Systems estimates.
Table A-138: Potential CH* Emission Estimates from the Natural Gas Processing Plants, and Reductions from the Natural
Gas STAR Program and Regulations (Gg)
Activity
1990
1995
2000
2005
2011
2012
Normal Fugitives
Plants
Reciprocating Compressors
Centrifugal Compressors (wet seals)
Centrifugal Compressors
(dry seals)
Vented and Combusted
Normal Operations
Compressor Exhaust
Gas Engines
Gas Turbines
AGR Vents
Kimray Pumps
Dehydrator Vents
Pneumatic Devices
Routine Maintenance
Slowdowns/Venting
42.3
324.9
240.3
137.1
3.9
16.5
3.7
22.7
2.4
59.5
37.5
338.4
248.6
0.8
142.8
4.0
14.6
3.8
23.6
2.1
52.8
32.5
349.5
251.3
|£J I.O
147.5
4.2
12.7
U.I
13
45.7
31.5
327.9
229.2
6.5
138.3
3.9
12.3
3.7
22.9
1.8
44.3
33.7
420.9
236.1
36.8
177.6
5.0
13.1
4.8
29.4
1.9
47.4
33.7
442.6
237.7
43.9
186.8
5.3
13.1
5.0
30.9
1.9
47.4
Regulatory Reductions
(12.9)
(12.1)
(15.5)
(16.3)
Voluntary Reductions
(1.5)
(21.8)
(42.8)
(155.5)
(140.4) (140.4)
Total Reductions
(1.5)
(21.8)
Total Potential Emissions
853.2
869.2
Total Net Emissions
851.8
847.3
(55.
877.1
821.
LZ)
ZJ
!1.3
(167.6)
(155.9) (156.8)
822.2
1,006.6 1,048.3
654.6
850.7
891.5
Note 1: Totals may not sum due to independent rounding.
Table A-139: Potential CH* Emission Estimates from the Natural Gas Transmission and Storage, and Reductions from the
Natural Gas STAR Program and Regulations (Gg)
Activity
Fugitives
Pipelines Leaks
Compressor Stations (Transmission)
Station
Recip Compressor
Centrifugal Compressor (wet seals)
Centrifugal Compressor (dry seals)
Compressor Stations (Storage)
Station
Recip Compressor
Centrifugal Compressor (wet seals)
Centrifugal Compressor (dry seals)
Wells (Storage)
M&R (Trans. Co. Interconnect)
M&R (Farm Taps + Direct Sales)
Vented and Combusted
Normal Operation
Dehydrator Vents (Transmission)
Dehydrator Vents (Storage)
Compressor Exhaust
Engines (Transmission)
Turbines (Transmission)
Engines (Storage)
Turbines (Storage)
Generators (Engines)
Generators (Turbines)
Pneumatic Devices Transmission + Storage
Pneumatic Devices Trans
Pneumatic Devices Storage
Routine Maintenance/Upsets
1990 1995
3.2
106.9
744.7
246.7
54.6
157.8
33.2
13.6
72.8
16.9
2.0
4.2
176.9
1.0
21.3
0.2
8.7
0.0
213.1
44.4
3.2B
108.8
757.5
249.7
0.8
60.41
174.3
36.6
nil
15.01
74.01
17.21
2.ol
1
204.9
1.2l
23.5>
0.2l
10.uB
o.ol
216jl
49. ll
2000
3.3
109.5
762.7
243.0
6.2
62.2
179.6
34.4
2.5
15.4
74.5
17.3
'
1.2
24.2
0.2
"
218.2
50.6
2005
3.3
110.1
766.5
234.1
12.7
60.1
173.5
30.9
4.1
14.9
74.9
17.4
4.6
203.1
1.2
23.4
0.2
9.9
0.0
219.3
48.8
2011
3.3
111.7
778.0
234.4
15.0
58.7
169.4
26.5
6.5
14.6
76.0
17.7
2.1
4.5
225.0
1.3
22.9
0.2
11.0
0.0
222.6
47.7
2012
3.3
111.0
773.3
232.8
15.0
52.0
150.2
22.3
6.5
12.9
75.6
17.5
2.1
4.0
235.3
1.3
20.3
0.2
11.5
0.0
221.3
42.3
A-194 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Activity
Pipeline Venting
Station venting Transmission + Storage
Station Venting Transmission
Station Venting Storage
LNG Storage
LNG Stations
LNG Reciprocating Compressors
LNG Centrifugal Compressors
LNG Compressor Exhaust
LNG Engines
LNG Turbines
LNG Station Venting
LNG Import Terminals
LNG Stations
LNG Reciprocating Compressors
LNG Centrifugal Compressors
LNG Compressor Exhaust
LNG Engines
LNG Turbines
LNG Station Venting
1990
178.0
145.5
30.3
9.2
34.5
11.81
2.6
0.0
5.1
0.2
1.0
0.3
1.7
0.0
0.1
1995
181.0
148.0
33.5
2000
182.2
149.0
34.5
10.3
38.8
13.3J
2.7
0.0
57
0.2
1.0
0.3
4.4
0.0
0.1
2005
183.2
149.71
33.4
10.6
40.1
13.8
0.1
0.2
2011
185.9
152.0
32.6
10.6
40.1
13.8
2.7
0.0
5.9
1.2
5.6
1.4
6.9
0.0
0.6
2012
184.8
151.1
28.9
10.6
40.1
13.8
2.7
0.0
5.9
1.2
5.6
1.4
3.6
0.0
0.6
Regulatory Reductions
Voluntary Reductions
(107.7)
(264.0)
(506.8)
(355.0)
(390.1)
Total Reductions
(107.7)
(264.0)
(506.8)
(355.0)
(390.1)
Total Potential Emissions
Total Net Emissions
2,342.6
2,441.6
2,480.4
2,470.0
2,342.6
2,333.9
2,216.4
1,963.2
2,508.2
2,153.1
2,461.1
2,071.0
Note: Totals may not sum due to independent rounding.
Table A-140: Potential CH* Emission Estimates from the Natural Gas Distribution Stage, and Reductions from the Natural
Gas STAR Program, and Regulations tugl
Activity
1990
Pipeline Leaks
Mains—Cast Iron
Mains—Unprotected steel
Mains—Protected steel
Mains—Plastic
Services—Unprotected steel
Services Protected steel
Services—Plastic
Services—Copper
Meter/Regulator (City Gates)
M&R >300
M&R 100-300
M&R<100
Reg >300
R-Vault >300
Reg 100-300
R-Vault 100-300
Reg 40-100
R-Vault 40-100
Reg <40
Customer Meters
Residential
Commercial/Industry
Routine Maintenance
Pressure Relief Valve Releases
Pipeline Blowdown
Upsets
Mishaps (Dig-ins)
1995
2000
2005
2011
2012
268.0
231.2
27.51
59.4
250.0
67.21
3.4J
Iv.
214.2
5.2
108.7
0.5
82.3
0.2
6.3
0.5
0.3
103.5
4.0
0.9
2.4
37.21
232.7
199.6
29.71
67.5
201.5
71.41
4.7l
1
122.0
236.6
57I
120.1
o.el
90.9
0.2
7.0
0.5
0.4
114.31
4.8
1.0
2.6
41.1
205.7
175.7
27.91
85.8
185.9
60.7
57
125.6
243.8
5.9l
123.7
o.el
93.6
0.2
7.2
0.5
0.4
117.71
1
1.0
42.3
182.3
153.8
29.0
106.9
180.4
56.2
6.9
6.1
121.41
235.5
57|
119.5
0.6
90.4
0.2
7.0
0.5
0.4
113.71
3.9
1.1
2.6
40.9
154.4
136.0
28.8
123.1
135.6
51.9
7.9
5.2
118.5
230.0
5.5
116.7
0.6
88.3
0.2
6.8
0.5
0.4
111.1
4.1
1.2
2.6
39.9
149.0
135.2
28.8
126.2
128.3
50.8
8.1
4.9
105.1
203.9
4.9
103.5
0.5
78.3
0.2
6.0
0.4
0.3
98.5
4.1
1.2
2.3
35.4
Regulatory Reductions
A-195
-------
Activity
Voluntary Reductions
Total Reductions
Total Potential Emissions
Total Net Emissions
1990
-
-
1,591.1
1,591.1
1995
(19.7)
(19.7)
1,561.9
1,542.1
2000
(29.9)
(29.9)
1,524.3
1,494.4
2005
(48.4)
(48.4)
1,464.9
1,416.5
2011
(58.1)
(58.1)
1,369.4
1,311.3
2012
(44.8)
(44.8)
1,276.1
1,231.3
Note: Totals may not sum due to independent rounding.
Table A-141: Net emissions for select sources (Gg)
Stage/Activity
Production
Hydraulic Fracturing Completions and Workow
Liquids Unloading
Dehydrator Vents
Condensate Tanks
Pipeline Leaks
Pneumatic Device Vents
Chemical Injection Pumps
Gas Engines
Compressor Starts
Other Production
Processing
Fugitives Reciprocating Compressors
Gas Engines
AGR Vents
Dehydrator Vents
Other Processing
Transmission and Storage
Fugitives Reciprocating Compressors
Engines
Pneumatic Device Vents (Transmission)
Pipeline Vents
Other Transmission
Distribution
Fugitives Cast Iron
Mishaps (Dig-ins)
Other Distribution
Total
1990
2,664.5
222.1
893.0
51.7
93.2
89.1
524.2
30.0
117.6
2.9
640.7
851.8
324.9
137.1
16.5
21.3
351.9
2,342.6
744.7
176.9
213.1
178.0
1,030.0
1,591.1
268
37
1,286
7,450
1995
2,849.8
201.6
929.0
64.2
69.7
105.7
616.9
35.8
125.3
34
698.2
847.3
338.4
142.8
14.6
23.6
327.9
2,333.9
756.9
192.4
211.4
144.7
1,028.6
1,542.1
233
41
1,268
7,573
2000
2,926.5
308.9
869.1
50.4
59.3
118.8
627.4
42.1
108.8
3.8
737.9
821.3
349.5
147.4
12.7
11.3
300.5
2,216.4
762.7
166.0
209.3
149.0
929.4
1,494.4
206
42
1,246
7,459
2005
3,206.0
434.1
750.7
65.1
87.3
143.8
747.1
55.5
116.0
4.8
801.5
654.6
327.9
137.2
12.3
8.6
168.6
1,963.2
766.3
119.9
208.9
58.3
809.8
1,416.5
1821
41
1,194
7,240
2011
2,027.7
218.1
273.2
82.0
164.9
175.5
402.2
65.7
129.6
5.6
510.8
850.7
420.9
171.5
13.1
4.5
240.8
2,153.1
777.7
103.4
209.6
126.9
935.4
1,311.3
154
35
1,222
6,343
2012
1,991.8
217.1
273.6
82.2
164.9
175.5
334.4
64.6
125.8
5.6
548.1
891.5
442.6
180.7
13.1
5.2
249.9
2071.0
772.6
111.4
207.2
84.7
895.2
1231.3
149
34.7
1,048
6,186
Note: This table presents net emissions for each natural gas system stage, and also presents net emissions for select emissions sources for which disaggregated
Gas STAR data and/or regulation reduction data can be matched to an Inventory source category, and sources for which emissions are calculated using net
emission factors. In general, the Inventory uses aggregated Gas STAR reductions by natural gas system stage (i.e., production, processing, transmission and
storage, and distribution). In some cases, emissions reductions reported to Gas STAR have been matched to potential emissions calculated in the Inventory, to
provide a net emissions number for specific emissions sources. This table presents sources for which Gas STAR reductions and/or regulatory reductions can be
matched to Inventory emissions sources. Net emission values presented here were calculated by deducting the voluntary reductions (Table A-135) and the
regulatory reductions (Table A-136) from the potential emissions values in Table A-137 throughTable A-140. Some reported Gas STAR reduction activities are
cross-cutting and cover multiple Inventory sources. It is not possible to attribute those reductions to specific Inventory source categories, and they are included in
the "Other" category.
Table A-142: U.S. Production Sector C0? Content in Natural Gas by HEMS Region and Formation Type for all years
Formation Types North East Midcontinent Gulf Coast
U.S. Region
SouthWest Rocky Mountain West Coast Lower-48 States
Conventional
Non-conventional*
All types
0.92%
7.42%
3.04%
0.79%
0.31%
0.79%
2.17%
0.23%
2.17%
3.81%
NA
3.81%
7.95%
0.64%
7.58%
0.16%
NA
0.16%
3.41%
4.83%
3.45%
Source: GRI-01/0136 GTI's Gas Resource Database: Unconventional Natural Gas and Gas Composition Databases. Second Edition. August, 2001
*ln GTI, this refers to shale, coal bed methane, and tight geologic formations.
Table A-143: C02 Emission Estimates from tbe Natural Gas Production Stage tCgl
Activity 1990
Normal Fugitives
Gas Wells
Non-Associated Gas Wells
1.0
1995 2000
,.,
2005
2011 2012
1.3
1.5
1.5
A-196 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Activity
Gas Wells with Hydraulic Fracturing
Field Separation Equipment
Heaters
Separators
Dehydrators
Meters/ Piping
Gathering Compressors
Small Reciprocating Compressors
Large Reciprocating Compressors
Large Reciprocating Stations
Pipeline Leaks
Vented and Combusted
Drilling, Well Completion, and Well
Workover
Gas Well Completions without
Hydraulic Fracturingb
Gas Well Workovers without Hydraulic
Fracturingb
Gas Well Completions with Hydraulic
Fracturing
Gas Well Workovers with Hydraulic
Fracturing
Well Drilling
Produced Water from Coal Bed
Methane Wells
Powder River1
Black Warrior3
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without Control
Device
Condensate Tanks with Control Device
Compressor Exhaust Vented
Gas Engines3
Liquids Unloading
Liquids Unloading - Vent with plunger
Lifts
Liquids Unloading - Vent without
plunger Lifts
Slowdowns
Vessel Blowdownsb
Pipeline Slowdowns
Compressor Blowdowns
Compressor Starts
Upsets
Pressure Relief Valvesb
Mishaps
Flaring Emissions - Onshore
Production and Processing
Offshore
Shallow water Gas Platforms (Gulf of
Mexico & Pacific)
Deepwater Gas Platforms (Gulf of
Mexico & Pacific) b
Flaring Emissions - Offshore
Total
1990
0.4
5.9
1.4
6.3
3.0
0.8
0.1
9.8
106.8
16.9
0.1
NE
NE
52.4
3.1
16.2
I1"
U.I
0.3
0.0
0.1
0.0
230.4
9,834.9
1995 2000 2005 2011
0.5 0.7 0.9 1.0
2.2
6.6
1.6
6.8
3.3
0.9
0.1
10.9
0.0
0.0
96.5
21.5
0.1
NE
NE
61.5
3.6
19.7
6.1
8.8
1.8
NE
1.7
244.3
0.0
0.2
0.1
0.3
0.0
0.1
2.8
8.2
2.0
8.3
4.1
1.0
0.1
12.6
0.0
0.0
211.3
28.2
0.1
NE
NE
75.4
4.6
23.6
7.4
9.3
1.9
NE
4.2
220.8
0.0
0.2
0.2
0.4
0.0
0.1
17,167.8 5,525.0
1.6 1.6
0.0 0.1
4.2
12.0
2.8
11.9
6.0
1.3
0.1
16.7
0.0
0.0
296.6
43.1
0.2
NE
NE
108.0
6.8
32.7
10.2
10.3
2.1
NE
8.9
183.2
0.1
0.3
0.2
0.5
0.1
0.1
5.3
15.3
3.6
15.0
7.5
1.7
0.1
20.1
0.0
0.0
242.5
44.1
0.1
NE
NE
134.8
8.6
40.6
12.7
15.7
3.1
NE
14.7
27.2
0.1
0.3
0.3
0.6
0.1
0.2
7,193.0 12,534.7
i.el 1.4
O.ll 0.1
197.2 204.3 180.7 358.0
17,866.9 6,359.7 8,135.8 13,511.1
2012
1.0
5.2
15.2
3.5
14.9
7.4
1.7
0.1
20.1
0.0
0.0
208.9
26.1
0.1
NE
NE
134.3
8.5
40.7
12.7
15.7
3.1
NE
14.6
27.1
0.1
0.3
0.3
0.6
0.1
0.2
12,738.8
1.4
0.1
358.0
13,662.6
A-197
-------
a Energy use C02emissions not estimated to avoid double counting. NE = not estimated.
b Emissions are not actually 0, but too small to show at this level of precision.
Note: Totals may not sum due to independent rounding.
Table A-144: C02 Emission Estimates from the Natural Gas Processing Stage tGgl
Activity
1990
1995
2000
2005
2011
2012
Normal Fugitives
Plants - Before C02 removal 2.6
Plants - After C02 removal 0.6
Reciprocating Compressors -
Before C02 removal 19.7
Reciprocating Compressors - After
C02 removal 4.4
Centrifugal Compressors (wet
seals) - Before C02 removal 14.5
Centrifugal Compressors (wet
seals) - After C02 removal 3.2
Centrifugal Compressors (dry seals)
- Before C02 removal
Centrifugal Compressors (dry seals)
- After C02 removal
Vented and Combusted
Normal Operations
Compressor Exhaust
Gas Engines3 NE
Gas Turbines3 NE
AGR Vents 27,708.2
Kimray Pumps 0.4
Dehydrator Vents 2.4
Pneumatic Devices 0.3
Routine Maintenance
Slowdowns/Venting 6.4
2.3
0.5
20.5
45
15.0
33
0.0
0.0
NE
NEJ
24,576.9
0.4
2.5
0.3
24,576.9
I i
5.6
2.0
0.4
212
4.7
152
34
0.2
0.0
NE
NE
23,288.2
0.4
2.61
0.2
4.9
1.9
0.4
19.8
4.4
13.9
3.1
0.4
0.1
NE
NEJ
21,694.3
0.4
2.4
0.2
4.7
2.0
0.5
25.5
5.7
14.3
3.2
2.2
0.5
NE
NE
21,403.6
0.5
3.1
0.2
5.1
2.0
0.5
26.8
5.9
14.4
3.2
2.7
0.6
NE
NE
21,403.6
0.5
3.3
0.2
5.1
Total
27,762.6
24,632.0
23,343.5
21,746.1
aEnergy use C02emissions not estimatedto avoid double counting. NE = not estimated.
Note: Totals may not sum due to independent rounding.
21,466.3
21,468.8
Table A-145: C02 Emission Estimates from the Natural Gas Transmission and Storage Stage tGgl
Activity 1990 1995 2000 2005 2011 2012
Fugitives
Pipelines Leaks
Compressor Stations (Transmission)
Station
Reciprocating Compressor
Centrifugal Compressor (wet seals)
Centrifugal Compressor (dry seals)
Compressor Stations (Storage)
Station
Reciprocating Compressor
Centrifugal Compressor (wet seals)
Centrifugal Compressor (dry seals)
Wells (Storage)
M&R (Trans. Co. Interconnect)
M&R (Farm Taps + Direct Sales)
Vented and Combusted
Normal Operation
Dehydrator Vents (Transmission)
Dehydrator Vents (Storage)
Compressor Exhaust
Engines (Transmission)1
Turbines (Transmission)1
Engines (Storage)1
Turbines (Storage)1
Generators (Engines)1
Generators (Turbines)1
0.1
3.1
21.5
7.1
0.1
3.1
21.9
72
0.0
1.6
4.6
1.0
0.4
2.1
0.5
I 1
1.7|
c n
0.1
0.1
NE
NE
NE
NE
NE
NE
I
1.7
5.0
1.1
0.0
0.4
2.1 [
0.5
0.1
0.1
NE
NE
NE
NE
NE
NE
0.1
32
22.0
7.01
0.2
1.8
5.2
1.0
0.11
0.4
2.1 [
0.5
NE
NE
NE
NE
NE
NE
0.1
3.2
22.1
6.81
0.4J
1.7
5.0
0.9
0.1 [
0.4|
2.2
0.5
0.1
0.1
NE
NE
NE
NE
NE
NE
0.1
3.2
22.4
6.8
0.4
1.7
4.9
0.8
0.2
0.4
2.2
0.5
0.1
0.1
NE
NE
NE
NE
NE
NE
0.1
3.2
22.3
6.7
0.4
1.5
4.3
0.6
0.2
0.4
2.2
0.5
0.1
0.1
NE
NE
NE
NE
NE
NE
A-198 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Activity
Pneumatic Devices Transmission + Storage
Pneumatic Devices Transmission
Pneumatic Devices Storage
Routine Maintenance/Upsets
Pipeline Venting
Station Venting Transmission + Storage
Station Venting Transmission
Station Venting Storage
LNG Storage
LNG Stations
LNG Reciprocating Compressors
LNG Centrifugal Compressors
LNG Compressor Exhaust
LNG Engines1
LNG Turbines1
LNG Station Venting
LNG Import Terminals
LNG Stations
LNG Reciprocating Compressors
LNG Centrifugal Compressors
LNG Compressor Exhaust
LNG Engines1
LNG Turbines1
LNG Station Venting2
Total
1990
6.1
I"
.._
0.9
0.3
1.2
0.4
I
1
1995
6.3
1.4
2000
5.3
2005
631
1.4
5.3
4.3
1.0
O.J
1.3
0.5
NE
NE
0.2
0.0
0.1
0.0
NE
NE
0.0
63.7
64.4
64.3
2011
6.4
1.4
5.4
4.4
0.9
0.4
1.3
0.5
NE
NE
0.2
0.0
0.2
0.0
NE
NE
0.0
2012
6.4
1.2
5.3
4.4
0.8
0.4
1.3
0.5
NE
NE
0.2
0.0
0.2
0.0
NE
NE
0.0
64.9
63.4
' Energy use C02emissions not estimated to avoid double counting. NE = not estimated.
2 Emissions are not actually 0, but too small to show at this level of precision.
Note: Totals may not sum due to independent rounding.
Table A-146: C02 Emission Estimates from the Natural Gas Distribution Stage tGgl
Activity 1990 1995 2000 2005 2011 2012
Pipeline Leaks
Mains—Protected steel 0.81 0.91 0.81 0.81 0.8 0.8
Pressure Relief Valve Releases 0.01 0.01 0.01 0.01 0.0 0.0
Mishaps (Dig-ins) 11 12 12 12 1.2 1.0
A-199
-------
Activity 1990 1995 2000 2005 2011 2012
Total 45.9 45J 44.0 42.3 39.5 36.8
Note: Totals may not sum due to independent rounding.
References
AGA (1991 through 1998) Gas Facts. American Gas Association. Washington, DC.
Alabama (2013) Alabama State Oil and Gas Board. Available online at .
API/ANGA (2012) Characterizing Pivotal Sources of Methane Emissions from Natural Gas Production — Summary and
Analysis of API and ANGA Survey Responses. Final Report. American Petroleum Institute and America's Natural Gas
Alliance. September 21.
BOEMRE (2004) Gulfwide Emission Inventory Study for the Regional Haze and Ozone Modeling Effort. DCS Study
MMS 2004-072.
BOEMRE (201 la) Gulf of Mexico Region Offshore Information. Bureau of Ocean Energy Management, Regulation
and Enforcement, U.S. Department of Interior.
BOEMRE (201 Ib) Pacific OCS Region Offshore Information. Bureau of Ocean Energy Management, Regulation
and Enforcement, U.S. Department of Interior.
BOEMRE (20 lie) GOM and Pacific OCS Platform Activity. Bureau of Ocean Energy Management, Regulation and
Enforcement, U.S. Department of Interior.
BOEMRE (20 lid) Pacific OCS Region. Bureau of Ocean Energy Management, Regulation and Enforcement, U.S.
Department of Interior.
Drillinglnfo (2014) DI Desktop® February 2014 Download. Drillinglnfo, Inc.
EIA (2012a) Formation crosswalk. Energy Information Administration, U.S. Department of Energy, Washington,
DC. Provided July 7.
EIA (2012b) Lease Condensate Production, 1989-2011, Natural Gas Navigator. Energy Information Administration,
U.S. Department of Energy, Washington, DC. Available online at
.
EIA (2013a) "Table 1—Summary of natural gas supply and disposition in the United States 2008-2013." Natural
Gas Monthly, Energy Information Administration, U.S. Department of Energy, Washington, DC. Available
online at .
EIA (2013b) "Table 2—Natural Gas Consumption in the United States 2008-2013." Natural Gas Monthly, Energy
Information Administration, U.S. Department of Energy, Washington, DC. Available online at
.
EIA (2013c) "Table 7— Marketed production of natural gas in selected states and the Federal Gulf of Mexico, 2008-
2013." Natural Gas Monthly, Energy Information Administration, U.S. Department of Energy, Washington,
DC. Available online at .
EIA (2014a) U.S. Imports by Country. Energy Information Administration, U.S. Department of Energy,
Washington, DC. Available online at .
EIA (2014b) Natural Gas Gross Withdrawals and Production. Energy Information Administration, U.S. Department of
Energy, Washington, DC. Available online at .
EIA (2005) "Table 5—U.S. Crude Oil, Natural Gas, and Natural Gas Liquids Reserves, 1977-2003." Energy
Information Administration, Department of Energy, Washington, DC.
EIA (2004) USING Markets and Uses. Energy Information Administration, U.S. Department of Energy,
Washington, DC. June 2004. Available online at .
A-200 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
EIA (2001) "Documentation of the Oil and Gas Supply Module (OGSM)." Energy Information Administration, U.S.
Department of Energy, Washington, DC. Available online at
.
EIA (1996) "Emissions of Greenhouse Gases in the United States" Carbon Dioxide Emissions. Energy Information
Administration, U.S. Department of Energy, Washington, DC.
EPA (2012) Greenhouse Gas Reporting Program- Subpart W - Petroleum and Natural Gas Systems. Environmental
Protection Agency.
EPA (2013a) Oil and Natural Gas Sector: Standards of Performance for Crude Oil and Natural Gas Production,
Transmission, and Distribution. Background Supplemental Technical Support Document for the Final New Source
Performance Standards. Environmental Protection Agency. September 2013.
EPA (2013b) Oil and Natural Gas Sector: New Source Performance Standards and National Emission Standards for
Hazardous Air Pollutants Reviews. Environmental Protection Agency, 40 CFR Parts 60 and 63, [EPA-HQ-OAR-
2010-0505; FRL-9665-1], RTN 2060-AP76.
EPA (2013c) Natural GasSTAR Reductions 1990-2012. Natural GasSTAR Program. September 2013.
EPA (2013d) Updating GHG Inventory Estimate for Hydraulically Fractured Gas Well Completions and Workovers.
Available online at: http://www.epa.gov/climatechange/Downloads/ghgemissions/memo-update-emissions-for-
hydraulically -worko vers. pdf.
EPA (2013e) Greenhouse Gas Reporting Program- Subpart W-Petroleum and Natural Gas Systems. Environmental
Protection Agency. September 2013.
EPA (1999) Estimates of Methane Emissions from the U.S. Oil Industry (Draft Report). Prepared by ICF-Kaiser,
Office of Air and Radiation, U.S. Environmental Protection Agency. October 1999.
EPA/GRI (1996) Methane Emissions from the Natural Gas Industry. Prepared by Harrison, M, T. Shires, J.
Wessels, and R. Cowgill, eds., Radian International LLC for National Risk Management Research Laboratory,
Air Pollution Prevention and Control Division, Research Triangle Park, NC. EPA-600/R-96-080a.
FERC (2011) North American LNG Terminals. Federal Energy Regulatory Commission, Washington, DC.
GTI (2001) Gas Resource Database: Unconventional Natural Gas and Gas Composition Databases. Second Edition.
GRI-01/0136.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, H.S. Eggleston, L. Buenida, K. Miwa, T Ngara, and K. Tanabe, eds.; Institute for
Global Environmental Strategies (IGES). Hayama, Kanagawa, Japan.
OGJ (1997-2013) "Worldwide Gas Processing." Oil & Gas Journal, PennWell Corporation, Tulsa, OK. Available
online at .
PHMSA (2013a) Transmission Annuals Data. Pipeline and Hazardous Materials Safety Administration, U.S. Department
of Transportation, Washington, DC. Available online at < http://phmsa.dot.gov/pipeline/library/data-stats >.
PHMSA (2013b) Gas Distribution Annual Data. Pipeline and Hazardous Materials Safety Administration, U.S.
Department of Transportation, Washington, DC. Available online at < http://phmsa.dot.gov/pipeline/library/data-stats
>
Wyoming (2013) Wyoming Oil and Gas Conservation Commission. Available online at
.
A-201
-------
3.6. Methodology for Estimating Cm and C02 Emissions from Petroleum Systems
The methodology for estimating CH4 and non-combustion CC>2 emissions from petroleum systems is based on
the 1999 EPA draft report, Estimates of Methane Emissions from the U.S. Oil Industry (EPA 1999) and the study, Methane
Emissions from the U.S. Petroleum Industry (EPA/GRI 1996). Sixty-four activities that emit CH4 and thirty activities that
emit non-combustion CC>2 from petroleum systems were examined from these reports. Most of the activities analyzed
involve crude oil production field operations, which accounted for over 98 percent of total oil industry CH4 emissions.
Crude transportation and refining accounted for the remaining CH4 emissions of less than 0.4 and less than 1.3 percent,
respectively. Non-combustion CC>2 emissions were analyzed for production operations and asphalt blowing in refining
operations. Non-combustion CC>2 emissions from transportation operations are not included because they are negligible.
The following steps were taken to estimate CH^and CC>2 emissions from petroleum systems.
Step 1: Calculate Potential Methane and Carbon Dioxide
Activity Data
Activity levels change from year to year. Some data changes in proportion to crude oil rates: production,
transportation, refinery runs. Some change in proportion to the number of facilities: oil wells, petroleum refineries. Some
factors change proportional to both the rate and number of facilities.
For most sources, activity data for 1995 found in EPA/GRI 1996 are extrapolated to other years using publicly-
available data sources. For the remaining sources, the activity data are obtained directly from publicly available data.
For both sets of data, a determination was made on a case-by-case basis as to which measure of petroleum
industry activity best reflects the change in annual activity. Publicly-reported data from the Bureau of Ocean Energy
Management (BOEM), Energy Information Administration (EIA), American Petroleum Institute (API), the Oil & Gas
Journal (O&GJ), the Interstate Oil and Gas Compact Commission (IOGCC), and the U.S Army Corps of Engineers
(USAGE) were used to extrapolate the activity data from the base year to each year between 1990 and 2012. Data used
include total domestic crude oil production, number of domestic crude oil wells, total imports and exports of crude oil,
total petroleum refinery crude runs, and number of oil-producing offshore platforms. The activity data for the total crude
transported in the transportation sector is not available. In this case, all the crude oil that was transported was assumed to
go to refineries. Therefore, the activity data for the refining sector was used also for the transportation sector. In the few
cases where no data was located, oil industry data based on expert judgment was used. In the case of non-combustion CO2
emission sources, the activity factors are the same as for CFU emission sources.
Potential methane factors and emission factors
The CH4 emission factors for the majority of the activities are taken from the 1999 EPA draft report, which
contained the most recent and comprehensive determination of CFU emission factors for the 64 CFU-emitting activities in
the oil industry at that time. Emission factors for pneumatic devices in the production sector were recalculated in 2002
using emissions data in the EPA/GRI 1996c study. The gas engine emission factor is taken from the EPA/GRI 1996b
study. The oil tank venting emission factor is taken from the API E&P Tank Calc weighted average for API gravity less
than 45 API degrees with the distribution of gravities taken from a sample of production data from the FIPDI database.
Offshore emissions from shallow water and deep water oil platforms are taken from analysis of the Gulf-wide Offshore
Activity Data System (GOADS) report (EPA 2005, BOEMRE 2004). The emission factors were assumed to be
representative of emissions from each source type over the period 1990 through 2012. Therefore, the same emission
factors are used for each year throughout this period.
The CO2 emission factors were derived from the corresponding source CFU emission factors. The amount of CO2
in the crude oil stream changes as it passes through various equipment in petroleum production operations. As a result,
four distinct stages/streams with varying CO2 contents exist. The four streams that are used to estimate the emissions
factors are the associated gas stream separated from crude oil, hydrocarbons flashed out from crude oil (such as in storage
tanks), whole crude oil itself when it leaks downstream, and gas emissions from offshore oil platforms. The standard
approach used to estimate CO2 emission factors was to use the existing CFU emissions factors and multiply them by a
conversion factor, which is the ratio of CO2 content to methane content for the particular stream. Ratios of CO2 to CFU
volume in emissions are presented in Table A-151. The two exceptions are the emissions factor for storage tanks, which is
estimated using API E&P Tank Calc simulation runs of tank emissions for crude oil of different gravities less than 45 API
degrees; and the emissions factor for uncontrolled asphalt blowing, which is estimated using the data and methods
provided by API (2009).
A-202 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Step 2: Compile Reductions Data
The methane emissions calculated in Step 1 above generally represent expected emissions from an activity in the
absence of emissions controls, and do not take into account any use of technologies or practices that reduce emissions. To
take into account use of such technologies, data were collected on voluntary reductions. Voluntary reductions included in
the Petroleum Sector calculations were those reported to Gas STAR for the following activities: Artificial lift: gas lift,
Artificial lift: use compression, Artificial lift: use pumping unit, Consolidate crude oil prod and water storage tanks, Lower
heater-treater temperature, Re-inject gas for enhanced oil recovery, Re-inject gas into crude, and Route casinghead gas to
VRU or compressor.
Industry partners report CH4 emission reductions by project to the Natural Gas STAR Program. The reductions
from the implementation of specific technologies and practices are calculated by the reporting partners using actual
measurement data or equipment-specific emission factors. The reductions undergo quality assurance and quality control
checks to identify errors, inconsistencies, or irregular data before being incorporated into the Inventory. The Inventory
uses aggregated Gas STAR reductions for the petroleum sector.
Step 3: Calculate Net Methane and Carbon Dioxide Emissions for Each Activity for Each Year
Annual CH4 emissions from each of the 64 petroleum system activities and CC>2 emissions from the 30 petroleum
system activities were estimated by multiplying the activity data for each year by the corresponding emission factor.
These annual emissions for each activity were then summed to estimate the total annual CH4 and CC>2 emissions,
respectively. Gas Star reductions data is summed for each year and deducted from the potential CH4 calculated in Step 1
to estimate net CH4 emissions for the Inventory.
Table A-147, Table A-148, Table A-149, and Table A-152 provide 2012 activity data, emission factors, and
emission estimates and Table A-150 and Table A-153 provide a summary of emission estimates for the years 1990, 1995,
2000, and 2005 through 2012. Table A-151 provides the CC>2 content in natural gas for equipment in different crude
streams to estimate CC>2 emission factors using CILi emission factors.
The tables provide references for emission factors and activity data in footnotes (the lettered footnotes). The
tables also provide information on which method was used for supplying activity data for 2012 (the numbered footnotes).
Key to table notations on methods for supplying activity data for 2012 for all tables:
1 . Ratios relating other factors for which activity data are available. For example, EPA (1 996) found that the
number of heater treaters (a source of CFL emissions) is related to both number of producing wells and
annual production. To estimate the activity data for heater treaters, reported statistics for wells and
production were used, along with the ratios developed for EPA (1996).
2. Activity data for 2012 available from source.
3. Activity data was held constant from 1990 through 2012 based on EPA (1999).
4. 2009, 2010, or 201 1 activity data are used to determine some or all of the 2012 activity data.
Table A-147: 2012 CH* Emissions from Petroleum Production Field Operations
Activity/Equipment
2072 EPA Inventory Values
Emission Factor
Vented Emissions
Oil Tanks
Pneumatic Devices, High Bleed
Pneumatic Devices, Low Bleed
Chemical Injection Pumps
Vessel Slowdowns
Compressor Slowdowns
Compressor Starts
Stripper wells
7.39 scf of Cl-Wbbl crude3
330 scfd CI-Wdevicef
52 scfd CHWdevice'
248 scfd CI-Wpumph
78 scfy Cm/vessel"
3,775 scf/yr of Cl-Wcompressor11
8,443 scf/yr of Cl-Wcompressor11
2,345 scf/yr of CH4/stripper well'
Activity Data
MMbbl/yr (non stripper
1,878 wells)b'°'d'1'4
No. of high-bleed
145,179 devices0-6*1
No. of low-bleed
269,618 devices0'6*1
28,702 No. ofpumpsS'i'P1
189,710 No. of vessels0*1-1
2,582 No. of compressors0*1-1
2,582 No. of compressors0*'-1
No. of stripper wells
315,213 vented'-1'4
Emissions
(Bcf/yr)
71.623
13.890
17.508
5.117
2.599
0.015
0.010
0.022
0.739
Emissions
(Gg/yr)
1,377.373
267.121
336.692
98.411
49.973
0.285
0.187
0.419
14.21
A-203
-------
Activity/Equipment
Well Completion Venting
Well Workovers
Pipeline Pigging
Offshore Platforms, Shallow water Oil,
fugitive, vented and combusted
Offshore Platforms, Deepwater oil,
fugitive, vented and combusted
2072 EPA Inventory Values
Emission Factor
733 scf/completionh
96 scfCHWworkover
2.40 scfd of CH
-------
Table A-148: 2012 CH* Emissions from Petroleum Transportation
Emission
Activity/Equipment Factor
Vented Emissions
Tanks
Truck Loading
Marine Loading
Rail Loading
Pump Station Maintenance
Pipeline Pigging
0.021
0.520
2.544
0.520
36.80
39
Fugitive Emissions
Pump Stations
Pipelines
Floating Roof Tanks
25
NE
58,965
Combustion Emissions
Pump Engine Drivers
Heaters
0.24
0.521
Total
Units
scf Cl-Wyr/bbl of crude delivered to refineries3
scf Cl-Wyr/bbl of crude transported by truck0
scf CI-W1000 gal crude marine loadings0
scf CH4/yr/bbl of crude transported by rail0
scfCI-Wstation/yr'
scfd of Cm/pig station11
scf d-Wmile/yrf
scf Cl-Wbbl crude transported by pipeline'
scf Cl-Wfloating roof tank/yr
scfCH4/hp-hri
scf Cl-Wbbl burned*
Activity
Factor
5,490
131.3
17,518,599
30.1
513
1,027
51,349
7,471
824
NE
NE
Units
MMbbl crude feed/yr6-2
MMbbl crude trans, by
truck"-2
1, 000 gal/yr loaded6-1-4
MMbbl Crude by
rail/yr"'2
No. ofpumpstations9>1
No. of pig stationsS'1
No. of miles of crude
P/|9>2
MMbbl crude pipeds-2
No. of floating roof
tanks3
No. of hp-hrs
No. of bbl Burned
Emissions
(Bcf/yr)
0.256
0.113
0.068
0.045
0.016
0.000*
0.015
0.050
0.001
NE
0.049
NE
NE
NE
0.306
Emissions
(Gg/yr)
4.928
2.175
1.313
0.857
0.301
0.000*
0.281
0.959
0.025
NE
0.934
NE
NE
NE
5.887
"API (1992)
b Energy Information Administration (EIA) Petroleum Supply Annual, Volume 1.
CEPA, AP 42 Compilation of Air Pollutant Emission Factors
d EIA Refinery Capacity Report
eEIA Monthly Energy Review
'Radian (1996)
sOGJ Petroleum Economics Issue
hCAPP(1992)
'API TANK
JGRI/EPA (1996)
k EPA/ICF International (1999)
* Emissions are not actually 0, but too small to show at this level of precision.
NE: Not estimated for lack of data
Table A-149:2012 Clh Emissions from Petroleum Refining
Activity/Equipment
2072 EPA Inventory Values
Emission Factor
Vented Emissions
Tanks
System Slowdowns
Asphalt Blowing
20.6
137
2,555
Fugitive Emissions
Fuel Gas System
Floating Roof Tanks
Wastewater Treating
Cooling Towers
439
587
1.88
2.36
Combustion Emissions
Atmospheric Distillation
3.61
scfCHWMbbl3
scfCHWMbbl0
scfCHWMbbl0
Mscf CI-Wrefinery/yra
scf Cl-Wfloating roof tank/yre
scfCHWMbbl0
scfCHWMbbl0
scfCHWMbbl0
1,930
15,040
35
144
767
15,040
15,040
15,362
Activity Factor
Mbbl/calendar day heavy crude
feedb>0'1
Mbbl/calendar day refinery
feedb'2
Mbbl/calendar day production11'2
Refineries^2
No. of floating roof tanks3
Mbbl/calendar day refinery
feedb>2
Mbbl/calendar day refinery
feedb>2
Mbbl/calendar day refinery
Emissions
(Bcf/yr)
0.798
0.015
0.751
0.033
0.087
0.063
0.000*
0.010
0.013
0.096
0.020
Emissions
(Gg/yr)
15.349
0.279
14.443
0.627
1.672
1.216
0.009
0.199
0.249
1.842
0.389
A-205
-------
Activity/Equipment
Vacuum Distillation
Thermal Operations
Catalytic Cracking
Catalytic Reforming
Catalytic Hydrocracking
Hydro refining
Hydro treating
Alkylation/Polymerization
Aromatics/lsomeration
Lube Oil Processing
Engines
Flares
2072 EPA Inventory Values
3.61
6.01
5.17
7.22
7.22
2.17
6.50
12.6
1.80
0.00
0.006
0.189
Total
Emission Factor
scfCHVMbbl"
scfCHVMbbl"
scfCHVMbbl"
scfCH
-------
Activity/Equipment
Pneumatic Devices, Low Bleed
Chemical Injection Pumps
Vessel Slowdowns
Compressor Slowdowns
Compressor Starts
Stripper wells
Well Completion Venting
Well Workovers
Pipeline Pigging
Offshore Platforms, Shallow water Oil,
fugitive, vented and combusted
Offshore Platforms, Deepwater oil,
fugitive, vented and combusted
2072 EPA Inventory Values
Emission Factor
1.055
5.033
1.583
77
171
48
14.87
1.95
NE
358
1,701
Fugitive Emissions
Oil Wellheads (heavy crude)
Oil Wellheads (light crude)
Separators (heavy crude)
Separators (light crude)
Heater/Treaters (light crude)
Headers (heavy crude)
Headers (light crude)
Floating Roof Tanks
Compressors
Large Compressors
Sales Areas
Pipelines
Well Drilling
Battery Pumps
0.003
0.337
0.003
0.281
0.319
0.002
0.220
17,490
2.029
332
2.096
NE
NE
0.012
Process Upset Emissions
Pressure Relief Valves
Well Blowouts Onshore
1.794
0.051
Refining Emissions
Asphalt Blowingt
20,736
Total
scfd C02/devicef
scfd C02/pumph
scfy C02/vesselh
scf/yr of C02/compressorh
scf/yr of C02/compressorh
scf/yr of C02/stripper well'
scf/completionh
scf C02/workover
scfd of C02/pig station
scfd C02/platformk
scfd C02/platformk
scfd/welle>m
scfd/welle>m
scfd Comparator6-"1
scfd Comparator6-"1
scfd CO^heater6'"1
scfd C02/headere'm
scfd CO^header6'"1
scf C02/floating roof
tank/yr"1'"
scfd C02/compressore
scfd C02/compressore
scf C02/loadinge
scfd of C02/mile of
pipeline
scfd of C02/oil well drilled
scfd of C02/pumpm
scf/yr/PR valve11
MMscf/blowout6
scfC02/Mbblm
Activity Factor
269,618
28,702
189,710
2,582
2,582
315,213
15,753
40,200
NE
1,447
29
15,565
205,222
11,142
101,402
77,166
13,982
43,344
24
2,582
0
1,974,334
14,077
17,774
160,800
197,931
59.0
35
No. of low-bleed
devices0-6*1
No. ofpumpss.i'1
No. of vessels0*'-1
No. of compressors0*'-1
No. of compressors0*'-1
No. of stripper wells
vented'-1'4
Oil well completions0-2
Oil well workoversS'1'1
No. of crude pig stations
No. of shallow water oil
platforms1'4
No. of deep water oil
platforms1'4
No. of hvy. crude wellsd*''1'4
No. of It. crude wellsd*'''1'4
No. of hvy. crude seps.0*'-1
No. of It. crude seps.0*'-1
No. of heater treaters0*1'1
No. of hvy. crude hdrs.s-''1
No. of It. crude hdrs.s-1'1
No. of floating roof tanks6'3
No. of compressors0*''-1
No. of large comprs.6-3
Loadings/year0-1
Miles of gathering line0'2
No. of oil wells drilled0-2
No. of battery pumpsS'6'1
No. of PR valves0'6-1
No. of blowouts/yr0'6'1
Mbbl/calendar day
production?-2
Emissions
(Bcf/yr)
0.104
0.053
0.000*
0.000*
0.000*
0.015
0.000*
0.000*
NE
0.189
0.018
0.055
0.000*
0.025
0.000*
0.010
0.009
0.000*
0.003
0.000*
0.002
0.000
0.004
NE
NE
0.001
0.003
0.000*
0.003
0.265
0.265
7.686
Emissions
(Gg/yr)
5.491
2.788
0.016
0.010
0.023
0.793
0.012
0.004
NE
10.00
0.960
2.929
0.001
1.337
0.001
0.550
0.475
0.000*
0.184
0.023
0.101
0.000
0.219
NE
NE
0.039
0.178
0.019
0.159
14.00
13.998
406.468
= TankCALC
b EPA/ICF International (1999)
c EIA Monthly Energy Review
d IOGCC Marginal Wells Report
'Consensus of Industrial Review Panel
'Expert Judgement
a EIA Annual Energy Review
"GRI/ EPA (1996)
1 Radian (1996)
iCAPP(1992)
"Adapted from the MMS GOADS by ICF (2005)
'BOEM
"API (1996)
A-207
-------
n EPA, AP 42 Compilation of Air Pollutant Emission Factors
°OGJ Petroleum Economics Issue
p EIA Petroleum Supply Annual, Volume 1
* Emissions are not actually 0, but too small to show at this level of precision.
t Asphalt Blowing emissions are the only significant vented emissions from the refining sector; other sources are too small to show at this level of precision.
NE: Not estimated for lack of data
Energy use C02 emissions not estimated to avoid double counting with fossil fuel combustion
Table A-153: Summary of Clh Emissions from Petroleum Systems tGgl
Activity 1990 1995 2000 2007 2008 2009 2010 2011 2012
Production Field Operations 376 341 323 293 284 306 317 332 392"
Pneumatic device venting 271 261 24 22 23 23 23 24 24
Tank venting 32sB 296 • 2811 253 243 265 276 291 350
Misc. venting & fugitives 18 18 1/B 16 16 16 16 16 16
Wellhead fugitives ll ll ll 111111
Refining 18 19 211 18 16 14 15 15 14
Asphalt Blowing 18 19 21 18 16 14 15 15 14
Total 394 360 344 311 300 320 332 347 406~
A-208 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
3.7. Methodology for Estimating C02, N20 and Cm Emissions from the Incineration of
Waste
Emissions of CC>2 from the incineration of waste include CC>2 generated by the incineration of plastics, synthetic
rubber and synthetic fibers in municipal solid waste (MSW), and incineration of tires (which are composed in part of
synthetic rubber and C black) in a variety of other combustion facilities (e.g., cement kilns). Incineration of waste also
results in emissions of N2O and CH/i. The methodology for calculating emissions from each of these waste incineration
sources is described in this Annex.
C02 from Plastics Incineration
In the Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures
reports (EPA 1999 through 2003, 2005 through 2014), the flows of plastics in the U.S. waste stream are reported for seven
resin categories. For 2012, the quantity generated, recovered, and discarded for each resin is shown in Table A-154. The
data set for 1990 through 2012 is incomplete, and several assumptions were employed to bridge the data gaps. The EPA
reports do not provide estimates for individual materials landfilled and incinerated, although they do provide such an
estimate for the waste stream as a whole. To estimate the quantity of plastics landfilled and incinerated, total discards were
apportioned based on the proportions of landfilling and incineration for the entire U.S. waste stream for each year in the
time series according to Biocycle's State of Garbage in America (van Haaren et al. 2010). For those years when
distribution by resin category was not reported (1990 through 1994), total values were apportioned according to 1995 (the
closest year) distribution ratios. Generation and recovery figures for 2002 and 2004 were linearly interpolated between
surrounding years' data.
Table A-154:2012 Plastics in the Municipal Solid Waste Stream by Resin tGgl
Waste Pathway
Generation
Recovery
Discard
Landfill
Combustion
Recovery*
Discard*
Landfill*
Combustion*
PET
4,101
798
3,302
3,011
291
19%
81%
73%
7%
HOPE
5,017
517
4,500
4,103
397
10%
90%
82%
8%
PVC
789
0
789
720
70
0%
100%
91%
9%
LDPE/
LLDPE
6,668
354
6,314
5,757
557
5%
95%
86%
8%
PP
6,523
36
6,486
5,915
572
1%
99%
91%
9%
PS
2,032
18
2,014
1,836
178
1%
99%
90%
9%
Other
3,629
816
2,812
2,564
248
23%
78%
71%
7%
Total
28,758
2,540
26,218
23,907
2,311
9%
91%
83%
8%
*As a percent of waste generation.
Note: Totals may not sum due to independent rounding. Abbreviations: PET (polyethylene terephthalate), HOPE (high density polyethylene), PVC (polyvinyl
chloride), LDPE/LLDPE (linear low density polyethylene), PP (polypropylene), PS (polystyrene).
Fossil fuel-based CC>2 emissions were calculated as the product of plastic combusted, C content, and fraction
oxidized (see Table A-155). The C content of each of the six types of plastics is listed, with the value for "other plastics"
assumed equal to the weighted average of the six categories. The fraction oxidized was assumed to be 98 percent.
Table A-155:2012 Plastics Incinerated tGgl, Carbon Content [%1. Fraction Oxidized [%1 and Carbon Incinerated tGgl
LDPE/
Factor PET HOPE PVC LLDPE PP PS Other Total
Quantity Combusted
Carbon Content of Resin
Fraction Oxidized
Carbon in Resin Combusted
Emissions (Tg C02 Eq.)
291
63%
98%
178
0.7
397
86%
98%
333
1.2
70
38%
98%
26
0.1
557
86%
98%
468
1.7
572
86%
98%
480
1.8
178
92%
98%
161
0.6
248
66%
98%
160
0.6
2,311
-
-
1,806
6.6
' Weighted average of other plastics produced.
Note: Totals may not sum due to independent rounding.
C02 from Incineration of Synthetic Rubber and Carbon Black in Tires
Emissions from tire incineration require two pieces of information: the amount of tires incinerated and the C
content of the tires. "U.S. Scrap Tire Management Summary 2005-2009" (RMA 2011) reports that 2,085 thousand of the
4,391 thousand tons of scrap tires generated in 2009 (approximately 47 percent of generation) were used for fuel purposes.
A-209
-------
Using RMA's estimates of average tire composition and weight, the mass of synthetic rubber and C black in scrap tires
was determined:
• Synthetic rubber in tires was estimated to be 90 percent C by weight, based on the weighted average C contents
of the major elastomers used in new tire consumption.60 Table A-156 shows consumption and C content of
elastomers used for tires and other products in 2002, the most recent year for which data are available.
• C black is 100 percent C (Aslett Rubber Inc. n.d.).
Multiplying the mass of scrap tires incinerated by the total C content of the synthetic rubber, C black portions of
scrap tires, and then by a 98 percent oxidation factor, yielded CC>2 emissions, as shown in Table A- 157. The disposal rate
of rubber in tires (0.4 Tg C/yr) is smaller than the consumption rate for tires based on summing the elastomers listed in
Table A-154 (1.3 Tg/yr); this is due to the fact that much of the rubber is lost through tire wear during the product's
lifetime and may also reflect the lag time between consumption and disposal of tires. Tire production and fuel use for
1990 through 2009 were taken from RMA 2006, RMA 2009, RMA 2011; where data were not reported, they were linearly
interpolated between bracketing years' data or, for the ends of time series, set equal to the closest year with reported data.
In 2009, RMA changed the reporting of scrap tire data from millions of tires to thousands of short tons of scrap
tire. As a result, the average weight and percent of the market of light duty and commercial scrap tires was used to convert
the previous years from millions of tires to thousands of short tons (STMC 1990 through 1997; RMA 2002 through 2006,
2012a).
Table A-156: Elastomers Consumed in 2002 tugl
Elastomer
Styrene butadiene rubber solid
For Tires
For Other Products*
Polybutadiene
For Tires
For Other Products
Ethylene Propylene
For Tires
For Other Products
Polychloroprene
For Tires
For Other Products
Nitrile butadiene rubber solid
For Tires
For Other Products
Polyisoprene
For Tires
For Other Products
Others
For Tires
For Other Products
Total
For Tires
Consumed Carbon Content Carbon Equivalent
768
660
108
583
408
175
301
6
295
54
0
54
84
1
83
58
48
10
367
184
184
2,215
1,307
91%
91%
91%
89%
89%
89%
86%
86%
86%
59%
59%
59%
77%
77%
77%
88%
88%
88%
88%
88%
88%
-
-
700
602
98
518
363
155
258
5
253
32
0
32
65
1
64
51
42
9
323
161
161
1,950
1,174
* Used to calculate C content of non-tire rubber products in municipal solid waste.
- Not applicable
Note: Totals may not sum due to independent rounding.
Table A-157: Scrap Tire Constituents and GO? Emissions from Scrap Tire Incineration in 2012
Material
Synthetic Rubber
Carbon Black
Total
Weight of Material
(Tg)
0.4
0.5
1.0
Fraction Oxidized
98%
98%
-
Carbon Content
90%
100%
-
Emissions (Tg C02Eq.)
1.6
1.9
3.5
- Not applicable
60
The carbon content of tires (1,174 Gg C) divided by the mass of rubber in tires (1,307 Gg) equals 90 percent.
A-210 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
C02 from Incineration of Synthetic Rubber in Municipal Solid Waste
Similar to the methodology for scrap tires, CC>2 emissions from synthetic rubber in MSW were estimated by
multiplying the amount of rubber incinerated by an average rubber C content. The amount of rubber discarded in the
MSW stream was estimated from generation and recycling data61 provided in the Municipal Solid Waste Generation,
Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 2005 through 2014)
and unpublished backup data (Schneider 2007). The reports divide rubber found in MSW into three product categories:
other durables (not including tires), non-durables (which includes clothing and footwear and other non-durables), and
containers and packaging. EPA (2014) did not report rubber found in the product category "containers and packaging;"
however, containers and packaging from miscellaneous material types were reported for 2009 through 2012. As a result,
EPA assumes that rubber containers and packaging are reported under the "miscellaneous" category; and therefore, the
quantity reported for 2009 through 2012 were set equal to the quantity reported for 2008. Since there was negligible
recovery for these product types, all the waste generated is considered to be discarded. Similar to the plastics method,
discards were apportioned into landfilling and incineration based on their relative proportions, for each year, for the entire
U.S. waste stream. The report aggregates rubber and leather in the MSW stream; an assumed synthetic rubber content of
70 percent was assigned to each product type, as shown in Table A-158.62 A C content of 85 percent was assigned to
synthetic rubber for all product types (based on the weighted average C content of rubber consumed for non-tire uses), and
a 98 percent fraction oxidized was assumed.
Table A-158: Rubber and Leather in Municipal Solid Waste in 2012
Product Type
Durables (not Tires)
Non-Durables
Clothing and Footwear
Other Non-Durables
Containers and Packaging
Total
Incinerated
(Gg)
280
81
62
19
2
363
Synthetic Carbon Content Fraction Oxidized
Rubber (%) (%) (%)
70%
70%
70%
70%
-
85%
85%
85%
85%
-
98%
98%
98%
98%
-
Emissions
(TgC02Eq.)
0.9
0.3
0.2
0.1
0.0
1.1
- Less than 0.05 Tg C02 Eq.
- Not applicable.
C02 from Incineration of Synthetic Fibers
CC>2 emissions from synthetic fibers were estimated as the product of the amount of synthetic fiber discarded
annually and the average C content of synthetic fiber. Fiber in the MSW stream was estimated from data provided in the
Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999
through 2003, 2005 through 2014) for textiles. Production data for the synthetic fibers was based on data from the
American Chemical Society (FEE 2009). The amount of synthetic fiber in MSW was estimated by subtracting (a) the
amount recovered from (b) the waste generated (see Table A-159). As with the other materials in the MSW stream,
discards were apportioned based on the annually variable proportions of landfilling and incineration for the entire U.S.
waste stream, as found in van Haaren et al. (2010). It was assumed that approximately 55 percent of the fiber was
synthetic in origin, based on information received from the Fiber Economics Bureau (DeZan 2000). An average C content
of 70 percent was assigned to synthetic fiber using the production-weighted average of the C contents of the four major
fiber types (polyester, nylon, olefin, and acrylic) produced in 1999 (see Table A-160). The equation relating CC>2
emissions to the amount of textiles combusted is shown below.
CC>2 Emissions from the Incineration of Synthetic Fibers = Annual Textile Incineration (Gg) x
(Percent of Total Fiber that is Synthetic) x (Average C Content of Synthetic Fiber) x
(44g CO2/12 g C)
Discards = Generation minus recycling.
62 As a sustainably harvested biogenic material, the incineration of leather is assumed to have no net CO2 emissions.
A-211
-------
Table A-159: Synthetic Textiles in MSWtGgl
Year
1990
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Generation
2,884
3,674
3,832
4,090
4,269
4,498
4,706
4,870
5,123
5,297
5,451
5,714
5,893
6,041
6,305
6,424
6,508
6,513
7,114
Recovery
328
447
472
526
556
611
655
715
750
774
884
913
933
953
968
978
998
1,003
1,117
Discards
2,557
3,227
3,361
3,564
3,713
3,887
4,051
4,155
4,373
4,522
4,567
4,800
4,959
5,088
5,337
5,446
5,510
5,510
5,997
Incineration
332
442
467
458
407
406
417
432
459
472
473
480
479
470
470
480
486
486
529
Table A-160: Synthetic Fiber Production in 1999
Fiber
Polyester
Nylon
Olefin
Acrylic
Total
Production (Tg)
1.8
1.2
1.4
0.1
4.5
Carbon Content
63%
64%
86%
68%
70%
N20 and Cmfrom Incineration of Waste
Estimates of N2O emissions from the incineration of waste in the United States are based on the methodology
outlined in the EPA's Compilation of Air Pollutant Emission Factors (EPA 1995) and presented in the Municipal Solid
Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003,
2005 through 2014) and unpublished backup data (Schneider 2007). According to this methodology, emissions of N2O
from waste incineration are the product of the mass of waste incinerated, an emission factor of N2O emitted per unit mass
of waste incinerated, and an N2(D emissions control removal efficiency. The mass of waste incinerated was derived from
the information published in BioCycle (van Haaren et al. 2010). For waste incineration in the United States, an emission
factor of 50 g N2O/metric ton MSW based on the 2006 IPCC Guidelines and an estimated emissions control removal
efficiency of zero percent were used (IPCC 2006). It was assumed that all MSW incinerators in the United States use
continuously-fed stoker technology (Bahor 2009, ERG 2009).
Estimates of CFU emissions from the incineration of waste in the United States are based on the methodology
outlined in IPCC's 2006 Guidelines for National Greenhouse Gas Inventories (IPCC 2006). According to this
methodology, emissions of CFU from waste incineration are the product of the mass of waste incinerated and an emission
factor of CH4 emitted per unit mass of waste incinerated. Similar to the N2O emissions methodology, the mass of waste
incinerated was derived from the information published in Biocycle (van Haaren et al. 2010). For waste incineration in the
United States, an emission factor of 0.20 kg CFU/Gg MSW was used based on the 2006 IPCC Guidelines and assuming
that all MSW incinerators in the United States use continuously-fed stoker technology (Bahor 2009, ERC 2009). No
information was available on the mass of waste incinerated from Biocycle in 2009 through 2012, so these values were
assumed to remain constant at the 2008 level.
Despite the differences in methodology and data sources, the two series of references (EPA's and BioCycle's)
provide estimates of total solid waste incinerated that are relatively consistent (see Table A-160).
A-212 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-161: U.S. Municipal Solid Waste Incinerated, as Reported by EPA and BioCycle (Metric Tons)
Year
1990
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
NA (Not Available)
EPA
28,939,680
32,241,888
32,740,848
33,294,240
31,216,752
30,881,088
30,599,856
30,481,920
30,255,120
30,028,320
28,585,872
28,685,664
28,985,040
29,003,184
28,622,160
26,317,872
26,544,672
26,544,672
26,544,672
BioCycle
30,632,057
29,639,040
29,707,171
27,798,368
25,489,893
24,296,249
25,974,978
25,942,036=
25,802,917
25,930,542"
26,037,823
25,973,520'
25,853,401
24,788,5391
23,674,017
NA
NA
NA
NA
' Interpolated between 2000 and 2002 values.
b Interpolated between 2002 and 2004 values.
c Interpolated between 2004 and 2006 values.
d Interpolated between 2006 and 2008 values
A-213
-------
3.8. Methodology for Estimating Emissions from International Bunker Fuels used by
the U.S. Military
Bunker fuel emissions estimates for the Department of Defense (DoD) are developed using data generated by the
Defense Energy Support Center (DESC) for aviation and naval fuels. The DESC of the Defense Logistics Agency (DLA)
prepared a special report based on data in the Fuels Automated System (FAS), a database that recently replaced the
Defense Fuels Automated Management System (DFAMS). Data for intermediate fuel oil, however, currently remains in
the original DFAMS database. DFAMS/FAS contains data for 1995 through 2012, but the data set was not complete for
years prior to 1995. Fuel quantities for 1990 to 1994 were estimated based on a back-calculation of the 1995 DFAMS
values using DLA aviation and marine fuel procurement data. The back-calculation was refined in 1999 to better account
for the j et fuel conversion from JP4 to JP8 that occurred within DoD between 1992 and 1995.
Step 1: Omit Extra-Territorial Fuel Deliveries
Beginning with the complete FAS data set for each year, the first step in the development of DoD-related
emissions from international bunker fuels was to identify data that would be representative of international bunker fuel
consumption as that term is defined by decisions of the UNFCCC (i.e., fuel sold to a vessel, aircraft, or installation within
the United States or its territories and used in international maritime or aviation transport). Therefore, fuel data were
categorized by the location of fuel delivery in order to identify and omit all international fuel transactions/deliveries (i.e.,
sales abroad).
Step 2: Allocate JP-8 between Aviation and Land-based Vehicles
As a result of DoD63 and NATO64 policies on implementing the Single Fuel For the Battlefield concept, DoD
activities have been increasingly replacing diesel fuel with JP8 (a type of jet fuel) in compression ignition and turbine
engines in land-based equipment. Based on this concept and examination of all data describing jet fuel used in land-based
vehicles, it was determined that a portion of JP8 consumption should be attributed to ground vehicle use. Based on
available Service data and expert judgment, it was determined that a small fraction of the total JP8 use should be
reallocated from the aviation subtotal to a new land-based jet fuel category for 1997 and subsequent years. The amount of
JP8 reallocated was determined to be between 1.78 and 2.7 times the amount of diesel fuel used, depending on the Service.
As a result of this reallocation, the JP8 use reported for aviation will be reduced and the total fuel use for land-based
equipment will increase. DoD's total fuel use will not change.
Table A-162 displays DoD's consumption of fuels that remain at the completion of Step 1, summarized by fuel
type. Table A-162 reflects the adjustments for jet fuel used in land-based equipment, as described above.
Step 3: Omit Land-Based Fuels
Navy and Air Force land-based fuels (i.e., fuel not used by ships or aircraft) were also omitted for the purpose of
calculating international bunker fuels. The remaining fuels, listed below, were considered potential DoD international
bunker fuels.
• Marine: naval distillate fuel (F76), marine gas oil (MGO), and intermediate fuel oil (IFO).
• Aviation: jet fuels (JP8, JP5, JP4, JAA, JA1, and JAB).
Step 4: Omit Fuel Transactions Received by Military Services that are not Considered to be International Bunker
Fuels
Next, the records were sorted by Military Service. The following assumptions were used regarding bunker fuel
use by Service, leaving only the Navy and Air Force as users of military international bunker fuels.
• Only fuel delivered to a ship, aircraft, or installation in the United States was considered a potential
international bunker fuel. Fuel consumed in international aviation or marine transport was included in the
63 DoD Directive 4140.43, Fuel Standardization, 1998; DoD Directive 4140.25, DoD Management Policy for Energy Commodities and
Related Services, 1999.
64 NATO Standard Agreement NATO STANAG 4362, Fuels for Future Ground Equipments Using Compression Ignition or Turbine
Engines, 1987.
A-214 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
bunker fuel estimate of the country where the ship or aircraft was fueled. Fuel consumed entirely within a
country's borders was not considered a bunker fuel.
• Based on discussions with the Army staff, only an extremely small percentage of Army aviation emissions,
and none of its watercraft emissions, qualified as bunker fuel emissions. The magnitude of these emissions
was judged to be insignificant when compared to Air Force and Navy emissions. Based on this, Army
bunker fuel emissions were assumed to be zero.
• Marine Corps aircraft operating while embarked consumed fuel reported as delivered to the Navy. Bunker
fuel emissions from embarked Marine Corps aircraft were reported in the Navy bunker fuel estimates.
Bunker fuel emissions from other Marine Corps operations and training were assumed to be zero.
• Bunker fuel emissions from other DoD and non-DoD activities (i.e., other federal agencies) that purchased
fuel from DLA Energy were assumed to be zero.
Step 5: Determine Bunker Fuel Percentages
Next it was necessary to determine what percent of the marine and aviation fuels were used as international
bunker fuels. Military aviation bunkers include international operations (i.e., sorties that originate in the United States and
end in a foreign country), operations conducted from naval vessels at sea, and operations conducted from U.S. installations
principally over international water in direct support of military operations at sea (e.g., anti-submarine warfare flights).
For the Air Force, a bunker fuel weighted average was calculated based on flying hours by major command. International
flights were weighted by an adjustment factor to reflect the fact that they typically last longer than domestic flights. In
addition, a fuel use correction factor was used to account for the fact that transport aircraft burn more fuel per hour of
flight than most tactical aircraft. The Air Force bunker fuel percentage was determined to be 13.2 percent. This
percentage was multiplied by total annual Air Force aviation fuel delivered for U.S. activities, producing an estimate for
international bunker fuel consumed by the Air Force. The Naval Aviation bunker fuel percentage of total fuel was
calculated using flying hour data from Chief of Naval Operations Flying Hour Projection System Budget for fiscal year
1998, and estimates of bunker fuel percent of flights provided by the fleet. The Naval Aviation bunker fuel percentage,
determined to be 40.4 percent, was multiplied by total annual Navy aviation fuel delivered for U.S. activities, yielding
total Navy aviation bunker fuel consumed.
For marine bunkers, fuels consumed while ships were underway were assumed to be bunker fuels. In 2000, the
Navy reported that 79 percent of vessel operations were underway, while the remaining 21 percent of operations occurred
in port (i.e., pierside). Therefore, the Navy maritime bunker fuel percentage was determined to be 79 percent. The
percentage of time underway may vary from year-to-year. For example, for years prior to 2000, the bunker fuel
percentage was 87 percent. Table A-163 and Table A-164 display DoD bunker fuel use totals for the Navy and Air Force.
Step 6: Calculate Emissions from International Bunker Fuels
Bunker fuel totals were multiplied by appropriate emission factors to determine GHG emissions. CC>2 emissions
from Aviation Bunkers and distillate Marine Bunkers are the total of military aviation and marine bunker fuels,
respectively.
The rows labeled "U.S. Military" and "U.S. Military Naval Fuels" in the tables in the International Bunker Fuels
section of the Energy Chapter were based on the totals provided in Table A-163 and Table A-164, below. CC>2 emissions
from aviation bunkers and distillate marine bunkers presented in Table A-167, and are based on emissions from fuels
tallied in Table A-163 and Table A-164
A-215
-------
Table A-162: Transportation Fuels from Domestic Fuel Deliveries3 (Million Gallons)
Vehicle Type/Fuel
Aviation
Total Jet Fuels
JP8
JP5
Other Jet Fuels
Aviation Gasoline
Marine
Middle Distillate (MGO)
Naval Distillate (F76)
Intermediate Fuel Oil
(IFO)"
Other0
Diesel
Gasoline
Jet Fuel d
1990
4,598.4
4,598.4
285.7
1,025.4
3,287.3
+
686.8
+
686.8
+
717.1
93.0
624.1
+
1995 1996 1997
3,099.9 2,941.9 2,685.6
3,099.9 2,941.9 2,685.6
2,182.8 2,253.1 2,072.0
691.2 615.8 552.8
225.9 72.9 60.9
+ + +
438.9 493.3 639.8
+ 38.5 47.5
438.9 449.0 583.4
+ 5.9 9.0
310.9 276.9 263.3
119.9 126.1 132.6
191.1 150.8 119.0
+ + 11.7
1998 1999
2,741.4 2,635.2
2,741.4 2,635.2
2,122.5 2,066.5
515.6 505.5
103.3 63.3
+ +
674.2 598.9
51.1 49.2
608.4 542.9
14.7 6.7
256.8 256.0
139.5 146.8
93.9 74.1
23.4 35.0
2000 2001
2,664.4 2,900.6
2,664.4 2,900.6
2,122.7 2,326.2
472.1 503.2
69.6 71.2
+ +
454.4 418.4
48.3 33.0
398.0 369.1
8.1 16.3
248.2 109.8
126.6 26.6
74.8 24.7
46.7 58.4
2002 2003
2,609.8 2,615.0
2,609.6 2,614.9
2,091.4 2,094.3
442.2 409.1
76.1 111.4
0.1 0.1
455.8 609.1
41.2 88.1
395.1 460.9
19.5 60.2
211.1 221.2
57.7 60.8
27.5 26.5
125.9 133.9
2004 2005
2,703.1 2,338.1
2,703.1 2,338.0
2,126.2 1,838.8
433.7 421.6
143.2 77.6
+ 0.1
704.5 604.9
71.2 54.0
583.5 525.9
49.9 25.0
170.9 205.6
46.4 56.8
19.4 24.3
105.1 124.4
2006 2007
2,092.0 2,081.0
2,091.9 2,0809
1,709.3 1,618.5
325.5 376.1
57.0 86.3
0.1 0.2
531.6 572.8
45.8 45.7
453.6 516.0
32.2 11.1
107.3 169.0
30.6 47.3
11.7 19.2
65.0 102.6
2008
2,067.8
2,067.7
1,616.2
362.2
89.2
0.1
563.4
55.2
483.4
24.9
173.6
49.1
19.7
104.8
2009
1,814.5
18143
1,358.2
361.2
94.8
0.2
485.8
56.8
399.0
30.0
206.8
58.3
25.2
123.3
2010
1,663.9
1,663.7
1,100.1
399.3
164.3
0.2
578.8
48.4
513.7
16.7
224.0
64.1
25.5
134.4
2011 2012
1,405.0 1,449.7
1,404.8 1,449.5
882.8 865.2
372.3 362.5
149.7 221.8
0.2 0.3
489.9 490.4
37.3 52.9
440.0 428.4
12.5 9.1
208.6 193.8
60.9 57.9
22.0 19.6
125.6 116.2
Total (Including
Bunkers) 6,002.4 3,849.8 3,712.1 3,588.8 3,672.4 3,490.1 3,367.0 3,428.8 3,276.7 3,445.3 3,578.5 3,148.6 2,730.9 2,822.8 2,804.9 2,507.1 2,466.7 2,103.5 2,133.9
Note: Totals may not sum due to independent rounding.
" Includes fuel consumption in the United States and U.S. Territories.
b Intermediate fuel oil (IFO 180 and IF0380) is a blend of distillate and residual fuels. IFO is used by the Military Sealift Command.
c Prior to 2001, gasoline and diesel fuel totals were estimated using data provided by the military Services for 1990 and 1996. The 1991 through 1995 data points were interpolated from the Service inventory data. The
1997 through 1999 gasoline and diesel fuel data were initially extrapolated from the 1996 inventory data. Growth factors used for other diesel and gasoline were 5.2 and -21.1 percent, respectively. However, prior diesel fuel
estimates from 1997 through 2000 were reduced according to the estimated consumption of jet fuel that is assumed to have replaced the diesel fuel consumption in land-based vehicles. Data sets for other diesel and
gasoline consumed by the military in 2000 were estimated based on ground fuels consumption trends. This method produced a result that was more consistent with expected consumption for 2000. In 2001, other gasoline
and diesel fuel totals were generated by DESC/DLA.
d The fraction of jet fuel consumed in land-based vehicles was estimated using Service data, DESC/DLA Energy data, and expert judgment.
+ Does not exceed 0.05 million gallons.
A-216 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-163: Total U.S. Military Aviation Bunker Fuel (Million Gallons)
Fuel Type/Service 1990 1995
JP8 56.7 300.4
Navy 56.7 38.3
Air Force + 262.2
JP5 370.5 249.8
Navy 365.3 246.3
Air Force 5.3 3.5
JP4 420.8 21.5
Navy + +
Air Force 420.8 21.5
JAA 13.7 9.2
Navy 8.5 5.7
Air Force 5.3 3.5
JA1 + +
Navy + +
Air Force + +
JAB + +
Navy + +
Air Force + +
Navy Subtotal 430.5 290.2
Air Force Subtotal 431.3 290.7
Total 861.8 580.9
+ Does not exceed 0.05 million gallons.
Note: Totals may not sum due to independent
1996 1997
308.8 292.0
39.8 46.9
269.0 245.1
219.4 194.2
216.1 191.2
3.3 3.0
1.1 0.1
+ +
1.1 0.1
10.3 9.4
6.6 5.9
3.7 3.5
+ +
+ +
+ +
+ +
+ +
+ +
262.5 244.0
277.0 251.7
539.5 495.6
rounding.
1998
306.4
53.8
252.6
184.4
181.4
3.0
+
+
+
10.8
6.6
4.2
+
+
+
+
+
+
241.8
259.9
501.7
1999
301.4
55.5
245.9
175.4
170.6
4.8
+
+
+
10.8
6.3
4.5
+
+
+
+
+
+
232.4
255.2
487.5
2000
307.6
53.4
254.2
160.3
155.6
4.7
+
+
+
12.5
7.9
4.5
+
+
+
+
+
+
216.9
263.5
480.4
2001
341.2
73.8
267.4
169.7
163.7
6.1
+
+
+
12.6
8.0
4.6
0.1
+
0.1
+
+
+
245.5
278.1
523.6
2002
309.5
86.6
222.9
158.3
153.0
5.3
+
+
+
13.7
9.8
3.8
0.6
+
0.6
+
+
+
249.4
232.7
482.1
2003
305.1
76.3
228.7
146.1
141.3
4.9
+
+
+
21.7
15.5
6.2
0.2
+
0.2
+
+
+
233.1
239.9
473.0
2004
309.8
79.2
230.6
157.9
153.8
4.1
+
+
+
30.0
21.5
8.6
0.5
+
0.5
+
+
+
254.4
243.7
498.1
2005
285.6
70.9
214.7
160.6
156.9
3.7
+
+
+
15.5
11.6
3.9
0.5
+
0.5
+
+
+
239.4
222.9
462.3
2006
262.5
64.7
197.8
125.0
122.8
2.3
+
+
+
11.7
9.1
2.6
0.4
+
0.4
+
+
+
196.6
203.1
399.7
2007 2008
249.1 229.4
62.7 59.2
186.5 170.3
144.5 139.2
141.8 136.5
2.7 2.6
+ +
+ +
+ +
15.6 16.8
11.7 12.5
3.9 4.3
1.1 1.0
0.1 0.1
1.0 0.8
+ +
+ +
+ +
216.3 208.3
194.0 178.1
410.3 386.3
2009 2010
211.4 182.5
55.4 60.8
156.0 121.7
137.0 152.5
133.5 149.7
3.5 2.8
+ 0.1
+ +
+ 0.1
18.1 31.4
12.3 13.7
5.9 17.7
0.6 0.3
0.1 0.1
0.5 0.1
+ +
+ +
+ +
201.3 224.4
165.9 142.4
367.2 366.7
2011 2012
143.4 141.2
47.1 50.4
96.2 90.8
144.9 141.2
143.0 139.5
1.8 1.7
+ +
+ +
+ +
31.1 38.6
14.6 14.8
16.5 23.8
-+ -+
-+ -+
-+ -+
+ +
+ +
+ +
204.3 204.5
114.5 116.3
318.8 320.8
Table A-164: Total U.S. BoB Maritime Bunker Fuel (Million Gallons)
Marine Distillates 1990 1995
Navy— MGO + +
Navy-F76 522.4 333.8
Navy— IFO + +
Total 522.4 333.8
1996 1997
30.3 35.6
331.9 441.7
4.6 7.1
366.8 484.3
1998
31.9
474.2
11.6
517.7
1999
39.7
466.0
5.3
511.0
2000
23.8
298.6
6.4
328.8
2001
22.5
282.6
12.9
318.0
2002
27.1
305.6
15.4
348.2
2003
63.7
347.8
47.5
459.0
2004
56.2
434.4
39.4
530.0
2005
38.0
413.1
19.7
470.7
2006
33.0
355.9
25.4
414.3
2007 2008
31.6 40.9
404.1 376.9
8.8 19.0
444.4 436.7
2009 2010
39.9 32.9
311.4 402.2
23.1 12.9
374.4 448.0
2011 2012
25.5 36.5
346.6 337.9
9.5 6.1
381.5 380.6
H Does not exceed 0.05 million gallons.
Note: Totals may not sum due to independent rounding.
A-217
-------
Table A-165: Aviation and Marine Carbon Contents tTg Carhon/QBtul and Fraction Oxidized
Mode (Fuel) Carbon Content Fraction
Coefficient Oxidized
Aviation (Jet Fuel) Variable 1.00
Marine (Distillate) 20.17 1.00
Marine (Residual) 20.48 1.00
Source: EPA (2010) and IPCC (2006)
Table A-166: Annual Variable Carbon Content Coefficient for Jet FueUTg Carhon/QBtul
Fuel 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Jet Fuel 19.40 19.34 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70
Source: EPA (2010)
Table A-167: Total U.S. DoD Clh Emissions from Bunker Fuels tTg Clh Eq.l
Mode 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Aviation 8.1 5.5 5.2 4.8 4.9 4.7 4.7 5.1 4.7 4.6 4.8 4.5 3.9 4.0 3.8 3.6 3.6 3.1 3.1
Marine 5.4 3.4 3.8 5.0 5.3 5.2 3.4 3.3 3.6 4.7 5.4 4.8 4.2 4.6 4.5 3.8 4.6 3.9 3.9
Total 13.4 9.0 9.0 9.8 10.2 10.0 8.0 8.3 8.3 9.3 10.3 9.3 8.1 8.5 8.2 7.4 8.2 7.0 7.0
Note: Totals may not sum due to independent rounding.
A-218 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
3.9. Methodology for Estimating HFC and RFC Emissions from Substitution of Ozone
Depleting Substances
Emissions of HFCs and PFCs from the substitution of ozone depleting substances (ODS) are developed using a
country-specific modeling approach. The Vintaging Model was developed as a tool for estimating the annual chemical
emissions from industrial sectors that have historically used ODS in their products. Under the terms of the Montreal
Protocol and the United States' Clean Air Act Amendments of 1990, the domestic U.S. consumption of ODS—
chlorofluorocarbons (CFCs), halons, carbon tetrachloride, methyl chloroform, and hydrochlorofluorocarbons (HCFCs)—
has been drastically reduced, forcing these industrial sectors to transition to more ozone friendly chemicals. As these
industries have moved toward ODS alternatives such as hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs), the
Vintaging Model has evolved into a tool for estimating the rise in consumption and emissions of these alternatives, and the
decline of ODS consumption and emissions.
The Vintaging Model estimates emissions from five ODS substitute end-use sectors: air-conditioning and
refrigeration, foams, aerosols, solvents, and fire-extinguishing. Within these sectors, there are 60 independently modeled
end-uses. The model requires information on the market growth for each of the end-uses, a history of the market transition
from ODS to alternatives, and the characteristics of each end-use such as market size or charge sizes and loss rates. As
ODS are phased out, a percentage of the market share originally filled by the ODS is allocated to each of its substitutes.
The model, named for its method of tracking the emissions of annual "vintages" of new equipment that enter into
service, is a "bottom-up" model. It models the consumption of chemicals based on estimates of the quantity of equipment
or products sold, serviced, and retired each year, and the amount of the chemical required to manufacture and/or maintain
the equipment. The Vintaging Model makes use of this market information to build an inventory of the in-use stocks of
the equipment and ODS and ODS substitute in each of the end-uses. The simulation is considered to be a "business-as-
usual" baseline case, and does not incorporate measures to reduce or eliminate the emissions of these gases other than
those regulated by U.S. law or otherwise common in the industry. Emissions are estimated by applying annual leak rates,
service emission rates, and disposal emission rates to each population of equipment. By aggregating the emission and
consumption output from the different end-uses, the model produces estimates of total annual use and emissions of each
chemical.
The Vintaging Model synthesizes data from a variety of sources, including data from the ODS Tracking System
maintained by the Stratospheric Protection Division and information from submissions to EPA under the Significant New
Alternatives Policy (SNAP) program. Published sources include documents prepared by the United Nations Environment
Programme (UNEP) Technical Options Committees, reports from the Alternative Fluorocarbons Environmental
Acceptability Study (AFEAS), and conference proceedings from the International Conferences on Ozone Protection
Technologies and Earth Technologies Forums. EPA also coordinates extensively with numerous trade associations and
individual companies. For example, the Alliance for Responsible Atmospheric Policy; the Air-Conditioning, Heating and
Refrigeration Institute; the Association of Home Appliance Manufacturers; the American Automobile Manufacturers
Association; and many of their member companies have provided valuable information over the years. In some instances
the unpublished information that the EPA uses in the model is classified as Confidential Business Information (CBI). The
annual emissions inventories of chemicals are aggregated in such a way that CBI cannot be inferred. Full public
disclosure of the inputs to the Vintaging Model would jeopardize the security of the CBI that has been entrusted to the
EPA.
The following sections discuss the emission equations used in the Vintaging Model for each broad end-use
category. These equations are applied separately for each chemical used within each of the different end-uses. In the
majority of these end-uses, more than one ODS substitute chemical is used.
In general, the modeled emissions are a function of the amount of chemical consumed in each end-use market.
Estimates of the consumption of ODS alternatives can be inferred by determining the transition path of each regulated
ODS used in the early 1990s. Using data gleaned from a variety of sources, assessments are made regarding which
alternatives have been used, and what fraction of the ODS market in each end-use has been captured by a given
alternative. By combining this with estimates of the total end-use market growth, a consumption value can be estimated
for each chemical used within each end-use.
A-219
-------
Methodology
The Vintaging Model estimates the use and emissions of ODS alternatives by taking the following steps:
1. Gather historical data. The Vintaging Model is populated with information on each end-use, taken
from published sources and industry experts.
2. Simulate the implementation of new, non-ODS technologies. The Vintaging Model uses detailed
characterizations of the existing uses of the ODS, as well as data on how the substitutes are replacing the ODS, to simulate
the implementation of new technologies that enter the market in compliance with ODS phase-out policies. As part of this
simulation, the ODS substitutes are introduced in each of the end-uses over time as seen historically and as needed to
comply with the ODS phase-out.
3. Estimate emissions of the ODS substitutes. The chemical use is estimated from the amount of
substitutes that are required each year for the manufacture, installation, use, or servicing of products. The emissions are
estimated from the emission profile for each vintage of equipment or product in each end-use. By aggregating the
emissions from each vintage, a time profile of emissions from each end-use is developed.
Each set of end-uses is discussed in more detail in the following sections.
Refrigeration and Air-Conditioning
For refrigeration and air conditioning products, emission calculations are split into two categories: emissions
during equipment lifetime, which arise from annual leakage and service losses, and disposal emissions, which occur at the
time of discard. Two separate steps are required to calculate the lifetime emissions from leakage and service, and the
emissions resulting from disposal of the equipment. For any given year, these lifetime emissions (for existing equipment)
and disposal emissions (from discarded equipment) are summed to calculate the total emissions from refrigeration and air-
conditioning. As new technologies replace older ones, it is generally assumed that there are improvements in their leak,
service, and disposal emission rates.
Step 1: Calculate lifetime emissions
Emissions from any piece of equipment include both the amount of chemical leaked during equipment operation
and the amount emitted during service. Emissions from leakage and servicing can be expressed as follows:
ESJ = (la + I) x S Qcj.i+i for i = ;->£
Where:
Es = Emissions from Equipment Serviced. Emissions in yeary from normal leakage and servicing (including
recharging) of equipment.
4 = Annual Leak Rate. Average annual leak rate during normal equipment operation (expressed as a
percentage of total chemical charge).
ls = Service Leak Rate. Average leakage during equipment servicing (expressed as a percentage of total
chemical charge).
Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to charge new
equipment in a given year by weight.
/ = Counter, runs from 1 to lifetime (k).
j = Year of emission.
k = Lifetime. The average lifetime of the equipment.
Step 2: Calculate disposal emissions
The disposal emission equations assume that a certain percentage of the chemical charge will be emitted to the
atmosphere when that vintage is discarded. Disposal emissions are thus a function of the quantity of chemical contained
in the retiring equipment fleet and the proportion of chemical released at disposal:
Edj = Qcj-k+i x [1 - (rm x re)]
Where:
Ed = Emissions from Equipment Disposed. Emissions in year7 from the disposal of equipment.
A-220 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to charge new
equipment in yearj-k+1, by weight.
rm = Chemical Remaining. Amount of chemical remaining in equipment at the time of disposal (expressed as
a percentage of total chemical charge).
re = Chemical Recovery Rate. Amount of chemical that is recovered just prior to disposal (expressed as a
percentage of chemical remaining at disposal (rm)).
j = Year of emission.
k = Lifetime. The average lifetime of the equipment.
Step 3: Calculate total emissions
Finally, lifetime and disposal emissions are summed to provide an estimate of total emissions.
Where:
E = Total Emissions. Emissions from refrigeration and air conditioning equipment in year/
Es = Emissions from Equipment Serviced. Emissions in year j from leakage and servicing (including
recharging) of equipment.
Ed = Emissions from Equipment Disposed. Emissions in yeary from the disposal of equipment.
j = Year of emission.
Assumptions
The assumptions used by the Vintaging Model to trace the transition of each type of equipment away from ODS
are presented in Table A- 168, below. As new technologies replace older ones, it is generally assumed that there are
improvements in their leak, service, and disposal emission rates. Additionally, the market for each equipment type is
assumed to grow independently, according to annual growth rates.
A-221
-------
Table A-168: Refrigeration and Air-Conditioning Market Transition Assumptions
Initial Market
Segment
Primary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New Equipment
Maximum
Market
Penetration
Tertiary Substitute
Name of
Substitute
Start Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Growth
Rate
Centrifugal Chillers
CFC-11
CFC-12
R-500
CFC-114
HCFC-123
HCFC-22
HFC-134a
HFC-134a
HCFC-22
HCFC-123
HFC-134a
HCFC-22
HCFC-123
HFC-236fa
1993
1991
1992
1992
1991
1993
1992
1991
1993
1993
1993
1993
1993
1994
1994
1994
1994
1994
1994
1996
45%
16%
39%
53%
16%
31%
53%
16%
31%
100%
Unknown
HFC-134a
None
None
HFC-134a
Unknown
None
HFC-134a
Unknown
HFC-134a
2000
2000
2000
1998
2010
2010
2010
2009
100%
100%
100%
100%
None
None
None
None
0.5%
0.5%
0.5%
0.2%
Cold Storage
CFC-12
HCFC-22
R-502
HCFC-22
R-404A
R-507
HCFC-22
HCFC-22
R-404A
R-507
1990
1994
1994
1992
1990
1993
1994
1993
1996
1996
1993
1993
1996
1996
65%
26%
9%
100%
40%
45%
15%
R-404A
R-507
None
None
R-404A
R-507
R-404A
R-507
R-404A
R-507
Non-
ODP/GWP
None
None
1996
1996
1996
1996
2009
2009
1996
1996
1996
2010
2010
2009
2009
2010
2010
2010
2010
2010
75%
25%
8%
3%
68%
23%
38%
12%
50%
None
None
None
None
None
None
None
None
None
2.5%
2.5%
2.5%
Commercial Unitary Air Conditioners (Large)
HCFC-22
HCFC-22
1992
1993
100%
R-410A
R-407C
R-410A
R-407C
R-410A
2001
2006
2006
2009
2009
2005
2009
2009
2010
2010
5%
1%
9%
5%
81%
None
None
None
None
None
0.8%
Commercial Unitary Air Conditioners (Small)
HCFC-22
HCFC-22
1992
1993
100%
R-410A
R-410A
R-410A
R-410A
1996
2001
2006
2009
2000
2005
2009
2010
3%
18%
8%
71%
None
None
None
None
0.8%
Dehumidifiers
HCFC-22
HFC-134a
R-410A
1997
2007
1997
2010
89%
11%
None
None
0.2%
A-222 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Initial Market
Segment
Primary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New Equipment
Maximum
Market
Penetration
Tertiary Substitute
Name of
Substitute
Start Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Growth
Rate
Ice Makers
CFC-12 |HFC-134a
1993
1995
100%
I None
I 2.5%
Industrial Process Refrigeration
CFC-11
CFC-12
HCFC-22
HCFC-123
HFC-134a
HCFC-22
HCFC-22
HCFC-123
HFC-134a
R-401A
HFC-134a
R-404A
R-410A
R-507
HFC-134a
R-404A
R-410A
R-507
1992
1992
1991
1991
1992
1992
1995
1995
1995
1999
1995
2009
2009
2009
2009
1994
1994
1994
1994
1994
1994
1996
2009
2009
2009
2009
2010
2010
2010
2010
70%
15%
15%
10%
35%
50%
5%
2%
5%
2%
2%
14%
45%
18%
14%
Unknown
None
HFC-134a
HFC-134a
R-404A
R-410A
R-507
Unknown
None
HFC-134a
None
None
None
None
None
None
None
None
1995
1995
1995
1999
1995
1997
2010
2010
2010
2010
2010
2000
100%
15%
50%
20%
15%
100%
None
None
None
None
None
None
2.5%
2.5%
2.5%
Mobile Air Conditioners (Passenger Cars)
CFC-12
HFC-134a
1992
1994
100%
HFO-1234yf
HFO-1234yf
2012
2016
2015
2021
1%
99%
None
None
0.5%
Mobile Air Conditioners (Light Duty Trucks)
CFC-12
HFC-134a
1993
1994
100%
HFO-1234yf
HFO-1234yf
2012
2016
2015
2021
1%
99%
None
None
2%
Mobile Air Conditioners (School and Tour Buses)
CFC-12
HCFC-22
HFC-134a
1994
1994
1995
1997
0.5%
99.5%
HFC-134a
None
2006
2007
100%
None
2.6%
Mobile Air Conditioners (Transit Buses)
HCFC-22 |HFC-134a
1995
2009
100%
I None
I 2.6%
A-223
-------
Initial Market
Segment
Primary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New Equipment
Maximum
Market
Penetration
Tertiary Substitute
Name of
Substitute
Start Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Growth
Rate
Mobile Air Conditioners (Trains)
HCFC-22
HFC-134a
R-407C
2002
2002
2009
2009
50%
50%
None
None
2.6%
Packaged Terminal Air Conditioners and Heat Pumps
HCFC-22
R-410A
R-410A
2006
2009
2009
2010
10%
90%
None
None
0.8%
Positive Displacement Chillers
HCFC-22
CFC-12
HFC-134a
R-407C
HFC-134a
HCFC-22
2000
2000
2009
1993
2009
2009
2010
1993
9%
1%
81%
100%
R-407C
R-410A
None
R-407C
R-407C
HFC-134a
R-407C
HFC-134a
R-407C
2010
2010
2010
2009
2000
2000
2009
2009
Refrif
CFC-12 |HFC-134a
1994
1995
100%
None
2020
2020
2020
2010
2009
2009
2010
2010
60%
40%
60%
9%
9%
1%
81%
9%
None
None
None
R-410A
None
R-407C
R-410A
None
R-407C
R-410A
None
2010
2010
2010
2010
2010
2020
2020
2020
2020
2020
40%
60%
40%
60%
40%
0.5%
0.2%
erated Appliances
0.5%
Residential Unitary Air Conditioners
HCFC-22
HCFC-22
R-410A
R-410A
R-410A
2006
2000
2000
2006
2006
2005
2006
2006
70%
5%
5%
20%
R-410A
R-410A
R-410A
None
None
2007
2010
2006
2010
2010
2006
29%
71%
100%
None
None
None
0.8%
Retail Food (Large; Technology Transition)
DX®
DX
2000
2010
85%
DX
2010
2010
66%
None
0.8%
65 DX refers to direct expansion systems where the compressors are mounted together in a rack and share suction and discharge refrigeration lines that run throughout the store, feeding refrigerant to the
display cases in the sales area.
DR refers to distributed refrigeration systems that consist of multiple smaller units that are located close to the display cases that they serve such as on the roof above the cases, behind a nearby wall, or
on top of or next to the case in the sales area.
SLS refers to secondary loop systems wherein a secondary fluid such as glycol or carbon dioxide is cooled by the primary refrigerant in the machine room and then pumped throughout the store to
remove heat from the display equipment.
4 The CFC-12 large retail food market for new systems transitioned to R-502 from 1998 to 1990, and subsequently transitioned to HCFC-22 from 1990 to 1993. These transitions are not shown in the
table in order to provide the HFC transitions in greater detail.
HCFC-22 for new equipment after 2010 is assumed to be reclaimed material.
A-224 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Initial Market
Segment
Primary Substitute
Name of
Substitute
DR
SLS
Start
Date
2000
2000
Date of Full
Penetration in
New Equipment
2010
2010
Maximum
Market
Penetration
13.5%
1.5%
Secondary Substitute
Name of
Substitute
DR2
SLS3
None
None
Start
Date
2010
2010
Date of Full
Penetration in
New Equipment
2010
2010
Maximum
Market
Penetration
30%
4%
Tertiary Substitute
Name of
Substitute
None
None
Start Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Growth
Rate
Retail Food (Large; Refrigerant Transition)
CFC-12
and
R-502"
R-404A
R-507
HCFC-22
1995
1995
1995
2000
2000
2000
17.5%
7.5%
75%
R-404A
R-507
R-407A
R-404A
R-507
R-407A
R-404A
R-507
R-407A
R-404A
R-507
R-407A
2000
2000
2000
2000
2000
2000
2001
2001
2001
2009
2009
2009
2009
2009
2009
2009
2009
2009
2010
2010
2010
2010
2010
2010
17.9%
1.7%
0.4%
17.9%
1.7%
0.4%
17.9%
1.7%
0.4%
68%
8.0%
4.0%
None
None
None
None
None
None
None
None
None
R-404A
R-507
R-407A
R-404A
R-507
R-407A
R-404A
R-507
R-407A
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
35.8%
3.6%
0.7%
35.8%
3.6%
0.7%
35.8%
3.6%
0.7%
0.8%
Retail Food (Large Condensing Units)
HCFC-22
R-402A
R-404A
R-507
R-404A
R-507
1995
1995
1995
2008
2008
2005
2005
2005
2010
2010
5%
25%
10%
45%
15%
R-404A
None
None
None
None
2006
2006
100%
None
0.9%
Retail Food (Small Condensing Units)
HCFC-22 IR-401A
1995
2005
\ 6% |HFC-134a
2006
2006
100%
I None
0.9%
A-225
-------
Initial Market
Segment
Primary Substitute
Name of
Substitute
R-402A
HFC-134a
R-404A
R-404A
Start
Date
1995
1993
1995
2008
Date of Full
Penetration in
New Equipment
2005
2005
2005
2010
Maximum
Market
Penetration
4%
30%
30%
30%
Secondary Substitute
Name of
Substitute
HFC-134a
Start
Date
2006
Date of Full
Penetration in
New Equipment
2006
Maximum
Market
Penetration
100%
Tertiary Substitute
Name of
Substitute
None
Start Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Growth
Rate
Retail Food (Small)
CFC-12
HCFC-22
R-404A
R-507
1990
1993
1993
1993
1996
1996
90%
7.5%
2.5%
HFC-134a
R-404A
R-507
None
None
1993
2000
2000
1995
2009
2009
90%
7.5%
2.5%
C02
None
None
2010
2010
5%
0.8%
Transport Refrigeration
CFC-12
R-502
HFC-134a
HCFC-22
HFC-134a
R-404A
1993
1993
1993
1993
1995
1995
1995
1995
98%
2%
55%
45%
None
HFC-134a
None
None
1995
1999
100%
None
2.5%
2.5%
Water-Source and Ground-Source Heat Pumps
HCFC-22
R-407C
R-410A
HFC-134a
R-407C
R-410A
HFC-134a
R-407C
R-410A
2000
2000
2000
2006
2006
2009
2009
2009
2006
2006
2009
2009
2009
2010
2010
2010
5%
5%
2%
2.5%
4.5%
18%
22.5%
40.5%
None
None
None
None
None
None
None
None
0.8%
Window Units
HCFC-22
R-410A
R-410A
2008
2009
2009
2010
10%
90%
None
None
5.0%
A-226 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 169 presents the average equipment lifetimes and annual HFC emission rates (for servicing and leaks)
for each end-use assumed by the Vintaging Model.
Table A-169. Refrigeration and Air-conditioning Lifetime Assumptions
End-Use
Centrifugal Chillers
Cold Storage
Commercial Unitary A/C
Dehumidifiers
Ice Makers
Industrial Process Refrigeration
Mobile Air Conditioners
Positive Displacement Chillers
PTAC/PTHP
Retail Food
Refrigerated Appliances
Residential Unitary A/C
Transport Refrigeration
Water & Ground Source Heat Pumps
Window Units
Lifetime
(Years)
20-27
20-25
15
11
20
25
5-16
20
12
18-20
14
15
12
20
12
HFC Emission Rates
(%)
2.0-10.9
15.0
7.9-8.6
0.5
3.0
3.6-12.3
2.3-18.0
0.5-1.5
3.9
1.0-25
0.6
11.8
20.6-27.9
3.9
0.6
Aerosols
ODSs, HFCs and many other chemicals are used as propellant aerosols. Pressurized within a container, a nozzle
releases the chemical, which allows the product within the can to also be released. Two types of aerosol products are
modeled: metered dose inhalers (MDI) and consumer aerosols. In the United States, the use of CFCs in consumer aerosols
was banned in 1978, and many products transitioned to hydrocarbons or "not-in-kind" technologies, such as solid
deodorants and finger-pump hair sprays. However, MDIs can continue to use CFCs as propellants because their use has
been deemed essential. Essential use exemptions granted to the United States under the Montreal Protocol for CFC use in
MDIs are limited to the treatment of asthma and chronic obstructive pulmonary disease.
All HFCs and PFCs used in aerosols are assumed to be emitted in the year of manufacture. Since there is
currently no aerosol recycling, it is assumed that all of the annual production of aerosol propellants is released to the
atmosphere. The following equation describes the emissions from the aerosols sector.
Ej = QCJ
Where:
E = Emissions. Total emissions of a specific chemical in yearj from use in aerosol products, by weight.
Qc = Quantity of Chemical. Total quantity of a specific chemical contained in aerosol products sold in year
j, by weight.
j = Year of emission.
Transition Assumptions
Transition assumptions and growth rates for those items that use ODSs or HFCs as propellants, including vital
medical devices and specialty consumer products, are presented in Table A- 170.
A-227
-------
Table A-170. Aerosol Product Transition Assumptions
Initial Market
Segment
Primary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New Equipment
Maximum
Market
Penetration
Srowth Rate
MDIs
CFC Mix*
HFC-134a
Non-ODP/GWP
CFC Mix*
1997
1998
2000
1997
2007
2000
6%
7%
87%
None
None
HFC-134a
HFC-134a
HFC-227ea
HFC-134a
HFC-227ea
HFC-134a
HFC-227ea
HFC-134a
HFC-227ea
2002
2003
2006
2010
2010
2011
2011
2014
2014
2002
2009
2009
2011
2011
2012
2012
2014
2014
34%
47%
5%
6%
1%
3%
0.3%
3%
0.3%
0.8%
Consumer Aerosols (Non-MDIs)
NA**
HFC-152a
HFC-134a
1990
1995
1991
1995
50%
50%
None
HFC-152a
HFC-152a
1997
2001
1998
2005
44%
36%
2.0%
*CFC Mix consists of CFC-11, CFC-12 and CFC-114 and represents the weighted average of several CFCs consumed for essential use in MDIs from 1993 to
2008.
"Consumer Aerosols transitioned away from ODS prior to 1985, the year in which the Vintaging Model begins. The portion of the market that is now using HFC
propellents is modeled.
Solvents
ODSs, HFCs, PFCs and other chemicals are used as solvents to clean items. For example, electronics may need
to be cleaned after production to remove any manufacturing process oils or residues left. Solvents are applied by moving
the item to be cleaned within a bath or stream of the solvent. Generally, most solvents are assumed to remain in the liquid
phase and are not emitted as gas. Thus, emissions are considered "incomplete," and are a fixed percentage of the amount
of solvent consumed in a year. The remainder of the consumed solvent is assumed to be reused or disposed without being
released to the atmosphere. The following equation calculates emissions from solvent applications.
Where:
Emissions. Total emissions of a specific chemical in yeary from use in solvent applications, by weight.
Percent Leakage. The percentage of the total chemical that is leaked to the atmosphere, assumed to be
90 percent.
Qc = Quantity of Chemical. Total quantity of a specific chemical sold for use in solvent applications in the
yearj, by weight.
j = Year of emission.
Transition Assumptions
The transition assumptions and growth rates used within the Vintaging Model for electronics cleaning, metals
cleaning, precision cleaning, and adhesives, coatings and inks, are presented in Table A- 171.
A-228 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-171. Solvent Market Transition Assumptions
Initial Market
Segment
Primary Substitute
Name of Substitute
Start
Date
Date of Full
Penetration in
New Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Growth
Rate
Adhesives
CH3CCI3 I Non-ODP/GWP
1994
1995
| 100% [None
I 2.0%
Electronics
CFC-113
CHsCCb
Semi-Aqueous
HCFC-225ca/cb
HFC-43-10mee
HFE-7100
nPB
Methyl Siloxanes
No-Clean
Non-ODP/GWP
PFC/PFPE
1994
1994
1995
1994
1992
1992
1992
1996
1996
1995
1995
1996
1995
1996
1996
1996
1997
1997
52%
0.2%
0.7%
0.7%
5%
0.8%
40%
99.8%
0.2%
None
Unknown
None
None
None
None
None
None
Non-ODP/GWP
Non-ODP/GWP
2000
2005
2003
2009
90%
10%
2.0%
2.0%
Metals
CHsCCb
CFC-113
CCI4
Non-ODP/GWP
Non-ODP/GWP
Non-ODP/GWP
1992
1992
1992
1996
1996
1996
100%
100%
100%
None
None
None
2.0%
2.0%
2.0%
Precision
CHsCCb
CFC-113
Non-ODP/GWP
HFC-43-10mee
PFC/PFPE
Non-ODP/GWP
HCFC-225ca/cb
HFE-7100
1995
1995
1995
1995
1995
1995
1996
1996
1996
1996
1996
1996
99.3%
0.6%
0.1%
96%
1%
3%
None
None
Non-ODP/GWP
Non-ODP/GWP
None
Unknown
None
2000
2005
2003
2009
90%
10%
2.0%
2.0%
Non-ODP/GWP includes chemicals with 0 OOP and low GWP, such as hydrocarbons and ammonia, as well as not-in-kind alternatives such as "no clean"
technologies.
Fire Extinguishing
ODSs, HFCs, PFCs and other chemicals are used as fire-extinguishing agents, in both hand-held "streaming"
applications as well as in built-up "flooding" equipment similar to water sprinkler systems. Although these systems are
generally built to be leak-tight, some leaks do occur and of course emissions occur when the agent is released. Total
emissions from fire extinguishing are assumed, in aggregate, to equal a percentage of the total quantity of chemical in
operation at a given time. For modeling purposes, it is assumed that fire extinguishing equipment leaks at a constant rate
for an average equipment lifetime, as shown in the equation below. In streaming systems, non-halon emissions are
assumed to be 3.5 percent of all chemical in use in each year, while in flooding systems 2.5 percent of the installed base of
chemical is assumed to leak annually. Halon systems are assumed to leak at higher rates. The equation is applied for a
single year, accounting for all fire protection equipment in operation in that year. Each fire protection agent is modeled
separately. In the Vintaging Model, streaming applications have a 12-year lifetime and flooding applications have a 20-
year lifetime.
Ej• = r x 2j Qcj-i+i for i=l
Where:
E = Emissions. Total emissions of a specific chemical in yeary for streaming fire extinguishing equipment,
by weight.
r = Percent Released. The percentage of the total chemical in operation that is released to the atmosphere.
A-229
-------
Qc = Quantity of Chemical. Total amount of a specific chemical used in new fire extinguishing equipment in
a given year, _/-/+!, by weight.
/ = Counter, runs from 1 to lifetime (k).
j = Year of emission.
k = Lifetime. The average lifetime of the equipment.
Transition Assumptions
Transition assumptions and growth rates for these two fire extinguishing types are presented in Table A- 172.
Table A-172. Fire Extinguishing Market Transition Assumptions
Initial Market
Segment
Primary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Growth
Rate
Flooding Agents
Halon-1301
Halon-1301*
HFC-23
HFC-227ea
Non-ODP/GWP
Non-ODP/GWP
Non-ODP/GWP
C4Fio
HFC-125
1994
1994
1994
1994
1995
1998
1994
1997
1994
1999
1999
1994
2034
2027
1999
2006
4%
0.2%
18%
46%
10%
10%
1%
11%
Unknown
None
FK-5-1-12
HFC-125
FK-5-1-12
None
None
FK-5-1-12
None
2003
2001
2003
2003
2010
2008
2010
2003
10%
10%
7%
100%
2.2%
Streaming Agents
Halon-1211
Halon-1211*
HFC-236fa
Halotron
Non-ODP/GWP
Non-ODP/GWP
Non-ODP/GWP
1992
1997
1994
1993
1995
1999
1992
1999
1997
1994
2024
2018
5%
3%
4%
58%
20%
10%
Unknown
None
Non-ODP/GWP
HFC-236fa
None
None
None
2020
2020
2020
2020
25%
75%
3.0%
'Despite the 1994 consumption ban, a small percentage of new halon systems are assumed to continue to be built and filled with stockpiled or recovered
supplies.
Foam Blowing
ODSs, HFCs, and other chemicals are used to produce foams, including such items as the foam insulation panels
around refrigerators, insulation sprayed on buildings, etc. The chemical is used to create pockets of gas within a substrate,
increasing the insulating properties of the item. Foams are given emission profiles depending on the foam type (open cell
or closed cell). Open cell foams are assumed to be 100 percent emissive in the year of manufacture. Closed cell foams
are assumed to emit a portion of their total HFC content upon manufacture, a portion at a constant rate over the lifetime of
the foam, a portion at disposal, and a portion after disposal; these portions vary by end-use.
equation.
Step 1: Calculate manufacturing emissions (open-cell and closed-cell foams)
Manufacturing emissions occur in the year of foam manufacture, and are calculated as presented in the following
Where:
Enij = Emissions from manufacturing. Total emissions of a specific chemical in year j due to manufacturing
losses, by weight.
Im = Loss Rate. Percent of original blowing agent emitted during foam manufacture. For open-cell foams,
Im is 100%.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a
given year.
A-230 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
j = Year of emission.
Step 2: Calculate lifetime emissions (closed-cell foams)
Lifetime emissions occur annually from closed-cell foams throughout the lifetime of the foam, as calculated as
presented in the following equation.
EUJ = lu x -L Qcj-i+i for i=l—>k
Where:
Eitj = Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due to lifetime losses
during use, by weight.
lu = Leak Rate. Percent of original blowing agent emitted each year during lifetime use.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a
given year.
/ = Counter, runs from 1 to lifetime (k).
j = Year of emission.
k = Lifetime. The average lifetime of foam product.
Step 3: Calculate disposal emissions (closed-cell foams)
Disposal emissions occur in the year the foam is disposed, and are calculated as presented in the following
equation.
Edj = Id x Qcj.k
Where:
Edj = Emissions from disposal. Total emissions of a specific chemical in year j at disposal, by weight.
Id = Loss Rate. Percent of original blowing agent emitted at disposal.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a
given year.
j = Year of emission.
k = Lifetime. The average lifetime of foam product.
Step 4: Calculate post-disposal emissions (closed-cell foams)
Post-Disposal emissions occur in the years after the foam is disposed; for example, emissions might occur while
the disposed foam is in a landfill. Currently, the only foam type assumed to have post-disposal emissions is polyurethane
foam used as domestic refrigerator and freezer insulation, which is expected to continue to emit for 26 years post-disposal,
calculated as presented in the following equation.
Epj = Ip x 2j Qcj-m for m=k-^>k + 26
Where:
Epj = Emissions from post disposal. Total post-disposal emissions of a specific chemical in year j, by weight.
Ip = Leak Rate. Percent of original blowing agent emitted post disposal.
A-231
-------
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a
given year.
k = Lifetime. The average lifetime of foam product.
m = Counter. Runs from lifetime (k) to (k+26).
j = Year of emission.
Step 5: Calculate total emissions (open-cell and closed-cell foams)
To calculate total emissions from foams in any given year, emissions from all foam stages must be summed, as
presented in the following equation.
Ej = Errij + EUJ + Edj + Epj
Where:
EJ = Total Emissions. Total emissions of a specific chemical in yeary, by weight.
Em = Emissions from manufacturing. Total emissions of a specific chemical in year j due to manufacturing
losses, by weight.
Ettj = Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due to lifetime losses
during use, by weight.
Edj = Emissions from disposal. Total emissions of a specific chemical in year j at disposal, by weight.
Epj = Emissions from post disposal. Total post-disposal emissions of a specific chemical in year j, by weight.
Assumptions
The Vintaging Model contains 13 foam types, whose transition assumptions away from ODS and growth rates
are presented in Table A- 173. The emission profiles of these 13 foam types are shown in Table A- 174.
A-232 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-173. Foam Blowing Market Transition Assumptions
Initial
Market
Segment
Primary Substitute
Name of Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Tertiary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New Equipment
Maximum
Market
Penetration
Growth
Rate
Commercial Refrigeration Foam
CFC-11
HCFC-141b
HCFC-142b
HCFC-22
1989
1989
1989
1996
1996
1996
40%
8%
52%
HFC-245fa
Non-ODP/GWP
Non-ODP/GWP
HFC-245fa
Non-ODP/GWP
HFC-245fa
2002
2002
2009
2009
2009
2009
2003
2003
2010
2010
2010
2010
80%
20%
80%
20%
80%
20%
None
None
None
None
None
None
6.0%
Flexible PU Foam: Integral Skin Foam
CFC-11
HCFC-141b
1989
1990
100%
HFC-134a
HFC-134a
C02
C02
1993
1994
1993
1994
1996
1996
1996
1996
25%
25%
25%
25%
None
None
None
None
2.0%
Flexible PU Foam: Slabstock Foam, Moulded Foam
CFC-11
Non-ODP/GWP
1992
1992
100%
None
2.0%
Phenolic Foam
CFC-11
HCFC-141b
1989
1990
100% 1 Non-ODP/GWP
1992
1992
100%
None
2.0%
Polyolefin Foam
CFC-11 4
HFC-152a
HCFC-142b
1989
1989
1993
1993
10%
90%
Non-ODP/GWP
Non-ODP/GWP
2005
1994
2010
1996
100%
100%
None
None
2.0%
PU and PIR Rigid: Boardstock
CFC-11
HCFC-141b
1993
1996
100%
Non-ODP/GWP
HC/HFC-245fa Blend
2000
2000
2003
2003
95%
5%
None
None
6.0%
PU Rigid: Domestic Refrigerator and Freezer Insulation
CFC-11
HCFC-141b
1993
1995
100%
HFC-134a
HFC-245fa
HFC-245fa
Non-ODP/GWP
Non-ODP/GWP
Non-ODP/GWP
1996
2001
2006
2002
2006
2009
2001
2003
2009
2005
2009
2014
7%
50%
10%
10%
3%
20%
Non-ODP/GWP
Non-ODP/GWP
Non-ODP/GWP
None
None
None
2002
2015
2015
2003
2029
2029
100%
100%
100%
0.8%
A-233
-------
Initial
Market
Segment
Primary Substitute
Name of Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Tertiary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New Equipment
Maximum
Market
Penetration
Growth
Rate
PU Rigid: One Component Foam
CFC-12
HCFC-142b/22
Blend
HCFC-22
1989
1989
1996
1996
70%
30%
Non-ODP/GWP
HFC-134a
HFC-152a
Non-ODP/GWP
HFC-134a
HFC-152a
2009
2009
2009
2009
2009
2009
2010
2010
2010
2010
2010
2010
80%
10%
10%
80%
10%
10%
None
None
None
None
None
None
4.0%
PU Rigid: Other: Slabstock Foam
CFC-11
HCFC-141b
1989
1996
100%
C02
Non-ODP/GWP
HCFC-22
1999
2001
2003
2003
2003
2003
45%
45%
10%
None
None
Non-ODP/GWP
2009
2010
100%
2.0%
PU Rigid: Sandwich Panels: Continuous and Discontinuous
CFC-11
HCFC-141b
HCFC-22
1989
1989
1996
1996
82%
18%
HCFC-22/Water
Blend
HFC-245fa/C02
Blend
Non-ODP/GWP
HFC-134a
HFC-245fa/C02
Blend
Non-ODP/GWP
C02
HFC-134a
2001
2002
2001
2002
2009
2009
2009
2009
2003
2004
2004
2004
2010
2010
2010
2010
20%
20%
40%
20%
40%
20%
20%
20%
HFC-245fa/C02
Blend
Non-ODP/GWP
None
None
None
None
None
None
None
2009
2009
2010
2010
50%
50%
6.0%
PU Rigid: Spray Foam
CFC-11
HCFC-141b
1989
1996
100%
HFC-245fa
HFC-245fa/C02
Blend
Non-ODP/GWP
2002
2002
2001
2003
2003
2003
30%
60%
10%
None
None
None
6.0%
XPS: Boardstock Foam
CFC-12
HCFC-142b/22
Blend
HCFC-142b
1989
1989
1994
1994
10%
90%
HFC-134a
HFC-152a
C02
Non-ODP/GWP
HFC-134a
HFC-152a
C02
Non-ODP/GWP
2009
2009
2009
2009
2009
2009
2009
2009
2010
2010
2010
2010
2010
2010
2010
2010
70%
10%
10%
10%
70%
10%
10%
10%
None
None
None
None
None
None
None
None
2.5%
XPS: Sheet Foam
A-234 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Initial
Market
Segment
CFC-12
Primary Substitute
Name of Substitute
C02
Non-ODP/GWP
Start
Date
1989
1989
Date of Full
Penetration in
New
Equipment
1994
1994
Maximum
Market
Penetration
1%
99%
Secondary Substitute
Name of Substitute
None
C02
HFC-152a
Start
Date
1995
1995
Date of Full
Penetration in
New
Equipment
1999
1999
Maximum
Market
Penetration
9%
10%
Tertiary Substitute
Name of
Substitute
None
None
Start
Date
Date of Full
Penetration in
New Equipment
Maximum
Market
Penetration
Growth
Rate
2.0%
A-235
-------
Table A-174. Emission profile for the foam end-uses
Loss at
Foam End-Use Manufacturing (%)
Flexible PU Foam: Slabstock Foam, Moulded Foam
Commercial Refrigeration
Rigid PU: Spray Foam
Rigid PU: Slabstock and Other
Phenolic Foam
Polyolefin Foam
Rigid PU: One Component Foam
XPS: Sheet Foam*
XPS: Boardstock Foam
Flexible PU Foam: Integral Skin Foam
Rigid PU: Domestic Refrigerator and Freezer Insulation*
PU and PIR Rigid: Boardstock
PU Sandwich Panels: Continuous and Discontinuous
100
6
15
37.5
23
95
100
40
25
95
4
6
5.5
Annual Leakage
Rate (%)
0
0.25
1.5
0.75
0.875
2.5
0
2
0.75
2.5
0.25
1
0.5
Leakage
Lifetime
(years)
1
15
56
15
32
2
1
25
50
2
14
50
50
Loss at Disposal Total*
(%) (%)
0
90.25
1
51.25
49
0
0
0
37.5
0
40.0
44
69.5
100
100
100
100
100
100
100
90
100
100
47.5
100
100
PIR (Polyisicyanurate)
PU (Polyurethane)
XPS (Extruded Polystyrene)
*ln general, total emissions from foam end-uses are assumed to be 100 percent, although work is underway to investigate that assumption. In the XPS
Sheet/Insulation Board end-use, the source of emission rates and lifetimes did not yield 100 percent emission; it is unclear at this time whether that was
intentional. In the Rigid PU Appliance Foam end-use, the source of emission rates and lifetimes did not yield 100 percent emission; the remainder is anticipated
to be emitted at a rate of 2.0%/year post-disposal for the next 26 years.
Sterilization
Sterilants kill microorganisms on medical equipment and devices. The principal ODS used in this sector was a
blend of 12% ethylene oxide (EtO) and 88% CFC-12, known as "12/88." In that blend, ethylene oxide sterilizes the
equipment and CFC-12 is a dilutent solvent to form a non-flammable blend. The sterilization sector is modeled as a single
end-use. For sterilization applications, all chemicals that are used in the equipment in any given year are assumed to be
emitted in that year, as shown in the following equation.
Where:
E = Emissions. Total emissions of a specific chemical in year j from use in sterilization equipment, by
weight.
Qc = Quantity of Chemical. Total quantity of a specific chemical used in sterilization equipment in year j, by
weight.
j = Year of emission.
Assumptions
The Vintaging Model contains 1 sterilization end-use, whose transition assumptions away from ODS and growth rates are
presented in Table A- 175
A-236 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-175. Sterilization Market Transition Assumptions
Initial
Market
Segment
Primary Substitute
Name of Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Tertiary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New Equipment
Maximum
Market
Penetration
Growth
Rate
Commercial Refrigeration Foam
12/88
EtO
Non-ODP/GWP
HCFC/EtO Blends
1994
1994
1993
1995
1995
1994
95%
1%
4%
None
None
Non-ODP/GWP
2010
2010
100%
None
2.0%
A-237
-------
Model Output
By repeating these calculations for each year, the Vintaging Model creates annual profiles of use and emissions
for ODS and ODS substitutes. The results can be shown for each year in two ways: 1) on a chemical-by-chemical basis,
summed across the end-uses, or 2) on an end-use or sector basis. Values for use and emissions are calculated both in
metric tons and in teragrams of CC>2 equivalents (Tg CC>2 Eq.). The conversion of metric tons of chemical to Tg CC>2 Eq.
is accomplished through a linear scaling of tonnage by the global warming potential (GWP) of each chemical.
Throughout its development, the Vintaging Model has undergone annual modifications. As new or more
accurate information becomes available, the model is adjusted in such a way that both past and future emission estimates
are often altered.
Bank of ODS and ODS Substitutes
The bank of an ODS or an ODS substitute is "the cumulative difference between the chemical that has been
consumed in an application or sub-application and that which has already been released" (IPCC 2006). For any given
year, the bank is equal to the previous year's bank, less the chemical in equipment disposed of during the year, plus
chemical in new equipment entering the market during that year, less the amount emitted but not replaced, plus the amount
added to replace chemical emitted prior to the given year, as shown in the following equation:
BCJ = Bcj-i-Qdj+Qpj+Ee-Qr
Where:
BCJ = Bank of Chemical. Total bank of a specific chemical in yeary, by weight.
Qdj = Quantity of Chemical in Equipment Disposed. Total quantity of a specific chemical in equipment
disposed of in yeary, by weight.
QPi = Quantity of Chemical Penetrating the Market. Total quantity of a specific chemical that is entering the
market in yeary, by weight.
Ee = Emissions of Chemical Not Replaced. Total quantity of a specific chemical that is emitted during year j
but is not replaced in that year. The Vintaging Model assumes all chemical emitted from refrigeration,
air conditioning and fire extinguishing equipment is replaced in the year it is emitted, hence this term is
zero for all sectors except foam blowing.
Qr = Chemical Replacing Previous Year's Emissions. Total quantity of a specific chemical that is used to
replace emissions that occurred prior to year j. The Vintaging Model assumes all chemical emitted
from refrigeration, air conditioning and fire extinguishing equipment is replaced in the year it is
emitted, hence this term is zero for all sectors.
j = Year of emission.
Table A- 176 provides the bank for ODS and ODS substitutes by chemical grouping in metric tons (MT) for 1990-2012.
A-238 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-176. Banks of ODS and ODS Substitutes, 1990-2012 (MT)
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
CFC
711,534
807,182
674,734
645,931
620,996
596,076
570,703
547,210
523,580
502,928
487,552
480,177
466,203
452,162
437,702
HCFC
283,302
497,582
923,281
992,260
1,045,199
1,081,664
1,118,168
1,159,858
1,197,264
1,226,849
1,246,198
1,241,550
1,206,140
1,161,244
1,115,819
HFC
868
53,696
197,661
227,436
257,279
293,046
330,397
368,806
412,438
456,497
497,005
544,053
612,013
679,111
753,596
References
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
A-239
-------
3.10. Methodology for Estimating Cm Emissions from Enteric Fermentation
Methane emissions from enteric fermentation were estimated for seven livestock categories: cattle, horses, sheep,
swine, goats, American bison, and the non-horse equines (mules and asses). Emissions from cattle represent the majority
of U.S. emissions from enteric fermentation; consequently, a more detailed IPCC Tier 2 methodology was used to estimate
emissions from cattle. The IPCC Tier 1 methodology was used to estimate emissions for the other types of livestock,
including horses, goats, sheep, swine, American bison, and mules and asses.
Estimate Methane Emissions from Cattle
This section describes the process used to estimate CH4 emissions from enteric fermentation from cattle using the
Cattle Enteric Fermentation Model (CEFM). The CEFM was developed based on recommendations provided in
IPCCAJNEP/OECD/IEA (1997), IPCC (2000) and IPCC (2006), and uses information on population, energy
requirements, digestible energy, and CFU conversion rates to estimate CFU emissions.66 The emission methodology
consists of the following three steps: (1) characterize the cattle population to account for animal population categories with
different emission profiles; (2) characterize cattle diets to generate information needed to estimate emission factors; and
(3) estimate emissions using these data and the IPCC Tier 2 equations.
Step 1: Characterize U.S. Cattle Population
The state-level cattle population estimates are based on data obtained from the U.S. Department of Agriculture's
(USDA) National Agricultural Statistics Service Quick Stats database (USDA 2012). A summary of the annual average
populations upon which all livestock-related emissions are based is provided in Table A-177. Cattle populations used in
the Enteric Fermentation source category were estimated using the cattle transition matrix in the CEFM, which uses
January 1 USDA population estimates and weight data to simulate the population of U.S. cattle from birth to slaughter,
and results in an estimate of the number of animals in a particular cattle grouping while taking into account the monthly
rate of weight gain, the average weight of the animals, and the death and calving rates. The use of supplemental USDA
data and the cattle transition matrix in the CEFM results in cattle population estimates for this sector differing slightly
from the January 1 or July 1 USDA point estimates and the cattle population data obtained from the Food and Agriculture
Organization of the United Nations (FAO).
Table A-177: Cattle Population Estimates from the CEFM Transition Matrix for1990-201211,000 head!
Livestock Type 1990 1995 2000 2005 2009 2010 2011 2012
Dairy
Dairy Calves (4-6 months)
Dairy Cows
Dairy Replacements 7-1 1 months
Dairy Replacements 12-23 months
Beef
Beef Calves (4-6 months)
Bulls
Beef Cows
Beef Replacements 7-1 1 months
Beef Replacements 12-23 months
Steer Stackers
Heifer Stackers
Feedlot Cattle
5,369
10,015
1,214
2,915
16,909
2,160
32,455
1,269
2,967
10,321
5,946
9,549
5,091
9,482
1,216
2,892
18,177
2,385
35,190
1,493
3,637
11,716
6,699
11,064
4,951
9,183
1,196
2,812
17,431
2,293
33,575
1,313
3,097
8,724
5,371
13,006
4,628
9,004
1,257
2,905
16,918
2,214
32,674
1,363
3,171
8,185
5,015
12,652
4,791
9,333
1,327
3,101
16,051
2,184
31,712
1,290
3,098
8,515
5,059
12,953
4,666
9,086
1,347
3,179
16,043
2,190
31,371
1,239
3,055
8,223
5,054
13,191
4,706
9,150
1,362
3,210
15,795
2,155
30,850
1,230
2,890
7,628
4,759
13,546
4,772
9,230
1,350
3,236
15,186
2,096
30,158
1,250
2,957
7,209
4,470
13,209
The population transition matrix in the CEFM simulates the U.S. cattle population over time and provides an
estimate of the population age and weight structure by cattle type on a monthly basis. Since cattle often do not remain in
a single population type for an entire year (e.g., calves become stackers, stackers become feedlot animals), and emission
profiles vary both between and within each cattle type, these monthly age groups are tracked in the enteric fermentation
model to obtain more accurate emission estimates than would be available from annual point estimates of population (such
as available from USDA statistics) and weight for each cattle type.
Additional information on the Cattle Enteric Fermentation Model can be found in ICF (2006).
Mature animal populations are not assumed to have significant monthly fluctuations, and therefore the populations utilized are the January
estimates downloaded from USDA (2013).
A-240 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The transition matrix tracks both dairy and beef populations, and divides the populations into males and females,
and subdivides the population further into specific cattle groupings for calves, replacements, stacker, feedlot, and mature
animals. The matrix is based primarily on two types of data: population statistics and weight statistics (including target
weights, slaughter weights, and weight gain). Using the weight data, the transition matrix simulates the growth of animals
over time by month. The matrix also relies on supplementary data, such as feedlot placement statistics, slaughter statistics,
death rates, and calving rates.
The basic method for tracking population of animals per category is based on the number of births (or graduates)
into the monthly age group minus those animals that die or are slaughtered and those that graduate to the next category
(such as stackers to feedlot placements).
Each stage in the cattle lifecycle was modeled to simulate the cattle population from birth to slaughter. This
level of detail accounts for the variability in CH4 emissions associated with each life stage. Given that a stage can last less
than one year (e.g., calves are usually weaned between 4 and 6 months of age), each is modeled on a per-month basis. The
type of cattle also influences CH4 emissions (e.g., beef versus dairy). Consequently, there is an independent transition
matrix for each of three separate lifecycle phases, 1) calves, 2) replacements and stackers, and 3) feedlot animals. In
addition, the number of mature cows and bulls are tabulated for both dairy and beef stock. The transition matrix estimates
total monthly populations for all cattle subtypes. These populations are then reallocated to the state level based on the
percent of the cattle type reported in each state in the January 1 USDA data. Each lifecycle is discussed separately below,
and the categories tracked are listed in Table A-178.
Table A-178: Cattle Population Categories Used for Estimating CHa Emissions
Dairy Cattle Beef Cattle
Calves (4-6 months) Calves (4-6 months)
Heifer Replacements Heifer Replacements
Cows Heifer and Steer Stackers
Animals in Feedlots (Heifers & Steer)
Cows
Bulls*
a Bulls (beef and dairy) are accounted for in a single category.
The key variables tracked for each of these cattle population categories are as follows:
Calves. Although only the emissions for calves ages 4 to 6 months are calculated in the inventory, it is necessary
to estimate populations from birth as estimates of populations for older cattle are reliant on understanding the available
supply of calves from birth. The number of animals born on a monthly basis was used to initiate monthly cohorts and to
determine population age structure. The number of calves born each month was obtained by multiplying annual births by
the percentage of births per month. Annual birth information for each year was taken from USDA (2013). For dairy
cows, the number of births is assumed to be distributed equally throughout the year (approximately 8.3 percent per
month), beef births are distributed according to Table A-179, based on estimates from the National Animal Health
Monitoring System (NAHMS) (USDA/APHIS/VS 1998, 1994, 1993). To determine whether calves were born to dairy or
beef cows, the dairy cow calving rate (USDA/APHIS/VS 2002, USDA/APHIS/VS 1996) was multiplied by the total dairy
cow population to determine the number of births attributable to dairy cows, with the remainder assumed to be attributable
to beef cows. Total annual calf births are obtained from USDA, and distributed into monthly cohorts by cattle type (beef
or dairy). Calf growth is modeled by month, based on estimated monthly weight gain for each cohort (approximately 61
pounds per month). The total calf population is modified through time to account for veal calf slaughter at 4 months and a
calf death loss of 0.35 percent annually (distributed across age cohorts up to six months of age). An example of a transition
matrix for calves is shown in Table A-180. Note that calves age one through six months available in January have been
tracked through the model based on births and death loss from the previous year.
Table A-179: Estimated Beef Cow Births by Month
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
7% 15% 28% 22% 9% 3% 2% 2% 3% 4% 3% 3%
A-241
-------
Table A-180: Example of Monthly Average Populations from Calf Transition Matrix (1,000 head)
Age (month) Jan
Feb
Mar
Apr May
Jun
Jul
Aug Sep
Oct
Nov
Dec
6
5
4
3
2
1
0
1,187
1,179
1,450
1,684
1,623
1,607
I 2,457
1,178
1,382
1,683
1,622
1,605
2,454
4,543
1,381
1,622
1,620
1,604
2,453
4,540
7,856
1,621
1,561
1,603
2,452
4,538
7,852
6,378
1,561
1,547
2,451
4,536
7,849
6,375
3,004
1,547
2,392
4,535
7,846
6,373
3,003
1,535
2,391 |
4,478
7,843
6,371
3,002
1,534
1,161
4,476
7,774
6,368
3,001
1,534
1,161
1,152
7,771
6,294
3,000
1,533
1,160
1,152
1,413
6,291
2,935
1,533
1,160
1,152
1,413
1,639
2,934 1,459
1,460 1,089
1,159 1,150
1,151 1,411
1,412 1,637
1,638 1,577
1,579 1,561
Note: The cohort starting at age 0 months on January 1 is tracked in order to illustrate how a single cohort moves through the transition matrix. Each month, the
cohort reflects the decreases in population due to the estimated 0.35% annual death loss, and between months 4 and 5, a more significant loss is seen than in
other months due to estimated veal slaughter.
Replacements and Stockers. At seven months of age, calves "graduate" and are separated into the applicable
cattle types. First the number of replacements required for beef and dairy cattle are calculated based on estimated death
losses and population changes between beginning and end of year population estimates. Based on the USDA estimates for
"replacement beef heifers" and "replacement dairy heifers," the transition matrix for the replacements is back-calculated
from the known animal totals from USDA, and the number of calves needed to fill that requirement for each month is
subtracted from the known supply of female calves. All female calves remaining after those needed for beef and dairy
replacements are removed become "stackers" that can be placed in feedlots (along with all male calves). During the
stacker phase animals are subtracted out of the transition matrix for placement into feedlots based on feedlot placement
statistics from USDA (2013).
The data and calculations that occur for the stacker category include matrices that estimate the population of
backgrounding heifers and steer, as well as a matrix for total combined stackers. The matrices start with the beginning of
year populations in January and model the progression of each cohort. The age structure of the January population is based
on estimated births by month from the previous two years, although in order to balance the population properly, an
adjustment is added that slightly reduces population percentages in the older populations. The populations are modified
through addition of graduating calves (added in month 7, bottom row of Table A-181) and subtraction through death loss
and animals placed in feedlots. Eventually, an entire cohort population of stackers may reach zero, indicating that the
complete cohort has been transitioned into feedlots. An example of the transition matrix for stackers is shown in Table A-
181.
Tahle A-181: Example of Monthly Average Populations from Stocker Transition Matrix 11,000 head!
(month)
Jan
Feb
Mar
Apr May
Jun
Jul
Aug Sep
Oct
Nov
Dec
23 177
22 306
21 248
20 117
19 60
18 45
17 45
16 55
15 64
14 61
13 61
12 946
11 1,750
10 3,026
9 2,456
8 1,157
7 | 3,520
187
152
71
37
28
27
34
39
38
37
813
1,543
2,665
2,158
1,023
3,091
853
106
50
26
19
19
23
27
26
26
568
1,396
2,412
1,938
903
2,601
704
846
36
19
14
14
17
20
19
19
413
1,078
2,125
1,707
802
2,306
521
534
1,018
15
11
11
14
16
15
15
334
907
1,782
1,506
707
1,930
419
387
647
1,232
9
9
11
13
13
12
272
774
1,513
1,275
585
1,540
297
210
384
787
1,179
7
9
11
10
10
222
666
1,294
1,088
499
1,118
226
148
188
421
710
1,168
7
8
8
8
177
569
1,096
920
422
847
48
48
79
236
417
737
1,920
5
5
5
110
424
804
671
309
550
48
48
59
88
205
385
1,509
3,764
2
2
54
305
562
465
216
304
48
48
59
68
84
207
1,308
3,326
6,676
0
3
195
339
276
130
78
48
48
59
68
65
86
1,153
2,856
6,107
5,368
0
188
326
264
125
64
48
48
59
68
65
65
1,014
2,368
5,180
4,910
2,401
Note: The cohort starting at age 7 months on January 1 is tracked in order to illustrate how a single cohort moves through the transition matrix. Each month, the
cohort reflects the decreases in population due to the estimated 0.35% annual death loss and loss due to placement in feedlots (the latter resulting in the majority
of the loss from the matrix).
In order to ensure a balanced population of both stackers and placements, additional data tables are utilized in the
stacker matrix calculations. The tables summarize the placement data by weight class and month, and is based on the total
number of animals within the population that are available to be placed in feedlots and the actual feedlot placement
statistics provided by USDA (2013). In cases where there are discrepancies between the USDA estimated placements by
weight class and the calculated animals available by weight, the model pulls available stackers from one higher weight
category if available. If there are still not enough animals to fulfill requirements the model pulls animals from one lower
A-242 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
weight category. In the current time series, this method was able to ensure that total placement data matched USDA
estimates, and no shortfalls have occurred.
In addition, average weights were tracked for each monthly age group using starting weight and monthly weight
gain estimates. Weight gain (i.e., pounds per month) was estimated based on weight gain needed to reach a set target
weight, divided by the number of months remaining before target weight was achieved. Birth weight was assumed to be
88 pounds for both beef and dairy animals. Weaning weights were estimated at 515 pounds. Other reported target
weights were available for 12, 15, 24, and 36 month-old animals, depending on the animal type. Beef cow mature weight
was taken from measurements provided by a major British Bos taurus breed (Enns 2008) and increased during the time
series through 2007. Bull mature weight was calculated as 1.5 times the beef cow mature weight (Doren et al. 1989).
Beef replacement weight was calculated as 70 percent of mature weight at 15 months and 85 percent of mature weight at
24 months. As dairy weights are not a trait that is typically tracked, mature weight for dairy cows was estimated at 1,500
pounds for all years, based on a personal communication with Kris Johnson (2010) and an estimate from Holstein
Association USA (2010).69 Dairy replacement weight at 15 months was assumed to be 875 pounds and 1,300 pounds at
24 months. Live slaughter weights were estimated from dressed slaughter weight (USDA 2013) divided by 0.63. This
ratio represents the dressed weight (i.e., weight of the carcass after removal of the internal organs), to the live weight (i.e.,
weight taken immediately before slaughter). The annual typical animal mass for each livestock type are presented in
Table A-182.
Weight gain for stacker animals was based on monthly gain estimates from Johnson (1999) for 1989, and from
average daily estimates from Lippke et al. (2000), Pinchack et al. (2004), Platter et al. (2003), and Skogerboe et al. (2000)
for 2000. Interim years were calculated linearly, as shown in Table A-183, and weight gain was held constant starting in
2000. Table A-183 provides weight gains that vary by year in the CEFM.
68 Mature beef weight is held constant after 2007 but future inventory submissions will incorporate known trends through 2007
and extrapolate to future years, as noted in the Planned Improvements section.
69 Mature dairy weight is based solely on Holstein weight, so could be higher than the national average. Future inventory
submissions will consider other dairy breeds, as noted in the Planned Improvements section.
A-243
-------
Table A-182:Typical Animal Mass libs)
Year/Cattle
Type
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Calves Dairy
Cows3
269 1,500
270 1,500
269 1,500
270 1,500
270 1,500
270 1,500
269 1,500
270 1,500
270 1,500
270 1,500
270 1,500
270 1,500
270 1,500
270 1,500
270 1,500
270 1,500
270 1,500
270 1,500
270 1,500
270 1,500
270 1,500
270 1,500
270 1,500
Dairy Beef
Replacements'1 Cows3
900 1,221
898 1,225
897 1,263
899 1,280
898 1,280
898 1,282
898 1,285
900 1,286
897 1,296
899 1,292
897 1,272
898 1,272
897 1,276
900 1,308
897 1,323
895 1,327
898 1,341
897 1,348
898 1,348
897 1,348
898 1,348
897 1,348
899 1,348
Bulls"
1,832
1,838
1,895
1,920
1,920
1,923
1,928
1,929
1,944
1,938
1,908
1,908
1,914
1,962
1,985
1,991
2,012
2,022
2,022
2,022
2,022
2,022
2,022
Beef
Replacements'1
820
822
841
852
854
858
859
861
866
862
849
850
852
872
878
880
890
895
895
894
897
892
893
Steer
Stackers'1
692
695
714
721
721
735
739
737
736
731
720
726
726
719
719
718
725
721
721
731
111
723
715
Heifer
Stackers'1
652
656
673
683
689
701
707
708
710
709
702
707
708
702
702
706
713
707
705
715
714
714
708
Steer
Feedlotb
923
934
984
983
1000
1019
1018
1010
1046
1050
1063
1081
1088
1087
1089
1087
1113
1133
1142
1145
1134
1137
1153
Heifer
Feedlotb
846
856
878
878
886
896
893
888
905
908
915
918
933
929
921
934
943
954
961
966
964
955
969
'Input into the model.
b Annual average calculated in model based on age distribution.
Table A-183:
Weight Gains that Vary by Year (Ibs)
Year/Cattle Type Steer Stockers to 1 2 Steer Stockers to 24
Heifer Stockers to 12
months(lbs/day) months (Ibs/day)
months(lbs/day)
Heifer Stockers to 24
months(lbs/day)
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000 onwards
1.53
1.56
1.59
1.62
1.65
1.68
1.71
1.74
1.77
1.80
1.83
1.23
1.29
1.35
1.41
1.47
1.53
1.59
1.65
1.71
1.77
1.83
1.23
1.29
1.35
1.41
1.47
1.53
1.59
1.65
1.71
1.77
1.83
1.08
1.15
1.23
1.30
1.38
1.45
1.53
1.60
1.68
1.75
1.83
Sources: Enns (2008), Johnson (1999), Lippkeet al. (2000), NRC (1999), Pinchacket al. (2004), Platter et al. (2003), Skogerboeet al. (2000).
Feedlot Animals. Feedlot placement statistics from USDA provide data on the placement of animals from the
stacker population into feedlots on a monthly basis by weight class. The model uses these data to shift a sufficient number
of animals from the stacker cohorts into the feedlot populations to match the reported placement data. After animals are
placed in feedlots they progress through two steps. First, animals spend 25 days on a step-up diet to become acclimated to
the new feed type (e.g., more grain than forage, along with new dietary supplements), during this time weight gain is
estimated to be 2.7 to 3 pounds per day (Johnson 1999). Animals are then switched to a finishing diet (concentrated, high
energy) for a period of time before they are slaughtered. Weight gain during finishing diets is estimated to be 2.9 to 3.3
pounds per day (Johnson 1999). The length of time an animal spends in a feedlot depends on the start weight (i.e.,
placement weight), the rate of weight gain during the start-up and finishing phase of diet, and the target weight (as
determined by weights at slaughter). Additionally, animals remaining in feedlots at the end of the year are tracked for
inclusion in the following year's emission and population counts. For 1990 to 1995, only the total placement data were
available, therefore placements for each weight category (categories displayed in Table A-184) for those years are based
on the average of monthly placements from the 1996 to 1998 reported figures. Placement data is available by weight class
for all years from 1996 onward. Table A-184 provides a summary of the reported feedlot placement statistics for 2012.
A-244 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-184: Feedlot Placements in the United States for 2012 (Number of animals placed in 1,000 Head)
Weight
Placed When:
< 600 Ibs
600-700lbs
700 -800 Ibs
> 800 Ibs
Total
Jan
445
430
525
447
1,847
Feb
400
335
469
510
1,714
Mar
390
300
500
602
1,792
Apr
355
250
380
536
1,521
May
520
365
530
669
2,084
Jun
460
320
390
494
1,664
Jul
500
325
470
627
1,922
Aug
482
385
475
665
2,007
Sep
515
355
444
690
2,004
Oct
680
505
435
560
2,180
Nov
655
450
385
453
1,943
Dec
495
415
379
375
1,664
Source: USDA(2013).
Note: Totals may not sum due to independent rounding.
Mature Animals. Energy requirements and hence, composition of diets, level of intake, and emissions for
particular animals, are greatly influenced by whether the animal is pregnant or lactating. Information is therefore needed
on the percentage of all mature animals that are pregnant each month, as well as milk production, to estimate CH4
emissions. A weighted average percent of pregnant cows each month was estimated using information on births by month
and average pregnancy term. For beef cattle, a weighted average total milk production per animal per month was
estimated using information on typical lactation cycles and amounts (NRC 1999), and data on births by month. This
process results in a range of weighted monthly lactation estimates expressed as pounds per animal per month. The
monthly estimates for daily milk production by beef cows are shown in Table A-9. Annual estimates for dairy cows were
taken from USDA milk production statistics. Dairy lactation estimates for 1990 through 2012 are shown in Table A-186.
Beef and dairy cow and bull populations are assumed to remain relatively static throughout the year, as large fluctuations
in population size are assumed to not occur. These estimates are taken from the USDA beginning and end of year
population datasets.
Table A-185: Estimates of Monthly Milk Production by Beef Cows (Pounds per Headl
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Beef Cow Milk Production (Ibs/head) 3.3 5.1 8.7 12.0 13.6 13.3 11.7 9.3 6.9 4.4 3.0 2.8
A-245
-------
Table A-186: Dairy Lactation Rates by State libs/ year/cow)
State/Year
1990
1995
2000
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
12,214
13,300
17,500
11,841
18,456
17,182
15,606
13,667
14,033
12,973
13,604
16,475
14,707
14,590
15,118
12,576
10,947
11,605
14,619
13,461
14,871
15,394
14,127
12,081
13,632
13,542
13,866
16,400
15,100
13,538
18,815
14,658
15,220
12,624
13,767
12,327
16,273
14,726
14,250
12,771
12,257
11,825
14,350
15,838
14,528
14,213
18,532
11,250
13,973
12,337
14,176
17,000
19,735
12,150
19,573
18,687
16,438
14,500
14,698
15,550
13,654
18,147
15,887
15,375
16,124
14,390
12,469
11,908
16,025
14,725
16,000
17,071
15,894
12,909
14,158
15,000
14,797
18,128
16,300
13,913
18,969
16,501
16,314
13,094
15,917
13,611
17,289
16,492
14,773
14,481
13,398
13,740
15,244
16,739
16,210
15,116
20,091
12,667
15,397
13,197
1J.U94
15,917 I
13,611 I
17,289
16,492
14,773 I
14,481 I
13,398
13,740 I
115,244
16,739
H I
13,920
14,500
21,820
12,436
21,130
21,618
17,778
14,747
15,688
16,284
14,358
20,816
17,450
16,568
18,298
16,923
12,841
12,034
17,128
16,083
17,091
19,017
17,777
15,028
14,662
17,789
16,513
19,000
17,333
15,250
20,944
17,378
16,746
14,292
17,027
14,440
18,222
18,081
15,667
16,087
15,516
14,789
16,503
17,573
17,199
15,833
22,644
15,588
17,306
13,571
14,000
12,273
22,679
13,545
21,404
22,577
19,200
16,622
16,591
17,259
12,889
22,332
18,827
20,295
20,641
20,505
12,896
12,400
18,030
16,099
17,059
21,635
18,091
15,280
16,026
19,579
17,950
21,680
18,875
16,000
21,192
18,639
18,741
14,182
17,567
16,480
18,876
18,722
17,000
16,000
17,741
15,743
19,646
18,875
18,469
16,990
23,270
14,923
18,500
14,878
2009
14,909
10,000
23,028
12,692
22,000
23,081
18,579
17,000
18,070
18,182
14,200
22,091
18,873
20,137
20,367
21,085
14,190
11,870
18,061
18,255
17,571
22,445
19,230
13,889
14,654
19,933
19,672
21,821
19,533
17,889
24,320
20,071
19,644
16,739
18,744
16,983
19,719
19,360
17,818
19,000
20,128
16,232
20,898
21,036
18,289
18,083
23,171
14,727
20,079
19,036
2010
14,455
11,833
23,441
12,750
23,025
23,664
19,158
16,981
18,658
17,671
13,316
22,658
19,170
20,094
20,724
20,975
14,769
11,750
18,344
18,537
17,286
23,277
19,366
13,118
14,596
20,643
19,797
23,500
19,600
17,500
24,551
20,807
19,636
18,286
19,446
17,125
20,331
19,847
17,727
17,875
20,478
16,346
21,375
21,400
18,537
18,095
23,510
15,700
20,630
20,067
2011
13,182
13,800
23,468
11,833
23,438
23,430
19,000
18,300
19,067
18,354
14,421
22,934
19,357
20,657
21,309
21,016
14,342
12,889
18,688
18,654
16,923
23,164
18,996
14,571
14,611
20,571
20,579
23,138
20,429
16,875
24,854
21,046
20,089
18,158
19,194
17,415
20,488
19,495
17,909
17,438
20,582
16,200
22,232
21,068
18,940
17,906
23,727
15,600
20,599
20,517
2012
13,200
14,250
23,979
13,300
23,457
23,978
19,889
19,143
19,008
19,125
14,200
23,376
19,510
21,366
21,730
21,675
15,135
13,176
18,576
19,196
18,250
23,704
19,508
14,357
14,936
21,357
21,179
22,966
19,643
18,571
24,694
21,633
20,435
19,278
19,833
17,688
20,431
19,576
18,300
17,313
21,391
16,100
22,009
21,678
19,316
17,990
23,794
15,800
21,436
20,817
Source: USDA (2013).
Step 2: Characterize U.S. Cattle Population Diets
To support development of digestible energy (DE, the percent of gross energy intake digested by the animal) and
CH4 conversion rate (Ym, the fraction of gross energy converted to CFU) values for each of the cattle population categories,
data were collected on diets considered representative of different regions. For both grazing animals and animals being
fed mixed rations, representative regional diets were estimated using information collected from state livestock specialists,
the United States Department of Agriculture, expert opinion, and other literature sources. The designated regions for this
analysis for dairy cattle for all years and foraging beef cattle from 1990 through 2006 are shown in Table A-187. For
foraging beef cattle from 2007 onwards, the regional designations were revised based on data available from the NAHMS
2007-2008 survey on cow-calf system management practices (USDA:APHIS:VS 2010) and are shown in and Table A-
A-246 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
188. The data for each of the diets (e.g., proportions of different feed constituents, such as hay or grains) were used to
determine feed chemical composition for use in estimating DE and Ym for each animal type.
Table A-187: Regions used for Characterizing the Diets of Dairy Cattle tall years! and Foraging Cattle from 1990-2006
West California Northern Great Midwestern Northeast Southcentral Southeast
Plains
Alaska California
Arizona
Hawaii
Idaho
Nevada
New Mexico
Oregon
Utah
Washington
Colorado
Kansas
Montana
Nebraska
North Dakota
South Dakota
Wyoming
Illinois
Indiana
Iowa
Michigan
Minnesota
Missouri
Ohio
Wisconsin
Connecticut
Delaware
Maine
Maryland
Massachusetts
New Hampshire
New Jersey
New York
Pennsylvania
Rhode Island
Vermont
West Virginia
Arkansas
Louisiana
Oklahoma
Texas
Alabama
Florida
Georgia
Kentucky
Mississippi
North Carolina
South Carolina
Tennessee
Virginia
Source: USDA (1996).
Table A-188: Regions used for Characterizing the Diets of Foraging Cattle from 2007-2012
West
Alaska
Arizona
California
Colorado
Hawaii
Idaho
Montana
Nevada
New Mexico
Oregon
Utah
Washington
Wyoming
Central
Illinois
Indiana
Iowa
Kansas
Michigan
Minnesota
Missouri
Nebraska
North Dakota
Ohio
South Dakota
Wisconsin
Northeast
Connecticut
Delaware
Maine
Maryland
Massachusetts
New Hampshire
New Jersey
New York
Pennsylvania
Rhode Island
Vermont
West Virginia
Southeast
Alabama
Arkansas
Florida
Georgia
Kentucky
Louisiana
Mississippi
North Carolina
Oklahoma
South Carolina
Tennessee
Texas
Virginia
Source: Based on data from USDA:APHIS:VS (2010).
Note: States in bold represent a change in region from the 1990-2006 assessment.
DE and Ym vary by diet and animal type. The IPCC recommends Ym values of 3.0+1.0 percent for feedlot cattle
and 6.5+1.0 percent for all other cattle (IPCC 2006). Given the availability of detailed diet information for different
regions and animal types in the United States, DE and Ym values unique to the United States were developed for dairy and
beef cattle. Digestible energy and Ym values were estimated across the time series for each cattle population category
based on physiological modeling, published values, and/or expert opinion.
For dairy cows, ruminant digestion models were used to estimate Ym. The three major categories of input
required by the models are animal description (e.g., cattle type, mature weight), animal performance (e.g., initial and final
weight, age at start of period), and feed characteristics (e.g., chemical composition, habitat, grain or forage). Data used to
simulate ruminant digestion is provided for a particular animal that is then used to represent a group of animals with
similar characteristics. The Ym values were estimated for 1990 using the Donovan and Baldwin model (1999), which
represents physiological processes in the ruminant animals, as well as diet characteristics from USDA (1996). The
Donovan and Baldwin model is able to account for differing diets (i.e., grain-based or forage-based), so that Ym values for
the variable feeding characteristics within the U.S. cattle population can be estimated. Subsequently, a literature review of
dairy diets was conducted and nearly 250 diets were analyzed from 1990 through 2009 across 23 states—the review
indicated highly variable diets, both temporally and spatially. Kebreab et al. (2008) conducted an evaluation of models
and found that the COWPOLL model was the best model for estimating Ym for dairy. The statistical analysis of the
COWPOLL model showed a trend in predicting Ym, and inventory team experts determined that the most comprehensive
approach was to use the 1990 baseline from Donovan and Baldwin and then scale Ym values for each of the diets beyond
A-247
-------
1990 with the COWPOLL model. A function based on the national trend observed from the analysis of the dairy diets was
used to calculate 1991 and beyond regional values based on the regional 1990 Ym values from Donovan and Baldwin. The
resulting scaling factor (incorporating both Donovan and Baldwin (1999) and COWPOLL) is shown below:
Y = Y (1990YEXP 7 — r \/EXP\
m m^- ' fv 1 non \
1.22
1^ (Year -1980) J 1^1990-1980)
DE values for dairy cows were estimated from the literature search based on the annual trends observed in the
data collection effort. The regional variability observed in the literature search was not statistically significant, and
therefore DE was not varied by region, but did vary over time, and was grouped by the following years 1990-1993, 1994-
1998, 1999-200270, 2004-2006, 2007, and 2008 onwards.
Considerably less data was available for dairy heifers and dairy calves. Therefore, for dairy heifers assumptions
were based on the relationship of the collected data in the literature on dairy heifers to the data on dairy cow diets. From
this relationship, DE was estimated as the mature cow DE minus three percent, and Ym was estimated as that of the mature
dairy cow plus 0.1 percent.
To calculate the DE values for grazing beef cattle, diet composition assumptions were used to estimate weighted DE
values for a combination of forage and supplemental diets. The forage portion makes up an estimated 85 to 95 percent of
grazing beef cattle diets, and there is considerable variation of both forage type and quality across the US. Currently there
is no comprehensive survey of this data, so for this analysis two regional DE values were developed to account for the
generally lower forage quality in the "West" region of the United States versus all other regions in Table A-l 1 (California,
Northern Great Plains, Midwestern, Northeast, Southcentral, Southeast) and Table A-12 (Central, Northeast, and
Southeast). For all non-western grazing cattle, the forage DE was an average of the estimated seasonal values for grass
pasture diets for a calculated DE of 64.2 percent. For foraging cattle in the west, the forage DE was calculated as the
seasonal average for grass pasture, meadow and range diets, for a calculated DE of 61.3 percent. The assumed specific
components of each of the broad forage types, along with their corresponding DE value and the calculated regional DE
values can be found in Table A-189. In addition, it was assumed that each region fed a supplemental diet, and two sets of
supplemental diets were developed, one for 1990 through 2006 (Donovan 1999) and one for 2007 onwards (Preston 2010,
Archibeque 2011, USDA:APHIS:VS 2010) as shown in
Table A-190 and Table A-l91 along with the percent of each total diet that is assumed to be made up of the supplemental
portion. By weighting the calculated DE values from the forage and supplemental diets, the DE values for the composite
diet were calculated.71 These values are used for steer and heifer stackers and beef replacements. Finally, for mature beef
cows and bulls, the DE value was adjusted downward by two percent to reflect the lower digestibility diets of mature cattle
based on Johnson (2002). Ym values for all grazing beef cattle were set at 6.5 percent based on Johnson (2002). The Ym
values and the resulting final weighted DE values by region for 2007 onwards are shown in Table A-192.
For feedlot animals, DE and Ym are adjusted over time as diet compositions in actual feedlots are adjusted based
on new and improved nutritional information and availability of feed types. Feedlot diets are assumed to not differ
significantly by state, and therefore only a single set of national diet values is utilized for each year. The DE and Ym values
for 1990 were estimated by Dr. Don Johnson (1999). In the CEFM, the DE values for 1991 through 1999 were linearly
extrapolated based on values for 1990 and 2000. DE and Ym values from 2000 through the current year were estimated
using the MOLLY model as described in Kebreab et al. (2008), based on a series of average diet feed compositions from
Galyean and Gleghorn (2001) for 2000 through 2006 and Vasconcelos and Galyean (2007) for 2007 onwards. In addition,
feedlot animals are assumed to spend the first 25 days in the feedlot on a "step-up" diet to become accustomed to the
higher quality feedlot diets. The step-up DE and Ym are calculated as the average of all state forage and feedlot diet DE
and Ym values.
For calves aged 4 through 6 months, a gradual weaning from milk is simulated, with calf diets at 4 months
assumed to be 25 percent forage, increasing to 50 percent forage at age 5 months, and 75 percent forage at age 6 months.
The portion of the diet allocated to milk results in zero emissions, as recommended by the IPCC (2006). For calves, the
DE for the remainder of the diet is assumed to be similar to that of slightly older replacement heifers (both beef and dairy
are calculated separately). The Ym for beef calves is also assumed to be similar to that of beef replacement heifers (6.5
percent), as literature does not provide an alternative Ym for use in beef calves. For dairy calves, the Ym is assumed to be
7.8 percent at 4 months, 8.03 percent at 5 months, and 8.27 percent at 6 months per estimates provided by Soliva (2006)
Due to inconsistencies in the 2003 literature values, the 2002 values were extended to include 2003 as well.
71 For example, the West has a forage DE of 61.3 which makes up 90 percent of the diet and a supplemented diet DE of 67.4 percent was
used for 10 percent of the diet, for atotal weighted DE of 61.9 percent, as shown in Table A-192.
A-248 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
for Ym at 4 and 7 months of age. Estimates for 5 and 6 months are the result of linear interpolation between the values
provided for 4 and 7 months.
Table A-193 shows the regional DE and Ym for U.S. cattle in each region for 2012.
Table A-189: Feed Components and Digestible Energytfalues Incorporated into Forage Diet Composition Estimates
Forage Type
Bahiagrass Paspalum notatum, fresh
Bermudagrass Cynodon dactylon, fresh
Bremudagrass, Coastal Cynodon dactylon, fresh
Bluegrass, Canada Poa compressa, fresh, early
vegetative
Bluegrass, Kentucky Poa pratensis, fresh, early
vegetative
Bluegrass, Kentucky Poa pratensis, fresh, mature
Bluestem Andropagon spp, fresh, early vegetative
Bluestem Andropagon spp, fresh, mature
Brome Bromus spp, fresh, early vegetative
Brome, Smooth Bromus inermis, fresh, early
vegetative
Brome, Smooth Bromus inermis, fresh, mature
Buffalograss, Buchloe dactyloides, fresh
Clover, Alsike Trifolium hybridum, fresh, early
vegetative
Clover, Ladino Trifolium repens, fresh, early
vegetative
Clover, Red Trifolium pratense, fresh, early bloom
Clover, Red Trifolium pratense, fresh, full bloom
Corn, Dent Yellow Zea mays indentata, aerial part
without ears, without husks, sun-cured,
(stover)(straw)
Dropseed, Sand Sporobolus cryptandrus, fresh,
stem cured
Fescue Festuca spp, hay, sun-cured, early
vegetative
Fescue Festuca spp, hay, sun-cured, early bloom
Grama Bouteloua spp, fresh, early vegetative
Grama Bouteloua spp, fresh, mature
Millet, Foxtail Setaria italica, fresh
Napiergrass Pennisetum purpureum, fresh, late
bloom
Needleandthread Stipa comata, fresh, stem cured
Orchardgrass Dactylis glomerata, fresh, early
vegetative
Orchardgrass Dactylis glomerata, fresh, midbloom
Pearlmillet Pennisetum glaucum, fresh
Prairie plants, Midwest, hay, sun-cured
Rape Brassica napus, fresh, early bloom
Rye Secale cereale, fresh
Ryegrass, Perennial Lolium perenne, fresh
Saltgrass Distichlis spp, fresh, post ripe
Sorghum, Sudangrass Sorghum bicolor
sudanense, fresh, early vegetative
Squirreltail Stanion spp, fresh, stem-cured
~ a> "> "> "H ._ =
tysss 5 .E . "7
° S. S.5 S. -3 -3 < -° 5 5 5
3! c/> ™ c/>£c/> * * "> "> is o> o o> o
•^>* ifi G (A ^ (A ^} ^} ^} ^} *•* ^} ^^ ^ ^^
m" 2 "E. 2 ! 2 = ro ro roro^- § 8 "E. 8
Q o co o co o u! c£ c£ o; o; co t£ ^ ^
61.38 x
66.29 x
65.53 x
73.99 x
756? y
1 \J.\Jt. A
59.00 x x
73.17 x
56.82 x x x x x
78.57 x
75.71 x
57.58 xx x
64.02 x x
70 6? y
1 \l.\Jt. A
73.22 x
71.27 x
67.44 x x
55.28 x
fi4 fiQ Y Y Y Y
Ut.UJ A A A A
67.39 x
53.57 x
67.02 x
63.38 xx x
68.20 x x
57.24 x x
60.36 xxx
75.54 x
60.13 x
68.04 x
55.53 x x
80.88 x
71.83 x
73.68 x
58.06 x x
73.27 x
62.00 x x
A-249
-------
?n **'•*. ~*^ i"
Forage Type ~ "Jo "Jo "Jo = -5 i" 5 .E .
°S.S.5S.^^<-|55 5
^ Sf S| S_ | | ||.l | |.= |
uf 2 a. 2 3 2 "TO ro ro raraS" raojo. o>
Q CD CO CD CO CD LL. ^ ^ C£ €£. S
Summercypress, Gray Kochia vestita, fresh, stem- .,-,,
cured 65'11
Timothy Phleum pratense, fresh, late vegetative 73.12 x
Timothy Phleum pratense, fresh, midbloom 66.87 x
Trefoil, Birdsfoot Lotus corniculatus, fresh 69.07 x
Vetch Vicia spp, hay, sun-cured 59.44 x
Wheat Triticum aestivum, straw 45.77 x
Wheatgrass, Crested Agropyron desertorum, ,„ ,„
fresh, early vegetative
Wheatgrass, Crested Agropyron desertorum, „,- gg
fresh, full bloom
Wheatgrass, Crested Agropyron desertorum, ,-„ gg
fresh, post ripe . x x x
Winterfat, Common Eurotia lanata, fresh, stem- .n SQ
cured *
Weighted Average DE 72.99 62.45 57.26 67.11 62.70 60.62 58.59 52.07 64.03 55.11
Forage Diet for West 61.3 10% 10% 10% 10% 10% 10% 10% 10% 10% 10%
Forage Diet for All Other Regions 64.2 33.3% 33.3% 33.3% -------
Sources: Preston (2010) and Archibeque (2011).
Note that forages marked with an x indicate that the DE from that a specific forage type is included in the general forage type for that column (e.g., grass
pasture, range, meadow or meadow by month or season).
Table A-190: DE Values with Representative Regional Diets forthe Supplemental Diet of Grazing Reef Cattle for 1990-2006
Source of DE
Feed (NRC1984)
Alfalfa Hay Table 8, feed #006
Barley
Bermuda Table 8, feed #030
Bermuda Hay Table 8, feed #031
Corn Table 8, feed #089
Corn Silage Table 8, feed #095
Cotton Seed
Meal
Grass Hay Table 8, feed #126,
170, 274
Orchard Table 8, feed #147
Soybean Meal
Supplement
Sorghum Table 8, feed #211
Soybean Hulls
Timothy Hay Table 8, feed #244
Whole Cotton
Seed
Wheat Middlings Table 8, feed #257
Wheat Table 8, feed #259
Weighted Supplement DE (%)
Percent of Diet that is Supplement
Unweighted
DE(%ofGE)
61.79
85.08
66.29
50.79
88.85
72.88
58.37
60.13
77.15
84.23
66.86
60.51
75.75
68.09
87.95
California*
65%
10%
10%
5%
10%
70.1
5%
West
30%
15%
10%
40%
5%
67.4
10%
Northern
Great Plains
30%
25%
25%
5%
15%
73.0
15%
Southcentral
29%
40%
11%
7%
13%
62.0
10%
Northeast
12%
13%
20%
50%
5%
67.6
15%
Midwest
30%
13%
20%
30%
7%
66.9
10%
Southeast
35%
40%
5%
20%
68.0
5%
Source of representative regional diets: Donovan (1999).
* Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above.
A-250 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-191: DEValues and Representative Regional Diets forthe Supplemental Diet of Grazing Beef Cattle for 2007-2012
Source of DE Unweighted
(NRC1984) DE(%ofGE)
Feed
West"
Central3
Northeast3 Southeast"
Alfalfa Hay
Bermuda
Bermuda Hay
Corn
Corn Silage
Grass Hay
Orchard
Protein supplement (West)
Protein Supplement (Central
and Northeast)
Protein Supplement
(Southeast)
Sorghum
Timothy Hay
Wheat Middlings
Wheat
Weighted Supplement DE
Table 8, feed #006
Table 8, feed #030
Table 8, feed #031
Table 8, feed #089
Table 8, feed #095
Table 8, feed #126, 170, 274
Table 8, feed #147
Table 8, feed #082, 134,225"
Table 8, feed #082, 134, 225 b
Table 8, feed #082, 134, 101"
Table 8, feed #211
Table 8, feed #244
Table 8, feed #257
Table 8, feed #259
61.79
66.29
50.79
88.85
72.88
58.37
60.13
81.01
80.76
77.89
84.23
60.51
68.09
87.95
Percent of Diet that is Supplement
65%
10%
10%
10%
5%
67.4
10%
30%
15%
35%
10%
5%
5%
73.1
15%
12%
13%
20%
10%
45%
68.9
5%
20%
20%
10%
30%
10%
10%
66.6
15%
Sources of representative regional diets: Donovan (1999), Preston (2010), Archibeque (2011), and USDA:APHIS:VS (2010).
" Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above.
b Not in equal proportions.
Table A-192: Foraging Animal DE [% of GE1 and Ym Values for Each Region and Animal Type for 2007-2012
Animal Type
Beef Repl. Heifers
Beef Calves (4-6 mo)
Steer Stackers
Heifer Stackers
Beef Cows
Bulls
Data
DE"
Ymb
DE
Ym
DE
Ym
DE
Ym
DE
Ym
DE
Ym
West'
61.9
6.5%
61.9
6.5%
61.9
6.5%
61.9
6.5%
59.9
6.5%
59.9
6.5%
Central
65.6
6.5%
65.6
6.5%
65.6
6.5%
65.6
6.5%
63.6
6.5%
63.6
6.5%
Northeast
64.5
6.5%
64.5
6.5%
64.5
6.5%
64.5
6.5%
62.5
6.5%
62.5
6.5%
Southeast
64.6
6.5%
64.6
6.5%
64.6
6.5%
64.6
6.5%
62.6
6.5%
62.6
6.5%
aDE is the digestible energy in units of percent of GE (MJ/Day).
b Ym is the methane conversion rate, the fraction of GE in feed converted to methane.
c Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above. To see the regional
designation per state, please see Table A-188.
Table A-193: Regional DE [% of GE1 and Ym Rates for Dairy and Feedlot Cattle by Animal Type for 2012
Animal Type
Dairy Repl. Heifers
Dairy Calves (4-6 mo)
Dairy Cows
Steer Feedlot
Heifer Feedlot
Data California0
DE"
Ymb
DE
Ym
DE
Ym
DE
Ym
DE
Ym
63.7
6.0%
63.7
Northern
West Great Plains Southcentral
63.7
6.0%
63.7
63.7
5.7%
63.7
63.7
6.5%
63.7
Northeast
63.7
6.4%
63.7
Midwest Southeast
63.7
5.7%
63.7
63.7
7.0%
63.7
7.8% (4 mo), 8.03% (5 mo), 8.27% (6 mo)-all regions
66.7
5.9%
82.5
3.9%
82.5
3.9%
66.7
5.9%
82.5
3.9%
82.5
3.9%
66.7
5.6%
82.5
3.9%
82.5
3.9%
66.7
6.4%
82.5
3.9%
82.5
3.9%
66.7
6.3%
82.5
3.9%
82.5
3.9%
66.7
5.6%
82.5
3.9%
82.5
3.9%
66.7
6.9%
82.5
3.9%
82.5
3.9%
' DE is the digestible energy in units of percent of GE (MJ/Day).
b Ym is the methane conversion rate, the fraction of GE in feed converted to methane.
A-251
-------
c Note that emissions are currently calculated on a state-by-state basis, but diets are applied in Table A-187 by the regions shown in the table above. To see the
regional designation for foraging cattle per state, please see Table A-187.
Step 3: Estimate ChU Emissions from Cattle
Emissions by state were estimated in three steps: a) determine gross energy (GE) intake using the Tier 2 IPCC
(2006) equations, b) determine an emission factor using the GE values, Ym and a conversion factor, and c) sum the daily
emissions for each animal type. Finally, the state emissions were aggregated to obtain the national emissions estimate.
The necessary data values for each state and animal type include:
• Body Weight (kg)
• Weight Gain (kg/day)
• Net Energy for Activity (Ca, MJ/day)72
• Standard Reference Weight (kg)73
• Milk Production (kg/day)
• Milk Fat (percent of fat in milk = 4)
• Pregnancy (percent of population that is pregnant)
• DE (percent of GE intake digestible)
• Ym (the fraction of GE converted to CH/i)
• Population
Step 3a: Determine Gross Energy, GE
As shown in the following equation, GE is derived based on the net energy estimates and the feed characteristics.
Only variables relevant to each animal category are used (e.g., estimates for feedlot animals do not require the NEi factor).
All net energy equations are provided in IPCC (2006).
GE =
NEa + NE, + NE,ort + NEp
REM REG
DE%
100
where,
GE = Gross energy (MJ/day)
NEm = Net energy required by the animal for maintenance (MJ/day)
NEa = Net energy for animal activity (MJ/day)
NEi = Net energy for lactation (MJ/day)
NEwork = Net energy for work (MJ/day)
NEp = Net energy required for pregnancy (MJ/day)
REM = Ratio of net energy available in a diet for maintenance to digestible energy consumed
NEg = Net energy needed for growth (MJ/day)
REG = Ratio of net energy available for growth in a diet to digestible energy consumed
DE = Digestible energy expressed as a percent of gross energy (percent)
Step 3b: Determine Emission Factor
The daily emission factor (DayEmit) was determined using the GE value and the methane conversion factor (Ym)
for each category. This relationship is shown in the following equation:
DayEmit = -
55.65
Zero for feedlot conditions, 0.17 for high quality confined pasture conditions, and 0.36 for extensive open range or hilly terrain
grazing conditions. Ca factor for dairy cows is weighted to account for the fraction of the population in the region that grazes during the
year (IPCC 2006).
73 Standard Reference Weight is the mature weight of a female animal of the animal type being estimated, used in the model to account
for breed potential.
A-252 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
where,
DayEmit = Emission factor (kg CHVhead/day)
GE = Gross energy intake (MJ/head/day)
Ym = CH4 conversion rate, which is the fraction of GE in feed converted to CH4 (%)
55.65 = A factor for the energy content of methane (MJ/kg CH4)
The daily emission factors were estimated for each animal type and state, calculated annual national emission
factors are shown by animal type in Table A-194.
Table A-194: Calculated Annual National Emission Factors for Cattle by Animal Type [kg CHJhead/yearl
Cattle Type 1990 1995 2000 2005 2009 2010 2011 2012
Dairy
Calves
Cows
Replacements 7-11 months
Replacements 12-23 months
Beef
Calves
Bulls
Cows
Replacements 7-11 months
Replacements 12-23 months
Steer Stackers
Heifer Stackers
Feedlot Cattle
12
132
46
70
91
56
66
58
60
40
12
133
45
67
94
59
68
58
60
40
12
140
46
70
11
98
95
60
70
58
60
45
12
142
46
69
11
98
95
60
70
58
60
45
12
142
46
69
11
98
95
60
70
58
60
44
12
143
46
69
11
98
95
60
70
58
60
45
Note: To convert to a daily emission factor, the yearly emission factor can be divided by 365 (the number of days in a year).
For quality assurance purposes, U.S. emission factors for each animal type were compared to estimates provided
by the other Annex I member countries of the United Nations Framework Convention on Climate Change (UNFCCC) (the
most recently available summarized results for Annex I countries are through 2011 only). Results, presented in Table A-
195 indicate that U. S. emission factors are comparable to those of other Annex I countries. Results are presented in Table
A-195 (along with Tier I emission factors provided by IPCC (2006). Throughout the time series, beef cattle in the United
States generally emit more enteric CFU than other Annex I member countries, while dairy cattle in the United States
generally emit comparable enteric CFU
Table A-195: Annex I Countries' Implied Emission Factors for Cattle by Year [kg CHJhead/yearl
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
United States Implied
Emission Factor
107
107
107
106
106
106
105
106
107
110
111
110
111
111
109
Dairy Cattle
Mean of Implied Emission Factors for
Annex I countries (excluding U.S.)
96
97
96
97
98
98
99
100
101
102
103
104
105
106
107
United States Implied
Emission Factor
71
71
72
73
73
73
73
73
73
73
73
73
73
74
74
Beef Cattle
Mean of Implied Emission Factors for
Annex I countries (excluding U.S.)
53
53
54
54
54
54
54
54
55
55
55
55
55
55
55
A-253
-------
2005
2006
2007
2008
2009
2010
2011
2012
Tier I
110
110
114
115
115
115
116
117
EFs For North America, from IPCC (2006)
109
110
111
112
112
113
113
N/A
121
74
74
75
76
76
75
75
75
55
55
55
55
56
55
55
N/A
53
Step 3c: Estimate Total Emissions
Emissions were summed for each month and for each state population category using the daily emission factor
for a representative animal and the number of animals in the category. The following equation was used:
where,
Emissionsstate
DayEmitstate
Days/Month
SubPopstate
Emissionsstate = DayEmitstate x Days/Month x SubPopst£
Emissions for state during the month (kg CH4)
Emission factor for the subcategory and state (kg CH4/head/day)
Number of days in the month
Number of animals in the subcategory and state during the month
This process was repeated for each month, and the monthly totals for each state subcategory were summed to
achieve an emission estimate for a state for the entire year and state estimates were summed to obtain the national total.
The estimates for each of the 10 subcategories of cattle are listed in Table A-196. The emissions for each subcategory
were then aggregated to estimate total emissions from beef cattle and dairy cattle for the entire year.
Table A-196: CHa Emissions from Cattle tGgl
Cattle Type
Dairy
Calves (4-6 months)
Cows
Replacements 7-1 1 months
Replacements 12-23
months
Beef
Calves (4-6 months)
Bulls
Cows
Replacements 7-1 1 months
Replacements 12-23
months
Steer Stackers
Heifer Stackers
Feedlot Cattle
Total
1990 1995
1,574
62
1,242
58
212
4,763
182
196
1,498
59l
1,185
56 1
202
5,438
193 1
225
2,884 3,222
69 85 1
12411
662
JUU. 3751
375 435
6,338 6,936
2000
1,519
59l
1,209
55
196
5,098
186
2151
3,058
74
204
509
323
530
6,617
2005
1,503
54l
1,197l
56
196
5,037
1791
2141
3,056
80
217
473
299
518
6,540
2009
1,639
58
1,304
61
216
5,062
169
216
3,002
78
216
491
300
592
6,701
2010
1,626
57
1,287
62
221
5,019
169
215
2,970
75
213
475
301
602
6,645
2011
1,643
57
1,301
63
222
4,911
166
211
2,921
74
202
439
283
615
6,555
2012
1,668
58
1,324
62
224
4,789
160
205
2,855
75
207
415
267
604
6,458
Notes: Totals may not sum due to independent rounding.
Emission Estimates from Other Livestock
"Other livestock" include horses, sheep, swine, goats, American bison, and mules and asses. All livestock
population data, except for American bison for years prior to 2002, were taken from the U.S. Department of Agriculture
(USDA) National Agricultural Statistics Service (NASS) agricultural statistics database (USDA 2013) or earlier census
data (USDA 1992, 1997). The Manure Management Annex discusses the methods for obtaining annual average
populations and disaggregating into state data where needed and provides the resulting population data for the other
livestock that were used for estimating all livestock-related emissions (See Table A-199). For each animal category, the
USDA publishes monthly, annual, or multi-year livestock population and production estimates. All data were downloaded
from the USDA-NASS agricultural database (USDA 2013) or taken from older census reports (USDA 1992, 1997).
American bison estimates prior to 2002 were estimated using data from the National Bison Association (1999).
Methane emissions from sheep, goats, swine, horses, mules and asses were estimated by multiplying national
population estimates by the default IPCC emission factor (IPCC 2006). For American bison the emission factor for
A-254 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
buffalo (IPCC 2006) was used and adjusted based on the ratio of live weights of 300 kg for buffalo (IPCC 2006) and 1,130
pounds (513 kg) for American Bison (National Bison Association 2011) to the 0.75 power. This methodology for
determining emission factors is recommended by IPCC (2006) for animals with similar digestive systems. Table A-197
shows the emission factors used for these other livestock.
National enteric fermentation emissions from all livestock types are shown in Table A-198 and Table A-199. Enteric
fermentation emissions from most livestock types, broken down by state, for 2012 are shown in Table A-200 and Table A-
201. Livestock Populations are shown in Table A- 202.
Table A-197: Emission Factors for Other Livestock [Kg CHVhead/year)
Livestock Type Emission Factor
Sheep 8
Goats 5
Horses 18
Swine 1.5
Mules and Asses 10.0
American Bison 82.2
Source: IPCC (2006), except American Bison, as described in text.
Table A-198: CH* Emissions from Enteric Fermentation (Tg Clh Eq.)
Livestock Type 1990 1995 2000 2006
Beef Cattle 100.0 114.2 107.1 107.5
Dairy Cattle 33. ll 31.51 31.91 32.2
Horses 0.8 1 1.ol 1.sl 1.5
Sheep 1.9 1.5l 1.2l 1.0
Swine 1.7 1.9l 1.9l 1.9
Goats 0.3 0.2l 0.3 1 0.3
American Bison 0.1 0.2 1 0.3J 0.4
Mules and Asses + + + 0.1
Total 137.9 1,150.5 1,143.9 144.9
Notes: Totals may not sum due to independent rounding.
+ indicates emissions are less than 0.05.
2007
108.4
33.6
1.5
1.0
2.1
0.3
0.3
0.1
147.4
2008
107.5
34.1
1.6
1.0
2.1
0.3
0.3
0.1
147.0
2009
106.3
34.4
1.6
1.0
2.1
0.3
0.3
0.1
146.1
2010
105.4
34.1
1.6
0.9
2.0
0.3
0.3
0.1
144.9
2011
103.1
34.5
1.6
0.9
2.1
0.3
0.3
0.1
143.0
2012
100.6
35.0
1.7
0.9
2.1
0.3
0.3
0.1
141.0
Table A-199: CH* Emissions from Enteric Fermentation (Gg)
Livestock Type 1990 1995 2000
Beef Cattle 4,763 5,438 5,098
Dairy Cattle 1,574 1,498 1,519
Horses 40 47 • 61 •
Sheep 91 • 72 56 •
Swine 81 • 88 88
Goats 13 12l 12l
American Bison 4 9l 16 1
Mules and Asses 1 11 H
Total 6,566 7,165 6,852
2006
5,117
1,534
71
50
93
15
17
2
6,899
2007
5,163
1,601
73
49
98
16
16
3
7,019
2008
5,119
1,622
74
48
101
16
16
3
6,999
2009
5,062
1,639
75
46
99
16
15
4
6,956
2010
5,019
1,626
77
45
97
16
15
4
6,898
2011
4,911
1,643
78
44
98
16
14
4
6,809
2012
4,789
1,668
79
43
100
16
14
5
6,714
Note: Totals may not sum due to independent rounding.
A-255
-------
Table A-200: CHa Emissions from Enteric Fermentation from Cattle tGgl, by State, for 2012
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Conn.
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Mass.
Michigan
Minnesota
Miss.
Missouri
Montana
Nebraska
Nevada
N. Hamp.
N. Jersey
N. Mexico
New York
N. Car.
N. Dakota
Ohio
Oklahoma
Oregon
Penn
Rlsland
S. Car.
S. Dakota
Tenn.
Texas
Utah
Vermont
Virginia
Wash.
W. Virg.
Wisconsin
Wyoming
Dairy Calves
63
3
1,203
70
11,270
829
117
32
760
494
12
3,679
627
1,108
1,298
779
475
114
203
329
76
2,349
2,944
89
589
89
355
184
89
47
2,121
3,862
285
114
1,710
329
779
3,419
7
101
570
317
2,754
570
842
608
1,665
63
8,009
38
Dairy Cows
1,312
57
28,933
1,306
265,624
18,872
2,668
706
18,932
12,347
216
87,096
12,615
23,508
27,816
16,664
10,502
2,127
4,440
7,349
1,649
53,067
59,246
1,910
10,266
1,880
7,484
4,300
2,005
1,040
51,957
92,408
7,393
2,278
34,729
7,136
17,013
77,175
151
2,400
12,098
7,222
67,646
12,890
18,863
14,698
39,858
1,272
170,261
794
g-cvj
cc v
£-°
ǤS
93
3
937
72
11,240
950
134
47
544
481
13
4,014
596
887
2,028
1,077
699
86
227
396
85
2,002
3,612
109
507
101
190
134
92
57
1,606
4,530
326
127
1,521
287
1,004
4,459
7
93
444
466
3,015
709
764
621
1,606
71
8,744
63
8-S
CC CN
l|
337
10
3,390
260
40,681
3,440
487
169
1,967
1,742
48
14,529
2,156
3,210
7,338
3,898
2,529
312
820
1,435
307
7,246
13,071
393
1,835
367
688
484
333
205
5,812
16,396
1,180
459
5,504
1,039
3,632
16,140
26
337
1,605
1,686
10,911
2,567
2,767
2,248
5,812
256
31,646
229
j/2
"B
CO
4,379
301
2,078
5,839
7,272
5,194
59
39
5,839
2,627
519
3,636
2,378
1,998
5,708
8,562
6,812
2,725
146
390
98
1,617
3,330
3,795
9,513
9,350
9,513
1,454
49
98
3,636
1,464
3,309
5,423
2,378
11,677
4,155
2,440
10
1,557
8,562
6,812
32,113
2,078
293
3,892
1,974
1,366
2,854
4,155
Beef Calves
3,426
31
1,024
4,791
3,528
4,318
24
19
4,955
2,699
444
2,668
1,700
1,001
4,596
7,430
5,244
2,382
53
227
37
560
1,874
2,562
9,536
8,284
9,674
1,343
21
42
2,475
529
1,898
4,426
1,541
9,372
3,112
846
7
970
8,267
5,007
24,061
1,878
53
3,500
1,235
1,031
1,361
4,062
Beef Cows
61,173
553
18,084
85,548
62,291
76,256
425
330
88,465
48,185
7,847
47,120
30,454
17,941
82,345
133,132
93,642
42,539
944
4,058
661
10,029
33,582
45,738
170,854
146,282
173,338
23,711
377
755
43,704
9,437
33,880
79,309
27,602
167,331
54,956
15,100
123
17,317
148,129
89,406
429,622
33,155
944
62,490
21,802
18,403
24,381
71,735
?t=
-------
Table A-201: CHa Emissions from Enteric Fermentation from Other Livestock [Ggl, by State, for 2012
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Conn.
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Mass.
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Penn.
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Horses
0.1
0.1
1.1
0.1
4.6
3.7
0.1
0.1
0.1
0.1
0.1
1.9
0.5
0.4
1.6
0.6
0.3
0.1
0.1
0.1
0.1
0.6
1.2
0.1
0.7
1.8
0.6
0.6
0.1
0.1
0.8
0.5
0.2
0.6
1.0
0.6
1.6
0.7
0.1
0.1
2.3
0.3
5.4
2.4
0.1
0.7
0.4
0.3
0.7
3.0
Sheep
0.1
0.1
1.1
0.1
4.6
3.7
0.1
0.1
0.1
0.1
0.1
1.9
0.5
0.4
1.6
0.6
0.3
0.1
0.1
0.1
0.1
0.6
1.2
0.1
0.7
1.8
0.6
0.6
0.1
0.1
0.8
0.5
0.2
0.6
1.0
0.6
1.6
0.7
0.1
0.1
2.3
0.3
5.4
2.4
0.1
0.7
0.4
0.3
0.7
3.0
Swine
0.2
0.0
0.3
0.2
0.2
1.1
0.0
0.0
0.0
0.2
0.0
0.0
7.0
5.7
30.6
2.8
0.5
0.0
0.0
0.0
0.0
1.6
11.7
0.6
4.2
0.3
4.7
0.0
0.0
0.0
0.0
0.1
13.5
0.2
3.2
3.5
0.0
1.7
0.0
0.4
1.9
0.2
1.3
1.1
0.0
0.3
0.0
0.0
0.5
0.1
American Mules and
Goats bison Asses
0.4
0.0
0.2
0.3
0.7
0.2
0.0
0.0
0.3
0.4
0.0
0.1
0.2
0.2
0.3
0.2
0.5
0.1
0.0
0.1
0.0
0.1
0.2
0.2
0.5
0.1
0.2
0.1
0.0
0.1
0.2
0.2
0.5
0.0
0.3
0.6
0.2
0.3
0.0
0.2
0.1
0.7
5.7
0.1
0.0
0.3
0.2
0.1
0.3
0.0
0.0
0.1
0.0
0.0
0.3
1.5
0.0
-
0.1
0.0
0.0
-
0.1
0.1
0.2
0.9
0.0
0.0
0.0
0.1
0.0
0.3
0.0
-
0.2
0.8
1.7
0.0
0.0
0.0
0.3
0.1
0.1
0.1
-
0.6
0.0
0.2
-
0.0
3.1
0.1
0.5
0.2
0.0
0.1
0.1
0.0
0.3
1.0
0.2
0.0
0.0
0.1
0.1
0.1
0.0
0.0
0.1
0.2
0.0
0.1
0.1
0.1
0.1
0.1
0.2
0.1
0.0
0.0
0.0
0.1
0.1
0.1
0.2
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.1
0.2
0.1
0.2
0.0
0.1
0.0
0.3
1.0
0.0
0.0
0.1
0.1
0.0
0.1
0.0
Total
1.0
0.3
2.7
0.8
10.5
10.3
0.2
0.2
0.7
1.0
0.2
4.0
8.4
6.9
34.4
5.2
1.8
0.4
0.2
0.4
0.2
3.3
14.4
1.1
6.5
4.9
7.8
1.3
0.2
0.3
2.1
1.4
14.6
1.5
5.6
6.1
3.5
3.8
0.2
0.9
9.7
1.9
19.3
6.2
0.2
2.2
1.2
0.7
2.6
1.0
A-257
-------
3.11. Methodology for Estimating ChU and N20 Emissions from Manure Management
The following steps were used to estimate methane (CFLi) and nitrous oxide (TSbO) emissions from the
management of livestock manure. Nitrous oxide emissions associated with pasture, range, or paddock systems and daily
spread systems are included in the emission estimates for Agricultural Soil Management (see the Agricultural Soils
Management Annex).
Step 1: Livestock Population Characterization Data
Annual animal population data for 1990 through 2012 for all livestock types, except American bison, goats,
horses, mules and asses were obtained from the USDA National Agricultural Statistics Service (NASS). The population
data used in the emissions calculations for cattle, swine, and sheep were downloaded from the USDA NASS Quick Stats
Database (USDA 2013a). Poultry population data were obtained from USDA NASS reports (USDA 1995a, 1995b, 1998,
1999, 2004a, 2004b, 2009b, 2009c, 2009d, 2009e, 2010a, 2010b, 201 la, 201 Ib, 2012a, 2012b, 2013b and 2013c). Goat
population data for 1992, 1997, 2002, and 2007 were obtained from the Census of Agriculture (USDA 2009a), as were
horse, mule and ass population data for 1987, 1992, 1997, 2002, and 2007, and American bison population for 2002 and
2007. American bison population data for 1990-1999 were obtained from the National Bison Association (1999).
Additional data sources used and adjustments to these data sets are described below.
Cattle: For all cattle groups (cows, heifers, steers, bulls, and calves), the USDA data provide cattle inventories
from January (for each state) and July (as a U.S. total only) of each year. Cattle inventories change over the course of the
year, sometimes significantly, as new calves are born and as cattle are moved into feedlots and subsequently slaughtered;
therefore, to develop the best estimate for the annual animal population, the populations and the individual characteristics,
such as weight and weight gain, pregnancy, and lactation of each animal type were tracked in the Cattle Enteric
Fermentation Model (CEFM—see section 6-1 Enteric Fermentation). For animals that have relatively static populations
throughout the year, such as mature cows and bulls, the January 1 values were used. For animals that have fluctuating
populations throughout the year, such as calves and growing heifers and steer, the populations are modeled based on a
transition matrix that uses annual population data from USDA along with USDA data on animal births, placement into
feedlots, and slaughter statistics.
Swine: The USDA provides quarterly data for each swine subcategory: breeding, market under 50 pounds (under
23 kg), market 50 to 119 pounds (23 to 54 kg), market 120 to 179 pounds (54 to 81 kg), and market 180 pounds and over
(greater than 82 kg). The average of the quarterly data was used in the emission calculations. For states where only
December inventory is reported, the December data were used directly.
Sheep: The USDA provides total state-level data annually for lambs and sheep. Population distribution data for
lamb and sheep on feed are not available after 1993 (USDA 1994). The number of lamb and sheep on feed for 1994
through 2012 were calculated using the average of the percent of lamb and sheep on feed from 1990 through 1993. In
addition, all of the sheep and lamb "on feed" are not necessarily on "feedlots;" they may be on pasture/crop residue
supplemented by feed. Data for those animals on feed that are in feedlots versus pasture/crop residue were provided only
for lamb in 1993. To calculate the populations of sheep and lamb in feedlots for all years, it was assumed that the
percentage of sheep and lamb on feed that are in feedlots versus pasture/crop residue is the same as that for lambs in 1993
(Anderson 2000).
Goats: Annual goat population data by state were available for 1992, 1997, 2002, and 2007 (USDA 2009a). The
data for 1992 were used for 1990 through 1992 and the data for 2007 were used for 2007 through 2012. Data for 1993
through 1996, 1998 through 2001, and 2003 through 2006 were extrapolated based on the 1992, 1997, and 2002 Census
data.
Horses: Annual horse population data by state were available for 1987, 1992, 1997, 2002, and 2007 (USDA
2009a). Data for 1990 through 1991, 1993 through 1996, 1998 through 2001, 2003 through 2006, and 2008 through 2012
were extrapolated based on the 1987, 1992, 1997, 2002, and 2007 Census data.
Mules and Asses: Annual mule and ass (burro and donkey) population data by state were available for 1987,
1992, 1997, 2002, and 2007 (USDA 2009a). Data for 1990 through 1991, 1993 through 1996, 1998 through 2001, 2003
through 2006, and 2008 through 2012 were extrapolated based on the 1987, 1992, 1997, 2002, and 2007 Census data.
American Bison: Annual American bison population data by state were available for 2002, and 2007 (USDA
2009a). Data for 1990 through 1999 were obtained from the Bison Association (1999). Data for 2000, 2001, 2003
through 2006, and 2008 through 2012 were extrapolated based on the Bison Association and 2002 and 2007 Census data.
A-258 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Poultry: The USDA provides population data for hens (one year old or older), pullets (hens younger than one
year old), other chickens, and production (slaughter) data for broilers and turkeys (USDA 1995a, 1995b, 1998, 1999,
2004a, 2004b, 2009b, 2009c, 2009d, 2009e, 2010a, 2010b, 201 la, 201 Ib, 2012a, 2012b, 2013b, and 2013c). All poultry
population data were adjusted to account for states that report non-disclosed populations to USDA NASS. The combined
populations of the states reporting non-disclosed populations are reported as "other" states. State populations for the non-
disclosed states were estimated by equally distributing the population attributed to "other" states to each of the non-
disclosed states.
Because only production data are available for boilers and turkeys, population data are calculated by dividing the
number of animals produced by the number of production cycles per year, or the turnover rate. Based on personal
communications with John Lange, an agricultural statistician with USDA NASS, the broiler turnover rate ranges from 3.4
to 5.5 over the course of the inventory. For turkeys, the turnover rate ranges from 2.4 to 3.0. A summary of the livestock
population characterization data used to calculate CH4 and N2(D emissions is presented in Table A- 202.
Step 2: Waste Characteristics Data
Methane and N2(D emissions calculations are based on the following animal characteristics for each relevant
livestock population:
• Volatile solids (VS) excretion rate;
• Maximum methane producing capacity (B0) for U.S. animal waste;
• Nitrogen excretion rate (Nex); and
• Typical animal mass (TAM).
Table A- 203 presents a summary of the waste characteristics used in the emissions estimates. Published sources
were reviewed for U.S.-specific livestock waste characterization data that would be consistent with the animal population
data discussed in Step 1. The USDA's Agricultural Waste Management Field Handbook (AWMFH; USDA 1996, 2008)
is one of the primary sources of waste characteristics. Data from the 1996 and 2008 USDA AWMFH were used to
estimate VS and Nex for most animal groups across the time series of the inventory, as shown in Table A- 204 (ERG
2010b and 2010c). The 1996 AWMFH data were based on measured values from U.S. farms; the 2008 AWMFH data
were developed using the calculation method created by the American Society of Agricultural and Biological Engineers,
which is based on U.S. animal dietary intake and performance measures. Since the values from each of the two AWMFHs
result from different estimation methods and reflect changes in animal genetics and nutrition over time, both data sources
were used to create a time series across the inventory as neither value would be appropriate to use across the entire span of
inventory years. Although the AWMFH values are lower than the IPCC values, these values are more appropriate for U.S.
systems because they have been calculated using U.S.-specific data. Animal-specific notes about VS and Nex are
presented below:
• Swine: The VS and Nex data for breeding swine are from a combination of the types of animals that make
up this animal group, namely gestating and farrowing swine and boars. It is assumed that a group of
breeding swine is typically broken out as 80 percent gestating sows, 15 percent farrowing swine, and 5
percent boars (Safley 2000).
• Poultry: Due to the change in USDA reporting of hens and pullets, new nitrogen and VS excretion rates
were calculated for the combined population of hens and pullets; a weighted average rate was calculated
based on hen and pullet population data from 1990 to 2004.
• Goats, Sheep, Horses, Mules and Asses: In cases where data were not available in the USDA documents,
data from the American Society of Agricultural Engineers, Standard D384.1 (ASAE 1998) or the 2006 IPCC
Guidelines were used as a supplement.
The method for calculating VS excretion and Nex from American bison, beef and dairy cows, bulls, heifers, and
steers is based on the relationship between animal performance characteristics such as diet, lactation, and weight gain and
energy utilization. The method used is outlined by the IPCC 2006 Guidelines Tier II methodology, and is modeled using
the CEFM described in the enteric fermentation portion of the inventory (documented in Moffroid and Pape 2013) in order
A-259
-------
to take advantage of the detailed diet and animal performance data assembled as part of the Tier II analysis for cattle. For
American bison, VS and Nex were assumed to be the same as beef NOF bulls.
The VS content of manure is the fraction of the diet consumed by cattle that is not digested and thus excreted as
fecal material; fecal material combined with urinary excretions constitutes manure. The CEFM uses the input of digestible
energy (DE) and the energy requirements of cattle to estimate gross energy (GE) intake and enteric CFU emissions. GE
and DE are used to calculate the indigestible energy per animal as gross energy minus digestible energy plus the amount of
gross energy for urinary energy excretion per animal (2 or 4 percent). This value is then converted to VS production per
animal using the typical conversion of dietary gross energy to dry organic matter of 18.45 MJ/kg, after subtracting out the
ash content of manure. The current equation recommended by the 2006IPCC Guidelines is:
VS production (kg) = [(GE - DE) + (UE x GE)] x l~ASH
where,
GE = Gross energy intake (MJ)
DE = Digestible energy (MJ)
(UE x GE) = Urinary energy expressed as fraction of GE, assumed to be 0.04 except for feedlots
which are reduced 0.02 as a result of the high grain content of their diet.
ASH = Ash content of manure calculated as a fraction of the dry matter feed intake
(assumed to be 0.08).
18.45 = Conversion factor for dietary GE per kg of dry matter (MJ per kg). This value is
relatively constant across a wide range of forage and grain-based feeds commonly
consumed by livestock.
Total nitrogen ingestion in cattle is determined by dietary protein intake. When feed intake of protein exceeds
the nutrient requirements of the animal, the excess nitrogen is excreted, primarily through the urine. To calculate the
nitrogen excreted by each animal type, the CEFM utilizes the energy balance calculations recommended by the IPCC
(2006) for gross energy and the energy required for growth along with inputs of weight gain, milk production, and the
percent of crude protein in the diets. The total nitrogen excreted is measured in the CEFM as nitrogen consumed minus
nitrogen retained by the animal for growth and in milk. The basic equation for calculating Nex is shown below, followed
by the equations for each of the constituent parts.
1ST = 1ST — (iST + 1ST )
excreted consumed V growth milk /
where,
N excreted = Daily N excreted per animal, kg per animal per day.
N consumed = Daily N intake per animal, kg per animal per day
N growth = Nitrogen retained by the animal for growth, kg per animal per day
N milk = Nitrogen retained in milk, kg per animal per day
The equation for N consumed is based on the 2006 IPCC Guidelines, and is estimated as:
'CP%\
GE
18.45
100
6.25
where,
N consumed = Daily N intake per animal, kg per animal per day
GE = Gross energy intake, as calculated in the CEFM, MJ per animal per day
18.45 = Conversion factor for dietary GE per kg of dry matter, MJ per kg.
CP% = Percent crude protein in diet, input into the CEFM
6.25 = Conversion from kg of dietary protein to kg of dietary N, kg feed per kg N
The portion of consumed N that is retained as product equals the nitrogen required for weight gain plus that in
milk. The nitrogen retained in body weight gain by stackers, replacements, or feedlot animals is calculated using the net
A-260 Inventory of U.S. Greenhouse Gas Emis
-------
energy for growth (NEg), weight gain (WG), and other conversion factors and constants. The equation matches current
2006 IPCC Guidelines recommendations, and is as follows:
WG
N = _ 10QQ
6.25
where,
N growth = Nitrogen retained by the animal for growth, kg per animal per day
WG = Daily weight gain of the animal, as input into the CEFM transition matrix, kg per day
268 = Constant from 2006 IPCC Guidelines
7.03 = Constant from 2006 IPCC Guidelines
NEg = Net energy required for growth, as calculated in the CEFM, MJ per animal per day
1 ,000 = Conversion from grams to kilograms
6.25 = Conversion from kg of dietary protein to kg of dietary N, kg feed per kg N
The N content of milk produced also currently matches the 2006 IPCC Guidelines, and is calculated using milk
production and percent protein, along with conversion factors. Milk N retained as product is calculated using the
following equation:
milk *
100
6.38
where,
= Nitrogen retained in milk, kg per animal per day
= Milk production, kg per day
pr% = Percent protein in milk, estimated from the fat content as 1.9 + 0.4 * %Fat
(Fat assumed to be 4%)
100 = Conversion from percent to value (e.g., 4% to 0.04)
6.38 = Conversion from kg Protein to kg N
The VS and N equations above were used to calculate VS and Nex rates for each state, animal type (heifers and
steer on feed, heifers and steer not on feed, bulls and American bison), and year. Table A- 205 presents the state-specific
VS and Nex production rates used for cattle in 2012.
Step 3: Waste Management System Usage Data
Table A- 206 summarizes 2012 manure distribution data among waste management systems (WMS) at beef
feedlots, dairies, dairy heifer facilities, and swine, layer, broiler, and turkey operations. Manure from the remaining
animal types (beef cattle not on feed, American bison, goats, horses, mules and asses and sheep) is managed on pasture,
range, or paddocks, on drylot, or with solids storage systems. Additional information on the development of the manure
distribution estimates for each animal type is presented below. Definitions of each WMS type are presented in Table A-
207.
Beef Cattle, Dairy Heifers and American Bison: The beef feedlot and dairy heifer WMS data were developed
using information from EPA's Office of Water's engineering cost analyses conducted to support the development of
effluent limitations guidelines for Concentrated Animal Feeding Operations (EPA 2002b). Based on EPA site visits and
state contacts supporting this work and additional personal communication with the national USDA office to estimate the
A-261
-------
percent of beef steers and heifers in feedlots (Milton 2000), feedlot manure is almost exclusively managed in drylots.
Therefore, for these animal groups, the percent of manure deposited in drylots is assumed to be 100 percent. In addition,
there is a small amount of manure contained in runoff, which may or may not be collected in runoff ponds. Using the
expert opinions and EPA and USDA data, the runoff from feedlots was calculated by region in Calculations: Percent
Distribution of Manure for Waste Management Systems (ERG 2000a) and was used to estimate the percentage of manure
managed in runoff ponds in addition to drylots; this percentage ranges from 0.4 to 1.3 percent. The percentage of manure
generating emissions from beef feedlots is therefore greater than 100 percent. The remaining population categories of beef
cattle outside of feedlots are managed through pasture, range, or paddock systems, which are utilized for the majority of
the population of beef cattle in the country. American bison WMS data were assumed to be the same as beef cattle not on
feed.
Dairy Cows: The WMS data for dairy cows were developed using data from the Census of Agriculture, EPA's
Office of Water, USDA, and expert sources. Farm-size distribution data are reported in the 1992, 1997, 2002, and 2007
Census of Agriculture (USDA 2009a). It was assumed that the Census data provided for 1992 were the same as that for
1990 and 1991, and data provided for 2007 were the same as that for 2008 through 2012. Data for 1993 through 1996,
1998 through 2001, and 2003 through 2006 were extrapolated using the 1992, 1997, 2002, and 2007 data. The percent of
waste by system was estimated using the USDA data broken out by geographic region and farm size.
Based on EPA site visits and the expert opinion of state contacts, manure from dairy cows at medium (200
through 700 head) and large (greater than 700 head) operations are managed using either flush systems or scrape/slurry
systems. In addition, they may have a solids separator in place prior to their storage component. Estimates of the percent
of farms that use each type of system (by geographic region) were developed by EPA's Office of Water, and were used to
estimate the percent of waste managed in lagoons (flush systems), liquid/slurry systems (scrape systems), and solid storage
(separated solids) (EPA 2002b).
Manure management system data for small (fewer than 200 head) dairies were obtained from USDA's Animal
and Plant Health Inspection Service (APHIS)'s National Animal Health Monitoring System (Ott 2000). These data are
based on a statistical sample of farms in the 20 U.S. states with the most dairy cows. Small operations are more likely to
use liquid/slurry and solid storage management systems than anaerobic lagoon systems. The reported manure
management systems were deep pit, liquid/slurry (includes slurry tank, slurry earth-basin, and aerated lagoon), anaerobic
lagoon, and solid storage (includes manure pack, outside storage, and inside storage).
Data regarding the use of daily spread and pasture, range, or paddock systems for dairy cattle were obtained from
personal communications with personnel from several organizations. These organizations include state NRCS offices,
state extension services, state universities, USDA NASS, and other experts (Deal 2000, Johnson 2000, Miller 2000,
Stettler 2000, Sweeten 2000, and Wright 2000). Contacts at Cornell University provided survey data on dairy manure
management practices in New York (Poe et al. 1999). Census of Agriculture population data for 1992, 1997, 2002, and
2007 (USDA 2009a) were used in conjunction with the state data obtained from personal communications to determine
regional percentages of total dairy cattle and dairy waste that are managed using these systems. These percentages were
applied to the total annual dairy cow and heifer state population data for 1990 through 2012, which were obtained from the
USDANASS (USDA2013a).
Of the dairies using systems other than daily spread and pasture, range, or paddock systems, some dairies
reported using more than one type of manure management system. Due to limitations in how USDA APHIS collects the
manure management data, the total percent of systems for a region and farm size is greater than 100 percent. However,
manure is typically partitioned to use only one manure management system, rather than transferred between several
different systems. Emissions estimates are only calculated for the final manure management system used for each portion
of manure. To avoid double counting emissions, the reported percentages of systems in use were adjusted to equal a total
of 100 percent using the same distribution of systems. For example, if USDA reported that 65 percent of dairies use deep
pits to manage manure and 55 percent of dairies use anaerobic lagoons to manage manure, it was assumed that 54 percent
(i.e., 65 percent divided by 120 percent) of the manure is managed with deep pits and 46 percent (i.e., 55 percent divided
by 120 percent) of the manure is managed with anaerobic lagoons (ERG 2000a).
Finally, the percentage of manure managed with anaerobic digestion (AD) systems with methane capture and
combustion was added to the WMS distributions. AD system data were obtained from EPA's AgSTAR Program's project
database (EPA 2012). This database includes basic information for AD systems in the U.S., based on publically available
data and data submitted by farm operators, project developers, financiers, and others involved in the development of farm
AD projects.
Swine: The distribution of manure managed in each WMS was estimated using data from a USDA APHIS report
and EPA's Office of Water site visits (Bush 1998, ERG 2000a). The USDA APHIS data are based on a statistical sample
of farms in the 16 U.S. states with the most hogs. For operations with less than 200 head, manure management system
A-262 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
data were obtained from USDA APHIS (Bush 1998), it was assumed that those operations use pasture, range, or paddock
systems. For swine operations with greater than 200 head, the percent of waste managed in each system was estimated
using the EPA and USDA data broken out by geographic region and farm size. Farm-size distribution data reported in the
1992, 1997, 2002, and 2007 Census of Agriculture (USDA 2009a) were used to determine the percentage of all swine
utilizing the various manure management systems. It was assumed that the swine farm size data provided for 1992 were
the same as that for 1990 and 1991, and data provided for 2007 were the same as that for 2008 through 2012. Data for
1993 through 1996, 1998 through 2001, and 2003 through 2006 were extrapolated using the 1992, 1997, 2002, and 2007
data. The manure management systems reported in the census were deep pit, liquid/slurry (includes above- and below-
ground slurry), anaerobic lagoon, and solid storage (includes solids separated from liquids).
Some swine operations reported using more than one management system; therefore, the total percent of systems
reported by USDA for a region and farm size was greater than 100 percent. Typically, this means that a portion of the
manure at a swine operation is handled in one system (e.g., liquid system), and a separate portion of the manure is handled
in another system (e.g., dry system). However, it is unlikely that the same manure is moved from one system to another,
which could result in increased emissions, so reported systems data were normalized to 100 percent for incorporation into
the WMS distribution, using the same method as described above for dairy operations. As with dairy, AD WMS were
added to the WMS distribution based on data from EPA's AgSTAR database (EPA 2012).
Sheep: WMS data for sheep were obtained from USDA NASS sheep report for years 1990 through 1993 (USDA
1994). Data for 2001 are obtained from USDA APHIS's national sheep report (USDA, APHIS 2003). The USDA APHIS
data are based on a statistical sampled of farms in the 22 U.S. states with the most sheep. The data for years 1994-2000
are calculated assuming a linear progression from 1993 to 2001. Due to lack of additional data, data for years 2002 and
beyond are assumed to be the same as 2001. Based on expert opinion, it was assumed that all sheep manure not deposited
in feedlots was deposited on pasture, range, or paddock lands (Anderson 2000).
Goats, Horses, and Mules and Asses: WMS data for 1990 to 2012 were obtained from Appendix H of Global
Methane Emissions from Livestock and Poultry Manure (EPA 1992). This report presents state WMS usage in
percentages for the major animal types in the U.S., based on information obtained from extension service personnel in
each state. It was assumed that all manure not deposited in pasture, range, or paddock lands was managed in dry systems.
For mules and asses, the WMS was assumed to be the same as horses.
Poultry—Hens (one year old or older), Pullets (hens less than one year old), and Other Chickens: WMS data for
1992 were obtained from Global Methane Emissions from Livestock and Poultry Manure (EPA 1992). These data were
also used to represent 1990 and 1991. The percentage of layer operations using a shallow pit flush house with anaerobic
lagoon or high-rise house without bedding was obtained for 1999 from a United Egg Producers voluntary survey (UEP
1999). These data were augmented for key poultry states (AL, AR, CA, FL, GA, IA, IN, MN, MO, NC, NE, OH, PA, TX,
and WA) with USDA data (USDA, APHIS 2000). It was assumed that the change in system usage between 1990 and
1999 is proportionally distributed among those years of the inventory. It was also assumed that system usage in 2000
through 2012 was equal to that estimated for 1999. Data collected for EPA's Office of Water, including information
collected during site visits (EPA 2002b), were used to estimate the distribution of waste by management system and
animal type. As with dairy and swine, using information about AD WMS from EPA's AgSTAR database (EPA 2012),
AD was added to the WMS distribution for poultry operations.
Poultry—Broilers and Turkeys: The percentage of turkeys and broilers on pasture was obtained from the Office
of Air and Radiation's Global Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that
one percent of poultry waste is deposited in pastures, ranges, and paddocks (EPA 1992). The remainder of waste is
assumed to be deposited in operations with bedding management. As with dairy, swine, and other poultry, AD systems
were added to the WMS distributions based on information from EPA's AgSTAR database (EPA 2012).
Step 4: Emission Factor Calculations
Methane conversion factors (MCFs) and N2O emission factors (EFs) used in the emission calculations were
determined using the methodologies presented below.
A-263
-------
Methane Conversion Factors (MCFs)
Climate-based IPCC default MCFs (IPCC 2006) were used for all dry systems; these factors are presented in
Table A- 208. A U.S.-specific methodology was used to develop MCFs for all lagoon and liquid systems.
For animal waste managed in dry systems, the appropriate IPCC default MCF was applied based on annual
average temperature data. The average county and state temperature data were obtained from the National Climate Data
Center (NCAA 2012) and each state and year in the inventory was assigned a climate classification of cool, temperate or
warm. Although there are some specific locations in the U.S. that may be included in the warm climate category, no
aggregated state-level annual average temperatures are included in this category. In addition, some counties in a particular
state may be included in the cool climate category, although the aggregated state-level annual average temperature may be
included in the temperate category. Although considering the temperatures at a state level instead of a county level may
be causing some specific locations to be classified into an inappropriate climate category, using the state level annual
average temperature provides an estimate that is appropriate for calculating the national average.
For anaerobic lagoons and other liquid systems a climate-based approach based on the van't Hoff-Arrhenius
equation was developed to estimate MCFs that reflects the seasonal changes in temperatures, and also accounts for long-
term retention time. This approach is consistent with the recently revised guidelines from IPCC (IPCC 2006). The van't
Hoff-Arrhenius equation, with a base temperature of 30°C, is shown in the following equation (Safley and Westerman
1990):
/ = exp|
where,
/ = van't Hoff-Arrhenius/factor, the proportion of VS that are biologically available for conversion to
CH4 based on the temperature of the system
Ti =303.15K
T2 = Ambient temperature (K) for climate zone (in this case, a weighted value for each state)
E = Activation energy constant (15,175 cal/mol)
R = Ideal gas constant (1.987 cal/K mol)
For those animal populations using liquid manure management systems or manure runoff ponds (i.e., dairy cow,
dairy heifer, layers, beef in feedlots, and swine) monthly average state temperatures were based on the counties where the
specific animal population resides (i.e., the temperatures were weighted based on the percent of animals located in each
county). County population data were calculated from state-level population data from NASS and county-state
distribution data from the 1992, 1997, 2002, and 2007 Census data (USDA 2009a). County population distribution data
for 1990 and 1991 were assumed to be the same as 1992; county population distribution data for 1993 through 1996 were
extrapolated based on 1992 and 1997 data; county population data for 1998 through 2001 were extrapolated based on 1997
and 2002 data; county population data for 2003 through 2006 were extrapolated based on 2002 and 2007 data; and county
population data for 2008 to 2012 were assumed to be the same as 2007.
Annual MCFs for liquid systems are calculated as follows for each animal type, state, and year of the inventory:
• The weighted-average temperature for a state is calculated using the county population estimates and average
monthly temperature in each county. Monthly temperatures are used to calculate a monthly van't Hoff-Arrhenius
/factor, using the equation presented above. A minimum temperature of 5°C is used for uncovered anaerobic
lagoons and 7.5°C is used for liquid/slurry and deep pit systems.
• Monthly production of VS added to the system is estimated based on the animal type, number of animals present,
and the volatile solids excretion rate of the animals.
• For lagoon systems, the calculation of methane includes a management and design practices (MDP) factor. This
factor, equal to 0.8, was developed based on model comparisons to empirical CH4 measurement data from
anaerobic lagoon systems in the United States (ERG 2001). The MDP factor represents management and design
factors which cause a system to operate at a less than optimal level.
• For all systems other than anaerobic lagoons, the amount of VS available for conversion to CFLi each month is
assumed to be equal to the amount of VS produced during the month (from Step 3). For anaerobic lagoons, the
amount of VS available also includes VS that may remain in the system from previous months.
A-264 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The amount of VS consumed during the month is equal to the amount available for conversion multiplied by the/
factor.
For anaerobic lagoons, the amount of VS carried over from one month to the next is equal to the amount
available for conversion minus the amount consumed. Lagoons are also modeled to have a solids clean-out once
per year, occurring in the month of October.
The estimated amount of CBu generated during the month is equal to the monthly VS consumed multiplied by
the maximum CBu potential of the waste (B0).
The annual MCF is then calculated as:
CH4 generated mnual
VS produced mnualxB0
where,
MCF annual = Methane conversion factor
VS produced annual = Volatile solids excreted annually
Bo = Maximum CBu producing potential of the waste
In order to account for the carry-over of VS from one year to the next, it is assumed that a portion of the VS from
the previous year are available in the lagoon system in the next year. For example, the VS from October, November, and
December of 2005 are available in the lagoon system starting January of 2006 in the MCF calculation for lagoons in 2006.
Following this procedure, the resulting MCF for lagoons accounts for temperature variation throughout the year, residual
VS in a system (carry-over), and management and design practices that may reduce the VS available for conversion to
CH/i. It is assumed that liquid-slurry systems have a retention time less than 30 days, so the liquid-slurry MCF calculation
doesn't reflect the VS carry-over.
The liquid system MCFs are presented in Table A- 209 by state, WMS, and animal group for 2012.
Nitrous Oxide Emission Factors
Direct N2O EFs for manure management systems (kg N2O-N/kg excreted N) were set equal to the most recent
default IPCC factors (IPCC 2006), presented in Table A- 210.
Indirect N2O EFs account for two fractions of nitrogen losses: volatilization of ammonia (NHs) and NOx (Fracgas)
and runoff/leaching (Fracnmoffiieach). IPCC default indirect N2O EFs were used to estimate indirect N2O emissions. These
factors are 0.010 kg N2O-N/kg N for volatilization and 0.0075 kg N2O/kg N for runoff/leaching.
Country-specific estimates of N losses were developed for Fracgas and Fracrunofflieach for the United States. The
vast majority of volatilization losses are NHs. Although there are also some small losses of NOx, no quantified estimates
were available for use and those losses are believed to be small (about 1 percent) in comparison to the NHs losses.
Therefore, Fracgas values were based on WMS-specific volatilization values estimated from U.S. EPA's National Emission
Inventory - Ammonia Emissions from Animal Agriculture Operations (EPA 2005). To estimate Fracmnoffleach, data from
EPA's Office of Water were used that estimate the amount of runoff from beef, dairy, and heifer operations in five
geographic regions of the country (EPA 2002b). These estimates were used to develop U.S. runoff factors by animal type,
WMS, and region. Nitrogen losses from leaching are believed to be small in comparison to the runoff losses and there are
a lack of data to quantify these losses. Therefore, leaching losses were assumed to be zero and Fracmnoffieach was set equal
to the runoff loss factor. Nitrogen losses from volatilization and runoff/leaching are presented in Table A- 211.
Step 5: Cm Emission Calculations
To calculate CBu emissions for animals other than cattle, first the amount of VS excreted in manure that is
managed in each WMS was estimated:
A-265
-------
VS excreted state> Animl> WMS = Populationstate ^^ x - x VS x WM Sx 365.25
where,
VS excreted state, Animal, WMS = Amount of VS excreted in manure managed in each WMS for each animal
type (kg/yr)
Population state, Animal = Annual average state animal population by animal type (head)
TAM = Typical animal mass (kg)
VS = Volatile solids production rate (kg VS/1000 kg animal mass/day)
WMS = Distribution of manure by WMS for each animal type in a state (percent)
365.25 = Days per year
Using the CEFM VS data for cattle, the amount of VS excreted in manure that is managed in each WMS was
estimated using the following equation:
VS excreted state, Animai,WMS = populationstate Anilml xVSx WMS
where,
VS excreted state, Animal, WMS = Amount of VS excreted in manure managed in each WMS for each animal
type (kg/yr)
Population state, Animal = Annual average state animal population by animal type (head)
VS = Volatile solids production rate (kg VS/animal/year)
WMS = Distribution of manure by WMS for each animal type in a state (percent)
For all animals, the estimated amount of VS excreted into a WMS was used to calculate CH4 emissions using the
following equation:
CH4 = V (VS excreted st^Andrail.WMS x B0 x MCFx 0.662)
State, Animal, WMS
where,
CFLi = CFLi emissions (kg CH4/yr)
VS excreted WMS, state = Amount of VS excreted in manure managed in each WMS (kg/yr)
Bo = Maximum CH4 producing capacity (m3 CH4/kg VS)
MCF animal, state, WMS = MCF for the animal group, state and WMS (percent)
0.662 = Density of methane at 25° C (kg CH/m3 CH4)
A calculation was developed to estimate the amount of CH4 emitted from AD systems utilizing CH4 capture and
combustion technology. First, AD systems were assumed to produce 90 percent of the maximum CH4 producing capacity.
This value is applied for all climate regions and AD system types. However, the actual amount of CH4 produced by each
AD system is very variable and will change based on operational and climate conditions and an assumption of 90 percent
is likely overestimating CH4 production from some systems and underestimating CH4production in other systems. The
CH4 production of AD systems is calculated using the equation below:
CH4 Production AD tem = Population AD stem x -^^ x VS x B0 x 0.662 x 365.25 x 0.90
1000
where,
CH4 Production AD AD system = CH4 production from a particular AD system, (kg/yr)
Population AD state = Number of animals on a particular AD system
VS = Volatile solids production rate (kg VS/1000 kg animal mass-day)
TAM = Typical Animal Mass (kg/head)
Bo = Maximum CH4 producing capacity (CH4 m3/kg VS)
0.662 = Density of CH4 at 25° C (kg CH/m3 CH4)
365.25 = Days/year
0.90 = CH4 production factor for AD systems
A-266 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The total amount of CH4 produced by AD is calculated only as a means to estimate the emissions from AD; i.e.,
only the estimated amount of CH4 actually entering the atmosphere from AD is reported in the inventory. The emissions
to the atmosphere from AD are a result of leakage and incomplete combustion and are calculated using the collection
efficiency (CE) and destruction efficiency (DE) of the AD system. The three primary types of AD systems in the U.S. are
covered lagoons, complete mix and plug flow systems. The CE of covered lagoon systems was assumed to be 75 percent,
and the CE of complete mix and plug flow AD systems was assumed to be 99 percent (EPA 2008). The CH4 DE from
flaring or burning in an engine was assumed to be 98 percent; therefore, the amount of CH4 that would not be flared or
combusted was assumed to be 2 percent (EPA 2008). The amount of CH4 produced by systems with AD was calculated
with the following equation:
r[CH4 Production ADAD8ystemxCEAD8ystenx(l-DE)h
CH4 Emissions AD = Z r . / M
State, Animal, AD Systems |^+[CH4 Production AD AD^t^X ^1 -CE^^,^]] J
where,
CH4 Emissions AD = CH4 emissions from AD systems, (kg/yr)
CH4 Production AD AD system = CH4 production from a particular AD system, (kg/yr)
C£AD system = Collection efficiency of the AD system, varies by AD system type
DE = Destruction efficiency of the AD system, 0.98 for all systems
Step 6: N20 Emission Calculations
In addition to CH4 emissions, total N2O emissions were also estimated from manure management systems. Total
N2O emissions were calculated by summing direct and indirect N2O emissions. The first step in estimating direct and
indirect N2(D emissions was calculating the amount of N excreted in manure and managed in each WMS. For calves and
animals other than cattle the following equation was used:
N excreted state; Animal; WMS = Populationstate ^^ x WMSx -^- x Nex x 365.25
where,
N excreted state, Animal, WMS = Amount of N excreted in manure managed in each WMS for each animal
type (kg/yr)
Population state = Annual average state animal population by animal type (head)
WMS = Distribution of manure by waste management system for each animal type
in a state (percent)
TAM = Typical animal mass (kg)
Nex = Total Kjeldahl nitrogen excretion rate (kg N/1000 kg animal mass/day)
365.25 = Days per year
Using the CEFM Nex data for cattle other than calves, the amount of N excreted was calculated using the
following equation:
N excreted Stete>Allinial>WMS = Populationstate>Amlml x WMSx Nex
where,
N excreted state, Animal, WMS = Amount of N excreted in manure managed in each WMS for each animal
type (kg/yr)
Population state = Annual average state animal population by animal type (head)
A-267
-------
WMS
Nex
Distribution of manure by waste management system for each animal type
in a state (percent)
Total Kjeldahl N excretion rate (kg N/animal/year)
For all animals, direct N2O emissions were calculated as follows:
Direct N2O= E N excreted state jAnimal WMS x EFWMS x
State, Animal, WMS V
44
where,
Direct N2O
N excreted state, Animal, WMS =
44/28
Direct N2O emissions (kg N2O/yr)
Amount of N excreted in manure managed in each WMS for each animal
type (kg/yr)
Direct N2O emission factor from IPCC guidelines (kg N2O-N /kg N)
Conversion factor of N2O-N to N2O
Indirect N2O emissions were calculated for all animals with the following equation:
Indirect N2O =
State, Animal, WMS
N excreted state>Anlmal)WMS x
|N excreted state>Anlmal)WMS x
,„„
1UU
44
X-"i-'Hvolatiliz£tion X ~o
Zo
h, WMS
1UU
__
X -^^
runnofflach
44
~
Zo
where,
Indirect N2O
N excreted state, Animal, WMS =
Fracgas,wMs =
FraCrlmon71each,WMS =
EFvolatilization =
EFnmoffleach =
44/28
Indirect N2O emissions (kg N2O/yr)
Amount of N excreted in manure managed in each WMS for each animal
type (kg/yr)
Nitrogen lost through volatilization in each WMS
Nitrogen lost through runoff and leaching in each WMS (data were not
available for leaching so the value reflects only runoff)
Emission factor for volatilization (0.010 kg N2O-N/kg N)
Emission factor for runoff/leaching (0.0075 kg N2O-N/kg N)
Conversion factor of N2O-N to N2O
Emission estimates of CFLi andN2O by animal type are presented for all years of the inventory in Table A- 212
and Table A- 213 respectively. Emission estimates for 2012 are presented by animal type and state in Table A- 214 and
Table A- 215 respectively.
A-268 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-202: Livestock Population (1,000 Head)
Animal Type
Dairy Cattle
Dairy Cows
Dairy Heifer
Swine3
Market <50 Ib.
Market 50-11 9 Ib.
Market 120-1 79 Ib.
Market >180lb.
Breeding
Beef Cattle"
Feedlot Steers
Feedlot Heifers
NOF Bulls
NOF Calves
NOF Heifers
NOF Steers
NOF Cows
Sheep
Sheep On Feed
Sheep NOF
Goats
Poultry0
Hens >1 yr.
Pullets
Chickens
Broilers
Turkeys
Horses
Mules and Asses
American Bison
1990
14,144
10,015
4,129
53,941
18,359
11,734
9,440
7,510
6,899
87,228
6,357
3,192
2,160
22,561
10,182
10,321
32,455
11,358
1,180
10,178
2,516
1,537,074
273,467
73,167
6,545
1,066,209
117,685
2,212
63
47
1995
13,590
9,482
4,108
58,899
19,656
12,836
10,545
8,937
6,926
95,683
7,233
3,831
2,385
23,499
11,829
11,716
35,190
8,989
1,771
7,218
2,357
1,826,977
299,071
81,369
7,637
1,331,940
106,960
2,632
101
104
2001
13,217
9,172
4,045
58,913
19,659
12,900
10,708
9,465
6,181
89,118
7,932
4,569
2,274
22,389
9,832
8,724
33,398
6,908
3,256
3,652
2,475
2,060,398
340,317
95,656
8,126
1,525,413
90,887
3,519
109
213
2002
13,165
9,106
4,060
60,028
19,863
13,284
11,013
9,738
6,129
89,102
8,116
4,557
2,244
22,325
9,843
8,883
33,134
6,623
3,143
3,480
2,530
2,097,691
340,209
95,289
8,353
1,562,015
91,826
3,644
105
232
2003
13,215
9,142
4,073
59,827
19,929
13,138
11,050
9,701
6,011
88,232
8,416
4,676
2,248
21,997
9,564
8,347
32,983
6,321
3,049
3,272
2,652
2,085,268
340,979
100,346
8,439
1,544,155
91,349
3,721
141
225
2004
13,021
8,988
4,033
60,735
20,222
13,400
11,227
9,922
5,963
86,441
8,018
4,521
2,201
21,781
9,321
8,067
32,531
6,065
2,923
3,142
2,774
2,130,877
343,922
101,429
8,248
1,589,209
88,069
3,798
177
218
2005
13,165
9,004
4,162
61,073
20,228
13,519
11,336
9,997
5,993
86,954
8,116
4,536
2,214
21,678
9,550
8,185
32,674
6,135
2,971
3,164
2,897
2,150,410
348,203
96,809
8,289
1,613,091
84,018
3,875
212
212
2006
13,398
9,104
4,294
61,887
20,514
13,727
11,443
10,113
6,090
88,070
8,724
4,801
2,258
21,621
9,716
8,248
32,703
6,200
3,026
3,174
3,019
2,154,236
349,888
96,596
7,938
1,612,327
87,487
3,952
248
205
2007
13,487
9,145
4,343
65,417
21,812
14,557
12,185
10,673
6,190
87,639
8,674
4,730
2,214
21,483
9,592
8,302
32,644
6,120
3,000
3,120
3,141
2,166,936
346,613
103,816
8,164
1,619,400
88,943
4,029
284
198
2008
13,658
9,257
4,401
67,408
19,964
17,219
12,931
11,193
6,102
86,450
8,481
4,589
2,207
21,155
9,350
8,233
32,435
5,950
2,911
3,039
3,141
2,175,990
339,859
99,458
7,589
1,638,055
91,029
4,106
319
192
2009
13,761
9,333
4,429
65,990
19,444
16,995
12,567
11,079
5,905
85,812
8,446
4,508
2,184
21,001
9,448
8,515
31,712
5,747
2,806
2,941
3,141
2,088,828
341,005
102,301
8,487
1,554,582
82,453
4,183
355
185
2010
13,612
9,086
4,526
64,768
19,124
16,699
12,313
10,854
5,778
85,183
8,563
4,628
2,190
20,861
9,348
8,223
31,371
5,620
2,778
2,842
3,141
2,104,335
341,884
105,738
7,390
1,567,927
81,396
4,260
391
179
2011
13,722
9,150
4,572
65,589
19,385
16,966
12,438
11,009
5,791
83,705
8,743
4,803
2,155
20,648
8,878
7,628
30,850
5,480
2,692
2,788
3,141
2,095,951
338,944
102,233
6,922
1,565,018
82,833
4,336
427
173
2012
13,816
9,230
4,586
66,516
19,580
17,257
12,660
11,181
5,837
81,443
8,515
4,695
2,096
20,094
8,677
7,209
30,158
5,365
2,661
2,704
3,141
2,074,269
345,730
103,058
6,817
1,534,164
84,500
4,413
462
167
Note: Totals may not sum due to independent rounding.
a Prior to 2008, the Market <50 Ibs category was <60 Ibs and the Market 50-119 Ibs category was Market 60-119 Ibs; USDA updated the categories to be more consistent with international animal categories.
b NOF = Not on Feed
c Pullets includes laying pullets, pullets younger than 3 months, and pullets older than 3 months.
A-269
-------
Table A-203: Waste Characteristics Data
Animal Group
Dairy Cows
Dairy Heifers
Feedlot Steers
Feedlot Heifers
NOF Bulls
NOF Calves
NOF Heifers
NOF Steers
NOF Cows
American Bison
Market Swine <50 Ibs.
Market Swine <60 Ibs.
Market Swine 50-1 19 Ibs.
Market Swine 60-1 19 Ibs.
Market Swine 120-1 79 Ibs.
Market Swine >180 Ibs.
Breeding Swine
Feedlot Sheep
NOF Sheep
Goats
Horses
Mules and Asses
Hens >/= 1 yr
Pullets
Other Chickens
Broilers
Turkeys
Typical Animal Mass, TAM
Value
(kg) Source
680 CEFM
406-408 CEFM
419-457 CEFM
384-430 CEFM
831-917 CEFM
118 ERG2003b
296-407 CEFM
314-335 CEFM
554-611 CEFM
578.5 Meagher1986
13 ERG2010a
16 Safley2000
39 ERG2010a
41 Safley2000
68 Safley2000
91 Safley2000
198 Safley2000
25 EPA 1992
80 EPA 1992
64 ASAE 1998
450 ASAE 1998
130 IPCC2006
1.8 ASAE 1998
1.8 ASAE 1998
1.8 ASAE 1998
0.9 ASAE 1998
6.8 ASAE 1998
Total Kjeldahl Nitrogen Excreted, Nexa
Value Source
Table A- 94 CEFM
Table A- 94 CEFM
Table A- 94 CEFM
Table A- 94 CEFM
Table A- 94 CEFM
Table A- 205 USDA 1996, 2008
Table A- 199 CEFM
Table A- 94 CEFM
Table A- 94 CEFM
Table A- 199 CEFM
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 ASAE 1 998, USDA 2008
Table A- 205 ASAE 1 998, USDA 2008
Table A- 205 ASAE 1998
Table A- 205 ASAE 1 998, USDA 2008
Table A- 205 IPCC2006
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Maximum Methane Generation Potential, Bo
Value
(m3 ChWkg VS added) Source
0.24 Morris 1976
0.17 Bryant etal. 1976
0.33 Hashimoto 1981
0.33 Hashimoto 1981
0.17 Hashimoto 1981
0.17 Hashimoto 1981
0.17 Hashimoto 1981
0.17 Hashimoto 1981
0.17 Hashimoto 1981
0.17 Hashimoto 1981
0.48 Hashimoto 1984
0.48 Hashimoto 1984
0.48 Hashimoto 1984
0.48 Hashimoto 1984
0.48 Hashimoto 1984
0.48 Hashimoto 1984
0.48 Hashimoto 1984
0.36 EPA 1992
0.19 EPA 1992
0.17 EPA 1992
0.33 EPA 1992
0.33 EPA 1992
0.39 Hill 1982
0.39 Hill 1982
0.39 Hill 1982
0.36 Hill 1984
0.36 Hill 1984
Volatile Solids Excreted, VS*
Value Source
Table A- 94 CEFM
Table A- 94 CEFM
Table A- 94 CEFM
Table A- 94 CEFM
Table A- 94 CEFM
Table A- 205 USDA 1996, 2008
Table A- 199 CEFM
Table A- 94 CEFM
Table A- 94 CEFM
Table A- 199 CEFM
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 ASAE 1 998, USDA 2008
Table A- 205 ASAE 1 998, USDA 2008
Table A- 205 ASAE 1998
Table A- 205 ASAE 1 998, USDA 2008
Table A- 205 IPCC2006
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
Table A- 205 USDA 1996, 2008
a Nex and VS values vary by year; Table A- 203 shows state-level values for 2012 only.
A-270 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 204: Estimated Volatile Solids (VS) and Total Kjeldahl Nitrogen Excreted (Hex) Production Rates by year for Swine, Poultry, Sheep, Goats, Horses, Mules and Asses, and
Cattle Calves [Kg/day/1000 Kg animal massl
Animal Type
VS
Swine, Market
<50 Ibs.
Swine, Market
50-1 19 Ibs.
Swine, Market
120-179 Ibs.
Swine, Market
>180 Ibs.
Swine, Breeding
NOF Cattle Calves
Sheep
Goats
Hens >1yr.
Pullets
Chickens
Broilers
Turkeys
Horses
Mules and Asses
Nex
Swine, Market
<50 Ibs.
Swine, Market
50-1 19 Ibs.
Swine, Market
120-179 Ibs.
Swine, Market
>180 Ibs.
Swine, Breeding
NOF Cattle Calves
Sheep
Goats
Hens >1yr.
Pullets
Chickens
Broilers
1990
8.8
5.4
5.4
5.4
2.6
6.4
9.2
9.5
10.1
10.1
10.8
15.0
9.7
10.0
7.2
0.42
0.42
0.42
0.24
0.30
0.42
0.45
0.70
0.70
0.83
1.10
0.74
1991
8.8
5.4
5.4
5.4
2.6
6.4
9.2
9.5
10.1
10.1
10.8
15.0
9.7
10.0
7.2
0.42
0.42
0.42
0.24
0.30
0.42
0.45
0.70
0.70
0.83
1.10
0.74
1992
8.8
5.4
5.4
5.4
2.6
6.4
9.2
9.5
10.1
10.1
10.8
15.0
9.7
10.0
7.2
0.42
0.42
0.42
0.24
0.30
0.42
0.45
0.70
0.70
0.83
1.10
0.74
1993
8.8
5.4
5.4
5.4
2.6
6.4
9.2
9.5
10.1
10.1
10.8
15.0
9.7
10.0
7.2
0.42
0.42
0.42
0.24
0.30
0.42
0.45
0.70
0.70
0.83
1.10
0.74
1994
8.8
5.4
5.4
5.4
2.6
6.4
9.2
9.5
10.1
10.1
10.8
15.0
9.7
10.0
7.2
0.42
0.42
0.42
0.24
0.30
0.42
0.45
0.70
0.70
0.83
1.10
0.74
1995
8.8
5.4
5.4
5.4
2.6
6.4
9.2
9.5
10.1
10.1
10.8
15.0
9.7
10.0
7.2
0.42
0.42
0.42
0.24
0.30
0.42
0.45
0.70
0.70
0.83
1.10
0.74
1996
8.8
5.4
5.4
5.4
2.6
6.4
9.2
9.5
10.1
10.1
10.8
15.0
9.7
10.0
7.2
0.42
0.42
0.42
0.24
0.30
0.42
0.45
0.70
0.70
0.83
1.10
0.74
1997
8.8
5.4
5.4
5.4
2.6
6.5
9.2
9.5
10.1
10.1
10.8
15.2
9.6
10.0
7.2
0.43
0.43
0.43
0.23
0.31
0.42
0.45
0.70
0.70
0.85
1.09
0.73
1998
8.8
5.4
5.4
5.4
2.6
6.6
9.2
9.5
10.1
10.1
10.8
15.3
9.5
10.0
7.2
0.44
0.44
0.44
0.23
0.33
0.42
0.45
0.71
0.71
0.88
1.08
0.72
1999
8.8
5.4
5.4
5.4
2.6
6.7
9.1
9.5
10.1
10.1
10.9
15.5
9.4
9.6
7.2
0.45
0.45
0.45
0.23
0.34
0.42
0.45
0.72
0.72
0.90
1.07
0.71
2000
8.8
5.4
5.4
5.4
2.6
6.8
9.0
9.5
10.1
10.1
10.9
15.7
9.3
9.2
7.2
0.46
0.46
0.46
0.22
0.35
0.43
0.45
0.73
0.73
0.92
1.05
0.70
2001
8.8
5.4
5.4
5.4
2.7
6.9
8.9
9.5
10.1
10.1
10.9
15.8
9.2
8.8
7.2
0.47
0.47
0.47
0.22
0.36
0.43
0.45
0.73
0.73
0.94
1.04
0.69
2002
8.8
5.4
5.4
5.4
2.7
7.1
8.8
9.5
10.1
10.1
10.9
16.0
9.1
8.4
7.2
0.48
0.48
0.48
0.22
0.38
0.43
0.45
0.74
0.74
0.97
1.03
0.68
2003
8.8
5.4
5.4
5.4
2.7
7.2
8.8
9.5
10.1
10.1
10.9
16.2
9.0
8.1
7.2
0.49
0.49
0.49
0.22
0.39
0.44
0.45
0.75
0.75
0.99
1.02
0.67
2004
8.8
5.4
5.4
5.4
2.7
7.3
8.7
9.5
10.1
10.1
10.9
16.3
8.9
7.7
7.2
0.50
0.50
0.50
0.21
0.40
0.44
0.45
0.76
0.76
1.01
1.01
0.66
2005
8.8
5.4
5.4
5.4
2.7
7.4
8.6
9.5
10.1
10.1
11.0
16.5
8.8
7.3
7.2
0.51
0.51
0.51
0.21
0.41
0.44
0.45
0.77
0.77
1.03
1.00
0.65
2006
8.8
5.4
5.4
5.4
2.7
7.5
8.5
9.5
10.2
10.2
11.0
16.7
8.7
6.9
7.2
0.52
0.52
0.52
0.21
0.43
0.44
0.45
0.77
0.77
1.06
0.98
0.64
2007
8.8
5.4
5.4
5.4
2.7
7.6
8.4
9.5
10.2
10.2
11.0
16.8
8.6
6.5
7.2
0.53
0.53
0.53
0.21
0.44
0.45
0.45
0.78
0.78
1.08
0.97
0.63
2008
8.8
5.4
5.4
5.4
2.7
7.7
8.3
9.5
10.2
10.2
11.0
17.0
8.5
6.1
7.2
0.54
0.54
0.54
0.20
0.45
0.45
0.45
0.79
0.79
1.10
0.96
0.63
2009
8.8
5.4
5.4
5.4
2.7
7.7
8.3
9.5
10.2
10.2
11.0
17.0
8.5
6.1
7.2
0.54
0.54
0.54
0.20
0.45
0.45
0.45
0.79
0.79
1.10
0.96
0.63
2010
8.8
5.4
5.4
5.4
2.7
7.7
8.3
9.5
10.2
10.2
11.0
17.0
8.5
6.1
7.2
0.54
0.54
0.54
0.20
0.45
0.45
0.45
0.79
0.79
1.10
0.96
0.63
2011
8.8
5.4
5.4
5.4
2.7
7.7
8.3
9.5
10.2
10.2
11.0
17.0
8.5
6.1
7.2
0.54
0.54
0.54
0.20
0.45
0.45
0.45
0.79
0.79
1.10
0.96
0.63
2012
8.8
5.4
5.4
5.4
2.7
7.7
8.3
9.5
10.2
10.2
11.0
17.0
8.5
6.1
7.2
0.54
0.54
0.54
0.20
0.45
0.45
0.45
0.79
0.79
1.10
0.96
0.63
A-271
-------
Animal Type
Turkeys
Horses
Mules and Asses
1990
0.30
0.30
0.42
1991
0.30
0.30
0.42
1992
0.30
0.30
0.42
1993
0.30
0.30
0.42
1994
0.30
0.30
0.42
1995
0.30
0.30
0.42
1996
0.30
0.30
0.42
1997
0.30
0.30
0.43
1998
0.30
0.30
0.44
1999
0.29
0.30
0.45
2000
0.29
0.30
0.46
2001
0.28
0.30
0.47
2002
0.28
0.30
0.48
2003
0.27
0.30
0.49
2004
0.27
0.30
0.50
2005
0.26
0.30
0.51
2006
0.26
0.30
0.52
2007
0.25
0.30
0.53
2008
0.25
0.30
0.54
2009
0.25
0.30
0.54
2010
0.25
0.30
0.54
2011
0.25
0.30
0.54
2012
0.25
0.30
0.54
A-272 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 205: Estimated Volatile Solids (VS) and Total Kjeldahl Nitrogen Excreted (Hex) Production Rates by State for Cattle (other than Calves) and American Bison3 for 2012
[hg/animal/yearl
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Volatile Solids
Dairy
Cow
2,140
2,164
2,891
2,097
2,833
2,890
2,585
2,529
2,574
2,582
2,160
2,845
2,557
2,695
2,723
2,718
2,284
2,088
2,487
2,533
2,462
2,870
2,556
2,226
2,215
2,695
2,681
2,815
2,567
2,487
2,944
2,715
2,680
2,539
Dairy Beef
Heifers NOFCow
1,255 1,664
1,255 1,891
1,255 1,891
1,255 1,664
1,255 1,891
1,255 1,891
1,255 1,674
1,255 1,674
1,255 1,664
1,255 1,664
1,255 1,891
1,255 1,891
1,255 1,589
1,255 1,589
1,255 1,589
1,255 1,589
1,255 1,664
1,255 1,664
1,255 1,674
1,255 1,674
1,255 1,674
1,255 1,589
1,255 1,589
1,255 1,664
1,255 1,589
1,255 1,664
1,255 1,891
1,255 1,589
1,255 1,891
1,255 1,674
1,255 1,674
1,255 1,891
1,255 1,674
1,255 1,664
Beef
NOF
Heifers
2,522
3,966
2,861
2,357
1,790
1,574
2,994
2,156
2,819
2,458
2,596
1,763
1,464
1,503
1,223
1,152
1,883
2,739
1,938
2,203
2,678
1,563
1,471
2,476
1,903
2,372
1,535
1,893
3,055
2,123
1,829
2,143
2,555
1,873
Beef
NOF Beef OF
Steer Heifers
968 675
1,112 675
1,112 675
968 670
1,112 675
1,112 675
974 675
974 675
968 676
968 675
1,112 675
1,112 675
921 675
921 675
921 675
921 675
968 675
968 675
974 676
974 675
974 675
921 675
921 675
968 676
921 675
968 675
1,112 675
921 675
1,112 676
974 675
974 675
1,112 675
974 675
968 675
Beef OF Beef
Steer NOF Bull
656 1,721
658 1,956
656 1,956
669 1,721
656 1,956
656 1,956
656 1,731
656 1,731
655 1,721
656 1,721
656 1,956
656 1,956
656 1,643
656 1,643
656 1,643
656 1,643
656 1,721
656 1,721
655 1,731
656 1,731
656 1,731
656 1,643
656 1,643
655 1,721
656 1,643
656 1,721
656 1,956
656 1,643
655 1,956
656 1,731
656 1,731
656 1,956
656 1,731
656 1,721
American
Bison
1,721
1,956
1,956
1,721
1,956
1,956
1,731
1,731
1,721
1,721
1,956
1,956
1,643
1,643
1,643
1,643
1,721
1,721
1,731
1,731
1,731
1,643
1,643
1,721
1,643
1,721
1,956
1,643
1,956
1,731
1,731
1,956
1,731
1,721
Nitrogen Excreted
Dairy Dairy
Cow Heifers
130 69
130 69
161 69
127 69
158 69
161 69
148 69
145 69
149 69
149 69
129 69
159 69
146 69
152 69
154 69
153 69
137 69
126 69
143 69
145 69
142 69
160 69
146 69
134 69
132 69
152 69
152 69
157 69
147 69
143 69
163 69
153 69
154 69
146 69
Beef
NOF Cow
73
59
59
73
59
59
74
74
73
73
59
59
75
75
75
75
73
73
74
74
74
75
75
73
75
73
59
75
59
74
74
59
74
73
Beef Beef
NOF NOF
Heifers Steer
50 41
42 33
41 33
50 41
39 33
38 33
52 42
50 42
51 41
50 41
41 33
39 33
49 43
49 43
48 43
47 43
49 41
51 41
50 42
51 42
51 42
49 43
49 43
50 41
51 43
50 41
38 33
51 43
41 33
50 42
49 42
40 33
51 42
49 41
Beef OF Beef OF
Heifers Steer
54 55
55 56
54 55
58 62
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 56
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
Beef American
NOF Bull Bison
83 83
69 69
69 69
83 83
69 69
69 69
84 84
84 84
83 83
83 83
69 69
69 69
85 85
85 85
85 85
85 85
83 83
83 83
84 84
84 84
84 84
85 85
85 85
83 83
85 85
83 83
69 69
85 85
69 69
84 84
84 84
69 69
84 84
83 83
A-273
-------
State
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Volatile Solids
Dairy
Cow
2,581
2,425
2,625
2,562
2,466
2,447
2,697
2,356
2,748
2,719
2,542
2,498
2,877
2,279
2,701
2,654
Dairy Beef
Heifers NOFCow
1,255 1,589
1,255 1,589
1,255 1,664
1,255 1,891
1,255 1,674
1,255 1,674
1,255 1,664
1,255 1,589
1,255 1,664
1,255 1,664
1,255 1,891
1,255 1,674
1,255 1,664
1,255 1,891
1,255 1,589
1,255 1,891
Beef
NOF
Heifers
1,698
1,624
2,019
2,076
2,678
2,527
1,637
2,083
1,429
2,135
2,062
2,299
1,546
2,452
2,183
2,331
Beef
NOF Beef OF
Steer Heifers
921 675
921 675
968 675
1,112 675
974 675
974 675
968 675
921 675
968 675
968 675
1,112 676
974 676
968 675
1,112 675
921 675
1,112 675
Beef OF Beef
Steer NOF Bull
656 1,643
656 1,643
656 1,721
656 1,956
656 1,731
656 1,731
656 1,721
656 1,643
656 1,721
656 1,721
656 1,956
656 1,731
656 1,721
656 1,956
656 1,643
656 1,956
American
Bison
1,643
1,643
1,721
1,956
1,731
1,731
1,721
1,643
1,721
1,721
1,956
1,731
1,721
1,956
1,643
1,956
Nitrogen Excreted
Dairy Dairy
Cow Heifers
147 69
141 69
149 69
147 69
143 69
143 69
152 69
140 69
155 69
153 69
146 69
146 69
160 69
134 69
153 69
151 69
Beef
NOF Cow
75
75
73
59
74
74
73
75
73
73
59
74
73
59
75
59
Beef Beef
NOF NOF
Heifers Steer
50 43
50 43
49 41
40 33
51 42
51 42
48 41
51 43
47 41
49 41
40 33
51 42
48 41
40 33
51 43
40 33
Beef OF Beef OF
Heifers Steer
54 55
54 55
54 55
54 55
54 56
54 55
54 56
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
54 55
Beef American
NOF Bull Bison
85 85
85 85
83 83
69 69
84 84
84 84
83 83
85 85
83 83
83 83
69 69
84 84
83 83
69 69
85 85
69 69
Source: CEFM
a Beef NOF Bull values were used for American bison Nex and VS.
Table A- 206:2012 Manure Distribution Among Waste Management Systems by Operation [Percent!
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Beef Feedlots
Dry Liquid/
Lot" Slurryb
100 1
100 1
100 0
100 1
100 1
100 0
100 1
100 1
100 1
100 1
100 1
100 0
100 1
100 1
100 1
100 1
100 1
Beef Not
on Feed
Operations
Pasture,
Range,
Paddock
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
Dairy Cow Farms3
Pasture,
Range, Daily Solid Liquid/ Anaerobic Deep
Paddock Spread Storage Slurry Lagoon Pit
51 16 7 10 16 0
5 9 34 19 24 9
0 10 9 19 61 0
60 14 10 7 90
1 11 9 20 59 0
1 1 11 23 64 0
6 43 16 20 13 2
6 44 19 19 10 2
13 22 7 15 43 0
37 18 9 12 23 0
10 0 9 23 57 0
0 0 11 23 65 0
4 6 39 31 16 5
5 8 29 31 24 3
4 8 34 30 20 4
2 3 21 37 36 2
60 14 14 7 32
Dairy Heifer Facilities
Pasture,
Daily Dry Liquid/ Range,
Spreadb Lotb Slurryb Paddockb
17 38 0 45
6 90 1 4
10 90 0 0
15 28 0 57
11 88 1 1
1 98 0 1
43 51 0 6
44 50 0 6
22 61 1 17
18 42 0 40
0 99 1 1
1 99 0 0
8 87 0 5
13 79 0 8
10 83 0 6
5 92 0 3
14 24 0 61
Swine Operations3
Pasture,
Range, Solid Liquid/ Anaerobic Deep
Paddock Storage Slurry Lagoon Pit
5 47 54 31
64 2 10 7 17
6 36 55 29
4 4 13 45 35
10 37 50 29
1 6 26 17 50
78 1 6 5 11
8 5 25 17 46
72 1 8 6 13
4 48 53 31
31 3 19 14 32
12 5 23 15 44
1 5 29 14 52
1 5 28 14 52
1 49 54 33
2 5 28 13 52
5 4 10 48 33
Layer Operations
Poultry
Anaerobic without
Lagoon Litter
42 58
25 75
60 40
0 100
12 88
60 40
5 95
5 95
42 58
42 58
25 75
60 40
2 98
0 100
0 100
2 98
5 95
Broiler and Turkey
Operations
Pasture, Poultry
Range, with
Paddock Litter
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
A-274 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
State
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New
Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Beef Feedlots
Dry Liquid/
Lot" Slurry"
100 1
100 1
100 1
100 1
100 1
100 1
100 1
100 1
100 0
100 1
100 0
100 1
100 1
100 0
100 1
100 1
100 1
100 1
100 0
100 1
100 1
100 1
100 1
100 1
100 1
100 0
100 0
100 1
100 1
100 1
100 1
100 1
100 0
Beef Not
on Feed
Operations
Pasture,
Range,
Paddock
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
Dairy Cow Farms3
Pasture,
Range, Daily Solid Liquid/ Anaerobic Deep
Paddock Spread Storage Slurry Lagoon Pit
59 15 10 7 91
7 45 20 17 10 2
7 44 22 16 83
7 44 22 16 83
2 4 24 38 29 3
5 8 39 28 17 4
54 15 10 8 12 0
7 12 42 22 11 5
2 4 19 28 42 4
2 4 26 35 29 3
0 0 10 24 65 0
7 44 19 18 10 2
7 45 25 13 63
0 10 9 19 61 0
6 44 17 18 13 2
46 17 11 15 10 2
7 11 38 26 15 4
6 11 38 26 15 4
0 7 21 22 45 4
16 0 11 22 50 1
8 46 24 12 62
9 47 25 13 52
47 17 8 11 18 0
3 4 24 36 31 2
58 15 12 9 42
0 9 11 22 58 1
1 1 15 26 56 2
6 44 17 19 13 2
56 15 11 10 52
11 0 11 22 56 1
8 46 23 14 72
5 9 38 28 17 4
4 6 19 23 43 4
Dairy Heifer Facilities
Pasture,
Daily Dry Liquid/ Range,
Spread" Lot" Slurry" Paddock"
14 26 0 60
45 48 0 7
44 49 0 7
45 47 0 7
6 91 0 3
10 84 0 6
15 28 0 57
14 77 0 8
4 93 0 3
6 90 0 4
0 99 0 0
44 49 0 7
45 47 0 8
10 90 0 0
45 48 0 7
15 31 0 54
11 83 0 6
14 78 0 8
6 94 0 0
0 80 1 20
47 44 0 9
47 44 0 9
15 31 0 54
8 87 0 5
15 26 0 59
8 92 0 0
1 98 0 1
44 49 0 7
15 28 0 57
0 83 1 17
45 48 0 7
12 82 0 7
12 81 0 7
Swine Operations3
Pasture,
Range, Solid Liquid/ Anaerobic Deep
Paddock Storage Slurry Lagoon Pit
88 1 3 36
65 2 10 7 16
7 5 25 17 47
56 2 12 9 20
4 5 26 17 48
1 5 26 18 50
2 46 58 31
2 5 28 13 52
3 5 25 17 49
1 5 28 14 51
34 3 18 14 31
64 2 10 8 17
36 3 18 14 30
100 0 0 00
13 5 23 15 44
0 47 57 32
5 5 25 17 48
3 5 28 14 51
1 46 58 31
48 2 14 11 24
4 5 26 18 48
72 1 8 6 13
3 47 55 31
1 5 26 17 50
13 3 11 41 32
3 46 57 30
1 6 26 17 51
63 2 10 8 18
4 47 54 31
43 3 15 11 28
59 2 11 7 20
13 4 23 17 42
4 5 25 16 49
Layer Operations
Poultry
Anaerobic without
Lagoon Litter
60 40
5 95
5 95
5 95
2 98
0 100
60 40
0 100
60 40
2 98
0 100
5 95
5 95
60 40
5 95
42 58
2 98
0 100
60 40
25 75
0 100
5 95
60 40
2 98
5 95
12 88
60 40
5 95
5 95
12 88
5 95
2 98
60 40
Broiler and Turkey
Operations
Pasture, Poultry
Range, with
Paddock Litter
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
A-275
-------
a In the methane inventory for manure management, the percent of dairy cows and swine with AD systems is estimated using data from EPA's AgSTAR Program.
b Because manure from beef feedlots and dairy heifers may be managed for long periods of time in multiple systems (i.e., both drylot and runoff collection pond), the percent of manure that generates emissions is greater than 100 percent.
A-276 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 207: Manure Management System Descriptions
Manure Management System
Description3
Pasture, Range, Paddock
Daily Spread
Solid Storage
Dry Lot
Liquid/Slurry
Anaerobic Lagoon
The manure from pasture and range grazing animals is allowed to lie as is, and is not managed. Methane
emissions are accounted for under Manure Management, but the ivbO emissions from manure deposited on
PRP are included under the Agricultural Soil Management category.
Manure is routinely removed from a confinement facility and is applied to cropland or pasture within 24 hours of
excretion. Methane emissions are accounted for under Manure Management, but the ivbO emissions during
storage and treatment are assumed to be zero. ivhO emissions from land application are covered under the
Agricultural Soil Management category.
The storage of manure, typically for a period of several months, in unconfined piles or stacks. Manure is able to
be stacked due to the presence of a sufficient amount of bedding material or loss of moisture by evaporation.
A paved or unpaved open confinement area without any significant vegetative cover where accumulating
manure may be removed periodically. Dry lots are most typically found in dry climates but also are used in
humid climates.
Manure is stored as excreted or with some minimal addition of water to facilitate handling and is stored in either
tanks or earthen ponds, usually for periods less than one year.
Uncovered anaerobic lagoons are designed and operated to combine waste stabilization and storage. Lagoon
supernatant is usually used to remove manure from the associated confinement facilities to the lagoon.
Anaerobic lagoons are designed with varying lengths of storage (up to a year or greater), depending on the
climate region, the VS loading rate, and other operational factors. Anaerobic lagoons accumulate sludge over
time, diminishing treatment capacity. Lagoons must be cleaned out once every 5 to 15 years, and the sludge is
typically applied to agricultural lands. The water from the lagoon may be recycled as flush water or used to
irrigate and fertilize fields. Lagoons are sometimes used in combination with a solids separator, typically for
dairy waste. Solids separators help control the buildup of nondegradable material such as straw or other
bedding materials.
Animal excreta with or without straw are collected and anaerobically digested in a large containment vessel
(complete mix or plug flow digester) or covered lagoon. Digesters are designed and operated for waste
stabilization by the microbial reduction of complex organic compounds to C02 and Cm, which is captured and
flared or used as a fuel.
Collection and storage of manure usually with little or no added water typically below a slatted floor in an
enclosed animal confinement facility. Typical storage periods range from 5 to 12 months, after which manure is
removed from the pit and transferred to a treatment system or applied to land.
Enclosed poultry houses use bedding derived from wood shavings, rice hulls, chopped straw, peanut hulls, or
other products, depending on availability. The bedding absorbs moisture and dilutes the
manure produced by the birds. Litter is typically cleaned out completely once a year. These manure systems
are typically used for all poultry breeder flocks and for the production of meat type chickens (broilers) and other
fowl.
In high-rise cages or scrape-out/belt systems, manure is excreted onto the floor below with no bedding to
absorb moisture. The ventilation system dries the manure as it is stored. When designed and operated
properly, this high-rise system is a form of passive windrow composting.
a Manure management system descriptions are based on the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Volume 4: Agriculture, Forestry
and Other Land Use, Chapter 10: Emissions from Livestock and Manure Management, Tables 10.18 and 10.21) and the Development Document for the Final
Revisions to the National Pollutant Discharge Elimination System Regulation and the Effluent Guidelines for Concentrated Animal Feeding Operations (EPA-821-
R-03-001, December 2002).
Table A- 208: Methane Conversion Factors (percent) for Dry Systems
Anaerobic Digester
Deep Pit
Poultry with Litter
Poultry without Litter
Waste Management System
Aerobic Treatment
Anaerobic Digester
Cattle Deep Litter (<1 month)
Cattle Deep Litter (>1 month)
Composting - In Vessel
Cool Climate MCF Temperate Climate MCF Warm Climate MCF
0
0
0.03
0.21
0.005
0
0
0.03
0.44
0.005
0
0
0.3
0.76
0.005
A-277
-------
Waste Management System
Composting - Static Pile
Cool Climate MCF Temperate Climate MCF Warm Climate MCF
Composting-Extensive/ Passive
Composting-lntensive
Daily Spread
Dry Lot
Fuel
Pasture
Poultry with bedding
Poultry without bedding
Solid Storage
Source: IPCC 2006
0.005
0.005
0.005
0.001
0.01
0.1
0.01
0.015
0.015
0.02
0.005
0.01
0.01
0.005
0.015
0.1
0.015
0.015
0.015
0.04
0.005
0.015
0.015
0.01
0.05
0.1
0.02
0.015
0.015
0.05
Table A- 209: Methane Conversion Factors by State for Liquid Systems for 2012 (percent)
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Anaerobic
Lagoon
0.77
0.47
0.79
0.76
0.75
0.65
0.70
0.76
0.80
0.78
0.76
0.67
0.73
0.71
0.70
0.75
0.74
0.78
0.65
0.74
0.68
0.68
0.68
0.77
0.74
0.61
0.71
0.69
0.66
0.73
0.75
0.68
0.75
0.66
0.71
0.77
0.63
0.71
0.71
0.77
0.69
0.74
0.77
0.67
0.65
0.73
0.63
0.71
Dairy
Liquid/Slurry and
Deep Pit
0.41
0.15
0.58
0.39
0.33
0.22
0.26
0.35
0.55
0.44
0.57
0.23
0.31
0.28
0.26
0.36
0.33
0.48
0.21
0.32
0.24
0.24
0.24
0.44
0.34
0.19
0.27
0.24
0.23
0.31
0.35
0.24
0.34
0.22
0.28
0.47
0.20
0.28
0.27
0.42
0.25
0.33
0.48
0.23
0.22
0.30
0.20
0.28
Swine
Anaerobic
Lagoon
0.76
0.47
0.78
0.77
0.73
0.68
0.71
0.76
0.80
0.77
0.76
0.65
0.72
0.72
0.70
0.75
0.74
0.78
0.65
0.74
0.69
0.69
0.69
0.76
0.74
0.63
0.71
0.71
0.67
0.74
0.73
0.69
0.77
0.66
0.72
0.77
0.64
0.72
0.71
0.78
0.70
0.75
0.78
0.69
0.65
0.75
0.64
0.71
Beef
Liquid/Slurry
and Deep Pit Liquid/Slurry
0.40
0.15
0.49
0.43
0.31
0.24
0.26
0.35
0.54
0.42
0.57
0.21
0.30
0.29
0.26
0.35
0.33
0.50
0.21
0.32
0.25
0.25
0.25
0.42
0.33
0.20
0.28
0.26
0.23
0.31
0.30
0.24
0.40
0.22
0.28
0.43
0.20
0.29
0.27
0.43
0.25
0.36
0.50
0.24
0.22
0.34
0.21
0.28
0.42
0.15
0.54
0.40
0.41
0.24
0.26
0.35
0.54
0.42
0.57
0.21
0.29
0.29
0.26
0.35
0.32
0.50
0.21
0.33
0.25
0.24
0.25
0.44
0.33
0.21
0.27
0.23
0.22
0.30
0.32
0.24
0.32
0.22
0.28
0.44
0.22
0.28
0.27
0.41
0.25
0.34
0.44
0.24
0.22
0.31
0.22
0.28
Poultry
Anaerobic
Lagoon
0.76
0.47
0.75
0.76
0.75
0.65
0.70
0.76
0.80
0.76
0.76
0.67
0.73
0.72
0.70
0.75
0.74
0.78
0.65
0.75
0.69
0.68
0.67
0.77
0.74
0.63
0.71
0.70
0.67
0.74
0.71
0.69
0.75
0.65
0.71
0.77
0.64
0.72
0.71
0.77
0.69
0.74
0.78
0.67
0.66
0.74
0.64
0.71
A-278 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Dairy
State
Wisconsin
Wyoming
Anaerobic
Lagoon
0.67
0.63
Liquid/Slurry and
Deep Pit
0.23
0.20
Swine
Anaerobic
Lagoon
0.68
0.64
Liquid/Slurry
and Deep Pit
0.24
0.21
Beef
Liquid/Slurry
0.24
0.22
Poultry
Anaerobic
Lagoon
0.68
0.64
Note: MCFs developed using Tier 2 methods described in IPCC 2006, Section 10.4.2.
A-279
-------
Table A- 210: Direct Nitrous Oxide Emission Factors for 2012 (kg N 0-N/kg Kjdl N)
Direct N20
Emission
Waste Management System Factor
Aerobic Treatment (forced aeration)
Aerobic Treatment (natural aeration)
Anaerobic Digester
Anaerobic Lagoon
Cattle Deep Bed (active mix)
Cattle Deep Bed (no mix)
Compostingjn vessel
Compostingjntensive
Composting_passive
Composting_static
Daily Spread
Deep Pit
Dry Lot
Fuel
Liquid/Slurry
Pasture
Poultry with bedding
Poultry without bedding
Solid Storage
0.005
0.01
0
0
0.07
0.01
0.006
0.1
0.01
0.006
0
0.002
0.02
0
0.005
0
0.001
0.001
0.005
Source: IPCC 2006
Table A- 211: Indirect Nitrous Oxide Loss Factors (percent)
Animal Type
Beef Cattle
Beef Cattle
Beef Cattle
Dairy Cattle
Dairy Cattle
Dairy Cattle
Dairy Cattle
Dairy Cattle
Dairy Cattle
Dairy Cattle
American Bison
Goats
Goats
Horses
Horses
Mules and Asses
Mules and Asses
Poultry
Poultry
Poultry
Poultry
Poultry
Poultry
Sheep
Sheep
Swine
Swine
Swine
Swine
Swine
Source: EPA 2002b, 2005.
Waste Management
System
Dry Lot
Liquid/Slurry
Pasture
Anaerobic Lagoon
Daily Spread
Deep Pit
Dry Lot
Liquid/Slurry
Pasture
Solid Storage
Pasture
Dry Lot
Pasture
Dry Lot
Pasture
Dry Lot
Pasture
Anaerobic Lagoon
Liquid/Slurry
Pasture
Poultry with bedding
Poultry without bedding
Solid Storage
Dry Lot
Pasture
Anaerobic Lagoon
Deep Pit
Liquid/Slurry
Pasture
Solid Storage
a Data for nitrogen losses due to leaching were not available,
Volatilization
Nitrogen Loss
23
26
0
43
10
24
15
26
0
27
0
23
0
23
0
23
0
54
26
0
26
34
8
23
0
58
34
26
0
45
Central
1.1
0
0
0.2
0
0
0.6
0.2
0
0.2
0
1.1
0
0
0
0
0
0.2
0.2
0
0
0
0
1.1
0
0.2
0
0.2
0
0
Runoff/Leaching
Nitrogen Loss"
Pacific Mid-Atlantic
3.9
0
0
0.8
0
0
2
0.8
0
0
0
3.9
0
0
0
0
0
0.8
0.8
0
0
0
0
3.9
0
0.8
0
0.8
0
0
3.6
0
0
0.7
0
0
1.8
0.7
0
0
0
3.6
0
0
0
0
0
0.7
0.7
0
0
0
0
3.6
0
0.7
0
0.7
0
0
Midwest
1.9
0
0
0.4
0
0
0.9
0.4
0
0
0
1.9
0
0
0
0
0
0.4
0.4
0
0
0
0
1.9
0
0.4
0
0.4
0
0
South
4.3
0
0
0.9
0
0
2.2
0.9
0
0
0
4.3
0
0
0
0
0
0.9
0.9
0
0
0
0
4.3
0
0.9
0
0.9
0
0
so the values represent only nitrogen losses due to runoff.
A-280 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 212: Methane Emissions from Livestock Manure Management tGgla
Animal Type
Dairy Cattle
Dairy Cows
Dairy Heifer
Swine
Market Swine
Market <50 Ibs.
Market 50-11 9 Ibs.
Market 120-1 79 Ibs.
Market >1 80 Ibs.
Breeding Swine
Beef Cattle
Feedlot Steers
Feedlot Heifers
NOF Bulls
NOF Calves
NOF Heifers
NOF Steers
NOF Cows
Sheep
Goats
Poultry
Hens >1 yr.
Total Pullets
Chickens
Broilers
Turkeys
Horses
Mules and Asses
American Bison
1990
599
592
7
624
484
102
101
136
145
140
128
14
7
5
8
12
12
69
7
1
131
73
25
4
19
10
9
+
+
1991
615
608
7
676
524
110
111
147
156
152
128
14
7
5
8
12
12
69
7
1
132
72
26
4
20
10
9
+
+
1992
598
591
7
639
500
104
105
140
152
139
131
14
7
5
8
13
13
70
7
1
127
70
23
4
20
10
9
+
+
1993
623
616
7
680
534
109
110
151
165
146
131
13
7
5
8
14
14
71
6
1
131
73
23
4
21
10
9
+
+
1994
663
656
7
741
585
119
120
164
182
156
137
14
8
5
9
14
13
74
6
1
131
72
23
4
22
9
10
+
+
1995
693
686
7
764
608
121
124
170
194
155
141
14
8
5
9
15
14
76
5
1
128
69
22
4
23
9
11
+
+
1996
702
695
7
730
582
116
117
163
185
148
139
14
8
5
8
15
14
76
5
1
126
68
21
3
24
9
11
+
+
1997
734
727
7
783
626
125
127
175
198
157
136
13
8
5
8
14
13
74
5
1
128
67
23
3
25
9
12
+
+
1998
781
774
7
892
720
141
144
201
235
172
139
13
8
5
9
15
13
76
5
1
130
70
23
4
26
8
13
+
+
1999
854
846
7
849
692
133
138
193
229
157
139
14
8
5
9
14
12
76
4
1
126
66
21
4
27
7
13
+
+
2000
900
893
7
834
680
131
136
189
225
155
133
15
9
5
9
13
11
71
4
1
127
67
22
3
28
7
13
+
+
2001
960
952
7
854
696
134
138
192
232
158
136
15
9
5
9
13
11
73
4
1
131
70
22
3
28
7
13
+
+
2002
997
990
7
879
720
137
144
199
240
158
133
15
9
5
9
13
11
71
4
1
129
67
22
4
29
7
13
+
+
2003
1047
1039
7
860
706
135
140
196
234
154
133
16
9
5
9
13
10
71
4
1
130
68
22
4
29
7
13
+
+
2004
1000
993
7
857
706
135
141
196
234
151
131
15
9
5
9
12
10
71
3
1
129
66
23
3
30
7
12
+
+
2005
1069
1062
7
914
753
142
150
210
251
161
135
15
9
5
9
13
10
73
3
1
129
66
22
3
31
7
12
+
+
2006
1101
1094
7
901
741
141
148
206
246
160
138
16
9
5
10
13
10
74
3
1
131
66
23
3
32
7
12
+
+
2007
1224
1216
8
982
814
155
163
227
269
168
136
16
9
5
10
13
11
73
3
1
134
67
25
3
32
7
11
+
+
2008
1238
1230
8
938
780
109
174
229
268
157
132
16
9
5
9
13
10
70
3
1
129
64
23
3
33
7
10
+
+
2009
1233
1225
8
896
748
103
167
218
259
148
131
16
9
5
9
13
11
69
3
1
128
64
23
4
31
6
11
+
+
2010
1239
1231
8
948
792
110
177
231
273
156
134
16
9
5
9
13
11
71
3
1
129
64
24
3
31
6
11
+
+
2011
1262
1254
8
941
787
110
176
228
272
154
132
17
9
5
9
12
10
70
3
1
127
64
23
3
31
6
11
+
+
2012
1291
1283
9
957
801
112
180
233
276
156
128
16
9
5
9
12
9
68
3
1
127
64
24
3
31
6
12
+
+
* Accounts for ChU reductions due to capture and destruction of ChU at facilities using anaerobic digesters.
H Emission estimate is less than 0.5 Gg.
A-281
-------
Table A- 213: Total (Direct and Indirect! Nitrous Oxide Emissions from Livestock Manure Management tGgl
Animal Type
Dairy Cattle
Dairy Cows
Dairy Heifer
Swine
Market Swine
Market <50 Ibs.
Market 50-11 9 Ibs.
Market 120-1 79 Ibs.
Market >180 Ibs.
Breeding Swine
Beef Cattle
Feedlot Steers
Feedlot Heifers
Sheep
Goats
Poultry
Hens >1 yr.
Total Pullets
Chickens
Broilers
Turkeys
Horses
Mules and Asses
American Bison
1990
17.1
10.0
7.0
4.0
3.0
0.6
0.6
0.9
0.9
1.0
19.8
13.4
6.4
0.4
0.1
4.7
1.0
0.3
+
2.2
1.2
0.3
+
NA
1991
17.0
10.1
6.9
4.2
3.1
0.6
0.7
0.9
1.0
1.1
20.3
13.6
6.6
0.4
0.1
4.8
1.0
0.3
+
2.3
1.2
0.3
+
NA
1992
17.0
10.0
7.1
4.3
3.3
0.6
0.7
0.9
1.0
1.1
20.1
13.5
6.6
0.4
0.1
4.9
1.0
0.3
+
2.4
1.2
0.3
+
NA
1993
17.3
10.0
7.2
4.4
3.3
0.6
0.7
1.0
1.0
1.1
19.1
12.8
6.3
0.4
0.1
5.0
1.0
0.3
+
2.5
1.1
0.3
+
NA
1994
17.4
10.1
7.3
4.6
3.5
0.6
0.7
1.0
1.1
1.1
20.9
13.9
7.0
0.6
0.1
5.1
1.0
0.3
+
2.6
1.1
0.3
+
NA
1995
17.7
10.3
7.4
4.5
3.5
0.6
0.7
1.0
1.1
1.1
21.8
14.4
7.4
0.7
0.1
5.1
1.0
0.3
+
2.7
1.1
0.4
+
NA
1996
17.7
10.3
7.4
4.4
3.3
0.6
0.7
1.0
1.1
1.0
21.4
14.0
7.4
0.8
0.1
5.3
1.0
0.3
+
2.8
1.1
0.4
+
NA
1997
17.9
10.4
7.4
4.7
3.6
0.7
0.8
1.0
1.2
1.1
21.5
13.9
7.6
0.9
0.1
5.3
1.1
0.3
+
2.8
1.1
0.4
+
NA
1998
18.0
10.5
7.5
5.1
4.0
0.7
0.8
1.1
1.3
1.1
21.6
14.1
7.6
0.9
0.1
5.3
1.1
0.3
+
2.9
1.0
0.4
+
NA
1999
17.6
10.2
7.4
5.0
4.1
0.7
0.8
1.1
1.3
1.0
24.0
15.5
8.5
1.0
0.1
5.3
1.1
0.3
+
2.9
0.9
0.4
+
NA
2000
17.9
10.5
7.5
5.0
4.1
0.8
0.8
1.1
1.3
0.9
25.0
16.1
8.9
1.1
0.1
5.3
1.1
0.3
+
2.9
0.9
0.5
+
NA
2001
18.2
10.5
7.7
5.1
4.2
0.8
0.8
1.2
1.4
0.9
24.1
15.4
8.6
1.2
0.1
5.3
1.2
0.3
+
2.9
0.9
0.5
+
NA
2002
18.5
10.6
7.8
5.3
4.4
0.8
0.9
1.2
1.5
0.9
24.8
16.0
8.7
1.2
0.1
5.4
1.2
0.3
+
3.0
0.9
0.5
+
NA
2003
18.7
10.8
7.9
5.4
4.5
0.9
0.9
1.3
1.5
0.9
25.0
16.3
8.8
1.2
0.1
5.3
1.2
0.4
+
2.9
0.9
0.5
+
NA
2004
17.8
10.3
7.5
5.6
4.7
0.9
0.9
1.3
1.6
0.9
23.6
15.3
8.4
1.1
0.1
5.4
1.2
0.4
+
2.9
0.8
0.5
+
NA
2005
18.3
10.5
7.8
5.7
4.9
0.9
1.0
1.4
1.6
0.9
24.0
15.5
8.5
1.2
0.1
5.4
1.3
0.4
+
3.0
0.8
0.5
+
NA
2006
18.9
10.8
8.1
5.9
5.0
1.0
1.0
1.4
1.6
0.9
25.7
16.7
9.0
1.2
0.1
5.4
1.3
0.4
+
2.9
0.8
0.5
+
NA
2007
18.9
10.8
8.1
6.3
5.5
1.1
1.1
1.5
1.8
0.9
25.6
16.7
8.9
1.2
0.1
5.4
1.3
0.4
+
2.9
0.8
0.5
+
NA
2008
18.6
10.6
8.0
6.5
5.6
0.8
1.3
1.6
1.9
0.8
25.2
16.5
8.7
1.2
0.1
5.4
1.3
0.4
+
2.9
0.8
0.5
+
NA
2009
18.8
10.8
8.0
6.3
5.5
0.8
1.2
1.6
1.9
0.8
25.1
16.5
8.6
1.1
0.1
5.2
1.3
0.4
+
2.7
0.7
0.5
+
NA
2010
18.9
10.6
8.2
6.2
5.4
0.8
1.2
1.6
1.8
0.8
25.3
16.6
8.7
1.1
0.1
5.2
1.3
0.4
+
2.8
0.7
0.5
+
NA
2011
19.1
10.8
8.3
6.3
5.5
0.8
1.2
1.6
1.9
0.8
25.8
16.8
9.0
1.1
0.1
5.2
1.3
0.4
+
2.8
0.7
0.5
+
NA
2012
19.4
11.0
8.4
6.4
5.6
0.8
1.3
1.6
1.9
0.8
25.6
16.6
9.0
1.1
0.1
5.1
1.3
0.4
+
2.7
0.7
0.5
+
NA
+ Emission estimate is less than 0.5 Gg.
Note: American bison are maintained entirely on unmanaged WMS; there are no American bison N20 emissions from managed systems.
A-282 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 214: Methane Emissions by State from Livestock Manure Management for 2012 tGgla
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Beef on
Feedlots
0.0336
0.0001
0.6397
0.0026
1.3823
1.7186
0.0006
0.0007
0.0187
0.0246
0.0048
0.3558
0.2545
0.1772
2.0677
3.9061
0.0180
0.0175
0.0018
0.0181
0.0007
0.2390
0.4858
0.0338
0.0878
0.0465
4.1448
0.0107
0.0004
0.0008
0.0406
0.0421
0.0149
0.0922
0.2825
0.8635
0.1238
Beef Not
on Feedb
2.4197
0.0212
1.0594
3.3507
4.0028
2.8226
0.0192
0.0112
3.3388
1.8624
0.3194
1.6026
0.9485
0.6055
3.0499
5.0245
2.5696
1.6358
0.0371
0.1329
0.0245
0.4683
1.3667
1.8562
4.5551
3.5868
7.0122
0.5581
0.0160
0.0235
1.1678
0.4686
0.9605
2.1982
0.8934
7.2239
1.4227
Dairy Cow
0.5926
0.0281
52.3809
0.3986
407.5697
28.3952
1.0254
0.3107
21.0759
7.4419
0.3757
121.2554
8.8046
14.2386
20.7412
21.7396
1.5901
0.7189
1.3859
2.6381
0.4907
46.7963
32.0661
0.6039
6.1014
1.7686
7.5325
6.6532
0.6541
0.2943
82.3978
31.8012
2.7022
1.2472
20.5114
9.6670
17.4305
Dairy
Heifer
0.0161
0.0003
0.1643
0.0133
2.1559
0.1119
0.0161
0.0059
0.1008
0.0841
0.0029
0.4488
0.0759
0.1118
0.2534
0.1402
0.0797
0.0167
0.0263
0.0493
0.0100
0.2481
0.4477
0.0190
0.0656
0.0119
0.0239
0.0150
0.0108
0.0070
0.1855
0.5337
0.0522
0.0156
0.1915
0.0459
0.1266
Swine-
Market
2.0727
0.0020
2.7052
0.6992
1.5979
4.8006
0.0040
0.0280
0.0581
2.2272
0.1056
0.1854
43.2643
35.9163
298.3233
21.5870
4.7269
0.0127
0.0096
0.1784
0.0428
8.8211
66.0579
5.8406
23.2430
1.1836
26.3869
0.0172
0.0111
0.0600
0.0003
0.5247
141.3090
0.7981
18.4559
35.0261
0.0458
Swine—
Breeding
0.5977
0.0010
0.7384
2.1303
0.1513
2.7184
0.0027
0.0191
0.0376
0.8186
0.0639
0.0941
10.7696
5.9036
31.2223
4.0160
1.1379
0.0099
0.0069
0.0565
0.0093
2.0014
10.9262
1.9556
7.8459
0.3580
7.7471
0.0042
0.0052
0.0160
0.0003
0.1025
32.2132
0.6028
3.3987
15.7762
0.0290
Layer
8.7998
0.1918
0.6810
0.5260
4.3435
3.5109
0.2534
0.0773
6.2130
14.7767
0.2996
0.6031
0.2349
0.8871
1.6782
0.0441
0.5523
2.1974
0.2850
0.2867
0.0120
0.7397
0.3392
7.6372
0.2893
0.3657
0.5646
0.0223
0.0705
0.0758
0.6441
0.5093
11.0142
0.0413
0.9321
3.4508
0.8411
Broiler
3.6453
+
+
3.5490
0.1939
+
+
0.7675
0.2161
4.9460
+
+
0.1932
0.1932
0.1932
+
1.1204
0.1939
+
1.1005
+
0.1932
0.1647
2.7289
0.9832
+
0.1932
+
+
+
+
0.1932
2.9047
+
0.2288
0.7693
0.1932
Turkey
0.0267
0.0266
0.0267
0.7252
0.3876
0.0266
0.0266
0.0266
0.0267
0.0267
0.0267
0.0266
0.0266
0.4113
0.0266
0.0266
0.0266
0.0267
0.0266
0.0266
0.0266
0.0266
1.1466
0.0267
0.4362
0.0266
0.0266
0.0266
0.0266
0.0266
0.0266
0.0266
0.9003
0.0266
0.1371
0.0267
0.0266
Sheep
0.0085
0.0057
0.0987
0.0085
0.4017
0.2161
0.0035
0.0057
0.0085
0.0085
0.0085
0.1128
0.0268
0.0258
0.0916
0.0329
0.0188
0.0085
0.0035
0.0057
0.0035
0.0371
0.0705
0.0085
0.0390
0.1057
0.0362
0.0329
0.0035
0.0057
0.0470
0.0291
0.0183
0.0343
0.0592
0.0493
0.0940
Goats
0.0302
0.0001
0.0158
0.0190
0.0490
0.0122
0.0011
0.0009
0.0216
0.0315
0.0034
0.0044
0.0084
0.0118
0.0140
0.0124
0.0245
0.0081
0.0015
0.0042
0.0021
0.0070
0.0092
0.0115
0.0241
0.0031
0.0086
0.0030
0.0010
0.0027
0.0089
0.0100
0.0369
0.0011
0.0174
0.0470
0.0095
Mules and American
Horses Asses Bison
0.3572
0.0057
0.2977
0.2814
0.7540
0.2869
0.0296
0.0098
0.4643
0.2609
0.0280
0.1420
0.2175
0.1400
0.1465
0.2461
0.4414
0.2407
0.0254
0.0779
0.0562
0.2132
0.1917
0.2098
0.3438
0.2543
0.1584
0.0453
0.0260
0.0731
0.1326
0.2075
0.3041
0.1009
0.2279
0.5946
0.1893
0.0203
0.0001
0.0039
0.0139
0.0133
0.0060
0.0005
0.0001
0.0122
0.0171
0.0005
0.0047
0.0053
0.0050
0.0047
0.0056
0.0146
0.0095
0.0005
0.0014
0.0013
0.0054
0.0046
0.0134
0.0145
0.0042
0.0031
0.0005
0.0009
0.0014
0.0022
0.0032
0.0167
0.0011
0.0072
0.0252
0.0050
0.0006
0.0038
0.0011
0.0016
0.0101
0.0412
0.0001
+
0.0047
0.0017
0.0006
+
0.0028
0.0033
0.0056
0.0210
0.0003
0.0005
0.0008
0.0024
0.0001
0.0059
0.0004
+
0.0038
0.0186
0.0468
0.0004
0.0001
0.0003
0.0074
0.0035
0.0013
0.0018
+
0.0202
0.0010
A-283
-------
State
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Beef on
Feedlots
0.1306
0.0001
0.0097
0.5241
0.0167
6.6601
0.0383
0.0019
0.0399
0.4017
0.0087
0.3756
0.1082
Beef Not
on Feedb
0.7368
0.0036
0.7102
4.2889
2.3424
19.1277
0.8478
0.0628
1.7150
0.7238
0.5604
1.1479
2.0454
Dairy Cow
17.4491
0.0358
1.2038
11.6592
1.4279
102.5671
16.9664
6.0471
2.9815
45.8262
0.3619
93.7919
0.8400
Dairy
Heifer
0.5388
0.0009
0.0161
0.0552
0.0533
0.4834
0.0793
0.0889
0.0696
0.2024
0.0086
1.0791
0.0074
Swine-
Market
10.7082
0.0039
4.8058
10.5354
2.2084
14.0303
5.6764
0.0131
3.9370
0.1247
0.0235
2.2459
0.2272
Swine—
Breeding
2.0211
0.0046
0.3713
3.4490
0.4569
3.0580
1.4043
0.0045
0.1651
0.0626
0.0185
0.8086
0.3644
Layer
0.7737
0.0741
4.9170
0.1377
0.2311
4.5769
3.2231
0.0183
0.3398
1.2451
0.1649
0.3269
0.0087
Broiler
0.5593
+
0.8093
+
0.6067
2.1884
+
+
0.8706
0.1932
0.3403
0.1872
+
Turkey
0.1745
0.0266
0.3001
0.1147
0.0266
0.0267
0.1022
0.0266
0.4237
0.0266
0.0823
0.0266
0.0266
Sheep
0.0418
0.0035
0.0085
0.1339
0.0164
0.4722
0.1433
0.0035
0.0395
0.0244
0.0155
0.0395
0.1739
Goats
0.0148
0.0002
0.0163
0.0027
0.0327
0.4273
0.0043
0.0016
0.0158
0.0082
0.0070
0.0140
0.0021
Mules and American
Horses Asses Bison
0.2612
0.0110
0.1506
0.1553
0.2965
1.6602
0.1275
0.0336
0.2177
0.2268
0.0955
0.3025
0.2147
0.0119
0.0001
0.0089
0.0021
0.0227
0.1112
0.0025
0.0011
0.0082
0.0046
0.0032
0.0070
0.0028
0.0042
+
0.0004
0.0721
0.0017
0.0194
0.0045
0.0003
0.0021
0.0029
0.0005
0.0073
0.0277
+ Emission estimate is less than 0.00005 Gg.
a Accounts for CHU reductions due to capture and destruction of CHU at facilities using anaerobic digesters.
b Beef Not on Feed includes calves.
A-284 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 215: Nitrous Oxide Emissions by State from Livestock Manure Management for 2012 [Ggl
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
Beef
Feedlot-
Heifer
0.0078
+
0.1737
0.0007
0.3213
0.7310
0.0002
0.0002
0.0040
0.0057
0.0010
0.1534
0.1017
0.0711
0.8372
1.5100
0.0064
0.0038
0.0007
0.0064
0.0002
0.0981
0.1984
0.0077
0.0342
0.0200
1.6696
0.0046
0.0002
0.0003
0.0168
0.0160
0.0053
0.0381
0.1130
0.2406
0.0456
0.0480
+
0.0023
Beef
Feedlot-
Steers
0.0144
0.0001
0.3229
0.0012
0.5971
1.3583
0.0004
0.0004
0.0074
0.0106
0.0019
0.2845
0.1887
0.1315
1.5524
2.8022
0.0119
0.0071
0.0013
0.0119
0.0005
0.1817
0.3680
0.0143
0.0637
0.0371
3.1011
0.0085
0.0003
0.0005
0.0312
0.0297
0.0099
0.0708
0.2099
0.4466
0.0845
0.0891
0.0001
0.0042
Dairy Cow
0.0041
0.0006
0.2326
0.0036
2.1362
0.1780
0.0148
0.0039
0.1001
0.0486
0.0019
0.7848
0.1418
0.2426
0.2943
0.1780
0.0297
0.0062
0.0240
0.0403
0.0092
0.5678
0.6451
0.0057
0.1082
0.0189
0.0815
0.0391
0.0109
0.0056
0.4155
0.4926
0.0267
0.0233
0.3585
0.0627
0.1379
0.3982
0.0008
0.0080
Dairy
Heifer
0.0053
0.0004
0.1465
0.0033
1.7345
0.1711
0.0114
0.0039
0.0501
0.0305
0.0023
0.6894
0.0952
0.1281
0.3101
0.1816
0.0257
0.0036
0.0180
0.0321
0.0066
0.3330
0.5568
0.0046
0.0718
0.0173
0.0315
0.0231
0.0075
0.0044
0.2511
0.3598
0.0150
0.0192
0.2187
0.0434
0.1400
0.3258
0.0005
0.0043
Swine-
Market
0.0104
+
0.0125
0.0038
0.0089
0.0507
+
0.0002
0.0003
0.0111
0.0006
0.0021
0.3919
0.3354
1.8140
0.1754
0.0268
0.0001
0.0001
0.0015
0.0004
0.0905
0.6568
0.0291
0.2076
0.0139
0.2539
0.0002
0.0001
0.0005
+
0.0053
0.7058
0.0086
0.1735
0.1695
0.0005
0.0975
+
0.0234
Swine-
Breeding
0.0022
+
0.0026
0.0085
0.0006
0.0212
+
0.0001
0.0001
0.0030
0.0002
0.0008
0.0713
0.0403
0.1390
0.0242
0.0047
+
0.0001
0.0003
0.0001
0.0153
0.0801
0.0070
0.0510
0.0031
0.0548
+
+
0.0001
+
0.0008
0.1187
0.0048
0.0234
0.0557
0.0002
0.0138
+
0.0013
Layer
0.0628
0.0033
0.0035
0.0743
0.0906
0.0210
0.0108
0.0031
0.0414
0.1042
0.0033
0.0035
0.0168
0.1232
0.2330
0.0031
0.0229
0.0112
0.0129
0.0117
0.0005
0.0547
0.0471
0.0397
0.0403
0.0022
0.0409
0.0031
0.0031
0.0031
0.0035
0.0220
0.0790
0.0031
0.1293
0.0178
0.0109
0.1075
0.0031
0.0252
Broiler
0.3225
+
+
0.3140
0.0172
+
+
0.0681
0.0191
0.4376
+
+
0.0172
0.0172
0.0172
+
0.0995
0.0172
+
0.0977
+
0.0172
0.0146
0.2414
0.0873
+
0.0172
+
+
+
+
0.0172
0.2570
+
0.0203
0.0681
0.0172
0.0496
+
0.0716
Turkey
0.0031
0.0031
0.0031
0.0840
0.0449
0.0031
0.0031
0.0031
0.0031
0.0031
0.0031
0.0031
0.0031
0.0478
0.0031
0.0031
0.0031
0.0031
0.0031
0.0031
0.0031
0.0031
0.1333
0.0031
0.0507
0.0031
0.0031
0.0031
0.0031
0.0031
0.0031
0.0031
0.1043
0.0031
0.0159
0.0031
0.0031
0.0203
0.0031
0.0348
Sheep
0.0046
0.0015
0.0154
0.0040
0.0710
0.0507
0.0029
0.0046
0.0046
0.0046
0.0015
0.0265
0.0187
0.0180
0.0640
0.0230
0.0152
0.0040
0.0029
0.0046
0.0029
0.0259
0.0492
0.0046
0.0272
0.0248
0.0253
0.0077
0.0029
0.0046
0.0110
0.0236
0.0099
0.0239
0.0477
0.0229
0.0249
0.0339
0.0029
0.0046
Goats
0.0024
+
0.0012
0.0015
0.0039
0.0014
0.0001
0.0001
0.0017
0.0025
0.0003
0.0005
0.0010
0.0014
0.0017
0.0015
0.0029
0.0006
0.0002
0.0005
0.0002
0.0008
0.0011
0.0009
0.0029
0.0004
0.0010
0.0004
0.0001
0.0003
0.0011
0.0012
0.0029
0.0001
0.0021
0.0037
0.0011
0.0018
+
0.0013
Mules and
Horses Asses
0.0123
0.0003
0.0102
0.0097
0.0259
0.0148
0.0015
0.0005
0.0160
0.0090
0.0010
0.0073
0.0112
0.0072
0.0075
0.0127
0.0228
0.0083
0.0013
0.0040
0.0029
0.0110
0.0099
0.0072
0.0177
0.0131
0.0082
0.0023
0.0013
0.0038
0.0068
0.0107
0.0104
0.0052
0.0117
0.0204
0.0098
0.0135
0.0006
0.0052
0.0007
+
0.0001
0.0005
0.0005
0.0003
+
+
0.0004
0.0006
+
0.0003
0.0003
0.0003
0.0003
0.0003
0.0008
0.0003
+
0.0001
0.0001
0.0003
0.0002
0.0005
0.0008
0.0002
0.0002
+
+
0.0001
0.0001
0.0002
0.0006
0.0001
0.0004
0.0009
0.0003
0.0006
+
0.0003
A-285
-------
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
0.2145
0.0059
1.8540
0.0163
0.0007
0.0143
0.1478
0.0032
0.1539
0.0462
0.3967
0.0109
3.4433
0.0303
0.0014
0.0266
0.2748
0.0059
0.2855
0.0860
0.1300
0.0209
0.5287
0.1216
0.1029
0.0432
0.3294
0.0069
1.7964
0.0075
0.0711
0.0185
0.4487
0.1203
0.0617
0.0260
0.2340
0.0056
1.3101
0.0094
0.1031
0.0126
0.0751
0.0584
0.0001
0.0212
0.0014
0.0002
0.0222
0.0041
0.0248
0.0019
0.0120
0.0113
+
0.0007
0.0005
0.0001
0.0059
0.0048
0.0101
0.0097
0.0919
0.0188
0.0008
0.0142
0.0298
0.0071
0.0242
0.0001
+
0.0539
0.1936
+
+
0.0773
0.0172
0.0302
0.0166
+
0.0133
0.0031
0.0031
0.0119
0.0031
0.0493
0.0031
0.0096
0.0031
0.0031
0.0935
0.0133
0.0739
0.0336
0.0029
0.0320
0.0065
0.0126
0.0276
0.0408
0.0003
0.0039
0.0337
0.0005
0.0002
0.0019
0.0010
0.0008
0.0017
0.0002
0.0080
0.0153
0.0570
0.0066
0.0017
0.0112
0.0117
0.0049
0.0156
0.0111
0.0001
0.0012
0.0039
0.0001
0.0001
0.0004
0.0002
0.0002
0.0004
0.0001
H Emission estimate is less than 0.00005 Gg.
A-286 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
3.12. Methodology for Estimating N20 Emissions and Soil Organic C Stock Changes
from Agricultural Soil Management (Cropland and Grassland)
Nitrous oxide (N2O) emissions from agricultural soils result from the interaction of the natural processes of
denitrification and nitrification with management practices that add or release mineral nitrogen (N) in the soil
profile. Emissions can occur directly in the soil where the N is made available or can be transported to another
location following volatilization, leaching, or runoff, and then converted into N2O. Management also influences soil
organic C stocks in agricultural soils by modifying the natural processes of photosynthesis (i.e., crop and forage
production) and microbial decomposition. This sub-annex describes the methodologies used to calculate N2O
emissions from agricultural soil management74 and annual carbon (C) stock changes from mineral and organic soils
classified as Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and
Land Converted to Grassland.75 This annex provides the underlying methodologies for both N2O emissions from
agricultural soil management and soil organic C stock change from mineral and organic soils. There is considerable
overlap in the methods and data sets used for these source categories, and the majority of emission are estimated
with the same inventory analysis using the DAYCENT biogeochemical simulation model.
A combination of Tier 1, 2 and 3 approaches is used to estimate direct and indirect N2O emissions, in
addition to C stock changes in agricultural soils. More specifically, the methodologies used to estimate soil N2O
emissions include:
1) Tier 3 method to estimate direct emissions from mineral soils for alfalfa hay, barley, corn, cotton, dry
beans, grass hay, grass-clover hay, oats, onions, peanuts, potatoes, rice, sorghum, soybeans, sugar beets,
sunflowers, tomatoes, and wheat, as well as non-federal grasslands and land use change between cropland
(with the crops listed above) and grassland, using the DAYCENT biogeochemical simulation model;
2) Tier 1 method to estimate direct N2O emissions from mineral soils for some vegetables, tobacco,
perennial/horticultural crops (i.e., all crops that are not listed in Item 1 above), and crops that are rotated
with these crops, in addition to federal grasslands (i.e., not simulated by DAYCENT);
3) Tier 1 method to estimate direct N2O emissions due to drainage of organic soils in croplands and
grasslands;
4) A combination of the Tier 3 and 1 methods to estimate indirect N2O emissions associated with management
of Tier 3 crops (i.e., simulated by DAYCENT; see list above), non-federal grasslands and land use change
between cropland and grassland; and
5) Tier 1 method to estimate indirect emissions from some vegetables, tobacco, perennial/horticultural crops,
and crops that are rotated with these crops, in addition to federal grasslands and all other land uses.
The methodologies used to estimate soil organic C stock changes include:
1) Tier 3 method to estimate soil organic C stock changes in mineral soils for crops (See crop list in Item 1 for
N2O emissions), non-federal grasslands and land use change between grasslands and croplands, using the
DAYCENT biogeochemical simulation model;
2) Tier 2 methods with country-specific stock change factors for estimating mineral soil organic C stock
changes for crop rotations and land use changes to cropland and grassland (other than the conversions
between cropland and grassland) that were not simulated with DAYCENT;
3) Tier 2 methods with country-specific emission factors for estimating losses of C from organic soils that are
partly or completely drained for agricultural production; and
4) Tier 2 methods for estimating additional changes in mineral soil C stocks due to sewage sludge additions to
soils and enrollment changes in the Conservation Reserve Program (CRP) after 2007.
^4 Direct Soil N2O methods from forestlands and settlements are described elsewhere in Forestland Remaining Forestland and
Settlements Remaining Settlements.
75 Soil C stock change methods for forestland are described elsewhere in the Forestland Remaining Forestland section.
A-287
-------
As described above, the Inventory uses a Tier 3 approach to estimate direct soil N2O emissions and C stock
changes for the majority of agricultural lands. This approach has several advantages over the IPCC Tier 1 or 2
approaches:
• It utilizes actual weather data at sub-county scales, rather than a broad climate region classification,
enabling quantification of inter-annual variability in N2O emissions and C stock changes at finer spatial
scales;
• The model uses a more detailed characterization of spatially-mapped soil properties that influence soil C
and N dynamics, as opposed to the broad soil taxonomic classifications of the IPCC methodology;
• The simulation approach provides a more detailed representation of management influences and their
interactions than are represented by a discrete factor-based approach in the Tier 1 and 2 methods; and
• Soil N2O emissions and C stock changes are estimated on a more continuous, daily basis as a function of
the interaction of climate, soil, and land management, compared with the linear rate changes that are
estimated with the Tier 1 and 2 methods.
The DAYCENT process-based simulation model (daily time-step version of the Century model) has been
selected for the Tier 3 approach based on several criteria:
• The model has been developed in the United States and extensively tested and verified for U.S. conditions
(e.g., Parton et al. 1987, 1993). In addition, the model has been widely used by researchers and agencies in
many other parts of the world for simulating soil C dynamics at local, regional and national scales (e.g.,
Brazil, Canada, India, Jordan, Kenya, Mexico), and soil N2O emissions (e.g., Canada, China, Ireland, New
Zealand) (Abdalla et al. 2010, Li et al., Smith et al. 2008, Stehfest and Muller 2004).
• The model is capable of simulating cropland, grassland, forest, and savanna ecosystems, and land-use
transitions between these different land uses. It is, thus, well suited to model land-use change effects.
• The model is designed to simulate management practices that influence soil C dynamics and direct N2O
emissions, with the exception of cultivated organic soils; cobbly, gravelly, or shaley soils; and a few crops
that have not been parameterized for DAYCENT simulations (e.g., some vegetables, tobacco,
perennial/horticultural crops, and crops that are rotated with these crops). For these latter cases, an IPCC
Tier 2 method has been used for soil C stock changes and IPCC Tier 1 method for N2O emissions. The
model can also be used estimate the amount of N leaching and runoff, as well as volatilization of N, which
is subject to indirect N2O emissions.
• Much of the data needed for the model is available from existing national databases. The exceptions are
CRP enrollment after 2007, management of federal grasslands, and sewage sludge amendments to soils,
which are not known at a sufficient resolution to use the Tier 3 model. Soil N2O emissions and C stock
changes associated with these practices are addressed with a Tier 1 and 2 method, respectively.
Overall, the Tier 3 approach is used to estimate approximately 82 to 88 percent of direct soil N2O emissions
and 85 to 87 percent of the land area associated with estimation of soil organic C stock changes under agricultural
management in the United States.
Tier 3 Method Description and Model Evaluation
The DAYCENT biogeochemical model (Parton et al. 1998; Del Grosso et al. 2001, 2011) simulates
biogeochemical C and N fluxes between the atmosphere, vegetation, and soil; and provides a more complete
estimation of soil C stock changes and N2O emissions than IPCC Tier 1/2 methods by more thoroughly accounting
for the influence of environmental conditions including soil characteristics and weather patterns, specific crop and
forage qualities that influence the C and N cycle, and management practices. The DAYCENT model utilizes the
soil C modeling framework developed in the Century model (Parton et al. 1987, 1988, 1994; Metherell et al. 1993),
but has been refined to simulate dynamics at a daily time-step. Carbon and N dynamics are linked in plant-soil
systems through biogeochemical processes of microbial decomposition and plant production (McGill and Cole
1981). Coupling the two source categories (i.e., agricultural soil C and N2O) in a single inventory analysis ensures
that there is a consistent treatment of the processes and interactions between C and N cycling in soils. For example,
plant growth is controlled by nutrient availability, water, and temperature stress. Plant growth along with the
residue management determines the C inputs to soils, which influence the C stock changes, and removal of mineral
N from the soil with plant growth influences the amount of N that can be converted into N2O. Nutrient supply is a
function of external nutrient additions as well as litter and soil organic matter (SOM) decomposition rates, and
A-288 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
increasing decomposition can lead to a reduction in soil organic C stocks due to microbial respiration, and greater
N2O emissions by enhancing mineral N availability in soils.
Key processes simulated by DAYCENT include plant production, organic matter formation and
decomposition, soil water and temperature regimes by layer, in addition to nitrification and denitrification processes
(Figure A-7). The plant-growth submodel simulates C assimilation through photosynthesis; N uptake; dry matter
production; partitioning of C within the crop or forage; senescence; and mortality. The primary function of the
growth submodel is to estimate the amount, type, and timing of organic matter inputs to soil, and to represent the
influence of the plant on soil water, temperature, and N balance. Yield and removal of harvested biomass are also
simulated. Separate submodels are designed to simulate herbaceous plants (i.e., agricultural crops and grasses) and
woody vegetation (i.e., trees and scrub). Maximum daily net primary production (NPP) is estimated using the
NASA-CASA production algorithm (Potter et all993, 2007) and MODIS Enhanced Vegetation Index (EVI)
products, MOD13Q1 and MYD13Q1, or an approximation of EVI data derived from the MODIS products (Gurung
et al. 2009). The NASA-CASA production algorithm is only used in the central United States for the following
major crops: corn, soybeans, sorghum, cotton and wheat.76 Other regions and crops are simulated with a single
value for the maximum daily net primary production (NPP), instead of the more dynamic NASA-CASA algorithm.
The maximum daily NPP rate is modified by air temperature and available water to capture temperature and
moisture stress. If the NASA-CASA algorithm is not used in the simulation, then production is further subject to
nutrient limitations (i.e., nitrogen). Model evaluation has shown that the NASA-CASA algorithm improves the
precision of NPP estimates using the EVI products to inform the production model. The r2 is 83 percent for the
NASA-CASA algorithm and 64 percent for the single parameter value approach).
Figure A-7: DAYGENT Model Flow Diagram
^^ Plant
Aboveground II Production
Submodel
EVI/PRDX
Dissolved Organic C, Dissolved Organic N, Mineral N
76 It is a planned improvement to estimate NPP for additional crops and grass forage with the NASA-CASA method
in the future.
A-289
-------
Figure A-8: Modeled versus measured net primary production (g G m2)
0 100 200 300 400 500 600 700 800
Yield Carbon from Published Data (g m )
700 !
600 •
500 -
400 '
300 •
200 :
100 i
0 ;
. •,
2* * '- m
* • «-• .
".*V" • • *
0 100 200 300 400 500 600 700 800
Yield Carbon from Published Data (g m )
Part a) presents results of fie NASA-CASA algorithm (r2 = 83°/§ and part b) presents the results of a single parameter
value for maximum net primary production (r2 =
The soil-water balance submodel calculates water balance components and changes in soil water
availability, which influences both plant growth and decomposition/nutrient cycling processes. The moisture
content of soils are simulated through a multi-layer profile based on precipitation, snow accumulation and melting,
interception, soil and canopy evaporation, transpiration, soil water movement, runoff, and drainage.
Dynamics of soil organic C and N (Figure A-7) are simulated for the surface and belowground litter pools
and soil organic matter in the top 20 cm of the soil profile; mineral N dynamics are simulated through the whole soil
profile. Organic C and N stocks are represented by two plant litter pools (metabolic and structural) and three soil
organic matter (SOM) pools (active, slow, and passive). The metabolic litter pool represents the easily
decomposable constituents of plant residues, while the structural litter pool is composed of more recalcitrant, ligno-
cellulose plant materials. The three SOM pools represent a gradient in decomposability, from active SOM
(representing microbial biomass and associated metabolites) having a rapid turnover (months to years), to passive
SOM (representing highly processed, humified, condensed decomposition products), which is highly recalcitrant,
with mean residence times on the order of several hundred years. The slow pool represents decomposition products
of intermediate stability, having a mean residence time on the order of decades and is the fraction that tends to
change the most in response to changes in land use and management. Soil texture influences turnover rates of the
slow and passive pools. The clay and silt-sized mineral fraction of the soil provides physical protection from
microbial decomposition, leading to enhanced SOM stabilization in finely textured soils. Soil temperature and
moisture, tillage disturbance, aeration, and other factors influence decomposition and loss of C from the soil organic
matter pools.
Soil mineral N dynamics are modeled based on N inputs from fertilizer inputs (synthetic and organic),
residue N inputs, soil organic matter mineralization, symbiotic and asymbiotic N fixation. Mineral N is available for
plant and microbial uptake, and is largely controlled by the specified stoichiometric limits for these organisms (i.e.,
C:N ratios). Mineral and organic N losses are simulated with leaching and runoff, and nitrogen can be volatilized
and lost from the soil during a variety of processes including nitrification and denitrification. N2O emissions from
A-290 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
denitrification are a function of soil NOs" concentration, water filled pore space (WFPS), heterotrophic (i.e.,
microbial) respiration, and texture. Nitrification is controlled by soil ammonium (NH4+) concentration, water filled
pore space, temperature, and pH (See Box 2 for more information).
The final main component of the model is the management submodel, which includes options for
specifying crop type, crop sequence (e.g., rotation), tillage, fertilization, organic matter addition (e.g., manure
amendments), harvest (with variable residue removal), drainage, irrigation, burning, and grazing intensity. An input
"schedule" file is used to simulate the timing of management activities and temporal trends; schedules can be
organized into discrete time blocks to define a repeated sequence of events (e.g., a crop rotation or a frequency of
disturbance such as a burning cycle for perennial grassland). Management options can be specified for any day of a
year within a scheduling block, where management codes point to operation-specific parameter files (referred to as
*.100 files), which contain the information used to simulate management effects with the model algorithms. User-
specified management activities can be defined by adding to or editing the contents of the *. 100 files. Additional
details of the model formulation are given in Partonetal. (1987, 1988, 1994, 1998), Del Grosso et al. (2001,2011)
and Metherell et al. (1993), and archived copies of the model source code are available.
[BEGIN TEXT BOX]
Box 2. DAYCENT Model Simulation of Nitrification and Denitrification
The DAYCENT model simulates the two biogeochemical processes, nitrification and denitrification, that
result in N2O emissions from soils (Del Grosso et al. 2000, Parton et al. 2001). Nitrification is calculated for the top
15 cm of soil, where nitrification mostly occurs, while denitrification is calculated for the entire soil profile
accounting for denitrification near the surface and subsurface as nitrate leaches through the profile. The equations
and key parameters controlling N2O emissions from nitrification and denitrification are described below.
Nitrification is controlled by soil ammonium (NH4+) concentration, WFPS, temperature (t), and pH
according to the following equation:
Nit = NH4 x Kmax x F(t) x F(WFPS) x F(pH)
where,
Nit = the soil nitrification rate (g N/m2/day)
NH4 = the model-derived soil ammonium concentration (g N/m2)
Kmax = the maximum fraction of NH4+nitrified (Kmax = 0.10/day)
F(t) = the effect of soil temperature on nitrification (Figure A-9a)
F(WFPS) = the effect of soil water content and soil texture on nitrification (Figure A-9b)
F(pH) = the effect of soil pH on nitrification (Figure A-9c)
The current parameterization used in the model assumes that 1.2 percent of nitrified N is converted to N2O.
The model assumes that denitrification rates are controlled by the availability of soil NOs" (electron
acceptor), labile C compounds (electron donor) and oxygen (competing electron acceptor). Heterotrophic soil
respiration is used as a proxy for labile C availability, while oxygen availability is a function of soil physical
properties that influence gas diffusivity, soil WFPS, and oxygen demand. The model selects the minimum of the
NOs" and CO2 functions to establish a maximum potential denitrification rate. These rates vary for particular levels
of electron acceptor and C substrate, and account for limitations of oxygen availability to estimate daily
denitrification rates according to the following equation:
Den = min[F(CO2), F(NO3)] x F(WFPS)
where,
Den = the soil denitrification rate (|ag N/g soil/day)
A-291
-------
F(CO2) = a function relating N gas flux to soil respiration (Figure A-lOa)
F(NOs) = a function relating N gas flux to nitrate levels (Figure A-lOb)
F(WFPS) = a dimensionless multiplier (Figure A-lOc).
The x inflection point of F(WFPS) is a function of respiration and soil gas diffusivity at field capacity
(DEC):
x inflection = 0.90 - M(CO2)
where,
M = a multiplier that is a function of DFc. In technical terms, the inflection point is the domain
where either F(WFPS) is not differentiable or its derivative is 0. In this case, the inflection point can be
interpreted as the WFPS value at which denitrification reaches half of its maximum rate.
Respiration has a much stronger effect on the water curve in clay soils with low DFc than in loam or sandy
soils with high DFC (Figure A-lOc). The model assumes that microsites in fine-textured soils can become anaerobic
at relatively low water contents when oxygen demand is high.
After calculating total N gas flux, the ratio of N2/N2O is estimated so that total N gas emissions can be
partitioned between N2O and N2:
RN2/N2o = Fr(N03/C02) x Fr(WFPS).
where,
RN2/N2o = the ratio of N2/N2O
Fr(NO3/CO2) = a function estimating the impact of the availability of electron donor relative to
substrate
Fr(WFPS) = a multiplier to account for the effect of soil water on N2:N2O.
For Fr(NO3/CO2), as the ratio of electron donor to substrate increases, a higher portion of N gas is assumed
to be in the form of N2O. For Fr(WFPS), as WFPS increases, a higher portion of N gas is assumed to be in the form
ofN2.
[End Box]
A-292 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure A-9: Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates
1.2-
1 -
0.8-
I 0.6-
0.4-
0.2-
0
Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates
10
20
30
Soil Temperature
40
SO
0.2
0.4
0.6
WFPS
6 7
pH
fine
—I
60
A-293
-------
Figure A-10: Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on
Denitrification Rates
Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on Denitrification Rates
o
z
J
o
en
—
2.
q,
z
35-
30-
25-
20-
15-
10-
5-
0
200
NQ pg N/g soil
—i—
300
400
CO; pg C/g soil/day
Comparison of model results and plot level data show that DAYCENT reliably simulates soil organic
matter levels (Ogle et al. 2007). The model was tested and shown to capture the general trends in C storage across
approximately 870 field plots from 47 experimental sites (Figure A-ll). Some biases and imprecision occur in
predictions of soil organic C, which is reflected in the uncertainty associated with DAYCENT model results.
Regardless, the Tier 3 approach has considerably less uncertainty than Tier 1 and 2 methods (Del Grosso et al., 201;
Figure A-ll).
Similarly, DAYCENT model results have been compared to trace gas N2O fluxes for a number of native
and managed systems (Del Grosso et al. 2001, 2005, 2010) (Figure A-12). In general, the model simulates accurate
A-294 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
emissions, but some bias and imprecision does occur in predictions, which is reflected in the uncertainty associated
with DAYCENT model results. Comparisons with measured data showed that DAYCENT estimated N2O emissions
more accurately and precisely than the IPCC Tier 1 methodology (See Figure 6-7: Comparison of Measured
Emissions at Field Sites and Modeled Emissions Using the DAYCENT Simulation Model and IPCC Tier 1
Approach in the main chapter text). The linear regression of simulated vs. measured emissions for DAYCENT had
higher r2 and a fitted line closer to a perfect 1:1 relationship between measured and modeled N2O emissions (Del
Grosso et al. 2005, 2008). This is not surprising, since DAYCENT includes site-specific factors (climate, soil
properties, and previous management) that influence N2O emissions. Furthermore, DAYCENT also simulated NOs-
leaching (root mean square error = 20 percent) more accurately than IPCC Tier 1 methodology (root mean square
error = 69 percent) (Del Grosso et al. 2005). Volatilization of N gases that contribute to indirect soil N2O emissions
is the only component that has not been thoroughly tested, which is due to a lack of measurement data. Thus, the
Tier 3 approach has reduced uncertainties in the agricultural soil C stock changes and N2O emissions compared to
using lower Tier methods.
A-295
-------
Figure A-11: Comparisons of Results from DAYGENT Model and Measurements of Soil Organic G Stocks
1.5
U
O
to
"D ^
IS u
1.5
Set-Aside
Hay/Pasture in Rotation
Other Cropland
Grassland
y=x .••
No-till
Fallow in Rotation
Organic Matter Additions
0.5
1.5
Sqrt Modeled SOC
(MgCha"1)
A-296 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure A-12: Comparison of Estimated Soil Organic G Stock Changes and Uncertainties using Tier 1 (IPGG 2006), Tier 2
(Ogle et al. 2003,2006) and Tier 3 Methods
T 80
t_
CM
O
O
en 60
^
(!)
O)
C
JS 40
O
O
o
O
O
20
Tier!
Tier 2
Tier 3
Source: Tier 1 (IPCC 2007), Tier 2 (Ogle et al. 2003, 2006), Tier 3 (Ogle et al. 2010).
Inventory Compilation Steps
There are five steps in estimating soil organic C stock changes for Cropland Remaining Cropland, Land
Converted to Cropland, Grassland Remaining Grassland and Land Converted to Grassland', direct N2O emissions
from cropland and grassland soils; and indirect N2O emissions from volatilization, leaching, and runoff from all
managed lands (i.e., croplands, grasslands, forest lands, and settlements). First, the activity data are derived from a
combination of land-use, livestock, crop, and grassland management surveys, as well as expert knowledge. In the
second, third, and fourth steps, soil organic C stock changes, direct and indirect N2O emissions are estimated using
DAYCENT and/or the Tier 1 and 2 methods. In the fifth step, total emissions are computed by summing all
components separately for soil organic C stock changes and N2O emissions. The remainder of this annex describes
the methods underlying each step.
Step 1: Derive Activity Data
The following describes how the activity data are derived to estimate soil organic C stock changes, in
addition to direct and indirect N2O emissions. The activity data requirements include: (1) land base and history data,
(2) crop-specific mineral N fertilizer rates,77 (3) crop-specific manure amendment N rates and timing, (4) other N
inputs, (5) tillage practices, (6) irrigation data, (7) Enhanced Vegetation Index (EVI), (8) daily weather data, and (9)
edaphic characteristics.
Step 1a: Activity Data for the Agricultural Land Base and Histories
The U.S. Department of Agriculture's National Resources Inventory (NRI) (USDA-NRCS 2009) provides
the basis for identifying the U.S. agricultural land base on non-federal lands, and classifying parcels into Cropland
Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to
Grassland. Note that the Inventory does not include estimates of C stock changes and N2O emissions for federal
grasslands (with the exception of soil N2O from PRP manure N, i.e., manure deposited directly onto pasture, range
or paddock by grazing livestock) and a minor amount of croplands on federal lands, even though these areas are part
No data are currently available at the national scale to distinguish the type of fertilizer applied or timing of applications rates.
It is a planned improvement to address variation in these practices in future inventories.
A-297
-------
of the managed land base for the United States. Methods are under development for estimating greenhouse gas
emissions from soils on federal croplands and grasslands, and will be included in future inventories.
Figure A-13: Comparisons of Results from DAYGENT Model and Measurements of Soil Nitrous Oxide Emissions
LLJ
o
T3
(U
O^
z^
00
3 -
2 -
1 -
0 -
-1 -
ra -2 •
-a
-3
5 -
4 -
3 -
-2 -
-3
Cropland
Grassland
-3
-2
-1
Ln Modeled N20 Emissions
(g N2O-N ha'1 day"1)
The NRI has a stratified multi-stage sampling design, where primary sample units are stratified on the basis
of county and township boundaries defined by the U.S. Public Land Survey (Nusser and Goebel 1997). Within a
primary sample unit, typically a 160-acre (64.75 ha) square quarter-section, three sample points are selected
according to a restricted randomization procedure. Each point in the survey is assigned an area weight (expansion
factor) based on other known areas and land-use information (Nusser and Goebel 1997). In principle, the expansion
factors represent the amount of area with the land use and land use change history that is the same as the point
location. It is important to note that the NRI uses a sampling approach, and therefore there is some uncertainty
associated with scaling the point data to a region or the country using the expansion factors. In general, those
uncertainties decline at larger scales, such as states compared to smaller county units, because of a larger sample
size. An extensive amount of soils, land-use, and land management data have been collected through the survey
A-298 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
(Nusser et al. 1998).78 Primary sources for data include aerial photography and remote sensing imagery as well as
field visits and county office records.
The annual NRI data product provides crop data for most years between 1979 and 2007, with the exception
of 1983, 1988, and 1993. These years are gap-filled using an automated set of rules so that cropping sequences are
filled with the most likely crop type given the historical cropping pattern at each NRI point location. Grassland data
are reported on 5-year increments prior to 1998, but it is assumed that the land use is also grassland between the
years of data collection (see Easter et al. 2008 for more information).
NRI points are included in the land base for the agricultural soil C and N2O emissions inventories if they
are identified as cropland or grassland79 between 1990 and 2007 (Table A-216).80 The NRI data are reconciled with
the Forest Inventory and Analysis Dataset, and in this process, the time series for Grassland Remaining Grassland
and Land Converted to Grassland is modified to account for differences in forest land area between the two national
surveys (See Section 7.1 for more information on the U.S. land representation). Overall, 529,687 NRI survey points
are included in the inventory.
For each year, land parcels are subdivided into Cropland Remaining Cropland, Land Converted to Cropland,
Grassland Remaining Grassland, and Land Converted to Grassland. Land parcels under cropping management in a
specific year are classified as Cropland Remaining Cropland if the parcel is cropland for at least 20 years. Similarly
land parcels under grassland management in a specific year of the inventory are classified as Grassland Remaining
Grassland if they are designated as grassland for at least 20 years.81 Otherwise, land parcels are classified as Land
Converted to Cropland or Land Converted to Grassland based on the most recent use in the inventory time period.
Lands are retained in the land-use change categories (i.e., Land Converted to Cropland and Land Converted to
Grassland) for 20 years as recommended by the IPCC guidelines (IPCC 2006). Lands converted into Cropland and
Grassland are further subdivided into the specific land use conversions (e.g., Forest Land Converted to Cropland).
Table A-216: Total Land Areas for the Agricultural Soil G and H?0 Inventory, Subdivided by Land Use Categories (Million
Hectares)
Category
1990 1991
1992
Land Areas (million ha)
1993 1994 1995 1996
1997 1998 1999 2000
Mineral Soils
349.74 349.63 349.49 344.46 344.20 344.25 344.34 344.45 338.83 339.00 339.11
Croplands
Cropland Remaining Cropland
Grassland Converted to Cropland
Forest Converted to Cropland
Other Lands Converted to Cropland
Settlements Converted to Croplands
Wetlands Converted to Croplands
Grasslands
152.87 152.66 152.41 148.11 146.95 146.62 146.26 145.87 141.61 141.65 141.67
12.82
0.62
0.11
0.24
0.08
13.12
0.62
0.11
0.24
0.08
13.38
0.62
0.11
0.24
0.08
15.19
1.24
0.25
0.66
0.22
16.75
1.24
0.25
0.66
0.22
16.97
1.24
0.25
0.66
0.22
17.31
1.24
0.25
0.66
0.22
17.46
1.24
0.25
0.66
0.22
18.08
0.41
0.13
0.33
0.10
17.64
0.41
0.13
0.33
0.10
17.25
0.41
0.13
0.33
0.10
Grasslands Remaining Grasslands
Croplands Converted to Grasslands
Forest Converted to Grasslands
Other Lands Converted to Grasslands
Settlements Converted to Grasslands
Wetlands Converted to Grasslands
Organic Soils
174.02
7.29
1.15
0.25
0.08
0.22
1.20
173.75
7.35
1.15
0.25
0.08
0.22
1.19
173.48
7.47
1.15
0.25
0.08
0.22
1.18
167.45
8.84
1.72
0.40
0.14
0.25
1.16
166.20
9.42
1.72
0.40
0.14
0.25
1.17
166.12
9.67
1.72
0.40
0.14
0.25
1.16
166.05
9.86
1.72
0.40
0.14
0.25
1.15
166.01
10.23
1.72
0.40
0.14
0.25
1.14
163.07
12.32
1.79
0.55
0.18
0.25
1.13
163.18
12.78
1.79
0.55
0.18
0.25
1.12
163.19
13.25
1.79
0.55
0.18
0.25
1.11
Croplands
78 In the current Inventory, NRI data only provide land-use and management statistics through 2007, but additional data will be
incorporated in the future to extend the time series of land use and management data.
7" Includes non-federal lands only, because federal lands are not classified into land uses as part of the NRI survey (i.e, they are only
designated as federal lands).
8" Land use for 2008 to 2012 is assumed to be the same as 2007, but will be updated after new NRI data are released.
81 NRI points are classified according to land-use history records starting in 1982 when the NRI survey began, and consequently the
classifications are based on less than 20 years from 1990 to 2001.
A-299
-------
Cropland Remaining Cropland
Grassland Converted to Cropland
Forest Converted to Cropland
Other Lands Converted to Cropland
Settlements Converted to Croplands
Wetlands Converted to Croplands
Grasslands
Grasslands Remaining Grasslands
Croplands Converted to Grasslands
Forest Converted to Grasslands
Other Lands Converted to Grasslands
Settlements Converted to Grasslands
Wetlands Converted to Grasslands
Total
0.59
0.06
0.02
0.00
0.00
0.02
0.44
0.05
0.01
0.00
0.00
0.01
350.94
0.59
0.07
0.02
0.00
0.00
0.02
0.43
0.05
0.01
0.00
0.00
0.01
350.82
0.58
0.06
0.02
0.00
0.00
0.01
0.43
0.05
0.01
0.00
0.00
0.01
350.67
0.56
0.07
0.02
0.00
0.00
0.02
0.42
0.05
0.01
0.00
0.00
0.01
345.62
0.55
0.08
0.02
0.00
0.01
0.02
0.41
0.06
0.01
0.00
0.00
0.01
345.37
0.55
0.08
0.02
0.00
0.01
0.02
0.40
0.07
0.01
0.00
0.00
0.02
345.41
0.54
0.08
0.02
0.00
0.01
0.02
0.39
0.06
0.01
0.00
0.00
0.02
345.49
0.54
0.08
0.02
0.00
0.01
0.02
0.38
0.07
0.01
0.00
0.00
0.02
345.58
0.52
0.09
0.02
0.00
0.01
0.02
0.37
0.08
0.01
0.00
0.00
0.02
339.96
0.52
0.09
0.02
0.00
0.01
0.01
0.36
0.08
0.01
0.00
0.00
0.02
340.12
0.52
0.09
0.02
0.00
0.01
0.01
0.36
0.08
0.01
0.00
0.00
0.02
340.23
Category
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Mineral Soils
339.09 339.12 339.23 338.85 338.35 337.90 337.53 337.30 337.06 336.83 336.60 336.37
Croplands
Cropland Remaining Cropland
Grassland Converted to Cropland
Forest Converted to Cropland
Other Lands Converted to Cropland
Settlements Converted to Croplands
Wetlands Converted to Croplands
Grasslands
141.68 142.22 144.37 143.42 143.68 143.96 144.57 144.57 144.57 144.57 144.57 144.57
13.12 12.27 12.27 12.27 12.27 12.27 12.27
0.15 0.15 0.15 0.15 0.15 0.15 0.15
0.06 0.06 0.06 0.06 0.06 0.06 0.06
0.18 0.18 0.18 0.18 0.18 0.18 0.18
0.04 0.04 0.04 0.04 0.04 0.04 0.04
16.96
0.41
0.13
0.33
0.10
16.34
0.41
0.13
0.33
0.10
14.21
0.15
0.06
0.18
0.04
14.08
0.15
0.06
0.18
0.04
13.60
0.15
0.06
0.18
0.04
Grasslands Remaining Grasslands
Croplands Converted to Grasslands
Forest Converted to Grasslands
Other Lands Converted to Grasslands
Settlements Converted to Grasslands
Wetlands Converted to Grasslands
Organic Soils
Croplands
Cropland Remaining Cropland
Grassland Converted to Cropland
Forest Converted to Cropland
Other Lands Converted to Cropland
Settlements Converted to Croplands
Wetlands Converted to Croplands
Grasslands
Grasslands Remaining Grasslands
Croplands Converted to Grasslands
Forest Converted to Grasslands
Other Lands Converted to Grasslands
Settlements Converted to Grasslands
Wetlands Converted to Grasslands
Total
163.03
13.67
1.79
0.55
0.18
0.25
1.10
0.52
0.11
0.01
0.00
0.01
0.01
0.32
0.09
0.01
0.00
0.00
0.01
340.19
163.56
13.25
1.79
0.55
0.18
0.25
1.09
0.52
0.10
0.01
0.00
0.01
0.01
0.32
0.10
0.00
0.00
0.00
0.01
340.20
165.91
12.57
1.07
0.39
0.13
0.15
1.06
0.54
0.10
0.00
0.00
0.01
0.01
0.31
0.09
0.00
0.00
0.00
0.01
340.29
165.79
13.38
1.07
0.39
0.13
0.15
1.05
0.53
0.09
0.00
0.00
0.01
0.01
0.31
0.10
0.00
0.00
0.00
0.01
339.90
165.53
13.38
1.07
0.39
0.13
0.15
1.05
0.53
0.09
0.00
0.00
0.01
0.01
0.31
0.10
0.00
0.00
0.00
0.01
339.40
165.47
13.18
1.07
0.39
0.13
0.15
1.04
0.53
0.09
0.00
0.00
0.01
0.01
0.30
0.09
0.00
0.00
0.00
0.01
338.94
165.86
12.66
1.07
0.39
0.13
0.15
1.04
0.53
0.08
0.00
0.00
0.01
0.01
0.30
0.08
0.00
0.00
0.00
0.01
338.56
165.69
12.60
1.07
0.39
0.13
0.15
1.03
0.53
0.08
0.00
0.00
0.01
0.01
0.30
0.08
0.00
0.00
0.00
0.01
338.32
165.51
12.55
1.07
0.39
0.13
0.15
1.03
0.53
0.08
0.00
0.00
0.01
0.01
0.30
0.08
0.00
0.00
0.00
0.01
338.09
165.33
12.50
1.07
0.39
0.13
0.15
1.03
0.53
0.08
0.00
0.00
0.01
0.01
0.30
0.08
0.00
0.00
0.00
0.01
337.86
165.15
12.44
1.07
0.39
0.13
0.15
1.03
0.53
0.08
0.00
0.00
0.01
0.01
0.30
0.08
0.00
0.00
0.00
0.01
337.63
164.97
12.39
1.07
0.39
0.13
0.15
1.03
0.53
0.08
0.00
0.00
0.01
0.01
0.30
0.08
0.00
0.00
0.00
0.01
337.39
Note: The area estimates are not consistent with the land representation chapter because the current Inventory does not cover all
of the managed land, including grassland and cropland in Alaska, as well as grasslands and croplands on federal lands in the
conterminous United States.
The Tier 3 method using the DAYCENT model is applied to estimate soil C stock changes and N2O
emissions for most of the NRI points that occur on mineral soils. Parcels of land that are not simulated with
DAYCENT are allocated to the Tier 2 approach for estimating soil organic C stock change, and a Tier 1 method
(IPCC 2006) to estimate soil N2O emissions (Table A- 214) (Note: Tier 1 method for soil N2O does not require land
area data with the exception of emissions from drainage and cultivation of organic soils so in practice it is only the
amount of N input to mineral soils that is addressed by the Tier 1 method and not the actual land area). The land
base that is not simulated with DAYCENT includes (1) land parcels occurring on organic soils; (2) land parcels that
include non-agricultural uses such as forest and federal lands in one or more years of the inventory; (3) land parcels
on mineral soils that are very gravelly, cobbly, or shaley (i.e., classified as soils that have greater than 35 percent of
soil volume comprised of gravel, cobbles, or shale); or (4) land parcels that are used to produce vegetables,
perennial/horticultural crops, tobacco or rice, which are either grown continuously or in rotation with other crops.
DAYCENT has not been fully tested or developed to simulate biogeochemical processes in soils used to produce
some annual (e.g., tobacco), horticultural (e.g., flowers), or perennial (e.g., vineyards, orchards) crops and
A-300 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
agricultural use of organic soils. In addition, DAYCENT has not been adequately tested for soils with a high gravel,
cobble, or shale content.
Table A-217: Total Land Area Estimated with Tier 2 and 3 Inventory Approaches (Million Hectares)
Land Areas (million ha)
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Mineral
Tier 1/2
31.78
31.78
31.78
26.90
26.90
26.90
26.90
26.90
21.50
21.50
21.50
21.50
21.50
21.63
21.63
21.63
21.63
21.63
21.63
21.63
21.63
21.63
21.63
TierS
317.96
317.85
317.71
317.56
317.30
317.35
317.44
317.55
317.33
317.50
317.61
317.58
317.61
317.59
317.21
316.71
316.27
315.89
315.66
315.43
315.20
314.96
314.73
Total
349.74
349.63
349.49
344.46
344.20
344.25
344.34
344.45
338.83
339.00
339.11
339.09
339.12
339.23
338.85
338.35
337.90
337.53
337.30
337.06
336.83
336.60
336.37
Organic
Tier 1/2
1.20
1.19
1.18
1.16
1.17
1.16
1.15
1.14
1.13
1.12
1.11
1.10
1.09
1.06
1.05
1.05
1.04
1.04
1.03
1.03
1.03
1.03
1.03
Total
350.94
350.82
350.67
345.62
345.37
345.41
345.49
345.58
339.96
340.12
340.23
340.19
340.20
340.29
339.90
339.40
338.94
338.56
338.32
338.09
337.86
337.63
337.39
NRI points on mineral soils are classified into specific crop rotations, continuous pasture/rangeland, and
other non-agricultural uses for the Tier 2 inventory analysis based on the survey data (Table A-218). NRI points are
assigned to IPCC input categories (low, medium, high, and high with organic amendments) according to the
classification provided in IPCC (2006). In addition, NPJ differentiates between improved and unimproved
grassland, where improvements include irrigation and interseeding of legumes. In order to estimate uncertainties,
probability distribution functions (PDFs) for the NPJ land-use data are constructed as multivariate normal based on
the total area estimates for each land-use/management category and associated covariance matrix. Through this
approach, dependencies in land use are taken into account resulting from the likelihood that current use is correlated
with past use. These dependencies occur because as some land use/management categories increase in area, the area
of other land use/management categories will decline. The covariance matrix addresses these relationships.
A-301
-------
Table A-218: Total Land Areas by Land-Use and Management System for the Tier 2 Mineral Soil Organic G Approach
[Million Hectares)
Land Areas (million ha)
1990-1992
Land-Use/Management System (Tier 2)
Cropland Systems
Aquaculture
Conservation Reserve Program
Continuous Hay
Continuous Hay with Legumes or Irrigation
Continuous Perennial or Horticultural Crops
Continuous Rice
Continuous Row Crops
Continuous Row Crops and Small Grains
Continuous Small Grains
Irrigated Crops
Low Residue Annual Crops (e.g., Tobacco or Cotton)
Miscellaneous Crop Rotations
Rice in Rotation with other crops
Row Crops and Small Grains in with Hay and/or Pasture
Row Crops and Small Grains with Fallow
Row Crops in Rotation with Hay and/or Pasture
Row Crops with Fallow
Small Grains in Rotation with Hay and/or Pasture
Small Grains with Fallow
Vegetable Crops
Grassland Systems
Rangeland
Continuous Pasture
Continuous Pasture with Legumes or Irrigation
CRP
Total
17.20
0.00
0.86
1.20
0.29
0.71
0.00
2.96
2.01
0.66
5.61
0.79
0.00
0.01
0.47
0.05
0.28
0.03
0.19
0.47
0.61
10.63
3.71
6.84
0.08
0.00
27.83
1993-1997
(Tier 2)
15.16
0.00
0.80
1.16
0.27
0.59
0.00
2.31
1.57
0.57
5.41
0.90
0.01
0.00
0.35
0.04
0.30
0.01
0.11
0.29
0.47
7.51
2.88
4.56
0.07
0.00
22.67
1998-2002
(Tier 2)
15.04
0.01
0.40
1.32
0.31
0.51
0.00
2.55
1.37
0.53
5.76
0.72
0.00
0.01
0.41
0.04
0.35
0.03
0.10
0.18
0.44
8.53
3.27
5.17
0.10
0.00
23.57
2003-2007
(Tier 2)
13.50
0.01
0.45
1.36
0.29
0.41
0.00
2.50
1.29
0.44
5.04
0.57
0.00
0.03
0.22
0.04
0.20
0.00
0.06
0.21
0.38
8.72
3.43
5.16
0.13
0.00
22.22
For the Tier 3 inventory estimates, the actual cropping and grassland histories are simulated with the DAYCENT
model so it is not necessary to classify NRI points into management systems. Uncertainty in the areas associated with
each management system is determined from the estimated sampling variance from the NRI survey (Nusser and Goebel
1997). See Step 2b for additional discussion.
Organic soils are also categorized into land-use systems based on drainage (IPCC 2006). Undrained soils are
treated as having no loss of organic C or soil N2(D emissions. Drained soils are subdivided into those used for cultivated
cropland, which are assumed to have high drainage and relatively large losses of C, and those used for managed pasture,
which are assumed to have less drainage with smaller losses of C. N2O emissions are assumed to be similar for both
drained croplands and grasslands. Overall, the area of organic soils drained for cropland and grassland has remained
relatively stable since 1990 (see Table A-219).
Table A-219: Total Land Areas for Drained Organic Soils By Land Management Category and Climate Region (Million
Hectaresl
Land Areas (million ha)
IPCC Land-Use Category for
Organic Soils 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Cold Temperate
Cultivated Cropland
(high drainage)
Managed Pasture
(low drainage)
Undrained
Total
0.37
0.31
0.05
0.72
0.36
0.30
0.05
0.72
0.36
0.30
0.05
0.71
0.36
0.30
0.04
0.70
0.37
0.30
0.03
0.70
0.37
0.29
0.03
0.69
0.36
0.29
0.04
0.69
0.37
0.28
0.03
0.68
0.36
0.29
0.03
0.68
0.36
0.29
0.03
0.67
0.35
0.28
0.04
0.67
0.35
0.27
0.03
0.65
0.34 0.34
0.28 0.27
0.03 0.02
0.64 0.63
Warm Temperate
Cultivated Cropland
(high drainage)
Managed Pasture
(low drainage)
Undrained
Total
0.09
0.07
0.01
0.17
0.09
0.07
0.01
0.17
0.09
0.07
0.01
0.17
0.08
0.07
0.01
0.16
0.09
0.07
0.01
0.17
0.09
0.08
0.01
0.17
0.09
0.07
0.01
0.17
0.08
0.07
0.01
0.16
0.09
0.07
0.00
0.17
0.09
0.07
0.00
0.17
0.09
0.07
0.01
0.17
0.08
0.07
0.00
0.16
0.09 0.09
0.07 0.07
0.00 0.00
0.16 0.16
Tropical
A-302 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Cultivated Cropland
(high drainage)
Managed Pasture
(low drainage)
Undrained
0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.16 0.17 0.17 0.17 0.19 0.19 0.19
0.13 0.12 0.12 0.12 0.12 0.12 0.12 0.11
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01
0.11 0.11 0.11 0.08 0.08 0.07
0.00 0.00 0.00 0.00 0.01 0.00
Total
0.30 0.30 0.30 0.30 0.30 0.29 0.29 0.28 0.28 0.28 0.28 0.28 0.28 0.26
IPCC Land-Use Category for
Organic Soils
Land Areas (million ha)
2004 2005 2006 2007 2008 2009 2010 2011 2012
Cold Temperate
Cultivated Cropland
(high drainage)
Managed Pasture
(low drainage)
Undrained
Total
0.33
0.27
0.02
0.63
0.33
0.28
0.02
0.63
0.32
0.27
0.03
0.62
0.32
0.27
0.03
0.62
0.32
0.27
0.03
0.62
0.32
0.27
0.03
0.62
0.32
0.27
0.03
0.62
0.32
0.27
0.03
0.62
0.32
0.27
0.03
0.62
Warm Temperate
Cultivated Cropland
(high drainage)
Managed Pasture
(low drainage)
Undrained
Total
0.09
0.07
0.00
0.17
0.09
0.07
0.00
0.17
0.09
0.07
0.00
0.16
0.09
0.07
0.00
0.16
0.09
0.07
0.00
0.16
0.09
0.07
0.00
0.16
0.09
0.07
0.00
0.16
0.09
0.07
0.00
0.16
0.09
0.07
0.00
0.16
Tropical
Cultivated Cropland
(high drainage)
Managed Pasture
(low drainage)
Undrained
Total
0.19
0.07
0.00
0.26
0.18
0.07
0.01
0.26
0.19
0.07
0.00
0.26
0.18
0.07
0.01
0.26
0.18
0.07
0.01
0.26
0.18
0.07
0.01
0.26
0.18
0.07
0.01
0.26
0.18
0.07
0.01
0.26
0.18
0.07
0.01
0.26
Step 1b: Obtain Management Activity Data for the Tier 3 Method to estimate Soil C Stock Changes and
A/20 Emissions from Mineral Soils
Synthetic N Fertilizer Application: Data on N fertilizer rates are based primarily on the USD A-Economic
Research Service Cropping Practices Survey (USDA-ERS 1997, 2011). In these surveys, data on inorganic N
fertilization rates are collected for crops simulated by DAYCENT (barley, corn, cotton, dry beans, oats, onions,
peanuts, potatoes, rice, sorghum, soybeans, sugar beets, sunflowers, tomatoes, and wheat) in the high production
states and for a subset of low production states. These data are used to build a time series of fertilizer application
rates for specific crops and states for the 1990 through 1999 time period and 2000 through 2012 time period. If only
a single survey is available for a crop, as is the case with sorghum, the rates for the one survey are used for both time
periods.
Mean fertilizer rates and standard deviations for irrigated and rainfed crops are produced for each state. If a
state is not surveyed for a particular crop or if there are not enough data to produce a state-level estimate, then data
are aggregated to USDA Farm Production Regions in order to estimate a mean and standard deviation for
fertilization rates (Farm Production Regions are groups of states in the United States with similar agricultural
commodities). If Farm Production Region data are not available, crop data are aggregated to the entire United States
(all major states surveyed) to estimate a mean and standard deviation for a particular crop in a state lacking
sufficient data. Standard deviations for fertilizer rates are used to construct probability distribution functions (PDFs)
with log-normal densities in order to address uncertainties in application rates (see Step 2a for discussion of
uncertainty methods). The survey summaries also present estimates for fraction of crop acres receiving fertilizer,
and these fractions are used to determine if a crop is receiving fertilizer. Alfalfa hay and grass-clover hay are
assumed to not be fertilized, but grass hay is fertilized according to rates from published farm enterprise budgets
(NRIAI2003). Total fertilizer application data are found in Table A- 220.
Simulations are conducted for the period prior to 1990 in order to initialize the DAYCENT model (see Step
2a), and crop-specific regional fertilizer rates prior to 1990 are based largely on extrapolation/interpolation of
fertilizer rates from the years with available data. For crops in some states, little or no data are available, and,
therefore, a geographic regional mean is used to simulate N fertilization rates (e.g., no data are available for the State
A-303
-------
of Alabama during the 1970s and 1980s for corn fertilization rates; therefore, mean values from the southeastern
United States are used to simulate fertilization to corn fields in this state).
Managed Livestock Manure*2Amendments: County-level manure addition estimates have been derived
from manure N addition rates developed by the USDA Natural Resources Conservation Service (NRCS) (Edmonds
et al. 2003). Working with the farm-level crop and animal data from the 1997 Census of Agriculture, USDA-NRCS
has coupled estimates of manure N produced with estimates of manure N recoverability by animal waste
management system to produce county-level rates of manure N application to cropland and pasture. Edmonds et al.
(2003) defined a hierarchy that included 24 crops, permanent pasture, and cropland used as pasture. They estimated
the area amended with manure and application rates in 1997 for both manure-producing farms and manure-receiving
farms within a county and for two scenarios—before implementation of Comprehensive Nutrient Management Plans
(baseline) and after implementation (Edmonds et al. 2003). The goal of nutrient management plans is to apply
manure nutrients at a rate meeting plant demand, thus limiting leaching losses of nutrients to groundwater and
waterways.
For DAYCENT simulations, the rates for manure-producing farms and manure-receiving farms have been
area-weighted and combined to produce a single county-level estimate for the amount of land amended with manure
and the manure N application rate for each crop in each county. The estimates were based on the assumption that
Comprehensive Nutrient Management Plans have not been fully implemented. This is a conservative assumption
because it allows for higher leaching rates due to some over-application of manure to soils. In order to address
uncertainty in these data, uniform probability distributions are constructed based on the proportion of land receiving
manure versus the amount not receiving manure for each crop type and pasture. For example, if 20 percent of land
producing corn in a county is amended with manure, randomly drawing a value equal to or greater than 0 and less
than 20 would lead to a simulation with a manure amendment, while drawing a value greater than or equal to 20 and
less than 100 would lead to no amendment in the simulation (see Step 2a for further discussion of uncertainty
methods).
Edmonds et al. (2003) only provides manure application rate data for 1997, but the amount of managed
manure available for soil application changes annually, so the area amended with manure is adjusted relative to 1997
to account for all the manure available for application in other years. Specifically, the manure N available for
application in other years is divided by the manure N available in 1997. If the ratio is greater than 1, there is more
manure N available in that county relative to the amount in 1997, and so it is assumed a larger area is amended with
manure. In contrast, ratios less than one imply less area is amended with manure because there is a lower amount
available in the year compared to 1997. The amendment area in each county for 1997 is multiplied by the ratio to
reflect the impact of manure N availability on the area amended. The amount of managed manure N available for
application to soils is calculated by determining the populations of livestock that are on feedlots or otherwise
housed, requiring collection and management of the manure, and the methods are described in the Manure
Management section (Section 6.2) and annex (Annex 3.11). The total managed manure N applied to soils is found in
Table A-221.
To estimate C inputs associated with manure N application rates derived from Edmonds et al. (2003),
carbon-nitrogen (C:N) ratios for livestock-specific manure types are adapted from the Agricultural Waste
Management Field Handbook (USDA 1996), On-Farm Composting Handbook (NRAES 1992), and recoverability
factors provided by Edmonds et al (2003). The C:N ratios are applied to county-level estimates of manure N
excreted by animal type and management system to produce a weighted county average C:N ratio for manure
amendments. The average C:N ratio is used to determine the associated C input for crop amendments derived from
Edmonds et al. (2003).
To account for the common practice of reducing inorganic N fertilizer inputs when manure is added to a
cropland soil, crop-specific reduction factors are derived from mineral fertilization data for land amended with
manure versus land not amended with manure in the ERS 1995 Cropping Practices Survey (ERS 1997). Mineral N
fertilization rates are reduced for crops receiving manure N based on a fraction of the amount of manure N applied,
depending on the crop and whether it is irrigated or rainfed. The reduction factors are randomly selected from PDFs
For purposes of the inventory, total livestock manure is divided into two general categories: (1) managed manure, and (2)
unmanaged manure. Managed manure includes manure that is stored in manure management systems such as drylots, pits and lagoons,
as well as manure applied to soils through daily spread manure operations. Unmanaged manure encompasses all manure deposited on
soils by animals on PRP.
A-304 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
with normal densities in order to address uncertainties in the dependence between manure amendments and mineral
fertilizer application.
PRP Manure N: Another key source of N for grasslands is Pasture/Range/Paddock (PRP) manure N
deposition (i.e., manure deposited by grazing livestock). The total amount of PRP manure N was estimated using
methods described in the Manure Management section (Section 6.2) and annex (Annex 3.11). Nitrogen from PRP
animal waste deposited on non-federal grasslands in a county was generated by multiplying the total PRP N (based
on animal type and population data in a county) by the fraction of non-federal grassland area in the county. PRP
manure N input rates for the Tier 3 DAYCENT simulations were estimated by dividing the total PRP manure N
amount by the land area associated with non-federal grasslands in the county from the NRI survey data. The total
PRP manure N added to soils is found in Table A- 221.
Residue N Inputs: Crop residue N, fixation by legumes, and N residue inputs from senesced grass litter
are included as sources of N to the soil, and are estimated in the DAYCENT simulations as a function of vegetation
type, weather, and soil properties. That is, while the model accounts for the contribution of N from crop residues to
the soil profile and subsequent N2O emissions, this source of mineral soil N is not activity data in the sense that it is
not a model input. The simulated total N inputs of above- and below-ground residue N and fixed N that is not
harvested and not burned (the DAYCENT simulations assumed that 3 percent of non-harvested above ground
residues for crops are burned83) are provided in Table A-222.
Other N Inputs: Other N inputs are estimated within the DAYCENT simulation, and thus input data are
not required, including mineralization from decomposition of soil organic matter and asymbiotic fixation of N from
the atmosphere. Mineralization of soil organic matter will also include the effect of land use change on this process
as recommended by the IPCC (2006). The influence of additional inputs of N are estimated in the simulations so that
there is full accounting of all emissions from managed lands, as recommended by IPCC (2006). The simulated total
N inputs from other sources are provided in Table A-222.
Tillage Practices: Tillage practices are estimated for each cropping system based on data from the
Conservation Technology Information Center84 (CTIC 2004). CTIC compiles data on cropland area under five
tillage classes by major crop species and year for each county. Because the surveys involve county-level aggregate
area, they do not fully characterize tillage practices as they are applied within a management sequence (e.g., crop
rotation). This is particularly true for area estimates of cropland under no-till, which include a relatively high
proportion of "intermittent" no-till, where no-till in one year may be followed by tillage in a subsequent year. For
example, a common practice in maize-soybean rotations is to use tillage in the maize crop while no-till is used for
soybean, such that no-till practices are not continuous in time. Estimates of the area under continuous no-till are
provided by experts at CTIC to account for intermittent tillage activity and its impact on soil C (Towery 2001).
Tillage practices are grouped into 3 categories: full, reduced, and no-tillage. Full tillage is defined as
multiple tillage operations every year, including significant soil inversion (e.g., plowing, deep disking) and low
surface residue coverage. This definition corresponds to the intensive tillage and "reduced" tillage systems as
defined by CTIC (2004). No-till is defined as not disturbing the soil except through the use of fertilizer and seed
drills and where no-till is applied to all crops in the rotation. Reduced tillage made up the remainder of the
cultivated area, including mulch tillage and ridge tillage as defined by CTIC and intermittent no-till. The specific
tillage implements and applications used for different crops, rotations, and regions to represent the three tillage
classes are derived from the 1995 Cropping Practices Survey by the Economic Research Service (ERS 1997).
Tillage data are further processed to construct probability distribution functions (PDFs). Transitions
between tillage systems are based on observed county-level changes in the frequency distribution of the area under
full, reduced, and no-till from the 1980s through 2004. Generally, the fraction of full tillage decreased during this
time span, with concomitant increases in reduced till and no-till management. Transitions that are modeled and
applied to NRI points occurring within a county are full tillage to reduced and no-till, and reduced tillage to no-till.
83 Another improvement is to reconcile the amount of crop residues burned with the Field Burning of Agricultural
Residues source category (Section 6.5).
National scale tillage data are no longer collected by CTIC, and a new data source will be needed, which is a
planned improvement.
A-305
-------
The remaining amount of cropland is assumed to have no change in tillage (e.g., full tillage remained in full tillage).
Transition matrices are constructed from CTIC data to represent tillage changes for three time periods, 1980-1989,
1990-1999, 2000-2007. Areas in each of the three tillage classes—full till (FT), reduced till (RT), no-till (NT)—in
1989 (the first year the CTIC data are available) are used for the first time period, data from 1997 are used for the
second time period, and data from 2004 are used for the last time period. Percentage areas of cropland in each
county are calculated for each possible transition (e.g., FT^FT, FT^RT, FT^NT, RT^RT, RT^NT) to obtain a
probability for each tillage transition at an NRI point. It is assumed that there are no transitions for NT—>FT or
NT—>NT after accounting for NT systems that have intermittent tillage. Uniform probability distributions are
established for each tillage scenario in the county. For example, a particular crop rotation had 80 percent chance of
remaining in full tillage over the two decades, a 15 percent chance of a transition from full to reduced tillage and a 5
percent chance of a transition from full to no-till. The uniform distribution is subdivided into three segments with
random draws in the Monte Carlo simulation (discussed in Step 2b) leading to full tillage over the entire time period
if the value is greater than or equal to 0 and less than 80, a transition from full to reduced till if the random draw is
equal to or greater than 80 and less than 95, or a transition from full to no-till if the draw is greater than or equal to
95. See step 2b for additional discussion of the uncertainty analysis.
Irrigation: NRI (USDA-NRCS 2009) differentiates between irrigated and non-irrigated land, but does not
provide more detailed information on the type and intensity of irrigation. Hence, irrigation is modeled by assuming
that applied water to field capacity with intervals between irrigation events where the soils drain to about 60 percent
of field capacity.
Daily Weather Data: Daily maximum/minimum temperature and precipitation data are based on gridded
weather data from the North America Regional Reanalysis Product (NARR) (Mesinger et al. 2006). It is necessary
to use computer-generated weather data because weather station data do not exist near all NRI points, and moreover
weather station data are for a point in space. The NARR product uses this information with interpolation algorithms
to derive weather patterns for areas between these stations. NARR weather data are available for the U.S. from
1980 through 2007 at a 32 km resolution. Each NRI point is assigned the NARR weather data for the grid cell
containing the point.
Enhanced Vegetation Index: The Enhanced Vegetation Index (EVI) from the MODIS vegetation products,
(MOD13Q1 and MYD13Q1) is an input to DAYCENT for estimating net primary production using the NASA-
CASA production algorithm (Potter et al. 1993, 2007). MODIS imagery is collected on a nominal 8 day-time
frequency when combining the two products. A best approximation of the daily time series of EVI data is derived
using a smoothing process based on the Savitzky-Golay Filter (Savitzky and Golay 1964) after pre-screening for
outliers and for cloud-free, high quality data as identified in the MODIS data product quality layer. The NASA-
CASA production algorithm is only used for the following crops: corn, soybeans, sorghum, cotton, wheat and other
close-grown crops such as barley and oats.85
The MODIS EVI products have a 250 m spatial resolution, and some pixels in images have mixed land
uses and crop types at this resolution, which is problematic for estimating NPP associated with a specific crop at a
NRI point. Therefore, a threshold of 90 percent purity in an individual pixel is the cutoff for estimating NPP using
the EVI data derived from the imagery (i.e., pixels with less than 90 percent purity for a crop are assumed to
generate bias in the resulting NPP estimates). The USDA-NASS Crop Data Layer (CDL) (Johnson and Mueller
2010) is used to determine the purity levels of the EVI data. CDL data have a 30 to 58 m spatial resolution,
depending on the year. The level of purity for individual pixels in the MODIS EVI products is determined by
aggregating the crop cover data in CDL to the 250m resolution of the EVI data. In this step, the percent cover of
individual crops is determined for the 250m EVI pixels. Pixels that did not meet a 90 percent purity level for any
crop are eliminated from the dataset. CDL did not provide full coverage of crop maps for the conterminous United
States until 2009 so it is not possible to evaluate purity for the entire cropland area prior to 2009.
The nearest pixel with at least 90 percent purity for a crop is assigned to the NRI point based on a 50 km
buffer surrounding the survey location. EVI data are not assigned to a point if there are no pixels with at least 90
percent purity within the 50 km buffer. Furthermore, MODIS products do not provide any data on EVI prior to
2000, which preceded the launch of the MODIS sensor on the Aqua and Terra Satellites. It is good practice to apply
a method consistently across a time series (IPCC 2006), and so a statistical model is used to estimate EVI for the
o c
Additional crops and grassland will be used with the NASA-CASA method in the future, as a planned improvement.
A-306 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
inventory time series prior to 2000 and also to fill gaps if no pixel has at least 90 percent purity within the 50 km
buffer due to purity limitations, lack of CDL data to evaluate purity, or low quality data (Gurung et al. 2009).
Soil Properties: Soil texture and natural drainage capacity (i.e., hydric vs. non-hydric soil characterization)
are the main soil variables used as input to the DAYCENT model. Texture is one of the main controls on soil C
turnover and stabilization in the DAYCENT model, which uses particle size fractions of sand (50-2,000 um), silt (2-
50 um), and clay (< 2 um) as inputs. Hydric condition specifies whether soils are poorly-drained, and hence prone to
water-logging, or moderately to well-drained (non-hydric), in their native (pre-cultivation) condition.86 Poorly
drained soils can be subject to anaerobic (lack of oxygen) conditions if water inputs (precipitation and irrigation)
exceed water losses from drainage and evapotranspiration. Depending on moisture conditions, hydric soils can
range from being fully aerobic to completely anaerobic, varying over the year. Decomposition rates are modified
according to a linear function that varies from 0.3 under completely anaerobic conditions to 1.0 under fully aerobic
conditions (default parameters in DAYCENT).87 Other soil characteristics needed in the simulation, such as field
capacity and wilting-point water contents, are estimated from soil texture data using a standardized hydraulic
properties calculator (Saxton et al. 1986). Soil input data are derived from Soil Survey Geographic Database
(SSURGO) (Soil Survey Staff 2011). The data are based on field measurements collected as part of soil survey and
mapping. Each NRI point is assigned the dominant soil component in the polygon containing the point from the
SSURGO data product.
Step 1c: Obtain Additional Management Activity Data for the Tier 1 Method to estimate Soil A/20
Emissions from Mineral Soils
Synthetic N Fertilizer: A process-of-elimination approach is used to estimate synthetic N fertilizer
additions to Tier 1 crops estimates. The total amount of fertilizer used on-farms has been estimated by the USGS
from 1990-2001 on a county scale from fertilizer sales data (Ruddy et al. 2006). For 2002 through 2012, county-
level fertilizer used on-farms is adjusted based on annual fluctuations in total U.S. fertilizer sales (AAPFCO 1995
through 2012). Fertilizer application data are available for crops and grasslands simulated by DAYCENT (discussed
in Step la section for Tier 3 crops and non-federal grasslands). Thus, the amount of N applied to Tier 1 crops (i.e.,
not simulated by DAYCENT) is assumed to be the remainder of the fertilizer used on farms after subtracting the
amount applied to Tier 3 crops and non-federal grasslands (i.e., simulated by DAYCENT). The differences are
aggregated to the state level, and PDFs are derived based on uncertainties in the amount of N applied to Tier 3 crops
and non-federal grasslands. Total fertilizer application to Tier 1 crops is found in Table A- 223.
Managed Livestock Manure and Other Organic Amendments: Manure N that is not applied to crops and
grassland simulated by DAYCENT is assumed to be applied to other crops that are included in the Tier 1 method.
Estimates of total national annual N additions from other commercial organic fertilizers are derived from organic
fertilizer statistics (TVA 1991 through 1994; AAPFCO 1995 through 2011). Commercial organic fertilizers include
dried blood, tankage, compost, and other; dried manure and sewage sludge that are used as commercial fertilizer are
subtracted from totals to avoid double counting. The dried manure N is counted with the non-commercial manure
applications, and sewage sludge is assumed to be applied only to grasslands. The organic fertilizer data, which are
recorded in mass units of fertilizer, had to be converted to mass units of N by multiplying the consumption values by
the average organic fertilizer N content of 0.5 percent (AAPFCO 2000a). The fertilizer consumption data are
recorded in "fertilizer year" totals, (i.e., July to June), but are converted to calendar year totals. This is done by
assuming that approximately 35 percent of fertilizer usage occurred from July to December and 65 percent from
January to June (TVA 1992b). Values for July to December are not available for calendar year 2012 so a "least
squares line" statistical extrapolation using the previous 5 years of data is used to arrive at an approximate value.
PDFs are derived for the organic fertilizer applications assuming a default ±50 percent uncertainty. Annual
consumption of other organic fertilizers is presented in Table A- 224. The fate of manure N is summarized in Table
A-221.
Artificial drainage (e.g., ditch- or tile-drainage) is simulated as a management variable.
Hydric soils are primarily subject to anaerobic conditions outside the plant growing season (i.e., in the absence of active plant water
uptake). Soils that are water-logged during much of the year are typically classified as organic soils (e.g., peat), which are not simulated
with the DAYCENT model.
A-307
-------
PRP Manure N: Soil N2O emissions from PRP manure N deposited on federal grasslands is estimated with
a Tier 1 method. PRP manure N data are derived using methods described in the Manure Management section
(Section 6.2) and Annex 3.11. PRP N deposited on federal grasslands is calculated using a process of elimination
approach. The amount of PRP N generated by DAYCENT model simulations of non-federal grasslands was
subtracted from total PRP N and this difference was assumed to be applied to federal grasslands. The total PRP
manure N added to soils is found in Table A- 221.
Sewage Sludge Amendments: Sewage sludge is generated from the treatment of raw sewage in public or
private wastewater treatment works and is typically used as a soil amendment or is sent to waste disposal facilities
such as landfills. In this Inventory, all sewage sludge that is amended to agricultural soils is assumed to be applied
to grasslands. Estimates of the amounts of sewage sludge N applied to agricultural lands are derived from national
data on sewage sludge generation, disposition, and N content. Total sewage sludge generation data for 1990-2011,
in dry mass units, are obtained from AAPFCO (1990-2011). Values for 2012 were not available so a "least squares
line" statistical extrapolation using the previous 5 years of data was used to arrive at an approximate value. The
total sludge generation estimates are then converted to units of N by applying an average N content of 69 percent
(AAPFCO 2000a), and disaggregated into use and disposal practices using historical data in EPA (1993) and
NEBRA (2007). The use and disposal practices are agricultural land application, other land application, surface
disposal, incineration, landfilling, ocean dumping (ended in 1992), and other disposal. The resulting estimates of
sewage sludge N applied to agricultural land are used to estimate N2O emissions from agricultural soil management;
the estimates of sewage sludge N applied to other land and surface-disposed are used in estimating N2O fluxes from
soils in Settlements Remaining Settlements (see section 7.5 of the Land Use, Land-Use Change, and Forestry
chapter). Sewage sludge disposal data are provided in Table A- 225).
Residue N Inputs: Soil N2O emissions for residue N inputs from crops that are not simulated by
DAYCENT are estimated with a Tier 1 method. Annual crop yield (metric tons per hectare) and area harvested
(hectare) statistics for N-fixing crops, including bean and pulse crops, are taken from U.S. Department of
Agriculture crop production reports (USDA 1994, 1998, 2003, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012).
Crop yield per hectare and area planted are multiplied to determine total crop yield for each crop, which is then
converted to tons of dry matter product using the residue dry matter fractions shown in Table A- 226. Dry matter
yield is then converted to tons of above- and below-ground biomass N. Above-ground biomass is calculated by
using linear equations to estimate above-ground biomass given dry matter crop yields, and below-ground biomass is
calculated by multiplying above-ground biomass by the below-to-above-ground biomass ratio. N inputs are
estimated by multiplying above- and below-ground biomass by respective N concentrations. All ratios and
equations used to calculate residue N inputs are from IPCC (2006) and Williams (2006). PDFs are derived
assuming a ±50 percent uncertainty in the yield estimates (NASS does not provide uncertainty), along with
uncertainties provided by the IPCC (2006) for dry matter fractions, above-ground residue, ratio of below-ground to
above-ground biomass, and residue N fractions. The resulting annual biomass N inputs are presented in Table A-
227.
Step 1d: Obtain Additional Management Activity Data for the Tier 2 Method to estimate Soil C Stock
Changes in Mineral Soils
Tillage Practices: For the Tier 2 method that is used to estimate soil organic C stock changes, PDFs are
constructed for the CTIC tillage data (CTIC 2004) as bivariate normal on a log-ratio scale to reflect negative
dependence among tillage classes. This structure ensured that simulated tillage percentages are non-negative and
summed to 100 percent. CTIC data do not differentiate between continuous and intermittent use of no-tillage, which
is important for estimating SOC storage. Thus, regionally based estimates for continuous no-tillage (defined as 5 or
more years of continuous use) are modified based on consultation with CTIC experts, as discussed in Step la
(downward adjustment of total no-tillage area based on the amount of no-tillage that is rotated with more intensive
tillage practices, Towery 2001).
Managed Livestock Manure Amendments: USDA provides information on the amount of land amended
with manure for 1997 based on manure production data and field-scale surveys detailing application rates that had
been collected in the Census of Agriculture (Edmonds et al. 2003). Similar to the DAYCENT model discussion in
Steplb, the amount of land receiving manure is based on the estimates provided by Edmonds et al. (2003), as a
proportion of crop and grassland amended with manure within individual climate regions. The resulting proportions
are used to re-classify a portion of crop and grassland into a new management category. Specifically, a portion of
medium input cropping systems is re-classified as high input, and a portion of the high input systems is re-classified
as high input with amendment. In grassland systems, the estimated proportions for land amended with manure are
A-308 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
used to re-classify a portion of nominally-managed grassland as improved, and a portion of improved grassland as
improved with high input. These classification approaches are consistent with the IPCC inventory methodology
(IPCC 2006). Uncertainties in the amount of land amended with manure are based on the sample variance at the
climate region scale, assuming normal density PDFs (i.e., variance of the climate region estimates, which are
derived from county-scale proportions).
Sewage Sludge Amendments: Sewage sludge is generated from the treatment of raw sewage in public or
private wastewater treatment facilities and is typically used as a soil amendment or is sent for waste disposal to
landfills. In this Inventory, all sewage sludge that is amended to agricultural soils is assumed to be applied to
grasslands. See section on sewage sludge in Step Ic for more information about the methods used to derive sewage
sludge N estimates, and the total amount of sewage sludge N is given in Table A- 225. Sewage sludge N is assumed
to be applied at the assimilative capacity provided in Kellogg et al. (2000), which is the amount of nutrients taken up
by a crop and removed at harvest, representing the recommended application rate for manure amendments. This
capacity varies from year to year, because it is based on specific crop yields during the respective year (Kellogg et
al. 2000). Total sewage sludge N available for application is divided by the assimilative capacity to estimate the
total land area over which sewage sludge had been applied. The resulting estimates are used for the estimation of
soil C stock change.
CRP Enrollment after 2007: The change in enrollment for the Conservation Reserve Program after 2007
is based on the amount of land under active contracts from 2008 through 2012 relative to 2007 (USDA-FS A 2012).
Wetland Reserve: Wetlands enrolled in the Conservation Reserve Program have been restored in the
Northern Prairie Pothole Region through the Partners for Wildlife Program funded by the U.S. Fish and Wildlife
Service. The area of restored wetlands is estimated from contract agreements (Euliss and Gleason 2002). While the
contracts provide reasonable estimates of the amount of land restored in the region, they do not provide the
information necessary to estimate uncertainty. Consequently, a ±50 percent range is used to construct the PDFs for
the uncertainty analysis.
Table A- 220: Synthetic Fertilizer N Added to Tier 3 Crops tGg HI
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Fertilizer N 8,994 8,760 8,906 8,540 8,908 8,748 9,052 8,719 8,594 9,594 9,609 9,724 9,842 9,620 9,823
Table A- 221: Fate of Livestock Manure Nitrogen tGg HI
Activity 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Managed Manure N
Applied to Tier 3
Crops and Non-
federal Grasslands'," 992 908 1,098 1,055 1,060 1,051 1,038 1,037 1,004 1,107 1,107 1,107 1,107 1,105 1,104
Managed Manure N
Applied to Tier 1
Crops<= 1,665 1,804 1,750 1,772 1,795 1,822 1,750 1,792 1,889 1,829 1,803 1,790 1,791 1,815 1,831
Managed Manure N
Applied to
Grasslands 62 • 61 • 60 61 62 60 59 59 59 57 57 57 57 57 57
Pasture, Range, &
Paddock Manure N 4,293 4,695 4,331 4,319 4,322 4,313 4,256 4,308 4,349 4,253 4,210 4,169 4,123 4,033 4,082
Total 7,012 7,468 7,239 7,208 7,239 7,246 7,104 7,196 7,300 7,247 7,177 7,124 7,078 7,010 7,075
a Accounts for N volatilized and leached/runoff during treatment, storage and transport before soil application.
b Includes managed manure and daily spread manure amendments
c Totals may not sum exactly due to rounding.
Table A-222: Crop Residue N and Other N Inputs to Tier 3 Crops as Simulated by DAYCENT tGg HI
Activity 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
'
Residue N= 578 • 623 619 631 589 636 664 657 629 628 628 628 628 628 628
Mineralization &
Asymbiotic
Fixation 11,001 11,834 10,455 11,653 11,300 11,303 12,061 11,343 11,642 11,116 11,116 11,116 11,116 11,116 11,116
a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
A-309
-------
Table A- 223: Synthetic Fertilizer N Added to Tier 1 Crops tug HI
Activity
Fertilizer N
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
2,532 2,311 2,261 2,594 2,554 2,450 2,630 2,298 1,914 1,656 1,866 1,974 1,640
Table A- 224: Other Organic Commercial Fertilizer Consumption on Agricultural Lands tug HI
Activity 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Other Commercial Organic
Fertilizer N° 4 10^ 9 7 8 8 9 10 12 15 12 10 10 10 9
a Includes dried blood, tankage, compost, other. Excludes dried manure and sewage sludge used as commercial fertilizer to avoid double
counting.
Table A- 225: Sewage Sludge Nitrogen by Disposal Practice tug HI
Disposal Practice
Applied to Agricultural Soils
Other Land Application
Surface Disposal
Total
1990
52
25
20
98
1995
69
28
16
113
2000 2001
84 86
30 30
10 9
124 125
2002
89
30
8
127
2003
91
30
6
128
2004
94
30
5
130
2005
98
31
5
134
2006 2007
101 104
31 32
4 4
136 139
2008
106
32
3
141
2009 2010
109 112
32 32
3 3
144 147
2011 2012
115 118
32 32
2 2
149 152
Note: Totals may not sum due to independent rounding.
Table A- 226: Key Assumptions for Production of Tier 1 Crops and Retention of Crop Residues
Crop
Dry Edible Peas
Austrian Winter Peas
Lentils
Wrinkled Seed Peas
Millet
Dry Matter
Fraction of
Harvested
Product
0.91
0.91
0.91
0.91
0.90
Above-ground Residue Ratio of Residue N Fraction
Below-ground
Residue to
Above-ground
Slope Intercept Biomass Above-ground Below-ground
1.13
1.13
1.13
1.13
1.43
0.85
0.85
0.85
0.85
0.14
0.19
0.19
0.19
0.19
0.22
0.008
0.008
0.008
0.008
0.007
0.008
0.008
0.008
0.008
0.009
Table A- 227: Nitrogen in Crop Residues Retained on Soils Producing Tier 1 Crops tug HI
Crop
Dry Edible Peas
Austrian Winter Peas
Lentils
Wrinkled Seed Peas
Millet
Total
1990 1995 2000
1
9
7 •
41
•
9
7
43
10
8
10
8
3
40
2001
10
8
10
8
7
43
2002
10
8
9
8
2
39
2003
11
8
9
8
5
41
2004
14
8
10
9
6
47
2005
15
8
11
8
5
48
2006
8
8
10
8
4
39
2007
8
8
10
8
6
41
2008
14
8
9
8
6
46
2009
17
8
11
9
4
48
2010
15
8
12
8
5
49
2011
11
8
10
8
4
42
2012
14
8
11
8
2
43
Step 1e: Additional Activity Data for Indirect AfeO Emissions from Managed Soils of all Land-Use Types
A portion of the N that is applied as synthetic fertilizer, livestock manure, sewage sludge, and other organic
amendments volatilizes as NH3 and NOX. In turn, this N is returned to soils through atmospheric deposition, thereby
increasing mineral N availability and enhancing N2O production. Additional N is lost from soils through leaching as
water percolates through a soil profile and through runoff with overland water flow. N losses from leaching and
runoff enter groundwater and waterways, from which a portion is emitted as N2O. However, N leaching is assumed
to be an insignificant source of indirect N2O in cropland and grassland systems where the amount of precipitation
plus irrigation does not exceed 80 percent of the potential evapotranspiration. These areas are typically semi-arid to
arid, and nitrate leaching to groundwater is a relatively uncommon event; moreover IPCC (2006) recommends
limiting the amount of nitrate leaching assumed to be a source of indirect N2O emissions based on precipitation,
irrigation and potential evapotranspiration.
The activity data for synthetic fertilizer, livestock manure, other organic amendments, residue N inputs,
sewage sludge N, and other N inputs are the same as those used in the calculation of direct emissions from
agricultural mineral soils, and may be found in Table A- 220 through Table A- 224, Table A- 227, and Table A-
225. The activity data for computing direct and indirect N2O emissions from settlements and forest lands are
described in the Land Use, Land-Use Change, and Forestry section of this report.
A-310 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Using the DAYCENT model, volatilization and leaching/surface run-off of N from soils is computed
internally for Tier 3 crops and non-federal grasslands. DAYCENT simulates the processes leading to these losses of
N based on environmental conditions (i.e., weather patterns and soil characteristics), management impacts (e.g.,
plowing, irrigation, harvest), and soil N availability. Note that the DAYCENT model accounts for losses of N from
all anthropogenic activity, not just the inputs of N from mineral fertilization and organic amendments, which are
addressed in the Tier 1 methodology. Similarly, the N available for producing indirect emissions resulting from
grassland management as well as deposited PRP manure is also estimated by DAYCENT. Estimated leaching losses
of N from DAYCENT are not used in the indirect N2O calculation if the amount of precipitation plus irrigation did
not exceed 80 percent of the potential evapotranspiration. Volatilized losses of N are summed for each day in the
annual cycle to provide an estimate of the amount of N subject to indirect N2O emissions. In addition, the daily
losses of N through leaching and runoff in overland flow are summed for the annual cycle. The implied emission
factor for N volatilization ranges from 7 to 9 percent for cropland (Table A-15, Tier 1 default value is 10 percent).
The implied emission factor for NOs" from leaching/runoff ranges from 25 to 31 percent for cropland (Table A-15,
Tier 1 default value is 30 percent). The implied emission factor for N volatilization ranges from 21 to 57 percent for
grassland (Table A-16, Tier 1 default value is 20 percent). The implied emission factor for NOs" from
leaching/runoff ranges from 14 to 22 percent for grassland (Table A-16, Tier 1 default value is 30 percent).
Uncertainty in the estimates is derived from uncertainties in the activity data for the N inputs (i.e., fertilizer and
organic amendments; see Step la for further information).
The Tier 1 method is used to estimate N losses from mineral soils due to volatilization and leaching/runoff
for crops, forestland, settlements, sewage sludge applications, and PRP manure on federal grasslands not accounted
for by DAYCENT simulations. To estimate volatilized losses, synthetic fertilizers, manure, sewage sludge, and
other organic N inputs are multiplied by the fraction subject to gaseous losses using the respective default values of
0.1 kg N/kg N added as mineral fertilizers and 0.2 kg N/kg N added as manure (IPCC 2006). Uncertainty in the
volatilized N ranges from 0.03-0.3 kg NH3-N+NOx-N/kg N for synthetic fertilizer and 0.05-0.5 kg NH3-N+NOx-
N/kg N for organic amendments (IPCC 2006). Leaching/runoff losses of N are estimated by summing the N
additions from synthetic and other organic fertilizers, manure, sewage sludge, and above- and below-ground crop
residues, and then multiplying by the default fraction subject to leaching/runoff losses of 0.3 kg N/kg N applied,
with an uncertainty from 0.1-0.8 kg NOs-N/kg N (IPCC 2006). However, N leaching is assumed to be an
insignificant source of indirect N2O emissions if the amount of precipitation plus irrigation did not exceed 80
percent of the potential evapotranspiration. PDFs are derived for each of the N inputs in the same manner as direct
N2O emissions, discussed in Steps la and Ic.
Volatilized N is summed for losses from Tier 3 crop types, Tier 1 crop types, federal and non-federal
grasslands, settlements, and forest lands. Similarly, the annual amounts of N lost from soil profiles through leaching
and surface runoff are summed to obtain the total losses for this pathway.
Step 2: Estimate Soil Organic C Stock Changes and Direct N20 Emissions from Mineral Soils
In this step, soil organic C stock changes and N2O emissions are estimated for Tier 3 crops, Tier 1 crops,
federal and non-federal grasslands. Three methods are used to estimate soil organic C stock changes and direct N2O
emissions from mineral soils. The DAYCENT process-based model is used for Tier 3 croplands and non-federal
grasslands. A Tier 2 method is used to estimate soil organic C stock changes for crop histories that included crops
that were not simulated by DAYCENT and land use change other than conversions between cropland and grassland.
A Tier 1 methodology is used to estimate N2O emissions from Tier 1 crops, which are grown on a considerably
smaller portion of land than the Tier 3 crops, as well as PRP manure N deposition on federal grasslands. Soil organic
C stock changes and N2O emissions are not estimated for federal grasslands (other than the effect of PRP manure
N), but are under evaluation as a planned improvement and may be estimated in future inventories.
Step 2a: Estimate Soil Organic C Stock Changes and NtO Emissions for Crops and Non-Federal Grassland with the
Tier 3 DAYCENT Model
Tier 3 crops include alfalfa hay, barley, corn, cotton, dry beans, grass hay, grass-clover hay, oats, onions,
peanuts, potatoes, rice, sorghum, soybeans, sugar beets, sunflowers, tomatoes, and wheat, which represent
approximately 85-87 percent of total cropland in the United States. The DAYCENT simulations also included all
non-federal grasslands in the United States.
A-311
-------
The methodology description is divided into two sub-steps. First, the model is used to establish the initial
conditions and C stocks for 1979, which is the last year before the NRI survey is initiated. In the second sub-step,
DAYCENT is used to estimate changes in soil organic C stocks and direct N2O emissions based on the land-use and
management histories recorded in the NRI from 1990 through 2007 (USDA-NRCS 2009).
Simulate Initial Conditions (Pre-NRI Conditions): DAYCENT model initialization involves two steps,
with the goal of estimating the most accurate stock for the pre-NRI history, and the distribution of organic C among
the pools represented in the model (e.g., Structural, Metabolic, Active, Slow, and Passive). Each pool has a different
turnover rate (representing the heterogeneous nature of soil organic matter), and the amount of C in each pool at any
point in time influences the forward trajectory of the total soil organic C storage. There is currently no national set
of soil C measurements that can be used for establishing initial conditions in the model. Sensitivity analysis of the
soil organic C algorithms showed that the rate of change of soil organic matter is relatively insensitive to the amount
of total soil organic C but is highly sensitive to the relative distribution of C among different pools (Parton et al.
1987). By simulating the historical land use prior to the inventory period, initial pool distributions are estimated in
an unbiased way.
The first step involves running the model to a steady-state condition (e.g., equilibrium) under native
vegetation, historical climate data based on the NARR product (1980-2007), and the soil physical attributes for the
NRI points. Native vegetation is represented at the MLRA level for pre-settlement time periods in the United States.
The model simulates 5,000 years in the pre-settlement era in order to achieve a steady-state condition.
The second step is to simulate the period of time from European settlement and expansion of agriculture to
the beginning of the NRI survey, representing the influence of historic land-use change and management,
particularly the conversion of native vegetation to agricultural uses. This encompasses a varying time period from
land conversion (depending on historical settlement patterns) to 1979. The information on historical cropping
practices used for DAYCENT simulations has been gathered from a variety of sources, ranging from the historical
accounts of farming practices reported in the literature (e.g., Miner 1998) to national level databases (e.g., NASS
2004). A detailed description of the data sources and assumptions used in constructing the base history scenarios of
agricultural practices can be found in Williams and Paustian (2005).
NRIHistory Simulations: After model initialization, DAYCENT is used to simulate the NRI land use and
management histories from 1979 through 2007.88 The simulations address the influence of soil management on
direct N2O emissions, soil organic C stock changes and losses of N from the profile through leaching/runoff and
volatilization. The NRI histories identify the land use and land use change histories for the NRI survey locations, as
well as cropping patterns and irrigation history (see Step la for description of the NRI data). The input data for the
model simulations also include the NARR weather dataset and SSURGO soils data, synthetic N fertilizer rates,
managed manure amendments to cropland and grassland, manure deposition on grasslands (i.e., PRP), tillage
histories and EVI data (See Step Ib for description of the inputs). The total number of DAYCENT simulations is
over 18 million with a 100 repeated simulations (i.e., iterations) for each NRI point location in a Monte Carlo
Analysis. The simulation system incorporates a dedicated MySQL database server and a 30-node parallel processing
computer cluster. Input/output operations are managed by a set of run executive programs written in PERL.
The simulations for the NRI history are integrated with the uncertainty analysis. Evaluating uncertainty is
an integral part of the analysis, and includes three components: (1) uncertainty in the main activity data inputs
affecting soil C and N2O emissions (input uncertainty); (2) uncertainty in the model formulation and
parameterization (structural uncertainty); and (3) uncertainty in the land-use and management system areas (scaling
uncertainty) (Ogle et al. 2010, Del Grosso et al. 2010). For component 1, input uncertainty is evaluated for
fertilization management, manure applications, and tillage, which are primary management activity data that are
supplemental to the NRI observations and have significant influence on soil organic C dynamics and N2O emissions.
As described in Step Ib, PDFs are derived from surveys at the county scale for the inputs in most cases. In addition,
uncertainty is included for predictions of EVI data that are needed to fill-data gaps and extend the time series (see
Enhanced Vegetation Index in Step Ib). To represent uncertainty in all of these inputs, a Monte-Carlo Analysis is
used with 100 iterations for each NRI point; random draws are made from PDFs for fertilizer, manure application,
tillage, and EVI predictions. As described above, an adjustment factor is also selected from PDFs with normal
densities to represent the dependence between manure amendments and N fertilizer application rates.
The estimated soil C stock change in 2007 is currently assumed to represent the changes between 2008 and 2012. New
estimates will be available in the future to extend the time series of land use and management data.
A-312 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
The second component deals with uncertainty inherent in model formulation and parameterization. This
component is the largest source of uncertainty in the Tier 3 model-based inventory analysis, accounting for more
than 80 percent of the overall uncertainty in the final estimates (Ogle et al. 2010, Del Grosso et al. 2010). An
empirically-based procedure is applied to develop a structural uncertainty estimator from the relationship between
modeled results and field measurements from agricultural experiments (Ogle et al. 2007). For soil organic C, the
DAYCENT model is evaluated with measurements from 84 long-term field experiments that have over 900
treatments, representing a variety of management conditions (e.g., variation in crop rotation, tillage, fertilization
rates, and manure amendments). There are 24 experimental sites available to evaluate structural uncertainty in the
N2O emission predictions from DAYCENT (Del Grosso et al. 2010). The inputs to the model are essentially known
in the simulations for the long-term experiments, and, therefore, the analysis is designed to evaluate uncertainties
associated with the model structure (i.e., model algorithms and parameterization). USDA is developing a national
soil monitoring network to evaluate the Inventory in the future (Spencer et al. 2011).
The relationship between modeled soil organic C stocks and field measurements are statistically analyzed
using linear-mixed effect modeling techniques. Additional fixed effects are included in the mixed effect model if
they explained significant variation in the relationship between modeled and measured stocks (i.e., if they met an
alpha level of 0.05 for significance). Several variables are tested, including land-use class; type of tillage; cropping
system; geographic location; climate; soil texture; time since the management change; original land cover (i.e.,
forest or grassland); grain harvest as predicted by the model compared to the experimental values; and variation in
fertilizer and residue management. The final cropland model includes variables for modeled soil organic C
inclusion of hay/pasture in cropping rotations, use of no-till, set-aside lands, organic matter amendments, and
inclusion of bare fallow in the rotation, which are significant at an alpha level of 0.05. The final grassland model
only included the model soil organic C. These fixed effects are used to make an adjustment to modeled values due to
biases that are creating significant mismatches between the modeled and measured stocks. For soil N2O, simulated
DAYCENT emissions are a highly significant predictor of the measurements, with a p-value of O.01. Several other
variables are considered in the statistical model to evaluate if DAYCENT exhibits bias under certain conditions
related to climate, soil types, and management practices. The type of crop is significant at an alpha level of 0.05,
demonstrating that DAYCENT tends to over-estimate emissions for small grains systems, but is accurate in
predicting the N2O emissions for other crops and grassland. Random effects are included in the model to capture the
dependence in time series and data collected from the same site, which are needed to estimate appropriate standard
deviations for parameter coefficients.
A Monte Carlo approach is used to apply the uncertainty estimator (Ogle et al. 2010). Parameter values for
the statistical equation (i.e., fixed effects) are selected from their joint probability distribution, as well as random
error associated with fine-scale estimates at NRI points, and the residual or unexplained error associated with the
linear mixed-effect model. The estimate and associated management information is then used as input into the
equation, and adjusted values are computed for each C stock and N2O emissions estimate. The variance of the
adjusted estimates is computed from the 100 simulated values from the Monte Carlo analysis.
The third element is the uncertainty associated with scaling the DAYCENT results for each NRI point to
the entire land base, using the expansion factors provided with the NRI survey dataset. The expansion factors
represent the number of hectares associated with the land-use and management history for a particular point. This
uncertainty is determined by computing the variances from a set of replicated weights for the expansion factor.
For the land base that is simulated with the DAYCENT model, soil organic C stock changes are provided in
Table A-228, and soil N2O emissions are provided in Table A-229.
Table A-228: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Land Base Simulated with
the Tier 3 DAYCENT Model-Based Approach tTg CQ2 Eq.l
Cropland
Year Estimate
1990 (69.39)
1991 (74.40)
1992 (66.23)
1993 (45.70)
1994 (53.00)
1995 (41.87)
Remaining Cropland
95% Cl
(11 1.58) to (27. 19)
(113.19) to (35.60)
(104. 48) to (27.97)
(82.77) to (8.64)
(86.46) to (19.53)
(78.60)to(5.15)
Land Converted to Cropland
Estimate 95% Cl
19.99
17.32
19.15
20.15
12.25
20.28
9.49 to 30.49
5.98 to 28.65
8.63 to 29.66
7.94 to 32.36
(1.92) to 26.42
7.18to33.38
Grassland Remaining Grassland
Estimate 95% Cl
(13.36)
(2.56)
(11.34)
(0.65)
(18.10)
1.68
(48.70) to 21. 97
(41. 30) to 36. 18
(46.88) to 24. 19
(35.56) to 34.26
(56.56) to 20.35
(36.44) to 39.81
Land Converted to Grassland
Estimate 95% Cl
(4.57)
(5.31)
(4.86)
(3.85)
(5.35)
(5.53)
(9.48) to 0.33
(10.02)to(0.60)
(10. 16) to 0.43
(9.06) to 1.36
(10.68) to (0.02)
(10.54)to(0.52)
A-313
-------
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
(51.50)
(46.79)
(38.32)
(41.27)
(57.32)
(45.18)
(37.86)
(35.71)
(38.94)
(48.71)
(50.85)
(50.58)
(50.58)
(50.58)
(50.58)
(50.58)
(50.58)
(90.24) to (12.77)
(85.92) to (7.66)
(78.24) to 1.59
(76.37)to(6.16)
(97.85)to(16.78)
(83.56) to (6.81)
(75.34) to (0.38)
(72.65) to 1.23
(77.67) to (0.21)
(87.49) to (9.93)
(90.12)to(11.58)
(93.40) to (7.75)
(93.40) to (7.75)
(93.40) to (7.75)
(93.40) to (7.75)
(93.40) to (7.75)
(93.40) to (7.75)
16.20
16.58
11.87
12.08
11.94
13.38
12.46
12.91
10.59
14.25
10.97
10.51
10.51
10.51
10.51
10.51
10.51
3.96 to 28.44
3.03to30.13
(1.95) to 25.70
(1.74) to 25.89
0.30 to 23.57
(1.54) to 28.30
0.97 to 23.95
0.60 to 25.21
(0.49) to 21. 66
2.01to26.50
(1.38) to 23.33
(.41) to 21.42
(.41) to 21.42
(.41) to 21.42
(.41) to 21.42
(.41) to 21.42
(.41) to 21.42
(21.59)
(8.46)
(9.66)
0.53
(33.46)
(11.47)
(14.69)
(10.48)
(0.20)
3.56
(19.74)
4.83
4.85
4.88
4.90
4.92
4.95
(61.91) to 18.73
(42.26) to 25.35
(45.71) to 26.39
(33.43) to 34.49
(72. 15) to 5.22
(46.22) to 23.28
(54.98) to 25.60
(48.22) to 27.26
(38.01) to 37.61
(35.96) to 43.08
(53.30) to 13.82
(30.74) to 40.40
(30.71) to 40.41
(30.68) to 40.43
(30.65) to 40.45
(30.62) to 40.46
(30.59) to 40.48
(6.34)
(6.03)
(6.20)
(6.62)
(8.67)
(7.32)
(6.98)
(6.96)
(8.18)
(7.02)
(7.04)
(7.32)
(7.28)
(7.24)
(7.20)
(7.15)
(7.11)
(12.09)to(0.58)
(11.91)to(0.14)
(14. 47) to 2.08
(14.01) to 0.77
(17.0)to(0.35)
(14. 44) to (0.20)
(15.62) to 1.65
(15.04) to 1.12
(16.58) to 0.21
(15.82) to 1.78
(15.70) to 1.62
(16.67) to 2.03
(16.59) to 2.03
(16.50) to 2.03
(16.42) to 2.02
(16.33) to 2.02
(16.25) to 2.02
Note: Estimates after 2007 are based on NRI data from 2007 and therefore do not fully reflect changes occurring in the latter part
of the time series.
Table A-229: Annual H?0 Emissions (95% Confidence Interval) for the Land Base Simulated with the Tier 3 DAYGENT Model-
Based Approach tTg Clh Eq.l
Year
Tier 3 Crops
Estimate 95% Cl
Non-Federal Grasslands
Estimate 95% Cl
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
132.1
144.6
146.3
154.6
138.1
141.0
151.4
147.0
125.4
129.3
121.1
133.6
131.5
130.0
138.4
137.4
136.8
144.1
143.7
143.7
139.6
137.9
138.2
123.77 to 144.01
135.44 to 157.83
136.76 to 160.06
144.86 to 168.56
129.86 to 150.44
132.4 to 153.96
142.2 to 165. 16
137.75 to 160.98
11 7.28 to 138.09
121.27 to 141.39
11 3.85 to 131. 88
125.47to145.5
123.57 to 143.07
122.3 to 141.21
130.06 to 150.46
128.97 to 149.53
128.43 to 148.91
135.14to156.8
134.78 to 156.45
134.75 to 156.42
130.73 to 152.46
129.08 to 150.83
129.33 to 151.09
74.3
89.7
73.1
79.3
68.0
77.4
84.8
86.3
67.1
66.0
61.5
69.8
64.2
63.9
77.9
74.3
71.3
86.8
86.6
86.3
86.1
85.8
85.6
67.37 to 84.96
82.14to101.2
68.03 to 80.41
74. 12 to 86.92
63.75 to 74.02
72.38 to 84.61
79.24 to 92.74
80.52 to 94.61
62.5 to 73.54
61.86 to 71.77
57.33 to 67.25
64.82 to 76.9
59.92 to 70.37
59.84 to 69.71
72.46 to 85.71
69.61 to 80.97
66.59 to 78.04
80.21 to 96.4
80.01 to 96.18
79.76 to 95.91
79.5 to 95.64
79.25 to 95.36
78.99 to 95.09
In D AYCENT, the model cannot distinguish among the original sources of N after the mineral N enters the
soil pools in order to determine which management activity led to specific N2O emissions. This means, for example,
that N2O emissions from applied synthetic fertilizer cannot be separated from emissions due to other N inputs, such
as crop residues. It is desirable, however, to report emissions associated with specific N inputs. Thus, for each NRI
point, the N inputs in a simulation are determined for anthropogenic practices discussed in IPCC (2006), including
synthetic mineral N fertilization, organic amendments, and crop residue N added to soils (including N-fixing crops).
The percentage of N input for anthropogenic practices is divided by the total N input, and this proportion is used to
determine the amount of N2O emissions assigned to each of the practices.89 For example, if 70 percent of the
This method is a simplification of reality to allow partitioning of N2O emissions, as it assumes that all N inputs have an
identical chance of being converted to N2O. This is unlikely to be the case, but DAYCENT does not track N2O emissions by source of
A-314 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
mineral N made available in the soil is due to mineral fertilization, then 70 percent of the N2O emissions are
assigned to this practice. The remainder of soil N2O emissions is reported under "other N inputs," which includes
mineralization due to decomposition of soil organic matter and litter, as well as asymbiotic N fixation from the
atmosphere. Asymbiotic N fixation by soil bacteria is a minor source of N, typically not exceeding 10 percent of
total N inputs to agroecosystems. Mineralization of soil organic matter is a more significant source of N, but is still
typically less than half of the amount of N made available in the cropland soils compared to application of synthetic
fertilizers and manure amendments, along with symbiotic fixation. Mineralization of soil organic matter accounts
for the majority of available N in grassland soils. Accounting for the influence of "other N inputs" is necessary in
order to meet the recommendation for reporting all emissions from managed lands (IPCC 2006). While this method
allows for attribution of N2O emissions to the individual N inputs to the soils, it is important to realize that sources
such as synthetic fertilization may have a larger impact on N2O emissions than would be suggested by the associated
level of N input for this source (Delgado et al. 2009). Further research will be needed to improve upon this
attribution method, however. The results are provided in Table A- 230 and Table A- 231 associated with
subdividing the N2O emissions based on N inputs.
mineral N so this approximation is the only approach that can be used currently for partitioning N2O emissions by source of N input.
Moreover, this approach is similar to the IPCC Tier 1 method (IPCC 2006), which uses the same direct emissions factor for most N
sources (e.g., PRP). Further research and model development may allow for other approaches in the future.
A-315
-------
Table A- 230: Direct M Emissions from Cropland Soils tTg Clh Eq.l
N Source
Total Mineral Soils
Tier 3 Crops
Synthetic Fertilizer
Managed Manure
Residue Na
Mineralization and Asymbiotic
Fixation
Tier 1 Crops
Synthetic Fertilizer
Managed Manure and Other
Organic Commercial Fertilizer
Residue N
Organic Soils
Total*
1990
150.4
132.1
55.9
5.8
3.3
67.0
18.3
9.6
8.1
0.6
4.7
155.1
1995
161.0
141.0
55.8
3^9
75.4
20.1
10.6
4.5
165.6
2000
142.6
121.1
50.6
6.1
3.4
61.0
21.5
12.3
8.6
0.6
4.4
146.9
2001
154.2
133.6
52.1
6.4
3.8
71.4
20.5
11.3
8.7
0.6
4.3
158.5
2002
151.9
131.5
53.3
6.4
3.4
68.3
20.4
11.0
8.8
0.6
4.3
156.1
2003
152.2
130.0
51.5
6.2
3.7
68.7
22.1
12.6
8.9
0.6
4.2
156.3
2004
160.0
138.4
54.8
6.5
3.9
73.2
21.6
12.4
8.6
0.6
4.2
164.2
2005
158.7
137.4
53.8
6.5
4.2
72.9
21.3
11.9
8.8
0.6
4.1
162.8
2006
159.5
136.8
51.7
6.5
3.9
74.7
22.6
12.8
9.3
0.6
4.1
163.5
2007
164.8
144.1
60.4
7.0
4.0
72.6
20.8
11.2
9.0
0.6
4.0
168.9
2008
162.5
143.7
60.1
7.0
4.0
72.5
18.8
9.3
8.8
0.6
4.0
166.5
2009
161.1
143.7
61.0
6.9
4.0
71.8
17.5
8.1
8.8
0.6
4.0
165.2
2010
158.1
139.6
59.5
6.6
3.9
69.5
18.5
9.1
8.8
0.6
4.0
162.1
2011
157.0
137.9
57.8
6.6
3.9
69.6
19.1
9.6
8.9
0.6
4.0
161.0
2012
155.7
138.2
59.3
6.6
3.8
68.5
17.5
8.0
9.0
0.6
4.0
159.8
+ Less than 0.05 Tg C02 Eq.
a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
Table A- 231: Direct M Emissions from Grasslands [Tg C0? Eq.l
N Source
TierS
Synthetic Fertilizer
PRP Manure
Managed Manure
Residue Na
Mineralization and
Asymbiotic Fixation
Tierl
PRP Manure
Sewage Sludge
Total
1990
74.3
0.5
13.4
+
2.0
58.2
11.3
11.0
0.3
85.6
1995
82.0
0.7 1
14.21
2 sl
64.41
15.5
15.21
0.3
97.6
2000
63.3
0.6
12.3
+
1.6
48.6
12.4
12.0
0.4
75.7
2001
71.9
0.7
14.1
+
2.0
54.8
11.9
11.5
0.4
83.8
2002
66.1
0.8
13.5
+
1.9
49.6
11.4
11.0
0.4
77.5
2003
66.6
0.7
12.3
+
1.9
51.5
12.3
11.9
0.4
78.9
2004
80.0
1.0
13.2
+
2.2
63.4
12.3
11.8
0.5
92.3
2005
77.8
1.0
13.2
+
2.4
60.8
12.7
12.3
0.5
90.5
2006
74.1
1.2
13.1
+
2.2
57.2
13.5
13.0
0.5
87.6
2007
90.4
1.0
14.3
+
2.6
72.2
13.2
12.7
0.5
103.6
2008
90.1
1.0
14.3
+
2.6
72.0
12.9
12.4
0.5
103.0
2009
89.9
1.0
14.2
+
2.6
71.9
12.6
12.1
0.5
102.5
2010
89.6
1.0
14.2
+
2.6
71.7
12.2
11.7
0.5
101.9
2011
89.4
1.0
14.1
+
2.5
71.5
11.5
10.9
0.6
100.9
2012
89.2
0.9
14.1
+
2.5
71.3
11.9
11.4
0.6
101.1
+ Less than 0.05 Tg C02 Eq.
a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
A-316 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Step 2b: Soil AfcO Emissions from Agricultural Lands on Mineral Soils Approximated with the Tier 1
Approach
To estimate direct N2O emissions from N additions to Tier 1 crops, the amount of N in applied synthetic
fertilizer, manure and other commercial organic fertilizers (i.e., dried blood, tankage, compost, and other) is added to
N inputs from crop residues, and the resulting annual totals are multiplied by the IPCC default emission factor of
0.01 kg N2O-N/kg N (IPCC 2006). The uncertainty is determined based on simple error propagation methods (IPCC
2006). The uncertainty in the default emission factor ranges from 0.3-3.0 kg N2O-N/kg N (IPCC 2006). For
flooded rice soils, the IPCC default emission factor is 0.003 kg N2O-N/kg N and the uncertainty range is 0.000-
0.006 kg N2O-N/kg N (IPCC 2006). Uncertainty in activity data is ± 20 percent for fertilizer additions (Mosier
2004).90 Uncertainties in the emission factor and fertilizer additions are combined with uncertainty in the equations
used to calculate residue N additions from above- and below-ground biomass dry matter and N concentration to
derive overall uncertainty.
The Tier 1 method is also used to estimate emissions from manure N deposited by livestock on federal
lands (i.e., PRP manure N), and from sewage sludge application to grasslands. These two sources of N inputs to
soils are multiplied by the IPCC (2006) default emission factors (0.01 kg N2O-N/kg N for sludge and horse, sheep,
and goat manure, and 0.02 kg N2O-N/kg N for cattle, swine, and poultry manure) to estimate N2O emissions (Table
A- 231). The uncertainty is determined based on the Tier 1 error propagation methods provided by the IPCC (2006)
with uncertainty in the default emission factor ranging from 0.007 to 0.06 kg N2O-N/kg N (IPCC 2006).
Step 2c: Soil Organic C Stock Changes in Agricultural Lands on Mineral Soils Approximated with the Tier 2
Approach
Mineral soil organic C stock values are derived for crop rotations that were not simulated by DAYCENT
and land converted from non-agricultural land uses to cropland or grassland in 1982, 1992, 1997, 2002 and 2007,
based on the land-use and management activity data in conjunction with appropriate reference C stocks, land-use
change, management, input, and wetland restoration factors. Each input to the inventory calculations for the Tier 2
approach has some level of uncertainty that is quantified in PDFs, including the land-use and management activity
data, reference C stocks, and management factors. A Monte Carlo Analysis is used to quantify uncertainty in soil
organic C stock changes for the inventory period based on uncertainty in the inputs. Input values are randomly
selected from PDFs in an iterative process to estimate SOC change for 50,000 times and produce a 95 percent
confidence interval for the inventory results.
Derive Mineral Soil Organic C Stock Change Factors: Stock change factors representative of U.S.
conditions are estimated from published studies (Ogle et al. 2003, Ogle et al. 2006). The numerical factors quantify
the impact of changing land use and management on SOC storage in mineral soils, including tillage practices,
cropping rotation or intensification, and land conversions between cultivated and native conditions (including set-
asides in the Conservation Reserve Program). Studies from the United States and Canada are used in this analysis
under the assumption that they would best represent management impacts for the Inventory.
The IPCC inventory methodology for agricultural soils divides climate into eight distinct zones based upon
average annual temperature, average annual precipitation, and the length of the dry season (IPCC 2006) (Table A-
232). Six of these climate zones occur in the conterminous United States and Hawaii (Eve et al. 2001).
Table A-232: Characteristics of the IPGG Climate Zones that Occur in the United States
Climate Zone
Cold Temperate, Dry
Cold Temperate, Moist
Warm Temperate, Dry
Annual Average
Temperature (°C)
10-20
Length of Dry Season
Average Annual Precipitation (mm) (months)
< Potential Evapotranspiration NA
> Potential Evapotranspiration NA
< 600 NA
Note that due to lack of data, uncertainties in managed manure N production, PRP manure N production, other commercial
organic fertilizer amendments, indirect losses of N in the DAYCENT simulations, and sewage sludge amendments to soils are currently
treated as certain; these sources of uncertainty will be included in future Inventories.
A-317
-------
Warm Temperate, Moist 10 - 20
Sub-Tropical, Dry3 > 20
Sub-Tropical, Moist (w/short dry season)3 >20
> Potential Evapotranspiration NA
< 1,000 Usually long
1,000-2,000 <5
1 The climate characteristics listed in the table for these zones are those that correspond to the tropical dry and tropical moist zones of the
IPCC. They have been renamed "sub-tropical" here.
Mean climate (1961-1990) variables from the PRISM data set (Daly et al. 1994) are used to classify climate
zones. Mean annual precipitation and annual temperature data are averaged (weighted by area) for each of the 4x4
km grid cells occurring within a MLRA region. These averages are used to assign a climate zone to each MLRA
according to the IPCC climate classification (Figure A-14). MLRAs represent geographic units with relatively
similar soils, climate, water resources, and land uses; and there are approximately 180 MLRAs in the United States
(NRCS 1981).
Figure A-14: Major Land Resource Areas by IPGG Climate Zone
Major Land Resource Areas by IPCC Climate Zone
IPCC Climate Zones
I ICTD
| ICTM
| ISTD
I ISTM
| | WTD
I I WTM
This figure shows the IPCC climate zone assigned to each ol the 180 Major Land Resource Areas (MLRAs) in the United States, based on
PRISM climate data averaged for each MRLA.
Soils are classified into one of seven classes based upon texture, morphology, and ability to store organic
matter (IPCC 2006). Six of the categories are mineral types and one is organic (i.e., Histosol). Reference C stocks,
representing estimates from conventionally managed cropland, are computed for each of the mineral soil types
across the various climate zones, based on pedon (i.e., soil) data from the National Soil Survey Characterization
Database (NRCS 1997) (Table A-233). These stocks are used in conjunction with management factors to estimate
the change in SOC stocks that result from management and land-use activity. PDFs, which represent the variability
in the stock estimates, are constructed as normal densities based on the mean and variance from the pedon data.
Pedon locations are clumped in various parts of the country, which reduces the statistical independence of individual
pedon estimates. To account for this lack of independence, samples from each climate by soil zone are tested for
spatial autocorrelation using the Moran's I test, and variance terms are inflated by 10 percent for all zones with
significant p-values.
A-318 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-233: U.S. Soil Groupings Based on the IPGG Categories and Dominant Taxonomic Soil, and Reference Carbon
Stocks [Metric Tons C/bal
Reference Carbon Stock in Climate Regions
Cold Cold Warm Warm
IPCC Inventory Soil USDA Taxonomic Temperate, Temperate, Temperate, Temperate, Sub-Tropical, Sub-Tropical,
Categories Soil Orders Dry Moist Dry Moist Dry Moist
High Clay Activity
Mineral Soils
Low Clay Activity
Mineral Soils
Sandy Soils
Volcanic Soils
Spodic Soils
Aquic Soils
Organic Soils3
Vertisols, Mollisols, Inceptisols,
Aridisols, and high base status
Alfisols
Ultisols, Oxisols, acidic Alfisols,
and many Entisols
Any soils with greater than 70
percent sand and less than 8
percent clay (often Entisols)
Andisols
Spodosols
Soils with Aquic suborder
Histosols
42 (n = 133)
45 (n = 37)
24 (n = 5)
124(n = 12)
86 (n=20)
86 (n = 4)
NA
65 (n = 526)
52(n = 113)
40 (n = 43)
114 (n = 2)
74(n = 13)
89(n = 161)
NA
37 (n = 203)
25 (n = 86)
16(n = 19)
124(n = 12)
86 (n=20)
48 (n = 26)
NA
51 (n = 424)
40 (n = 300)
30 (n = 102)
124(n = 12)
107 (n = 7)
51 (n = 300)
NA
42 (n = 26)
39(n = 13)
33(n = 186)
124(n = 12)
86 (n=20)
63 (n = 503)
NA
57 (n = 12)
47 (n = 7)
50 (n = 18)
128 (n = 9)
86 (n=20)
48 (n = 12)
NA
* C stocks are not needed for organic soils.
Notes: C stocks are for the top 30 cm of the soil profile, and are estimated from pedon data available in the National Soil Survey Characterization database
(NRCS 1997); sample size provided in parentheses (i.e., 'n' values refer to sample size).
To estimate the land use, management and input factors, studies had to report SOC stocks (or information
to compute stocks), depth of sampling, and the number of years since a management change to be included in the
analysis. The data are analyzed using linear mixed-effect modeling, accounting for both fixed and random effects.
Fixed effects included depth, number of years since a management change, climate, and the type of management
change (e.g., reduced tillage vs. no-till). For depth increments, the data are not aggregated for the C stock
measurements; each depth increment (e.g., 0-5 cm, 5-10 cm, and 10-30 cm) is included as a separate point in the
dataset. Similarly, time series data are not aggregated in these datasets. Linear regression models assume that the
underlying data are independent observations, but this is not the case with data from the same experimental site, or
plot in a time series. These data are more related to each other than data from other sites (i.e., not independent).
Consequently, random effects are needed to account for the dependence in time series data and the dependence
among data points representing different depth increments from the same study. Factors are estimated for the effect
of management practices at 20 years for the top 30 cm of the soil (Table A-234). Variance is calculated for each of
the U.S. factor values, and used to construct PDFs with a normal density. In the IPCC method, specific factor values
are given for improved grassland, high input cropland with organic amendments, and for wetland rice, each of which
influences C stock changes in soils. Specifically, higher stocks are associated with increased productivity and C
inputs (relative to native grassland) on improved grassland with both medium and high input.91 Organic
amendments in annual cropping systems also increase SOC stocks due to greater C inputs, while high SOC stocks in
rice cultivation are associated with reduced decomposition due to periodic flooding. There are insufficient field
studies to derive factor values for these systems from the published literature, and, thus, estimates from IPCC (2006)
are used under the assumption that they would best approximate the impacts, given the lack of sufficient data to
derive U.S.-specific factors. A measure of uncertainty is provided for these factors in IPCC (2006), which is used to
construct PDFs.
Table A-234: Soil Organic Carbon Stock Change Factors for the United States and the IPCC Default Values Associated with
Management Impacts on Mineral Soils
Land-Use Change Factors
Cultivated3
General Uncult.a,b (n=251)
Set-Asidea(n=142)
IPCC
default
1
1.4
1.25
Warm Moist
Climate
1
1 .42±0.06
1.31 ±0.06
U.S. Factor
Warm Dry
Climate
1
1 .37±0.05
1 .26±0.04
Cool Moist
Climate
1
1 .24±0.06
1.14±0.06
Cool Dry
Climate
1
1 .20±0.06
1.10±0.05
91
Improved grasslands are identified in the 2007 National Resources Inventory as grasslands that are irrigated or seeded with
legumes, in addition to those reclassified as improved with manure amendments.
A-319
-------
Improved Grassland Factors0
Medium Input
High Input
Wetland Rice Production Factorb
Tillage Factors
Conv. Till
Red. Till (n=93)
No-till (n=212)
Cropland Input Factors
Low (n=85)
Medium
High (n=22)
High with amendment11
1.1
NA
1.1
1
1.05
1.1
0.9
1
1.1
1.2
1.14±0.06
1.11 ±0.04
1.1
1
1 .08±0.03
1.13±0.02
0.94±0.01
1
1 .07±0.02
1 .38±0.06
1.14±0.06
1.11 ±0.04
1.1
1
1.01 ±0.03
1 .05±0.03
0.94±0.01
1
1 .07±0.02
1 .34±0.08
1.14±0.06
1.11 ±0.04
1.1
1
1 .08±0.03
1.13±0.02
0.94±0.01
1
1 .07±0.02
1 .38±0.06
1.14±0.06
1.11 ±0.04
1.1
1
1.01 ±0.03
1 .05±0.03
0.94±0.01
1
1 .07±0.02
1 .34±0.08
Note: The "n" values refer to sample size.
a Factors in the IPCC documentation (IPCC 2006) are converted to represent changes in SOC storage from a cultivated condition rather than a native condition.
b U.S.-specific factors are not estimated for land improvements, rice production, or high input with amendment because of few studies addressing the impact of
legume mixtures, irrigation, or manure applications for crop and grassland in the United States, or the impact of wetland rice production in the US. Factors
provided in IPCC (2006) are used as the best estimates of these impacts.
Wetland restoration management also influences SOC storage in mineral soils, because restoration leads to
higher water tables and inundation of the soil for at least part of the year. A stock change factor is estimated
assessing the difference in SOC storage between restored and unrestored wetlands enrolled in the Conservation
Reserve Program (Euliss and Gleason 2002), which represents an initial increase of C in the restored soils over the
first 10 years (Table A-235). A PDF with a normal density is constructed from these data based on results from a
linear regression model. Following the initial increase of C, natural erosion and deposition leads to additional
accretion of C in these wetlands. The mass accumulation rate of organic C is estimated using annual sedimentation
rates (cm/yr) in combination with percent organic C, and soil bulk density (g/cm3) (Euliss and Gleason 2002).
Procedures for calculation of mass accumulation rate are described in Dean and Gorham (1998); the resulting rate
and variance are used to construct a PDF with a normal density (Table A-235).
Table A-235: Factor Estimate for the Initial Increase and Subsequent Annual Mass Accumulation Rate (Mg G/ha-yr) in Soil
Organic C Following Wetland Restoration of Conservation Reserve Program
Variable Value
Factor (Initial Increase—First 10 Years) 1.22±0.18
Mass Accumulation (After Initial 10 Years) 0.79±0.05
Note: Mass accumulation rate represents additional gains in C for mineral soils after the first 10 years (Euliss and Gleason 2002).
Estimate Annual Changes in Mineral Soil Organic C Stocks: In accordance with IPCC methodology,
annual changes in mineral soil C are calculated by subtracting the beginning stock from the ending stock and then
dividing by 20.92 For this analysis, the base inventory estimate for 1990 through 1992 is the annual average of 1992
stock minus the 1982 stock. The annual average change between 1993 and 1997 is the difference between the 1997
and 1992 C stocks. The annual average change between 1998 and 2002 is the difference between the 1998 and 2002
C stocks. The annual average change between 2003 and 2012 is the difference between the 2003 and 2007. Using
the Monte Carlo approach, SOC stock changes for mineral soils are estimated 50,000 times between 1982 and 1992,
1993 and 1997, 1998 and 2002, and 2003 and 2007. From the final distribution of 50,000 values, a 95 percent
confidence interval is generated based on the simulated values at the 2.5 and 97.5 percentiles in the distribution
(Ogle et al. 2003). Soil organic C stock changes are provided in Table A-236.
The difference in C stocks is divided by 20 because the stock change factors represent change over a 20-year time period.
A-320 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-236: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Land Base Estimated with the Tier 2 Analysis using U.S. Factor Values and
Reference Carbon Stocks (Tg Clh Eq./yr)
Croplands:
Mineral Soils
1990-1992
1993-1997
1998-2002
2003-2012
Organic Soils
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Cropland Remaining Cropland
Estimate
-6.49
-7.64
-6.93
-2.82
23.98
23.72
23.74
23.01
22.40
22.19
21.83
21.69
21.72
21.64
21.52
21.96
21.92
22.92
22.61
22.39
22.29
22.14
22.14
22.14
22.14
22.14
22.14
95% Cl
(9.24) to (4.07)
(10.70) to (4.79)
(9.67) to (4.44)
(5.08) to (0.91)
15.43 to 34.78
15.21 to 34.31
15.27 to 34.52
14.68 to 33.58
14.36 to 32.77
14. 12 to 32.46
13.88 to 31.93
13.75 to 31.76
13.63 to 32.09
13.63 to 31.77
13.51 to 31.60
13.84 to 32. 17
13.85 to 32.08
14.50 to 33.46
14.24 to 33.46
14.06 to 33.01
13.98 to 32.83
14.05 to 32.46
14.05 to 32.46
14.05 to 32.46
14.05 to 32.46
14.05 to 32.46
14.05 to 32.46
Grassland Converted to
Cropland
Estimate
2.34
2.02
1.78
0.78
2.51
2.59
2.43
2.75
3.10
3.11
3.25
3.33
3.44
3.34
3.26
4.68
4.34
4.04
4.36
4.29
4.17
4.02
4.02
4.02
4.02
4.02
4.02
Note: Estimates after 2007 are based on NRI data from 2007
Grasslands'
Mineral Soils
1990-1992
1993-1997
1998-2002
Grassland
Remaining
Grassland
Estimate
-0.19
-0.08
-0.01
95% Cl
1.29 to 3.48
1.06 to 3.06
0.90 to 2.73
0.40 to 1.20
1.36 to 4.05
1.40 to 4.12
1.33 to 3.90
1.56 to 4.32
1.78 to 4.83
1.77 to 4.90
1.87 to 5.09
1.92 to 5.20
1.85 to 5.55
1.77 to 5.44
1.77 to 5.27
1.91 to 9.31
1.73 to 8.88
1.70 to 7.89
1.03 to 11. 27
0.95 to 11. 22
0.86 to 11. 10
0.69 to 10.93
0.69 to 10.93
0.69 to 10.93
0.69 to 10.93
0.69 to 10.93
0.69 to 10.93
Forest Converted to
Cropland
Estimate
1.47
1.39
0.82
0.26
0.83
0.83
0.77
0.81
0.85
0.81
0.93
0.93
0.83
0.76
0.70
0.42
0.29
0.26
0.29
0.27
0.22
0.23
0.23
0.23
0.23
0.23
0.23
95% Cl
0.81 to 2.18
0.73 to 2.12
0.41 to 1.25
0.1 3 to 0.40
0.34 to 1.51
0.35 to 1.51
0.29 to 1.43
0.32 to 1.50
0.35 to 1.55
0.32 to 1.52
0.40 to 1.67
0.40 to 1.67
0.30 to 1.58
0.31 to 1.39
0.27 to 1.31
0.14 to 0.80
0.04 to 0.63
0.02 to 0.60
0.0 to 0.95
0.0 to 0.91
0.0 to 0.81
0.0 to 0.81
0.0 to 0.81
0.0 to 0.81
0.0 to 0.81
0.0 to 0.81
0.0 to 0.81
Other Land Converted to
Cropland
Estimate 95% Cl
0.26 0.14 to 0.38
0.28 0.15 to 0.43
0.27 0.13 to 0.41
0.11 0.06 to 0.17
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Settlements Converted to
Cropland
Estimate
0.56
0.74
0.65
0.32
0.14
0.09
0.09
0.19
0.35
0.35
0.36
0.36
0.36
0.36
0.26
0.29
0.27
0.27
0.21
0.21
0.20
0.18
0.18
0.18
0.18
0.18
0.18
and therefore do not fully reflect changes occurring in the latter part of the time
Cropland Converted to
Grassland
95% Cl Estimate
(0.38) to (0.03)
(0.1 8) to 0.0
(0.08) to 0.06
-1.73
-1.56
-1.74
95% Cl
(2.41) to (1.06)
(2.18) to (0.94)
(2.47) to (1.03)
Forest Converted to
Grassland
Estimate
-1.07
-1.06
-0.79
95% Cl
(1.51) to (0.67)
(1.51) to (0.65)
(1.1 4) to (0.47)
Other Land Converted to
Grassland
Estimate 95% Cl
-0.19 (0.27) to (0.1 2)
-0.21 (0.31) to (0.1 3)
-0.26 (0.37) to (0.1 5)
95% Cl
.31 to .83
.39 to 1.13
.33 to 1.0
.16to.50
0.06 to 0.26
0.03 to 0.16
0.03 to 0.16
0.09 to 0.33
0.18 to 0.59
0.18 to 0.58
0.19 to 0.59
0.19 to 0.59
0.08 to 0.73
0.08 to 0.72
0.04 to 0.55
0.06 to 0.59
0.05 to 0.57
0.04 to 0.56
0.12 to 0.34
0.12 to 0.34
0.11 to 0.32
0.10 to 0.29
0.10 to 0.29
0.10 to 0.29
0.10 to 0.29
0.10 to 0.29
0.10 to 0.29
series.
Settlements Converted to
Grassland
Estimate
-0.41
-0.56
-0.63
95% Cl
(0.58) to (0.26)
(0.80) to (0.35)
(0.91) to (0.38)
Wetlands Converted to
Cropland
Estimate
0.18
0.25
0.21
0.08
0.67
0.67
0.62
0.68
0.79
0.80
0.81
0.81
0.86
0.67
0.62
0.62
0.48
0.30
0.30
0.30
0.30
0.36
0.36
0.36
0.36
0.36
0.36
95% Cl
.10to.27
.13to.38
.10(0.32
.04(0.12
0.37to1.09
0.36to1.09
0.34(0.98
0.39to1.06
0.47to1.18
0.49to1.20
0.49to1.22
0.49to1.21
0.25to1.68
0.28to1.26
0.24to1.20
0.24to1.19
0.17to1.01
0.15to0.50
0.14to0.50
0.14to0.50
0.14to0.50
0.17to0.61
0.17to0.61
0.17to0.61
0.17to0.61
0.17to0.61
0.17to0.61
Wetlands Converted to
Grassland
Estimate
-0.13
-0.19
-0.20
95% Cl
(0.1 9) to (0.08)
(0.27) to (0.1 2)
(0.29) to (0.1 2)
A-321
-------
2003-2012
0.10
0.01 to 0.21
-1.28 (1.86) to (0.71)
-0.42 (0.62) to (0.24)
-0.18 (0.27) to (0.10)
-0.52 (0.77) to (0.29)
-0.13 (0.19) to (0.07)
Organic Soils
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
4.60
4.50
4.47
4.40
4.29
4.14
4.04
3.91
3.80
3.73
3.69
3.28
3.24
3.08
3.05
3.06
2.97
3.03
3.03
3.03
3.03
3.03
3.03
2.54 to 7.33
2.47 to 7. 19
2.45 to 7. 14
2.42 to 7.03
2.37 to 6.87
2.28 to 6.61
2.22 to 6.47
2.14to 6.28
1.96 to 6.32
1.92 to 6.25
1.91 to 6.14
1.76 to 5.33
1.72 to 5.24
1.66 to 4.97
1.66 to 4.90
1.67 to 4.91
1.59 to 4.81
1.62 to 4.93
1.62 to 4.93
1.62 to 4.93
1.62 to 4.93
1.62 to 4.93
1.62 to 4.93
0.54
0.50
0.55
0.56
0.69
0.73
0.71
0.74
0.89
0.89
0.88
0.94
1.05
0.92
1.03
1.05
0.97
0.90
0.90
0.90
0.90
0.90
0.90
0.24 to 0.98
0.22 to 0.92
0.24 to 0.99
0.25 to 1.02
0.31 to 1.23
0.34 to 1.30
0.33 to 1.25
0.35 to 1.30
0.40 to 1.63
0.40 to 1.62
0.40 to 1.62
0.42 to 1.70
0.45 to 1.96
0.39 to 1.73
0.39 to 2.03
0.40 to 2.05
0.36 to 1.93
0.33 to 1.78
0.33 to 1.78
0.33 to 1.78
0.33 to 1.78
0.33 to 1.78
0.33 to 1.78
0.11
0.11
0.11
0.10
0.11
0.11
0.10
0.11
0.10
0.10
0.09
0.08
0.05
0.05
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.03 to 0.23
0.03 to 0.24 0.02 0.0 to 0.05
0.03 to 0.23
0.03 to 0.22
0.02 to 0.23
0.02 to 0.23
0.02 to 0.23
0.02 to 0.23 0.01 0.0 to 0.04
0.0 to 0.27
0.0 to 0.27
0.0 to 0.23
0.0 to 0.22
0.0 to 0.16
0.0 to 0.16 0.01 .0 to 0.05
0.0 to 0.24
0.0 to 0.24
0.0 to 0.24
0.0 to 0.24
0.0 to 0.24
0.0 to 0.24
0.0 to 0.24
0.0 to 0.24
0.0 to 0.24
0.01
0.01
0.01
0.02
0.02
0.02
0.02
0.01
0.02
0.02
0.03
0.02
0.02
0.02
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.0 to 0.05
0.0 to 0.05
0.0 to 0.05
0.0 to 0.06
0.0 to 0.06
0.0 to 0.06
0.0 to 0.06
0.0 to 0.03
0.0 to 0.06
0.0 to 0.06
0.01 to 0.07
0.0 to 0.06
0.0 to 0.06
0.0 to 0.06
0.0 to 0.07
0.0 to 0.07
0.0 to 0.07
0.0 to 0.07
0.0 to 0.07
0.0 to 0.07
0.0 to 0.07
0.0 to 0.07
0.0 to 0.07
0.10
0.10
0.10
0.11
0.16
0.17
0.17
0.17
0.18
0.17
0.17
0.16
0.16
0.09
0.11
0.12
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.02 to 0.22
0.02 to 0.22
0.01 to 0.24
0.03 to 0.24
0.07 to 0.31
0.07 to 0.32
0.07 to 0.32
0.07 to 0.32
0.05 to 0.38
0.05 to 0.38
0.05 to 0.38
0.04 to 0.35
0.04 to 0.34
0.05 to 0.1 6
0.05 to 0.1 9
0.06 to 0.22
0.05 to 0.21
0.05 to 0.20
0.05 to 0.20
0.05 to 0.20
0.05 to 0.20
0.05 to 0.20
0.05 to 0.20
Note: Estimates after 2007 are based on NRI data from 2007 and therefore do not fully reflect changes occurring in the latter part of the time series.
A-322 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Step 2d: Estimate Additional Changes in Soil Organic C Stocks Due to CRP Enrollment after 2007 and Sewage Sludge
Amendments
There are two additional land use and management activities in U.S. agricultural lands that are not
estimated in Steps 2a and 2b. The first activity involves the application of sewage sludge to agricultural lands.
Minimal data exist on where and how much sewage sludge is applied to U.S. agricultural soils, but national
estimates of mineral soil land area receiving sewage sludge can be approximated based on sewage sludge N
production data, and the assumption that amendments are applied at a rate equivalent to the assimilative capacity
from Kellogg et al. (2000). It is assumed that sewage sludge for agricultural land application is applied to grassland
because of the high heavy metal content and other pollutants found in human waste, which limits its application to
crops. The impact of organic amendments on SOC is calculated as 0.38 metric tonnes C/ha-yr. This rate is based on
the IPCC default method and country-specific factors (see Table A-238), by calculating the effect of converting
nominal, medium-input grassland to high input improved grassland. The assumptions are that reference C stock are
50 metric tonnes C/ha, which represents a mid-range value of reference C stocks for the cropland soils in the United
States,93 that the land use factor for grassland of 1.4 and 1.11 for high input improved grassland are representative of
typical conditions, and that the change in stocks are occurring over a 20 year (default value) time period (i.e., [50 x
1.4 x 1.11 - 50 x 1.4] / 20 = 0.38). A nominal ±50 percent uncertainty is attached to these estimates due to limited
information on application and the rate of change in soil C stock change with sewage sludge amendments. The
influence of sewage sludge on soil organic C stocks are provided in Table A-238.
The second activity is the change in enrollment for the Conservation Reserve Program after 2007 for
mineral soils. Relative to the enrollment in 2007, the total area in the Conservation Reserve Program has decreased
from 2008 to 2012 (USDA-FSA 2012). An average annual change in SOC of 0.5 metric tonnes C/ha-yr is used to
estimate the effect of the enrollment changes. This rate is based on the IPCC default method and country-specific
factors (see Table A-234) by estimating the impact of setting aside a medium input cropping system in the
Conservation Reserve Program. The assumptions are that reference C stock are 50 metric tonnes C/ha, which
represents a mid-range value for the dominant cropland soils in the United States, and the average country-specific
factor is 1.2 for setting-aside cropland from production, with the change in stocks occurring over a 20 year (default
value) time period equal to 0.5 (i.e., [50 x 1.2 - 50] / 20 = 0.5). A nominal ±50 percent uncertainty is attached to
these estimates due to limited information about the enrollment trends at subregional scales, which creates
uncertainty in the rate of soil C stock change (stock change factors for set-aside lands vary by climate region).
Estimates and uncertainties are provided in Table A-242.
Step 3: Estimate Soil Organic C Stock Changes and Direct N20 Emissions from Organic Soils
In this step, soil organic C losses and N2O emissions are estimated for organic soils that are drained for
agricultural production.
Step 3a: Direct A/20 Emissions Due to Drainage of Organic Soils in Cropland and Grassland
To estimate annual N2O emissions from drainage of organic soils in cropland and grassland, the area of
drained organic soils in croplands and grasslands for temperate regions is multiplied by the IPCC (2006) default
emission factor for temperate soils and the corresponding area in sub-tropical regions is multiplied by the average
(12 kg N2O-N/ha cultivated) of IPCC (2006) default emission factors for temperate (8 kg N2O-N/ha cultivated) and
tropical (16 kg N2O-N/ha cultivated) organic soils. The uncertainty is determined based on simple error propagation
methods (IPCC 2006), including uncertainty in the default emission factor ranging from 2-24 kg N2O-N/ha (IPCC
2006).
Step 3b: Soil Organic C Stock Changes Due to Drainage of Organic Soils in Cropland and Grassland
Change in soil organic C stocks due to drainage of cropland and grassland soils are estimated annually from
1990 through 2007, based on the land-use and management activity data in conjunction with appropriate loss rate
emission factors. Each input to the inventory for the Tier 2 approach has some level of uncertainty that is quantified
93
Reference C stocks are based on cropland soils for the Tier 2 method applied in this Inventory.
A-323
-------
in PDFs. A Monte Carlo Analysis is used to quantify uncertainty in soil organic C stock changes for the inventory
period based on uncertainty in the inputs. Input values are randomly selected from PDFs in an iterative process to
estimate SOC change for 50,000 times and produce a 95 percent confidence interval for the inventory results.
Derive Organic Soil Emission Factors: Organic Soil emission factors representative of U.S. conditions
have been estimated from published studies (Ogle et al. 2003), based on subsidence studies in the United States and
Canada (Table A-238). PDFs are constructed as normal densities based on the mean C loss rates and associated
variances.
Estimate Annual C Emissions from Organic Soils: Losses of C are estimated by applying the Monte Carlo
approach to the annual data from 1990 through 2007 from the NRI. The results for 2007 are applied to the years
2007 through 2012. Losses of soil organic C from drainage of cropland and grassland soils are provided in Table A-
239.
Step 4: Estimate Indirect N20 Emissions for All Land-Use Types
In this step, N2O emissions are estimated for the two indirect emission pathways (N2O emissions due to
volatilization, and N2O emissions due to leaching and runoff of N), which are summed to yield total indirect N2O
emissions from croplands, grasslands, forest lands, and settlements.
Step 4a: Indirect Soil A/20 Emissions Due to Volatilization
Indirect emissions from volatilization of N inputs from synthetic and commercial organic fertilizers, and
PRP manure, are calculated according to the amount of mineral N that is transported in gaseous forms from the soil
profile and later emitted as soil N2O following atmospheric deposition. See Step le for additional information about
the methods used to compute N losses due to volatilization. The estimated N volatilized for all land-use and
livestock activities is multiplied by the IPCC default emission factor of 0.01 kg N2O-N/kg N (IPCC 2006) to
estimate total N2O emissions from volatilization. The uncertainty is estimated using simple error propagation
methods (IPCC 2006), by combining uncertainties in the amount of N volatilized, with uncertainty in the default
emission factor ranging from 0.002-0.05 kg N2O-N/kg N (IPCC 2006). The estimates and uncertainties are
provided in Table A- 239.
Step 4b: Indirect Soil A/20 Emissions Due to Leaching and Runoff
The amount of mineral N from synthetic fertilizers, commercial organic fertilizers, PRP manure, crop
residue, N mineralization, asymbiotic fixation that is transported from the soil profile in aqueous form is used to
calculate indirect emissions from leaching of mineral N from soils and losses in runoff of water associated with
overland flow. See Step le for additional information about the methods used to compute N losses from soils due to
leaching and runoff in overland water flows.
The total amount of N transported from soil profiles through leaching and surface runoff is multiplied by
the IPCC default emission factor of 0.0075 kg N2O-N/kg N (IPCC 2006) to estimate emissions for this source. The
emission estimates are provided in Table A-241. The uncertainty is estimated based on simple error propagation
methods (IPCC 2006), including uncertainty in the default emission factor ranging from 0.0005 to 0.025 kg N2O-
N/kg N (IPCC 2006).
Step 5: Estimate Total Soil Organic C Stock Changes and N20 Emissions for U.S. Soils
Step 5a: Estimate Total Soil A/20 Emissions
Total emissions are estimated by adding total direct emissions (from Tier 3 crop types and Tier 1 crop types
on mineral cropland soils, drainage and cultivation of organic soils, and grassland management) to indirect
emissions for all land use and management activities. Uncertainties in the final estimate are combined using simple
error propagation methods (IPCC 2006), and expressed as a 95 percent confidence interval. Estimates and
uncertainties are provided in Table A- 237.
Direct and indirect simulated emissions of soil N2O vary regionally in both croplands and grasslands as a function of
N input amount and timing of fertilization, tillage intensity, crop rotation sequence, weather, and soil type. Note that
there are other management practices, such as fertilizer formulation (Halvorson et al. 2013), that influence emissions
but are not represented in the model simulations. The highest total N2O emissions from Tier 3 crops occur in Iowa,
A-324 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Illinois, Missouri, Minnesota, the Dakotas, and Kansas (Table A- 243). On a per area unit basis, direct N2O
emissions are high in the northeast and many of the Mississippi River Basin states where there are high N inputs to
hay, corn and soybean crops, and in some western states where irrigated crops are grown that require high N inputs
(Figure A-15). Note that although the total crop area in the northeast is relatively low, emissions are high on a per
unit area basis because a large portion of the cropped area in these states is used for hay production that receives
large N inputs from both fertilizer and symbiotic fixation. Indirect emissions tend to be high on a per unit of area
basis in some northeastern states and Florida because relatively high rainfall and coarse textured soils facilitates N
losses from leaching and runoff and in some Great Plains states where irrigation can contribute to leaching and
runoff (Figure A-16).
Direct and indirect emissions from non-federal grasslands are typically lower than those from croplands
(Table A- 243, Figure A-17, and Figure A-18) because N inputs tend to be lower, particularly from synthetic
fertilizer. Texas, Oklahoma, Kansas, Nebraska, Missouri, Colorado, South Dakota and Montana are the highest
emitters for this category because large land areas are suded for pastures and rangeland. On a per unit of area basis,
emissions are higher in the Northeastern United States and some of the Great Lakes and Midwestern states because
these grasslands are more intensively managed (legume seeding, fertilization) while western rangelands receive few,
if any, N inputs. Also, rainfall is limited in most of the western United States, and grasslands are not typically
irrigated so minimal leaching and runoff of N occurs in these grasslands, but N volatilization can be substantial.
Figure A-15: Tier 3 Crops,2012 Annual Direct H?0 Emissions, Estimated Using the DAYGENT Model, (kg H./ha/yearl
A-325
-------
Figure A-16: Tier 3 Crops, 2012 Annual N Losses Leading to Indirect H?0 Emissions, Estimated Using the DAYGENT Model,
(kgN/ha/year)
A-326 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure A-17: Non-federal Grasslands, 2012 Annual Direct N^O Emissions, Estimated Using the DAYGENT Model, (kg
N/ha/year)
A-327
-------
Figure A-18: Non-federal Grasslands, 2012 Annual N Losses Leading to Indirect N^O Emissions, Estimated Using the
DAYGENT Model, (kg N/ha/year)
Step 5b: Estimate Total Soil Organic Stock Change
The sum of total CO2 emissions and removals from the Tier 3 DAYCENT Model Approach, Tier 2 IPCC
Methods and additional land-use and management considerations are provided in Table A-242. The total change in soil
organic C stocks (as seen in the Land Use, Land-Use Change, and Forestry chapter) as well as per hectare rate of
change varies among the states (Figure A-19 and Figure A-20). The states with highest total amounts of C
sequestration are Illinois, Iowa, Kansas, Minnesota, Missouri, Ohio and Tennessee (Table A- 244). On a per hectare
basis, the highest rates of C accumulation occur in states found in the Southeast, Northeast and Midwest. For
organic soils, emission rates are highest in the regions that contain the majority of drained organic soils, including
California, Florida, Indiana, Michigan, Minnesota, North Carolina and Wisconsin. On a per unit of area basis, the
emission rate patterns are very similar to the total emissions in each state, with the highest rates in coastal states of
the Southeast, states surrounding the Great Lakes, and California.
A-328 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Figure A-19: Net G Stock Change, per Hectare, for Mineral Soils Under Agricultural Management, 2012
Mg CO2 Eq/ha/yr
Note: Values greater than zero represent emissions,
and values less than zero represent sequestration.
Map accounts for fluxes associated with the Tier 2
and 3 inventory computations. See methodology
for additional details.
-0.05 to 0
-0.1 to -0.05
-0.25 to -0.1
-0.5 to -0.25
< -0.5
A-329
-------
Figure A- 20: Net G Stock Change, per Hectare, for Organic Soils Under Agricultural Management, 2012
Note: Values greater than zero represent emissions.
Mg CO2 Eq/ha/yr
^B >40
I j 30 to 40
[~B 20 to 30
| | 10 to 20
| |0 to 10
| | No organic soils
A-330 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 237: Assumptions and Calculations to Estimate the Contribution to Soil Organic Carbon Stocks from Application of Sewage Sludge to Mineral Soils
Sewage Sludge N Applied to Agricultural
Land (Mg N)=
Assimilative Capacity (Mg N/ha)b
Area covered by Available Sewage Sludge
N (ha)'
Average Annual Rate of C storage (Mg
C/ha-yr)d
Contribution to Soil C (TgC02/yr)e.f
1990
52,198
0.12
434,985
0.38
-0.61
Sewage Sludge N Applied to Agricultural
Land (Mg N)=
Assimilative Capacity (Mg N/ha)b
Area covered by Available Sewage Sludge
N (ha)=
Average Annual Rate of C storage (Mg
C/ha-yr)d
Contribution to Soil C (TgC02/yr)e.f
2002
88,736
0.122
727,341
0.38
-1.01
1991
55,658
0.12
463,816
0.38
-0.65
2003
91,358
0.122
748,836
0.38
-1.04
1992
59,250
0.12
493,746
0.38
-0.69
2004
93,991
0.122
770,418
0.38
-1.07
1993
62,977
0.122
516,202
0.38
-0.72
2005
98,081
0.122
803,942
0.38
-1.12
1994
65,966
0.122
540,707
0.38
-0.75
2006
100,887
0.122
826,940
0.38
-1.15
1995
69,001
0.122
565,583
0.38
-0.79
2007
103,682
0.122
849,851
0.38
-1.18
1996
72,081
0.122
590,828
0.38
-0.82
2008
106,468
0.122
872,686
0.38
-1.22
1997
75,195
0.122
616,357
0.38
-0.86
2009
109,245
0.122
895,452
0.38
-1.25
1998
78,353
0.122
642,240
0.38
-0.89
2010
112,015
0.122
918,156
0.38
-1.28
1999
80,932
0.122
663,381
0.38
-0.92
2011
114,778
0.122
940,805
0.38
-1.31
2000
83,523
0.122
684,612
0.38
-0.95
2012
117,536
0.122
963,407
0.38
-1.34
2001
86,124
0.122
705,932
0.38
-0.98
Values in parentheses indicate net C storage.
a N applied to soils described in Step 1d.
b Assimilative Capacity is the national average amount of manure-derived N that can be applied on cropland without buildup of nutrients in the soil (Kellogg et al., 2000).
c Area covered by sewage sludge N available for application to soils is the available N applied at the assimilative capacity rate. The 1992 assimilative capacity rate was applied to
1990 -1992 and the 1997 rate was applied to 1993-2012.
d Annual rate of C storage based on national average increase in C storage for grazing lands that is attributed to organic matter amendments (0.38 Mg/ha-yr)
e Contribution to Soil C is estimated as the product of the area covered by the available sewage sludge N and the average annual C storage attributed to an organic matter
amendment.
f Some small, undetermined fraction of this applied N is probably not applied to agricultural soils, but instead is applied to forests, home gardens, and other lands.
A-331
-------
Table A-238: Carbon Loss Rates for Organic Soils Under Agricultural Management in the United States, and IPCC Default Rates (Metric Ton G/ha-yr)
Region
Cold Temperate, Dry & Cold Temperate, Moist
Warm Temperate, Dry & Warm Temperate, Moist
Sub-Tropical, Dry & Sub-Tropical, Moist
IPCC
1
10
1
Cropland
U.S. Revised
11.2±2.5
14.0±2.5
11.2±2.5
IPCC
0.25
2.5
0.25
Grassland
U.S. Revised
2.8±0.5"
3.5±0.8"
2.8±0.5"
a There are not enough data available to estimate a U.S. value for C losses from grassland. Consequently, estimates are 25 percent of the values for cropland, which is an assumption that is used for the IPCC default
organic soil C losses on grassland.
Table A- 239: Indirect M Emissions from Volatilization tTg C0? Eq.l
Activity
Croplands
Settlements
Forest Land
Grasslands
Total
1990 1995
15.1 16.1
0.1 0.2
+H +
4.9 5.6
20.2 21.9
1996
15.8
0.2
+
5.4
21.4
1997
15.9
0.2
+
5.4
21.5
1998
16.4
0.1
+
5.5
22.1
1999
15.8
0.1
+
5.1
21.0
2000
15.5
0.2
+
4.9
20.6
2001
15.5
0.2
+
5.0
20.8
2002
15.5
0.2
+
4.9
20.6
2003
16.0
0.2
+
5.2
21.4
2004
15.8
0.2
+
5.7
21.7
2005
15.9
0.2
+
5.5
21.6
2006
16.5
0.2
+
5.3
22.0
2007
15.9
0.2
+
5.5
21.7
2008
15.5
0.2
+
5.5
21.3
2009
15.3
0.2
+
5.5
21.0
2010
15.3
0.2
+
5.4
20.9
2011
15.5
0.2
+
5.3
21.0
2012
15.4
0.2
+
5.4
21.0
- Less than 0.05 Tg C02 Eq.
Table A- 240: Indirect N20 Emissions from Leaching and Runoff tTg C02 Eq.l
Activity
Croplands
Settlements
Forest Land
Grasslands
Total
1990
16.4
0.2
+
4.5
21.2
1995
21.3
0.3
+
6.2
27.9
1996
21.0
0.3
0.1
6.3
27.7
1997
16.6
0.3
0.1
5.8
22.8
1998
22.1
0.3
0.1
5.6
28.0
1999
24.4
0.3
0.1
5.3
30.1
2000
14.5
0.4
0.1
5.0
19.9
2001
17.9
0.4
0.1
4.8
23.2
2002
16.6
0.4
0.1
4.6
21.7
2003
14.7
0.4
0.1
4.9
20.1
2004
18.8
0.4
0.1
4.9
24.2
2005
16.8
0.4
0.1
5.1
22.4
2006
14.5
0.4
0.1
5.4
20.4
2007
23.6
0.4
0.1
5.2
29.3
2008
22.7
0.4
0.1
5.1
28.2
2009
22.3
0.4
0.1
5.0
27.8
2010
19.8
0.4
0.1
4.8
25.2
2011
19.8
0.4
0.1
4.5
24.8
2012
19.5
0.4
0.1
4.8
24.8
i- Less than 0.05 Tg C02 Eq.
A-332 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-241: Total N20 Emissions from Agricultural Soil Management tTg Clh Eq.l
Activity
Total Direct
Direct Emissions from Mineral
Cropland Soils
Synthetic Fertilizer
Organic Amendment3
Residue Nb
Mineralization and Asymbiotic Fixation
Direct Emissions from Drained
Organic Cropland Soils
Direct Emissions from Grasslands
Synthetic Mineral Fertilizer
PRP Manure
Managed Manure
Sewage Sludge
Residue11
Mineralization and Asymbiotic Fixation
Total Indirect
Volatilization
Leaching/Runoff
Total Emissions
1990
240.7
3.9
67.0
4.7
85.6
0.5
24.5
+
0.3
2.0
58.2
41.4
20.2
21.2
282.1
1995
263.2
161.0
66.4
14.7
4.5
75.4
4.5
97.6
0.7
29.4
+
0.3
2.5
64.4
49.8
21.9
27.9
312.9
1996
282.0
173.3
74.4
15.2
4.7
78.9
4.5
104.3
0.8
31.5
+
0.4
2.7
68.6
49.1
21.4
27.7
331.2
1997
278.1
169.0
72.2
15.5
4.6
76.7
4.4
104.7
0.7
29.8
+
0.4
2.6
70.9
44.3
21.5
22.8
322.5
1998
238.8
149.2
62.3
14.3
4.1
68.6
4.5
85.1
0.5
26.1
+
0.4
2.0
55.8
50.1
22.1
28.0
288.8
1999
239.6
153.2
66.0
14.9
4.4
67.9
4.4
82.0
0.7
26.3
+
0.4
2.0
52.3
51.1
21.0
30.1
290.7
2000
222.7
142.6
62.9
14.7
4.0
61.0
4.4
75.7
0.6
24.3
+
0.4
1.6
48.6
40.5
20.6
19.9
263.2
2001
242.3
154.2
63.3
15.1
4.4
71.4
4.3
83.8
0.7
25.6
+
0.4
2.0
54.8
43.9
20.8
23.2
286.2
2002
233.6
151.9
64.3
15.2
4.0
68.3
4.3
77.5
0.8
24.5
+
0.4
1.9
49.6
42.3
20.6
21.7
275.9
2003
235.2
152.2
64.1
15.1
4.3
68.7
4.2
78.9
0.7
24.1
+
0.4
1.9
51.5
41.5
21.4
20.1
276.7
2004
256.5
160.0
67.2
15.0
4.5
73.2
4.2
92.3
1.0
25.0
+
0.5
2.2
63.4
45.9
21.7
24.2
302.4
2005
253.3
158.7
65.8
15.3
4.8
72.9
4.1
90.5
1.0
25.5
+
0.5
2.4
60.8
44.0
21.6
22.4
297.3
2006
251.1
159.5
64.5
15.8
4.4
74.7
4.1
87.6
1.2
26.2
+
0.5
2.2
57.2
42.4
22.0
20.4
293.6
2007
272.4
164.8
71.6
16.0
4.6
72.6
4.0
103.6
1.0
27.0
+
0.5
2.6
72.2
51.0
21.7
29.3
323.4
2008
269.5
162.5
69.5
15.8
4.6
72.5
4.0
103.0
1.0
26.6
+
0.5
2.6
72.0
49.5
21.3
28.2
319.0
2009
267.6
161.1
69.0
15.7
4.6
71.8
4.0
102.5
1.0
26.3
+
0.5
2.6
71.9
48.8
21.0
27.8
316.4
2010
264.0
158.1
68.6
15.4
4.5
69.5
4.0
101.9
1.0
25.8
+
0.5
2.6
71.7
46.1
20.9
25.2
310.1
2011
261.9
157.0
67.4
15.5
4.5
69.6
4.0
100.9
1.0
25.0
+
0.6
2.5
71.5
45.8
21.0
24.8
307.8
2012
260.9
155.7
67.3
15.5
4.4
68.5
4.0
101.1
0.9
?54
+
0.6
2.5
71.3
45.7
21.0
24.8
306.6
i- Less than 0.05 Tg C02 Eq.
a Organic amendment inputs include managed manure amendments, daily spread manure and other commercial organic fertilizer (i.e., dried blood, tankage, compost, and other).
b Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
A-333
-------
Table A-242: Annual Soil G Stock Change in Cropland Remaining CroplandWW], Land Converted to Cropland[\S&\, Grassland Remaining Grassland\SM\, and Land
Converted to Grassland \i\£%\, in U.S. Agricultural Soils tTg C02 Eq.l
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Net emissions based on Tier 3 Century-based analysis (Step 2)
CRC (69.4) (74.4) (66.2) (45.7) (53.0) (41.9) (51.5) (46.8) (38.3) (41.3) (57.3) (45.2) (37.9) (35.7) (38.9) (48.7) (50.9) (50.6) (50.6) (50.6) (50.6) (50.6) (50.6)
GCC 20.0 17.3 19.1 20.2 12.3 20.3 16.2 16.6 11.9 12.1 11.9 13.4 12.5 12.9 10.6 14.3 11.0 10.5 10.5 10.5 10.5 10.5 10.5
GRG (13.4) (2.6) (11.3) (0.7) (18.1) 1.7 (21.6) (8.5) (9.7) 0.5 (33.5) (11.5) (14.7) (10.5) (0.2) 3.6 (19.7) 4.8 4.9 4.9 4.9 4.9 4.9
CCG (4.6) (5.3) (4.9) (3.8) (5.4) (5.5) (6.3) (6.0) (6.2) (6.6) (8.7) (7.3) (7.0) (7.0) (8.2) (7.0) (7.0) (7.3) (7.3) (7.2) (7.2) (7.2) (7.1)
Net emissions based on the IPCC Tier 2 analysis (Step 3)
Mineral Soils
CRC
GCC
FCC
OCC
sec
WCC
GRG
CCG
FCG
OCG
SCG
WCG
Organic Soils
CRC
GCC
FCC
OCC
sec
WCC
GRG
CCG
FCG
OCG
SCG
WCG
(6.5) (6.5) (6.5) (7.6) (7.6) (7.6) (7.6) (7.6) (6.9) (6.9) (6.9)
2.3 2.3 2.3 2.0 2.0 2.0 2.0 2.0 1.8 1.8 1.8
1.5 1.5 1.5 1.4 1.4 1.4 1.4 1.4 0.8 0.8 0.8
0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
(0.2) (0.2) (0.2) (0.1) (0.1) (0.1) (0.1) (0.1) (0.0) (0.0) (0.0)
(1.7) (1.7) (1.7) (1.6) (1.6) (1.6) (1.6) (1.6) (1.7) (1.7) (1.7)
(1.1) (1.1) (1.1) (1.1) (1.1) (1.1) (1.1) (1.1) (0.8) (0.8) (0.8)
(0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.3) (0.3) (0.3)
(0.4) (0.4) (0.4) (0.6) (0.6) (0.6) (0.6) (0.6) (0.6) (0.6) (0.6)
(0.1) (0.1) (0.1) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2)
24.0 23.7 23.7 23.0 22.4 22.2 21.8 21.7 21.7 21.6 21.5
2.5 2.6 2.4 2.7 3.1 3.1 3.3 3.3 3.4 3.3 3.3
(0.2) (0.2) 0.8 0.8 0.9 0.8 0.9 0.9 0.8 0.8 0.7
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
(0.0) (0.0) 0.1 0.2 0.4 0.4 0.4 0.4 0.4 0.4 0.3
(0.2) (0.2) 0.6 0.7 0.8 0.8 0.8 0.8 0.9 0.7 0.6
4.6 4.5 4.5 4.4 4.3 4.1 4.0 3.9 3.8 3.7 3.7
0.5 0.5 0.5 0.6 0.7 0.7 0.7 0.7 0.9 0.9 0.9
0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2
(6.9) (6.9)
1.8 1.8
0.8 0.8
0.3 0.3
0.7 0.7
0.2 0.2
(0.0) (0.0)
(1.7) (1.7)
(0.8) (0.8)
(0.3) (0.3)
(0.6) (0.6)
(0.2) (0.2)
22.0 21.9
4.7 4.3
0.4 0.3
0.0 0.0
0.3 0.3
0.6 0.5
3.3 3.2
0.9 1.1
0.1 0.1
0.0 0.0
0.0 0.0
0.2 0.2
(2.8)
0.8
0.3
0.1
0.3
0.1
0.1
(1.3)
(0.4)
(0.2)
(0.5)
(0.1)
22.9
4.0
0.3
0.0
0.3
0.3
3.1
0.9
0.1
0.0
0.0
0.1
(2.8)
0.8
0.3
0.1
0.3
0.1
0.1
(1.3)
(0.4)
(0.2)
(0.5)
(0.1)
22.6
4.4
0.3
0.0
0.2
0.3
3.1
1.0
0.1
0.0
0.0
0.1
(2.8)
0.8
0.3
0.1
0.3
0.1
0.1
(1.3)
(0.4)
(0.2)
(0.5)
(0.1)
22.4
4.3
0.3
0.0
0.2
0.3
3.1
1.0
0.1
0.0
0.0
0.1
(2.8)
0.8
0.3
0.1
0.3
0.1
0.1
(1.3)
(0.4)
(0.2)
(0.5)
(0.1)
22.3
4.2
0.2
0.0
0.2
0.3
3.0
1.0
0.1
0.0
0.0
0.1
(2.8)
0.8
0.3
0.1
0.3
0.1
0.1
(1.3)
(0.4)
(0.2)
(0.5)
(0.1)
22.1
4.0
0.2
0.0
0.2
0.4
3.0
0.9
0.1
0.0
0.0
0.1
(2.8)
0.8
0.3
0.1
0.3
0.1
0.1
(1.3)
(0.4)
(0.2)
(0.5)
(0.1)
22.1
4.0
0.2
0.0
0.2
0.4
3.0
0.9
0.1
0.0
0.0
0.1
(2.8)
0.8
0.3
0.1
0.3
0.1
0.1
(1.3)
(0.4)
(0.2)
(0.5)
(0.1)
22.1
4.0
0.2
0.0
0.2
0.4
3.0
0.9
0.1
0.0
0.0
0.1
(2.8)
0.8
0.3
0.1
0.3
0.1
0.1
(1.3)
(0.4)
(0.2)
(0.5)
(0.1)
22.1
4.0
0.2
0.0
0.2
0.4
3.0
0.9
0.1
0.0
0.0
0.1
(2.8)
0.8
0.3
0.1
0.3
0.1
0.1
(1.3)
(0.4)
(0.2)
(0.5)
(0.1)
22.1
4.0
0.2
0.0
0.2
0.4
3.0
0.9
0.1
0.0
0.0
0.1
(2.8)
0.8
0.3
0.1
0.3
0.1
0.1
(1.3)
(0.4)
(0.2)
(0.5)
(0.1)
22.1
4.0
0.2
0.0
0.2
0.4
3.0
0.9
0.1
0.0
0.0
0.1
Additional changes in net emissions from mineral soils based on application of sewage sludge to agricultural land (Step 4)
GRG
(0.6) (0.6) (0.7) (0.7) (0.8) (0.8) (0.8) (0.9) (0.9) (0.9) (1.0)
(1.0) (1.0)
(1.0)
(1.1)
(1.1)
(1.2)
(1.2)
(1.2)
(1.2)
(1.3)
(1.3)
(1.3)
Additional changes in net emissions from mineral soils based on additional enrollment of CRP land (Step 4)
CRC
_
-
-
-
-
-
-
1.4
2.0
3.6
3.7
4.8
Total Stock Changes by Land Use/Land-Use Change Category (Step 5)
CRC
GCC
FCC
OCC
sec
WCC
GRG
CCG
(51.9) (57.2) (49.0) (30.3) (38.2) (27.3) (37.3) (32.7) (23.5) (26.6) (42.7)
24.8 22.2 23.9 24.9 17.4 25.4 21.5 21.9 17.1 17.2 17.0
1.2 1.2 2.2 2.2 2.2 2.2 2.3 2.3 1.7 1.6 1.5
0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
0.5 0.5 0.6 0.9 1.1 1.1 1.1 1.1 1.0 1.0 0.9
0.0 (0.0) 0.8 0.9 1.0 1.0 1.1 1.1 1.1 0.9 0.8
(9.6) 1.1 (7.7) 3.0 (14.6) 5.0 (18.4) (5.5) (6.8) 3.3 (30.7)
(5.8) (6.5) (6.0) (4.8) (6.2) (6.4) (7.2) (6.8) (7.0) (7.5) (9.5)
(30.2) (22.9)
19.8 18.6
1.2 1.1
0.3 0.3
0.9 0.9
0.8 0.7
(9.2) (12.5)
(8.1) (7.7)
(15.6)
17.7
0.5
0.1
0.6
0.4
(8.3)
(7.3)
(19.2)
15.7
0.6
0.1
0.5
0.4
1.9
(8.4)
(29.1)
19.3
0.5
0.1
0.5
0.4
5.6
(7.3)
(31.4)
15.9
0.5
0.1
0.5
0.4
(17.8)
(7.3)
(31.3)
15.3
0.5
0.1
0.5
0.4
6.8
(7.7)
(29.8)
15.3
0.5
0.1
0.5
0.4
6.8
(7.7)
(29.2)
15.3
0.5
0.1
0.5
0.4
6.8
(7.6)
(27.6)
15.3
0.5
0.1
0.5
0.4
6.7
(7.6)
(27.5)
15.3
0.5
0.1
0.5
0.4
6.7
(7.5)
(26.5)
15.3
0.5
0.1
0.5
0.4
6.7
(7.5)
A-334 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
FCG (1.0) (1.0) (1.0) (1.0) (1.0) (1.0) (1.0) (1.0) (0.7) (0.7) (0.7) (0.7) (0.7) (0.4) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3)
OCG (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.3) (0.3) (0.3) (0.3) (0.3) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2)
SCG (0.4) (0.4) (0.4) (0.5) (0.5) (0.5) (0.5) (0.6) (0.6) (0.6) (0.6) (0.6) (0.6) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5)
WCG (0.0) (0.0) (0.0) (0.1) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0)
Total* (41.9) (39.9) (36.5) (4.7) (38.8) (0.4) (38.4) (20.1) (17.8) (11.3) (64.1) (26.0) (23.1) (13.0) (9.4) (11.0) (40.2) (16.4) (14.9) (14.3) (12.7) (12.5) (11.4)
Note: Totals may not sum due to independent rounding.
A-335
-------
Table A- 243: Total 2012 N20 Emissions (Direct and Indirect! from Agricultural Soil Management by State tTg Clh Eq.l
State
AL
AR
AZ
CA
CO
CT
DE
FL
GA
HI"
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
Ml
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PA
Rl
SC
SD
TN
TX
UT
VA
VT
WA
Wl
WV
WY
Croplands3
1.19
4.20
0.98
7.51
4.08
0.21
0.24
4.91
1.58
0.03
16.46
4.00
14.94
7.65
12.26
2.40
1.32
0.38
0.59
0.68
5.08
9.96
7.67
2.80
5.64
2.13
7.57
8.62
0.22
0.38
1.82
0.33
6.01
6.40
3.19
2.10
4.96
0.02
0.68
6.66
1.60
9.06
0.99
0.88
1.22
3.72
4.40
0.56
1.13
Grasslands'1
0.88
1.40
1.30
2.17
6.10
0.04
0.01
1.59
0.60
n.e.
2.23
2.96
1.44
2.71
4.63
2.20
0.89
0.06
0.19
0.14
1.31
1.48
6.07
0.74
10.59
0.56
1.83
5.45
0.04
0.07
3.10
1.12
1.82
1.18
5.79
3.90
0.93
0.01
0.29
4.70
1.33
14.01
1.73
1.47
0.28
2.57
1.58
0.47
4.51
Settlements0
0.03
0.02
0.01
0.18
0.02
0.02
0.01
0.21
0.01
n.e.
0.07
0.02
0.11
0.07
0.06
0.03
0.04
0.04
0.05
0.01
0.08
0.03
0.07
0.03
0.01
0.05
0.03
0.06
0.01
0.07
0.01
0.01
0.07
0.10
0.03
0.01
0.06
0.01
0.03
0.02
0.05
0.09
0.01
0.05
0.00
0.03
0.04
0.00
0.01
Forest Landsd Total
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
2.14
5.70
2.35
10.35
10.24
0.27
0.27
6.97
2.25
0.03
18.76
7.05
16.51
10.45
16.98
4.66
2.25
0.49
0.85
0.84
6.56
11.53
13.82
3.59
16.24
2.85
9.43
14.16
0.27
0.54
5.00
1.47
7.94
7.89
9.14
6.04
6.03
0.04
1.02
11.38
3.00
23.26
2.75
2.43
1.50
6.39
6.12
NE 1.06
NE 5.68
Lower
Bound
1.39
4.24
1.34
3.63
7.16
0.21
0.19
2.85
1.25
0.01
14.46
5.33
12.92
7.16
13.22
3.23
1.54
0.37
0.57
0.60
4.90
9.18
10.85
2.73
11.97
1.23
7.39
7.10
0.19
0.28
3.09
0.97
6.37
5.02
6.34
4.73
4.54
0.02
0.65
8.45
1.98
16.07
1.97
1.35
1.19
4.83
4.41
0.57
1.86
Upper
Bound
3.77
8.44
4.34
21.08
15.15
0.42
1.12
14.02
4.41
0.11
25.47
10.13
22.07
15.52
22.74
7.13
3.82
1.36
1.92
1.79
9.60
15.90
18.75
5.50
22.52
5.88
12.44
24.40
1.17
1.52
8.33
2.73
10.79
12.59
13.53
8.25
9.42
0.84
2.18
15.26
4.97
33.96
4.40
4.56
22.27
9.05
10.37
2.39
1.90
a Emissions from non-manure organic N inputs for minor crops were not estimated (n.e.) at the state level.
b Emissions from sewage sludge applied to grasslands and were not estimated (n.e.) at the state level
c Emissions from sewage sludge applied to settlements were not estimated (n.e.) at the state level.
d Forestland emissions were not estimated (n.e.) at the state level.
e ixbO emissions are not reported for Hawaii except from cropland organic soils.
A-336 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 244: Soil C Stock Change for Mineral and Organic Soils during 2012 within individual states (Tg 002 Eq.)
State
AL
AR
AZ
CA
CO
CT
DE
FL
GA
HI
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
Ml
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PA
Rl
SC
SD
TN
TX
UT
VA
VT
WA
Wl
WV
WY
Mineral Soil
(1.31)
(0.17)
0.20
(0.94)
0.16
(0.05)
(0.12)
0.28
(0.67)
(0.02)
(8.59)
(1.07)
(5.00)
(1.71)
(2.99)
(1.82)
(1.45)
(0.07)
(0.37)
(0.22)
(0.75)
(2.07)
(2.47)
(1.03)
1.72
(1.21)
(1.63)
(0.52)
(0.03)
(0.15)
1.13
(0.12)
0.19
(2.11)
(0.26)
(1.71)
(0.84)
(0.01)
(0.64)
(0.68)
(2.14)
0.07
0.48
(1.23)
(0.02)
(1.10)
0.48
(0.59)
0.93
Organic Soil
-
-
-
1.55
0.00
0.00
0.01
10.11
-
0.25
0.54
0.08
0.62
2.37
-
-
0.33
0.12
0.02
0.00
3.05
5.85
-
0.00
0.16
1.90
-
0.00
0.05
0.06
-
0.00
0.41
0.47
-
0.34
0.02
0.02
0.02
-
-
-
0.08
0.00
0.06
0.31
2.28
-
-
Total
(1.31)
(0.17)
0.20
0.61
0.17
(0.04)
(0.11)
10.39
(0.67)
0.24
(8.05)
(0.98)
(4.38)
0.67
(2.99)
(1.82)
(1.11)
0.05
(0.34)
(0.21)
2.29
3.78
(2.47)
(1.03)
1.87
0.68
(1.63)
(0.52)
0.02
(0.10)
1.13
(0.12)
0.61
(1.64)
(0.26)
(1.38)
(0.82)
0.01
(0.62)
(0.68)
(2.14)
0.07
0.56
(1.23)
0.03
(0.79)
2.76
(0.59)
0.93
Note: Parentheses indicate net C accumulation. Estimates do not include soil C stock change associated with CRP enrollment after 2007 or sewage sludge
application to soils, which were only estimated at the national scale. The sum of state results will not match the national results because state results are
generated in a separate programming package, the sewage sludge and CRP enrollment after 2007 are not included, and differences arise due to rounding of
values in this table.
A-337
-------
3.13. Methodology for Estimating Net Carbon Stock Changes in Forest Lands
Remaining Forest Lands
This sub-annex expands on the methodology used to estimate net changes in carbon (C) stocks in forest
ecosystems and in harvested wood products. Some of the details of C conversion factors and procedures for calculating
net CC>2 flux for forests are provided below; full details of selected topics may be found in the cited references.
Carbon Stocks and Net Changes in Forest Ecosystem Carbon Stocks
At least two forest inventories exist for most forest land in the United States. Carbon stocks are estimated based
on data from each inventory, at the level of permanent inventory plots. Carbon per hectare (for a sample location) is
multiplied by the total number of hectares that the plot represents, and then totals are summed for an area of interest, such
as the state of Maine. Net annual C stock changes are calculated by taking the difference between the inventories and
dividing by the number of years between the inventories for a selected state or sub-state area.
Forest inventory data
The estimates of forest C stocks are based on data from forest inventory surveys. Forest inventory data were
obtained from the USDA Forest Service, Forest Inventory and Analysis (FIA) program (Prayer and Furnival 1999, USDA
Forest Service 2013a, USDA Forest Service 2013b). Forest Inventory and Analysis data include remote sensing
information and collection of measurements in the field at sample locations called plots. Tree measurements include
diameter and species. On a subset of plots, additional measurements or samples are taken of downed dead wood, litter,
and soil C. However, the technical advances needed to estimate C stocks from these data are still under development (e.g.,
forest floor, Woodall et al. 2012). The field protocols are thoroughly documented and available for download from the
USDA Forest Service (2013c). Bechtold and Patterson (2005) provide the estimation procedures for standard forest
inventory results. The data are freely available for download at USDA Forest Service (201 Ib) as the Forest Inventory and
Analysis Database (FIADB) Version 5.1.6 (USDA Forest Service 2013b, USDA Forest Service 2013c); these data are the
primary sources of forest inventory used to estimate forest C stocks.
Forest surveys have begun in the U.S. territories and in Hawaii. Meanwhile this inventory assumes that these
areas account for a net C change of zero. Survey data are available for the temperate oceanic ecoregion of Alaska
(southeast and south central). Inventory data are publicly available for 6 million hectares of forest land, and these
inventoried lands, comprising an estimated 12 percent of the total forest land in Alaska, contribute to the forest C stocks
presented here.
Agroforestry systems are also not currently accounted for in the U.S. inventory, since they are not explicitly
inventoried by either of the two primary national natural resource inventory programs: the FIA program of the USDA
Forest Service and the National Resources Inventory (NRI) of the USDA Natural Resources Conservation Service (Perry
et al. 2005). The majority of these tree-based practices do not meet the size and definitions for forests within each of these
resource inventories. The size characteristics that exclude them from inventories also allow these systems to provide their
many services without taking the land out of agricultural production, making them an appealing C sequestration option.
Agroforestry in the United States has been defined as "intensive land-use management that optimizes the benefits
(physical, biological, ecological, economic, social) from bio-physical interactions created when trees and/or shrubs are
deliberately combined with crops and/or livestock" (Gold et al. 2000). In the United States, there are six categories of
agroforestry practices: riparian forest buffers, windbreaks, alley cropping, silvopasture, forest farming and special
applications.94 These practices are used to address many issues facing agricultural lands, such as economic diversification,
habitat fragmentation, and water quality. While providing these services and regardless of intent, these tree-based
plantings will also help reduce atmospheric CC>2. This occurs directly through CC>2 sequestration into woody biomass, and
indirectly through enhancement of agricultural production, trapping wind-blown and surface runoff sediments, and/or
reducing CC>2 emissions through fuel-use savings (Quam et al. 1992). The effects of these individual practices can
potentially be quite large when taken into account within a whole-farm or within an aggregating larger entity (i.e., state-
level) (Quam et al. 1992, Schoeneberger 2006). One estimate of the sequestration potential through agroforestry practices
in the United States is 90.3 Mt C/year by 2025 (Nair and Nair 2003).
More information on agroforestry practices can be found online at .
A-338 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Summing state-level C stocks to calculate United States net C flux in forest ecosystems
The overall approach for determining forest C stocks and stock change is essentially based on methodology and
algorithms coded into the computer tool described in Smith et al. (2010). A recent change in methods for the inventory
involves a modification of the downed dead wood estimates to incorporate population estimates of down woody material
measured on a subset of the inventory plots (Domke et al. 2013, Woodall and Monleon 2008, Woodall et al. 2013). The C
calculation tool focuses on estimating forest C stocks based on data from two or more forest surveys conducted several
years apart for each state or sub-state. There are generally two or more surveys available for download for each state.
Carbon stocks are calculated separately for each state based on available inventories conducted since 1990 and for the
inventory closest to, but prior to, 1990 if such data are available and consistent with these methods. This approach ensures
that the period 1990 (the base year) to present can be adequately represented. Surveys conducted prior to and in the early
to mid-1990s focused on land capable of supporting timber production (timberland).95 As a result, information on less
productive forest land or lands reserved from harvest was limited. Inventory field crews periodically measured all the
plots in a state at a frequency of every 5 to 14 years. Generally, forests in states with fast-growing (and therefore rapidly
changing) forests tended to be surveyed more often than states with slower-growing (and therefore slowly changing)
forests. Older surveys for some states, particularly in the West, also have National Forest System (NFS) lands or reserved
lands that were surveyed at different times than productive, privately-owned forest land in the state. Periodic data for each
state thus became available at irregular intervals and determining the year of data collection associated with the survey can
sometimes be difficult.
Table A-245: Source of Unique Forest Inventory and Average Year of Field Survey Used to Estimate Statewide Carbon
Stocks
State/Substate3
Alabama
Alaska, Coastal east non-reserved
Alaska, Coastal reserved
Alaska, Coastal west non-reserved
Arizona, NFS non-woodlands
Arizona, NFS woodlands
Source of Inventory Data,
Report/Inventory Yearb
FIADB 5.1.6, 1982
FIADB 5.1.6, 1990
FIADB 5.1.6, 2000
FIADB 5.1.6, 2005
FIADB 5.1.6, 2012
FIADB 5.1.6, 2003
FIADB 5.1.6, 2011
FIADB 5.1.6, 2011
FIADB 5.1.6, 2003
FIADB 5.1.6, 2011
1987 RPA
FIADB 5.1.6, 1999
FIADB 5.1.6, 2010
1987 RPA
Average Year
Assigned to
Inventory0
1982
1990
1999
2003
2009
1997
2008
2006
2001
2008
1985
1996
2006
1984
Forest land is defined as land at least 120 feet wide and 1 acre in size with at least 10 percent cover (or equivalent stocking by
live trees of any size, including land that formerly had such tree cover and that will be naturally or artificially regenerated. Forest
land includes transition zones, such as areas between forest and nonforest lands that have at least 10 percent cover (or equivalent
stocking) with live trees and forest areas adjacent to urban and built-up lands. Roadside, streamside, and shelterbelt strips of
trees must have a crown width of at least 120 feet and continuous length of at least 363 feet to qualify as forest land.
Unimproved roads and trails, streams, and clearings in forest areas are classified as forest if they are less than 120 feet wide or an
acre in size. Tree-covered areas in agricultural production settings, such as fruit orchards, or tree-covered areas in urban settings,
such as city parks, are not considered forest land (Smith et al. 2009). Timberland is the most productive type of forest land,
which is on unreserved land and is producing or capable of producing crops of industrial wood. Productivity is at a minimum
rate of 20 cubic feet of industrial wood per acre per year (Woudenberg and Farrenkopf 1995). There are about 203 million
hectares of timberland in the conterminous United States, which represents 81 percent of all forest lands over the same area
(Smith et al. 2009).
A-339
-------
Arizona, non-NFS non-woodlands
Arizona, non-NFS woodlands
Arkansas
California, NFS
California, non-NFS
Colorado, NFS non-woodlands
Colorado, NFS woodlands
Colorado, non-NFS non-woodlands
Colorado, non-NFS woodlands
Connecticut
Delaware
Florida
Georgia
Idaho, Caribou-Targhee NF
Idaho, Kootenai NF
Idaho, Payette NF
Idaho, Salmon-Challis NF
FIADB 5.1.6, 1999
FIADB 5.1.6, 2010
FIADB 5.1.6, 1985
FIADB 5.1.6, 1999
FIADB 5.1.6, 2010
FIADB 5.1.6, 1999
FIADB 5.1.6, 2010
FIADB 5.1.6, 1988
FIADB 5.1.6, 1995
FIADB 5.1.6, 2005
FIADB 5.1.6, 2010
IDB, 1990s
FIADB 5.1.6, 2010
IDB, 1990s
FIADB 5.1.6, 2010
1997 RPA
FIADB 5.1.6, 2011
FIADB 5.1.6, 2011
Westwide, 1983
FIADB 5.1.6, 2011
Westwide, 1983
FIADB 5.1.6, 2011
FIADB 5.1.6, 1985
FIADB 5.1.6, 1998
FIADB 5.1.6, 2007
FIADB 5.1.6, 2011
FIADB 5.1.6, 1986
FIADB 5.1.6, 1999
FIADB 5.1.6, 2008
FIADB 5.1.6, 2011
FIADB 5.1.6, 1987
FIADB 5.1.6, 1995
FIADB 5.1.6, 2007
FIADB 5.1.6, 2011
FIADB 5.1.6, 1989
FIADB 5.1.6, 1997
FIADB 5.1.6, 2004
FIADB 5.1.6, 2009
FIADB 5.1.6, 2012
Westwide, 1991
FIADB 5.1.6, 2011
1987 RPA
FIADB 5.1.6, 1991
FIADB 5.1.6, 2011
1987 RPA
FIADB 5.1.6, 2011
1987 RPA
FIADB 5.1.6, 2011
1996
2006
1986
1996
2007
1990
2006
1988
1996
2003
2008
1997
2006
1993
2006
1981
2007
2007
1980
2007
1983
2007
1985
1998
2006
2010
1986
1999
2007
2010
1987
1995
2005
2009
1989
1997
2002
2007
2010
1992
2008
1988
1995
2008
1982
2008
1978
2008
A-340 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Idaho, Sawtooth NF
Idaho, non-NFS non-woodlands
Idaho, non-NFS woodlands
Idaho, other NFS
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Westwide, 1991
FIADB 5.1.6, 1991
FIADB 5.1.6, 2011
FIADB 5.1.6, 1991
FIADB 5.1.6, 2011
FIADB 5.1.6, 1991
FIADB 5.1.6, 2011
Westwide, 1991
FIADB 5.1.6, 1991
FIADB 5.1.6, 2011
FIADB 5.1.6, 1985
FIADB 5.1.6, 1998
FIADB 5.1.6, 2005
FIADB 5.1.6, 2010
FIADB 5.1.6, 1986
FIADB 5.1.6, 1998
FIADB 5.1.6, 2003
FIADB 5.1.6, 2008
FIADB 5.1.6, 2012
FIADB 5.1.6, 1990
FIADB 5.1.6, 2003
FIADB 5.1.6, 2008
FIADB 5.1.6, 2012
FIADB 5.1.6, 1981
FIADB 5.1.6, 1994
FIADB 5.1.6, 2005
FIADB 5.1.6, 2010
FIADB 5.1.6, 1988
FIADB 5.1.6, 2004
FIADB 5.1.6, 2009
FIADB 5.1.6, 1984
FIADB 5.1.6, 1991
FIADB 5.1.6, 2005
Eastwide, 1982
FIADB 5.1.6, 1995
FIADB 5.1.6, 2003
FIADB 5.1.6, 2008
FIADB 5.1.6, 2012
FIADB 5.1.6, 1986
FIADB 5.1.6, 1999
FIADB 5.1.6, 2008
FIADB 5.1.6, 2011
FIADB 5.1.6, 1985
FIADB 5.1.6, 1998
FIADB 5.1.6, 2007
FIADB 5.1.6, 2011
1983
1996
2008
1990
2008
1982
2008
1988
2000
2008
1985
1998
2004
2008
1986
1998
2001
2007
2010
1990
2002
2006
2010
1981
1994
2003
2009
1987
2002
2008
1984
1991
2004
1983
1995
2002
2007
2010
1986
2000
2007
2010
1985
1998
2006
2010
Michigan
FIADB 5.1.6,1980
1980
A-341
-------
Minnesota
Mississippi
Missouri
Montana, NFS
Montana, non-NFS non-reserved
Montana, non-NFS reserved
Nebraska
Nevada, NFS non-woodlands
Nevada, NFS woodlands
Nevada, non-NFS non-woodlands
Nevada, non-NFS woodlands
New Hampshire
New Jersey
New Mexico, NFS non-woodlands
FIADB 5.1.6, 1993
FIADB 5.1.6, 2004
FIADB 5.1.6, 2009
FIADB 5.1.6, 2012
FIADB 5.1.6, 1990
FIADB 5.1.6, 2003
FIADB 5.1.6, 2008
FIADB 5.1.6, 2012
FIADB 5.1.6, 1987
FIADB 5.1.6, 1994
FIADB 5.1.6, 2006
FIADB 5.1.6, 2012
FIADB 5.1.6, 1989
FIADB 5.1.6, 2003
FIADB 5.1.6, 2008
FIADB 5.1.6, 2012
1987 RPA
FIADB 5.1.6, 1989
FIADB 5.1.6, 2011
FIADB 5.1.6, 1989
FIADB 5.1.6, 2011
1997 RPA
FIADB 5.1.6, 2011
FIADB 5.1.6, 1983
FIADB 5.1.6, 1994
FIADB 5.1.6, 2005
FIADB 5.1.6, 2010
1987 RPA
FIADB 5.1.6, 1989
FIADB 5.1.6, 2005
1987 RPA
FIADB 5.1.6, 1989
FIADB 5.1.6, 2005
1997 RPA
FIADB 5.1.6, 2005
FIADB 5.1.6, 1989
FIADB 5.1.6, 2005
FIADB 5.1.6, 1983
FIADB 5.1.6, 1997
FIADB 5.1.6, 2007
FIADB 5.1.6, 2012
FIADB 5.1.6, 1987
FIADB 5.1.6, 1999
FIADB 5.1.6, 2008
FIADB 5.1.6, 2011
1987 RPA
FIADB 5.1.6, 1999
FIADB 5.1.6, 2012
1993
2003
2007
2010
1989
2001
2006
2010
1987
1994
2007
2009
1988
2002
2006
2010
1988
1996
2008
1989
2008
1990
2008
1983
1995
2004
2008
1974
1997
2005
1978
1997
2005
1985
2005
1980
2005
1983
1997
2005
2011
1987
1999
2007
2010
1986
1997
2011
A-342 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
New Mexico, NFS woodlands
New Mexico, non-NFS non-timberlands
New Mexico, non-NFS timberlands
New York, non-reserved
New York, reserved
North Carolina
North Dakota
Ohio
Oklahoma, Central & West
Oklahoma, East
Oregon, NFS East
Oregon, NFS West
Oregon, non-NFS East
Oregon, non-NFS West
Pennsylvania
Rhode Island
1987 RPA
FIADB 5.1.6, 1999
FIADB 5.1.6, 2012
FIADB 5.1.6, 2012
FIADB 5.1.6, 1987
FIADB 5.1.6, 1999
FIADB 5.1.6, 2012
Eastwide, 1980
FIADB 5.1.6, 1993
FIADB 5.1.6, 2007
FIADB 5.1.6, 2011
1987 RPA
FIADB 5.1.6, 2007
FIADB 5.1.6, 2011
FIADB 5.1.6, 1984
FIADB 5.1.6, 1990
FIADB 5.1.6, 2002
FIADB 5.1.6, 2007
FIADB 5.1.6, 2012
FIADB 5.1.6, 1980
FIADB 5.1.6, 1995
FIADB 5.1.6, 2005
FIADB 5.1.6, 2010
FIADB 5.1.6, 1991
FIADB 5.1.6, 2006
FIADB 5.1.6, 2011
FIADB 5.1.6, 2011
FIADB 5.1.6, 1986
FIADB 5.1.6, 1993
FIADB 5.1.6, 2008
IDB, 1990s
FIADB 5.1.6, 2010
IDB, 1990s
FIADB 5.1.6, 2010
Westwide, 1992
IDB, 1990s
FIADB 5.1.6, 2010
Westwide, 1992
IDB, 1990s
FIADB 5.1.6, 2010
FIADB 5.1.6, 1989
FIADB 5.1.6, 2004
FIADB 5.1.6, 2009
FIADB 5.1.6, 1985
FIADB 5.1.6, 1998
FIADB 5.1.6, 2007
FIADB 5.1.6, 2011
1986
1997
2011
2011
1987
1999
2011
1981
1993
2005
2010
1988
2005
2010
1984
1990
2001
2006
2009
1979
1995
2003
2009
1991
2005
2010
2011
1986
1993
2008
1995
2006
1996
2006
1991
1999
2006
1989
1997
2006
1990
2003
2008
1985
1999
2006
2010
A-343
-------
South Carolina
South Dakota, NFS
South Dakota, non-NFS
Tennessee
Texas, Central & West
Texas, East
Utah, non-woodlands
Utah, woodlands
Vermont
Virginia
Washington, NFS East
Washington, NFS West
Washington, non-NFS East
Washington, non-NFS West
West Virginia
FIADB 5.1.6, 1986
FIADB 5.1.6, 1993
FIADB 5.1.6, 2001
FIADB 5.1.6, 2006
FIADB 5.1.6, 2011
1997 RPA
FIADB 5.1.6, 1995
FIADB 5.1.6, 2005
FIADB 5.1.6, 2010
1987 RPA
FIADB 5.1.6, 1995
FIADB 5.1.6, 2005
FIADB 5.1.6, 2010
FIADB 5.1.6, 1989
FIADB 5.1.6, 1999
FIADB 5.1.6, 2004
FIADB 5.1.6, 2009
FIADB 5.1.6, 2010
FIADB 5.1.6, 1986
FIADB 5.1.6, 1992
FIADB 5.1.6, 2003
FIADB 5.1.6, 2008
FIADB 5.1.6, 2012
FIADB 5.1.6, 1993
FIADB 5.1.6, 2009
FIADB 5.1.6, 1993
FIADB 5.1.6, 2009
FIADB 5.1.6, 1983
FIADB 5.1.6, 1997
FIADB 5.1.6, 2007
FIADB 5.1.6, 2011
FIADB 5.1.6, 1985
FIADB 5.1.6, 1992
FIADB 5.1.6, 2001
FIADB 5.1.6, 2007
FIADB 5.1.6, 2011
IDB, 1990s
FIADB 5.1.6, 2011
IDB, 1990s
FIADB 5.1.6, 2011
IDB, 1990s
FIADB 5.1.6, 2011
IDB, 1990s
FIADB 5.1.6, 2011
FIADB 5.1.6, 1989
FIADB 5.1.6, 2000
FIADB 5.1.6, 2008
FIADB 5.1.6, 2011
1986
1993
2001
2005
2009
1986
1999
2004
2009
1986
1995
2004
2008
1989
1998
2003
2008
2008
1986
1992
2003
2006
2010
1993
2005
1994
2005
1983
1997
2006
2010
1985
1991
2000
2005
2010
1996
2007
1996
2007
1992
2007
1990
2007
1988
2001
2007
2010
A-344 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Wisconsin
Wyoming, NFS
Wyoming, non-NFS non-reserved non-woodlands
Wyoming, non-NFS non-reserved woodlands
Wyoming, non-NFS reserved
FIADB 5.1.6, 1983
FIADB 5.1.6, 1996
FIADB 5.1.6, 2004
FIADB 5.1.6, 2009
FIADB 5.1.6, 2012
1997 RPA
FIADB 5.1.6, 2000
FIADB 5.1.6, 1984
FIADB 5.1.6, 2000
FIADB 5.1.6, 1984
FIADB 5.1.6, 2000
1997 RPA
FIADB 5.1.6, 2000
1982
1995
2002
2007
2010
1982
2000
1984
2002
1984
2002
1985
2000
Substate areas (Smith et al. 2010) include National Forests (NFS), all forest ownerships except National Forest (non-NFS), woodlands (forest land
dominated by woodland species, such as pinyon and juniper, where stocking cannot be determined (USDA Forest Service 2013c), non-woodlands (used
for clarity to emphasize that woodlands are classified separately), reserved (forest land withdrawn from timber utilization through statute, administrative
regulation, or designation, Smith et al. (2009)), and non-reserved (forest land that is not reserved, used for clarity). Some National Forests are listed
individually by name, e.g., Payette NF. Oregon and Washington were divided into eastern and western forests (east or west of the crest of the Cascade
Mountains). Oklahoma and Texas are divided into East versus Central & West according to forest inventory survey units (USDA Forest Service 2013d).
Alaska is represented by a portion of forest land, in the southcentral and southeast part of the state.
b FIADB 5.1.6 is the current, publicly available, format of FIA inventory data, and these files were downloaded from the Internet 11 July 2013 (USDA
Forest Service 2013b). IDB (Integrated Database) data are a compilation of periodic inventory data from the 1990s for California, Oregon, and
Washington (Waddell and Hiserote 2005). Eastwide (Hansen et al. 1992) and Westwide (Woudenberg and Farrenkopf 1995) inventory data are formats
that predate the FIADB data. RPA data are periodic national summaries. The year is the nominal, or reporting, year associated with each dataset.
c Average year is based on average measurement year of forest land survey plots and rounded to the nearest integer year.
A national plot design and annualized sampling (USDA Forest Service 2013a) was introduced by FIA with most
new surveys beginning after 1998. These surveys include sampling of all forest land including reserved and lower
productivity lands. Most states have annualized inventory data available as of July 2013. Annualized sampling means
that a portion of plots throughout the state is sampled each year, with the goal of measuring all plots once every 5 to 10
years, depending on the region of the United States. The full unique set of data with all measured plots, such that each
plot has been measured one time, is called a cycle. Sampling is designed such that partial inventory cycles provide usable,
unbiased samples of forest inventory, but with higher sampling errors than the full cycle. After all plots have been
measured once, the sequence continues with remeasurement of the first year's plots, starting the next new cycle. Most
Eastern states have completed one or two cycles of the annualized inventories and are providing annual updates to the
state's forest inventory with each year's remeasurements, such that one plot's measurements are included in subsequent
year's annual updates. Thus, annually updated estimates of forest C stocks are accurate, but estimates of stock change
cannot utilize the annually updated inventory measurements directly, as there is redundancy in the data used to generate
the annual updates of C stock. For example, a typical annual inventory update for an Eastern state will include new data
from remeasurement on 20 percent of plots; data from the remaining 80 percent of plots is identical to that included in the
previous year's annual update. The interpretation and use of the sequence of annual inventory updates can affect trends in
annualized stock and stock change. In general, the C stock and stock change calculations use annual inventory summaries
(updates) with unique sets of plot-level data (that is, without redundant sets); the most-recent annual update is the
exception because it is included in stock change calculations if at least half of the plots in a state include new
measurements. The specific surveys used in this report are listed in Table A-245, and this list can be compared with the
full set of summaries available for download (USDA Forest Service 2013b).
For each pool in each state in each year, C stocks are estimated by linear interpolation between survey years.
Similarly, fluxes, or net stock changes, are estimated for each pool in each state by dividing the difference between two
successive stocks by the number of intervening years between surveys. Thus, the number of separate stock change
estimates for each state or sub-state is one less than the number of available inventories. Annual estimates of stock and net
change since the most recent survey are based on linear extrapolation. Carbon stock and flux estimates for each pool are
summed over all forest land in all states as identified in the FIADB to form estimates for the United States. Summed net
annual stock change and stocks are presented in Table A-245 and Table A-246 , respectively. An estimate of forest area
based on the interpolation and extrapolation procedure described above is also provided in Table A-247. Estimated net
A-345
-------
stock change of non-soil forest ecosystem carbon for each of the states is shown in Table A-248, which also includes
estimated forest area and total non-soil forest C stock. The state-level forest areas and C stocks are from the most recent
inventory available (USDA Forest Service 201 la), and the estimate for net stock change is the 10-year mean of the 2003
through 2012 estimates from the C calculator (Smith et al. 2010).
A-346 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-246: Estimated Net Annual Changes in Carbon Stocks ITg Cyrl in Forest and Harvested Wood Pools, 1990-2012
Live, Live,
Year Total Net Flux Forest Total aboveground belowground Dead Wood
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
(192.2)
(189.8)
(188.3)
(188.9)
(195.6)
(197.0)
(193.3)
(184.3)
(169.5)
(152.7)
(147.1)
(175.2)
(210.8)
(240.3)
(250.4)
(252.9)
(249.0)
(244.8)
(237.6)
(231.6)
(233.4)
(236.5)
(236.3)
(156.2)
(156.0)
(154.5)
(155.9)
(162.2)
(164.7)
(162.7)
(152.3)
(138.3)
(120.2)
(116.3)
(149.7)
(184.0)
(214.7)
(222.0)
(224.8)
(219.6)
(217.0)
(216.9)
(216.9)
(217.2)
(218.2)
(218.2)
(96.7)
(96.6)
(97.5)
(103.4)
(106.5)
(109.7)
(109.1)
(110.1)
(105.2)
(101.2)
(100.3)
(109.4)
(112.0)
(118.5)
(120.3)
(120.6)
(119.1)
(118.2)
(118.6)
(118.6)
(118.6)
(118.6)
(118.6)
(18.9)
(18.9)
(19.1)
(20.3)
(20.9)
(21.6)
(21.5)
(21.7)
(20.7)
(20.0)
(19.8)
(21.6)
(22.0)
(23.3)
(23.7)
(23.7)
(23.6)
(23.5)
(23.6)
(23.6)
(23.6)
(23.6)
(23.6)
(13.8)
(14.0)
(14.1)
(14.9)
(14.9)
(14.2)
(19.9)
(18.6)
(18.6)
(18.2)
(18.6)
(19.0)
(20.2)
(19.3)
(18.7)
(17.6)
(19.2)
(19.9)
(20.0)
(20.0)
(20.3)
(21.3)
(21.3)
Soil Organic Harvested Products in
Litter Carbon Wood Total Use
(6.5)
(6.5)
(6.5)
(5.6)
(5.1)
(3.4)
(2.5)
(2.3)
1.7
5.0
6.8
1.5
(5.1)
(9.4)
(11.1)
(12.6)
(13.4)
(14.0)
(14.0)
(14.0)
(14.0)
(14.0)
(14.0)
(20.3)
(20.1)
(17.3)
(11.7)
(14.8)
(15.8)
(9.6)
0.4
4.6
14.2
15.5
(1.2)
(24.6)
(44.3)
(48.2)
(50.3)
(44.4)
(41.4)
(40.7)
(40.7)
(40.7)
(40.7)
(40.7)
(35.9)
(33.8)
(33.8)
(32.9)
(33.4)
(32.3)
(30.6)
(32.0)
(31.1)
(32.5)
(30.8)
(25.5)
(26.8)
(25.6)
(28.3)
(28.0)
(29.4)
(27.8)
(20.7)
(14.8)
(16.2)
(18.3)
(18.1)
(17.7)
(14.9)
(16.3)
(15.0)
(15.9)
(15.1)
(14.1)
(14.7)
(13.4)
(14.1)
(12.8)
(8.7)
(9.6)
(9.4)
(12.0)
(11.7)
(12.1)
(10.4)
(3.6)
1.8
0.3
(1.6)
(1.3)
SWDS
(18.3)
(18.8)
(17.4)
(17.9)
(17.5)
(17.2)
(16.5)
(17.3)
(17.7)
(18.4)
(18.0)
(16.8)
(17.2)
(16.2)
(16.3)
(16.3)
(17.3)
(17.4)
(17.0)
(16.6)
(16.5)
(16.7)
(16.8)
A-347
-------
Table A-247: Estimated Carbon Stocks (Tg G) in Forest and Harvested Wood Pools.1990-2013
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Total Carbon
Stock
40,826
41,018
41,208
41,396
41,585
41,781
41,978
42,171
42,356
42,525
42,678
42,825
43,000
43,211
43,451
43,701
43,954
44,203
44,448
44,686
44,917
45,151
45,387
45,623
Forest
Total
38,967
39,123
39,279
39,434
39,590
39,752
39,917
40,079
40,232
40,370
40,490
40,606
40,756
40,940
41,155
41,377
41,602
41,821
42,038
42,255
42,472
42,689
42,907
43,126
Live,
aboveground
12,318
12,415
12,511
12,609
12,712
12,819
12,928
13,038
13,148
13,253
13,354
13,454
13,564
13,676
13,794
13,915
14,035
14,154
14,272
14,391
14,510
14,628
14,747
14,866
Live,
belowground
2,437
2,456
2,475
2,494
2,515
2,535
2,557
2,579
2,600
2,621
2,641
2,661
2,682
2,704
2,728
2,751
2,775
2,799
2,822
2,846
2,869
2,893
2,916
2,940
Dead Wood
2,147
2,161
2,175
2,189
2,204
2,219
2,233
2,253
2,272
2,290
2,308
2,327
2,346
2,366
2,385
2,404
2,422
2,441
2,461
2,481
2,501
2,521
2,542
2,564
Litter
4,897
4,903
4,910
4,916
4,922
4,927
4,931
4,933
4,935
4,934
4,929
4,922
4,920
4,925
4,935
4,946
4,958
4,972
4,986
5,000
5,014
5,028
5,042
5,056
Soil Organic
Carbon
17,168
17,188
17,208
17,225
17,237
17,252
17,268
17,277
17,277
17,272
17,258
17,243
17,244
17,268
17,313
17,361
17,411
17,456
17,497
17,538
17,578
17,619
17,660
17,700
Harvested Wood
Total
1,859
1,895
1,929
1,963
1,996
2,029
2,061
2,092
2,124
2,155
2,188
2,218
2,244
2,271
2,296
2,325
2,353
2,382
2,410
2,430
2,445
2,461
2,480
2,498
Products in
Use SWDS
1,231 628
1,249 646
1,264 665
1,280 683
1,295 701
1,311 718
1,326 735
1,340 752
1,355 769
1,368 787
1,382 805
1,395 823
1,404 840
1,414 857
1,423 873
1,435 890
1,447 906
1,459 923
1,469 940
1,473 958
1,471 974
1,471 991
1,472 1,007
1,474 1,024
Forest Area
(1000 ha)
275,399
276,027
276,663
277,275
277,845
278,409
278,962
279,401
279,769
280,134
280,400
280,613
280,895
281,323
281,929
282,583
283,263
283,829
284,345
284,858
285,371
285,884
286,397
286,910
A-348 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-248: State-Level Forest Area, Carbon Stock, and Net Annual Stock Change. Estimates are Forest Ecosystem carbon
and Do Not Include Harvested Wood
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Mean year of Mean net annual
field data Forest area Nonsoil C nonsoil stock change
collection (1000 ha) stock (Tg C) 2003-201 2 (Tg C/yr)
2009
2007
2007
2010
2007
2007
2010
2010
2009
2010
2008
2010
2010
2010
2010
2010
2008
2010
2010
2010
2010
2010
2009
2010
2008
2009
2005
2011
2010
2011
2010
2009
2010
2010
2010
2007
2010
2010
2010
2011
2010
2008
2007
2010
2010
2007
2010
2010
2001
9,268
6,161
7,545
7,666
13,200
9,242
693
137
7,066
10,017
8,680
1,977
1,965
1,208
1,013
5,047
5,954
7,138
996
1,224
8,214
7,055
7,886
6,266
10,349
638
4,520
1,956
795
10,052
7,675
7,536
305
3,273
5,118
12,061
6,791
145
5,285
770
5,642
25,234
7,339
1,858
6,437
9,079
4,919
6,909
4,633
684
993
397
584
1,798
746
83
16
449
737
932
167
185
89
62
444
427
621
118
146
698
446
574
464
986
38
193
219
77
499
850
672
16
329
237
1,781
742
16
423
42
572
894
423
221
616
1,598
547
508
413
(7.0)
(5.0)
0.9
(4.1)
(6.5)
(1.1)
(0.8)
(0.1)
(4.3)
(6.2)
(0.2)
(3.2)
(2.5)
(1.6)
(1.4)
(4.3)
(2.7)
(3.0)
(1.0)
(1.3)
(9.8)
(4.8)
(8.6)
(5.9)
(7.3)
(1.1)
(1.2)
(1.4)
(0.7)
(0.0)
(7.6)
(7.4)
(0.1)
(4.1)
(1.4)
(5.8)
(7.0)
(0.3)
(5.8)
(0.7)
(4.4)
(0.9)
(4.1)
(1.5)
(5.0)
(8.5)
(7.3)
(6.0)
(1.1)
A-349
-------
Table A-249 shows average C density values for forest ecosystem C pools according to region and forest types
based on forest lands in this Inventory. These values were calculated by applying plot-level C estimation procedures as
described below to the most recent inventory per state as available 11 July 2013 (USDA Forest Service 2013b). Carbon
density values reflect the most recent survey for each state as available in the FIADB, not potential maximum C storage.
Carbon densities are affected by the distribution of stand sizes within a forest type, which can range from regenerating to
mature stands. A large proportion of young stands in a particular forest type are likely to reduce the regional average for C
density.
Table A-249: Average Carbon Density (Mg G/ha) by Carbon Pool and Forest Area (1000 ha) According to Region and
Forest Type, Based on the Most Recent Inventory Survey Available for Each State from FIA, Corresponding to an Average
Year of 2009
Region
(States) Above-ground
Forest Types Biomass
Below-
ground
Biomass Dead Wood
Litter
Soil
Organic
Carbon
Carbon Density (Mg C/ha)
Northeast
Forest
Area
(1,000 ha)
(CT,DE,MA,MD,ME,NH,NJ,NY,OH,PA,RI,VT,WV)
White/Red/Jack Pine
Spruce/Fir
Oak/Pine
Oak/Hickory
Elm/Ash/Cottonwood
Maple/Beech/Birch
Aspen/Birch
Minor Types and Nonstocked
All
Northern Lake States
(MI,MN,WI)
White/Red/Jack Pine
Spruce/Fir
Oak/Hickory
Elm/Ash/Cottonwood
Maple/Beech/Birch
Aspen/Birch
Minor Types and Nonstocked
All
Northern Prairie States
(IA,IL,IN,KS,MO,ND,NE,SD)
Ponderosa Pine
Oak/Pine
Oak/Hickory
Elm/Ash/Cottonwood
Minor Types and Nonstocked
All
South Central
(AL,AR,KY,LA,MS,OK,TN,TX)
Loblolly/Shortleaf Pine
Pinyon/Juniper
Oak/Pine
Oak/Hickory
Oak/Gum/Cypress
Elm/Ash/Cottonwood
Woodland Hardwoods
Minor Types and Nonstocked
All
Southeast
(FL,GA,NC,SC,VA)
Longleaf/Slash Pine
Loblolly/Shortleaf Pine
Oak/Pine
Oak/Hickory
Oak/Gum/Cypress
79.0
39.6
70.9
78.1
55.5
71.1
42.4
45.8
68.3
46.3
29.4
54.4
42.4
59.0
31.8
29.3
43.2
32.1
40.0
53.2
56.4
31.2
50.1
48.7
12.1
44.8
48.1
63.7
38.6
9.6
27.9
40.1
41.0
53.1
51.2
65.2
64.2
16.3
8.3
14.0
14.8
10.5
13.6
8.3
9.0
13.1
9.6
6.2
10.3
8.1
11.3
6.1
5.8
8.4
6.7
7.7
10.0
10.5
6.1
9.5
10.0
2.3
8.8
9.0
12.1
7.2
1.5
5.4
7.7
8.5
11.0
10.0
12.3
12.5
6.9
7.5
5.6
6.3
5.4
6.8
5.9
5.8
6.5
5.6
5.6
7.0
5.5
6.7
6.1
6.3
6.2
4.5
4.4
6.0
6.4
5.2
5.8
7.4
4.0
6.6
7.4
7.5
6.1
1.9
6.6
6.3
4.9
4.3
4.0
5.3
5.3
13.8
30.8
27.9
8.2
7.1
27.3
8.7
11.2
18.1
12.4
33.3
8.1
7.6
27.7
8.3
18.0
16.6
14.4
26.1
7.9
6.9
17.8
9.8
9.6
12.2
9.3
6.4
6.5
5.9
5.0
6.8
7.4
10.0
9.8
9.4
6.5
6.5
78.1
98.0
66.9
53.1
111.7
69.6
87.4
73.9
69.1
120.8
261.8
97.1
179.9
134.3
146.1
120.0
151.4
48.5
40.7
49.6
83.1
60.3
55.1
41.9
37.7
41.7
38.6
52.8
49.9
65.0
54.3
45.7
110.0
72.9
61.4
45.3
158.0
1,661
3,077
1,209
13,027
1,479
13,763
1,551
1,833
37,601
1,915
3,197
4,002
2,287
4,495
5,096
1,186
22,178
550
572
9,545
2,041
1,434
14,142
13,816
4,030
5,121
25,087
5,278
4,047
9,454
4,983
71,815
4,174
9,309
4,066
11,817
4,622
A-350 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Elm/Ash/Cottonwood
Minor Types and Nonstocked
All
Coastal Alaska
(approximately 12 percent of forest
land in Alaska)
Spruce/Fir
Fir/Spruce/Mountain Hemlock
Hemlock/Sitka Spruce
Aspen/Birch
Minor Types and Nonstocked
All
Pacific Northwest, Westside
(Western OR and WA)
Douglas-fir
Fir/Spruce/Mountain Hemlock
Hemlock/Sitka Spruce
Alder/Maple
Minor Types and Nonstocked
All
Pacific Northwest, Eastside
(Eastern OR and WA)
Douglas-fir
Ponderosa Pine
Fir/Spruce/Mountain Hemlock
Lodgepole Pine
Western Larch
Other Western Softwoods
Minor Types and Nonstocked
All
Pacific Southwest
(CA)
Pinyon/Juniper
Douglas-fir
Ponderosa Pine
Fir/Spruce/Mountain Hemlock
Redwood
Other Western Softwoods
California Mixed Conifer
Western Oak
Tanoak/Laurel
Minor Types and Nonstocked
All
Rocky Mountain, North
(ID,MT)
Douglas-fir
Ponderosa Pine
Fir/Spruce/Mountain Hemlock
Lodgepole Pine
Western Larch
Other Western Softwoods
Aspen/Birch
Minor Types and Nonstocked
All
Rocky Mountain, South
(AZ,CO,NM,NV,UT,WY)
Pinyon/Juniper
Douglas-fir
Ponderosa Pine
Fir/Spruce/Mountain Hemlock
Lodgepole Pine
Aspen/Birch
49.1
31.4
55.9
9.3
6.0
10.9
3.9
7.0
4.9
5.6
5.7
8.0
95.7
110.9
79.8
867
1,485
36,341
14.7
64.4
116.5
28.7
29.8
81.1
2.9
13.5
24.5
5.4
5.8
17.0
7.8
17.2
27.4
8.3
7.3
20.2
33.5
43.0
50.4
10.6
22.9
42.9
62.1
62.1
116.3
42.5
74.6
86.7
395
2,275
2,808
294
389
6,161
140.0
130.3
169.2
78.9
56.0
127.2
29.3
27.5
35.6
15.4
11.0
26.5
31.4
35.7
41.5
16.0
17.5
30.1
32.2
38.5
37.8
7.7
12.9
28.9
94.8
62.1
116.3
115.2
86.0
95.4
5,954
1,210
1,545
1,180
1,234
11,124
62.9
41.1
74.1
35.7
72.4
12.0
27.3
46.8
13.1
8.5
15.6
7.5
15.1
2.2
5.3
9.7
18.3
9.7
27.1
13.0
20.8
3.5
17.0
15.2
36.3
22.5
37.9
21.1
36.1
36.3
24.4
30.0
94.8
50.7
62.1
52.0
45.1
78.8
80.7
67.9
2,020
2,809
1,797
1,019
211
1,143
1,016
10,017
13.7
144.6
53.5
111.3
243.2
23.0
105.8
49.3
126.0
48.5
75.4
2.5
30.0
11.1
23.5
50.8
4.4
22.1
9.3
24.7
9.7
15.3
2.4
23.8
9.6
29.6
35.9
5.5
21.3
5.3
12.4
15.5
13.6
21.1
35.5
22.4
38.4
60.4
37.8
37.9
30.0
28.3
26.0
32.2
26.3
40.1
41.3
51.9
53.8
49.8
49.8
27.6
27.6
37.0
38.7
739
455
912
817
287
822
3,157
3,827
805
1,378
13,200
11.2
50.9
32.2
55.5
44.8
111
22.2
33.2
44.1
2.2
10.7
6.6
11.7
9.5
5.7
4.1
6.7
9.2
1.8
13.1
8.0
21.8
16.4
13.1
14.7
21.0
16.0
21.1
37.0
22.9
37.4
22.9
38.8
26.8
25.2
31.4
41.7
38.8
34.3
44.1
37.2
31.4
56.6
41.7
40.2
638
5,473
1,733
4,708
2,715
648
504
2,609
19,029
15.1
49.7
37.0
55.9
46.8
39.9
3.0
10.5
7.7
11.8
9.9
7.6
1.8
12.8
7.5
19.9
18.7
10.6
21.1
38.3
23.8
38.9
23.9
28.5
19.7
30.9
24.1
31.5
27.0
58.8
20,800
1,694
3,264
4,210
1,974
2,569
A-351
-------
Woodland Hardwoods 13.9 2.5 4.9 28.0 25.9 5,728
Minor Types and Nonstocked 13.1 2.4 9.6 22.8 25.5 3,092
All 247 5.0 6.7 25.3 25.5 43,330
United States (forest land included in
Inventory) 50J 10.0 8.7 17.6 61.7 284,938
Note: The forest area values in this table do not equal the forest area values reported in Table A-247, because the forest area values in this table
are estimated using the most recent dataset per state, with an average year of 2009. The time series of forest area values reported in Table A-247,
in contrast, is constructed following the CCT methods used to construct the C stock series. The forest area values reported in Table A-247 and
Table A-249 would only be identical if all states were measured simultaneously or they all had identical rates of change.
The Inventory is derived primarily from the FIADB 5.1.6 data (USDA Forest Service 2013b), but it also draws
on older FIA survey data where necessary. The Resources Planning Act Assessment (RPA) database, which includes
periodic summaries of state inventories, is one example. Information about the RPA data is available on the Internet
(USDA Forest Service 2013a), and compilations of analytical estimates based on these databases are found in Waddell et
al. (1989) and Smith et al. (2001). Some differences between the RPA database and the FIADB are that the FIADB
includes individual-tree data and includes additional land use categories such as "other wooded lands".
Having only plot-level information (such as volume per hectare) limits the conversion to biomass. This does
not constitute a substantial difference for the overall state-wide estimates, but it does affect plot-level precision (Smith et
al. 2004). In the past, FIA made their data available in tree-level Eastwide (Hansen et al. 1992) or Westwide (Woudenberg
and Farrenkopf 1995) formats, which included inventories for Eastern and Western states, respectively. The current
Inventory estimates rely, in part, on older tree-level data that are not available on the current FIADB site. The Integrated
Database (IDE) is a compilation of periodic forest inventory data from the 1990s for California, Oregon, and Washington
(Waddell and Hiserote 2005). These data were identified by Heath et al. (2011) as the most appropriate non-FIADB
sources for these three states.
A historical focus of the FIA program was to provide information on timber resources of the United States. For
this reason, prior to 1998, some forest land, which were less productive or reserved (i.e., land where harvesting was
prohibited by law), were less intensively surveyed. This generally meant that on these less productive lands, forest type
and area were identified but data were not collected on individual tree measurements. The practical effect that this
evolution in inventories has had on estimating forest C stocks from 1990 through the present is that some older surveys of
lands do not have the individual-tree data or even stand-level characteristics such as stand age. Any data gaps identified in
the surveys taken before 1998 were filled by assigning average C densities calculated from the more complete, later
inventories from the respective states. The overall effect of this necessary approach to generate estimates for C stock is
that no net change in C density occurs on those lands with gaps in past surveys (for further discussion see Domke et al. In
Review). This approach to filling gaps in older data also extends to timberlands where individual-tree data was not
available (e.g., standing dead trees).
Estimating C stocks from forest inventory data
For each inventory summary in each state, data are converted to C units or augmented by other ecological data.
Most of the conversion factors and models used for inventory-based forest C estimates (Smith et al. 2010, Heath et al.
2011) were initially developed as an offshoot of the forest C simulation model FORCARB (Heath et al. 2010) and are
incorporated into a number of applications (Birdsey and Heath 1995, Birdsey and Heath 2001, Heath et al. 2003, Smith et
al. 2004, Hoover and Rebain 2008). The conversion factors and model coefficients are usually categorized by region and
forest type. Classifications for both of these categories are subject to change depending on the particular coefficient set.
Thus, region and type are specifically defined for each set of estimates. Factors are applied to the survey data at the scale
of FIA inventory plots. The results are estimates of C density (Mg per hectare) for the various forest pools. Carbon
density for live trees, standing dead trees, understory vegetation, downed dead wood, litter, and soil organic matter are
estimated. All non-soil pools except litter can be separated into aboveground and belowground components. The live tree
and understory C pools are pooled as biomass in this inventory. Similarly, standing dead trees and downed dead wood are
pooled as dead wood in this inventory. C stocks and fluxes for Forest Land Remaining Forest Land are reported in pools
following IPCC (2003).
Live tree C pools
Live tree C pools include aboveground and belowground (coarse root) biomass of live trees with diameter at
diameter breast height (d.b.h.) of at least 2.54 cm at 1.37 m above the forest floor. Separate estimates are made for above-
A-352 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
and below-ground biomass components. If inventory plots include data on individual trees, tree C is based on Woodall et
al. (2011), which is also known as the component ratio method (CRM), and is a function of volume, species, diameter,
and, in some regions, tree height and site quality. The estimated sound volume provided in the tree table of the FIADB is
the principal input to the CRM biomass calculation for each tree. The estimated volumes of wood and bark are converted
to biomass based on the density of each. Additional components of the trees such as tops, branches, and coarse roots, are
estimated according to adjusted component estimates from Jenkins et al. (2003). Live trees with d.b.h of less than 12.7 cm
do not have estimates of sound volume in the FIADB, and CRM biomass estimates follow a separate process (see Woodall
et al. 201 1 for details). An additional component of foliage, which was not explicitly included in Woodall et al. (201 1),
was added to each tree following the same CRM method. Carbon is estimated by multiplying the estimated oven-dry
biomass by a C constant of 0.5 because biomass is 50 percent of dry weight (IPCC/UNEP/OECD/IEA 1997). Further
discussion and example calculations are provided in Woodall et al. 201 1 and Domke et al. 2012.
Some of the older forest inventory data in use for these estimates do not provide measurements of individual
trees. Examples of these data include plots with incomplete or missing tree data (e.g., some of the non-timberland plots in
older surveys) or the RPA plot-level summaries. The C estimates for these plots are based on average densities (metric
tons C per hectare) obtained from plots of more recent surveys with similar stand characteristics and location. This applies
to less than 5 percent of the forest land inventory -plot-to -C conversions within the 193 state-level surveys utilized here.
Understory vegetation
Understory vegetation is a minor component of total forest ecosystem biomass. Understory vegetation is defined
as all biomass of undergrowth plants in a forest, including woody shrubs and trees less than one-inch d.b.h. In this
inventory, it is assumed that 10 percent of understory C mass is belowground. This general root-to-shoot ratio (0.11) is
near the lower range of temperate forest values provided in IPCC (2006) and was selected based on two general
assumptions: ratios are likely to be lower for light-limited understory vegetation as compared with larger trees, and a
greater proportion of all root mass will be less than 2 mm diameter.
Estimates of C density are based on information in Birdsey (1996), which was applied to FIA permanent plots.
These were fit to the model:
Ratio =
e c density))
In this model, the ratio is the ratio of understory C density (Mg C/ha) to live tree C density (above- and below-
ground) according to Jenkins et al. (2003) and expressed in Mg C/ha. An additional coefficient is provided as a maximum
ratio; that is, any estimate predicted from the model that is greater than the maximum ratio is set equal to the maximum
ratio. A full set of coefficients are in Table A-250. Regions and forest types are the same classifications described in
Smith et al. (2003). As an example, the basic calculation for understory C in aspen-birch forests in the Northeast is:
Understory (Mg C/ha) = (live tree C density)
e c densl'y»
This calculation is followed by three possible modifications. First, the maximum value for the ratio is set to 2.02
(see value in column "maximum ratio"); this also applies to stands with zero tree C, which is undefined in the above
model. Second, the minimum ratio is set to 0.005 (Birdsey 1996). Third, nonstocked and piny on/juniper stands are set to
coefficient A, which is a C density (Mg C/ha) for these types only.
Table A-250: Coefficients for Estimating the Ratio of Carbon Density of Understory Vegetation (above- and belowground,
MgC/ha)a by Region and Forest Type. The Ratio is Multiplied by Tree Carbon Density on Each Plot to Produce Understory
Vegetation
Regionb
NE
NLS
Forest Typeb
Aspen-Birch
MBB/Other Hardwood
Oak-Hickory
Oak-Pine
Other Pine
Spruce-Fir
White-Red-Jack Pine
Nonstocked
Aspen-Birch
Lowland Hardwood
A
0.855
0.892
0.842
1.960
2.149
0.825
1.000
2.020
0.777
0.650
B
1.032
1.079
1.053
1.235
1.268
1.121
1.116
2.020
1.018
0.997
Maximum
ratio0
2.023
2.076
2.057
4.203
4.191
2.140
2.098
2.060
2.023
2.037
A-353
-------
NPS
PSW
PWE
PWW
RMN
RMS
SC
SE
Maple-Beech-Birch
Oak-Hickory
Pine
Spruce-Fir
Nonstocked
Conifer
Lowland Hardwood
Maple-Beech-Birch
Oak-Hickory
Oak-Pine
Nonstocked
Douglas-fir
Fir-Spruce
Hardwoods
Other Conifer
Pinyon-Juniper
Redwood
Nonstocked
Douglas-fir
Fir-Spruce
Hardwoods
Lodgepole Pine
Pinyon-Juniper
Ponderosa Pine
Nonstocked
Douglas-fir
Fir-Spruce
Other Conifer
Other Hardwoods
Red Alder
Western Hemlock
Nonstocked
Douglas-fir
Fir-Spruce
Hardwoods
Lodgepole Pine
Other Conifer
Pinyon-Juniper
Ponderosa Pine
Nonstocked
Douglas-fir
Fir-Spruce
Hardwoods
Lodgepole Pine
Other Conifer
Pinyon-Juniper
Ponderosa Pine
Nonstocked
Bottomland Hardwood
Misc. Conifer
Natural Pine
Oak-Pine
Planted Pine
Upland Hardwood
Nonstocked
Bottomland Hardwood
Misc. Conifer
Natural Pine
Oak-Pine
Planted Pine
Upland Hardwood
Nonstocked
0.863
0.965
0.740
1.656
1.928
1.189
1.370
1.126
1.139
2.014
2.052
2.084
1.983
1.571
4.032
4.430
2.513
4.431
1.544
1.583
1.900
1.790
2.708
1.768
4.315
1.727
1.770
2.874
2.157
2.094
2.081
4.401
2.342
2.129
1.860
2.571
2.614
2.708
2.099
4.430
5.145
2.861
1.858
3.305
2.134
2.757
3.214
4.243
0.917
1.601
2.166
1.903
1.489
2.089
4.044
0.834
1.601
1.752
1.642
1.470
1.903
4.033
1.120
1.091
1.014
1.318
1.928
1.190
1.177
1.201
1.138
1.215
2.052
1.201
1.268
1.038
1.785
4.430
1.312
4.431
1.064
1.156
1.133
1.257
2.708
1.213
4.315
1.108
1.164
1.534
1.220
1.230
1.218
4.401
1.360
1.315
1.110
1.500
1.518
2.708
1.344
4.430
2.232
1.568
1.110
1.737
1.382
2.757
1.732
4.243
1.109
1.129
1.260
1.190
1.037
1.235
4.044
1.089
1.129
1.155
1.117
1.036
1.191
4.033
2.129
2.072
2.046
2.136
2.117
2.114
2.055
2.130
2.072
4.185
2.072
4.626
4.806
4.745
4.768
4.820
4.698
4.626
4.626
4.806
4.745
4.823
4.820
4.768
4.626
4.609
4.807
4.768
4.745
4.745
4.693
4.589
4.731
4.749
4.745
4.773
4.821
4.820
4.776
4.773
4.829
4.822
4.745
4.797
4.821
4.820
4.820
4.797
1.842
4.191
4.161
4.173
4.124
4.170
4.170
1.842
4.191
4.178
4.195
4.141
4.182
4.182
A-354 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
'Prediction of ratio of understory C to live tree C is based on the model: Ratio=exp(A - B x ln(tree_carbon_tph)), where "ratio" is the ratio of understory C density
to live tree (above-and below- ground) C density, and "tree_carbon_density" is live tree (above-and below- ground) C density in Mg C/ha.
b Regions and types as defined in Smith et al. (2003).
'Maximum ratio: any estimate predicted from the model that is greater than the maximum ratio is set equal to the maximum ratio.
Dead Wood
The standing dead tree estimates are primarily based on plot-level measurements (Domke et al. 2011, Woodall et
al. 2011). This C pool includes aboveground and belowground (coarse root) mass and includes trees of at least 12.7 cm
d.b.h. Calculations follow the basic CRM method applied to live trees (Woodall et al. 2011) with additional modifications
to account for decay and structural loss. In addition to the lack of foliage, two characteristics of standing dead trees that
can significantly affect C mass are decay, which affects density and thus specific C content (Domke et al. 2011, Harmon et
al. 2011), and structural loss such as branches and bark (Domke et al. 2011). Dry weight to C mass conversion is by
multiplying by 0.5.
Some of the older forest inventory data in use for these estimates do not provide measurements of individual
standing dead trees. In addition to the RPA data, which are plot-level summaries, some of the older surveys that otherwise
include individual-tree data may not completely sample dead trees on non-timberlands and in some cases timberlands.
The C estimates for these plots are based on average densities (metric tons C per hectare) obtained from plots of more
recent surveys with similar stand characteristics and location. This applies to 23 percent of the forest land inventory-plot-
to-C conversions within the 193 state-level surveys utilized here.
Downed dead wood, inclusive of logging residue, are sampled on a subset of FIA plots. Despite a reduced
sample intensity, a single down woody material population estimate (Woodall et al. 2010, Domke et al. 2013, Woodall et
al. 2013) per state is now incorporated into these empirical downed dead wood estimates. Downed dead wood is defined
as pieces of dead wood greater than 7.5 cm diameter, at transect intersection, that are not attached to live or standing dead
trees. It also includes stumps and roots of harvested trees. Ratio estimates of downed dead wood to live tree biomass
were developed using FORCARB2 simulations and applied at the plot level (Smith et al. 2004). Estimates for downed
dead wood correspond to the region and forest type classifications described in Smith et al. (2003). A full set of ratios is
provided in Table A-251. An additional component of downed dead wood is a regional average estimate of logging
residue based on Smith et al. (2006) applied at the plot level. These are based on a regional average C density at age zero
and first order decay; initial densities and decay coefficients are provided in Table A-252. These amounts are added to
explicitly account for downed dead wood following harvest. The sum of these two components are then adjusted by the
ratio of population totals; that is, the ratio of plot-based to modeled estimates (Domke et al. 2013). An example of this 3-
part calculation for downed dead wood in a 25-year-old naturally regenerated loblolly pine forest with 82.99 Mg C/ha in
live trees (Jenkins et al. 2003) in Louisiana is as follows:
First, an initial estimate from live tree C density and Table A-251 (SC, Natural Pine)
C density = 82.99 x 0.068 = 5.67 (Mg C/ha)
Second, an average logging residue from age and Table A-252 (SC, softwood) C density = 5.5 x e(-25/i7.9) _ j 37
(Mg C/ha)
Third, adjust the sum by the downed dead wood ratio plot-to-model for Louisiana, which was 27.6/31.1 = 0.886
C density = (5.67 + 1.37) x 0.886 = 6.24 (Mg C/ha)
Table A-251: Ratio for Estimating Down Dead Wood by Region and Forest Type. The Ratio is Multiplied by the Live Tree
Carbon Density on a Plot to Produce Down Dead Wood Carbon Density [Hg C/hal
Region3
NE
Forest type3
Aspen-Birch
MBB/Other Hardwood
Oak-Hickory
Oak-Pine
Other Pine
Spruce-Fir
White-Red-Jack Pine
Nonstocked
Ratio
0.078
0.071
0.068
0.061
0.065
0.092
0.055
0.019
Region
(cont'd)
PWW
RMN
Forest type (cont'd)
Douglas-fir
Fir-Spruce
Other Conifer
Other Hardwoods
Red Alder
Western Hemlock
Nonstocked
Douglas-fir
Ratio
(cont'd)
0.100
0.090
0.073
0.062
0.095
0.099
0.020
0.062
A-355
-------
Aspen-Birch
Lowland Hardwood
Maple-Beech-Birch
NLS Oak-Hickory
Pine
Spruce-Fir
Nonstocked
Conifer
Lowland Hardwood
Mp~ Maple-Beech-Birch
lwb Oak-Hickory
Oak-Pine
Nonstocked
Douglas-fir
Fir-Spruce
Hardwoods
PSW Other Conifer
Pinyon-Juniper
Redwood
Nonstocked
Douglas-fir
Fir-Spruce
Hardwoods
PWE Lodgepole Pine
Pinyon-Juniper
Ponderosa Pine
Nonstocked
0.081
0.061
0.076
0.077
0.072
0.087
0.027
0.073
0.069
0.063
0.068
0.069
0.026
0.091
0.109
0.042
0.100
0.031
0.108
0.022
0.103
0.106
0.027
0.093
0.032
0.103
0.024
Fir-Spruce
Hardwoods
Lodgepole Pine
Other Conifer
Pinyon-Juniper
Ponderosa Pine
Nonstocked
Douglas-fir
Fir-Spruce
Hardwoods
D. .„ Lodgepole Pine
RMS Other Conifer
Pinyon-Juniper
Ponderosa Pine
Nonstocked
Bottomland Hardwood
Misc. Conifer
Natural Pine
SC Oak-Pine
Planted Pine
Upland Hardwood
Nonstocked
Bottomland Hardwood
Misc. Conifer
Natural Pine
SE Oak-Pine
Planted Pine
Upland Hardwood
Nonstocked
0.100
0.112
0.058
0.060
0.030
0.087
0.018
0.077
0.079
0.064
0.098
0.060
0.030
0.082
0.020
0.063
0.068
0.068
0.072
0.077
0.067
0.013
0.064
0.081
0.081
0.063
0.075
0.059
0.012
a Regions and types as defined in Smith et al. (2003).
Table A-252: Coefficients for Estimating Logging Residue Component of Down Dead Wood
Region3
Alaska
Alaska
NE
NE
NLS
NLS
NPS
NPS
PSW
PSW
PWE
PWE
PWW
PWW
RMN
RMN
RMS
RMS
SC
SC
SE
SE
Forest Type Groupb Initial Carbon
(softwood/hardwood) Density (Mg/ha) Decay Coefficient
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
6.9
8.6
13.9
12.1
9.1
7.2
9.6
6.4
9.8
17.5
3.3
9.5
18.1
23.6
7.2
9.0
5.1
3.7
4.2
5.5
6.4
7.3
12.1
32.3
12.1
17.9
12.1
17.9
12.1
17.9
12.1
32.3
12.1
32.3
12.1
32.3
43.5
18.1
43.5
18.1
8.9
17.9
8.9
17.9
' Regions are defined in Smith et al. (2003) with the addition of coastal Alaska.
b Forest types are according to majority hardwood or softwood species.
A-356 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Litter carbon
Carbon of the litter layer is currently sampled on a subset of the FIA plots. However, these data are not yet
available electronically for general application to all inventories in Table A-245. Litter C is the pool of organic C
(including material known as duff, humus, and fine woody debris) above the mineral soil and includes woody fragments
with diameters of up to 7.5 cm. Estimates therefore continue to be based on models of Smith and Heath (2002) and
applied at the plot level. The models describe processes for decay or loss of forest floor following harvest and the net
accumulation of new forest floor material following stand growth. For example, total forest floor C at a given number of
years after a clearcut harvest for aspen-birch forests in the North is:
Total forest floor C (Mg C/ha) = (18.4 x years)/(53.7 +years) + 10.2 x e(-yeals + 92>
See Table 4 of Smith and Heath (2002) for the complete set of coefficients. Note that these are direct estimates
of C density; the 0.5 conversion does not apply to litter.
Soil organic carbon
Soil organic carbon (SOC) is currently sampled to a 20 cm depth on subsets of FIA plots, however, these data are
not available for the entire United States. Thus, estimates of SOC are based on the national STATSGO spatial database
(USDA 1991), and the general approach described by Amichev and Galbraith (2004). In their procedure, SOC was
calculated for the conterminous United States using the STATSGO database, and data gaps were filled by representative
values from similar soils. Links to region and forest type groups were developed with the assistance of the USDA Forest
Service FIA Geospatial Service Center by overlaying FIA forest inventory plots on the soil C map.
Carbon in Harvested Wood Products
Estimates of the harvested wood product (HWP) contribution to forest C sinks and emissions (hereafter called
"HWP Contribution") are based on methods described in Skog (2008) using the WOODCARB II model. These methods
are based on IPCC (2006) guidance for estimating HWP C. The 2006 IPCC Guidelines provide methods that allow Parties
to report HWP Contribution using one of several different accounting approaches: production, stock change, and
atmospheric flow, as well as a default method. The various approaches are described below. The approaches differ in
how HWP Contribution is allocated based on production or consumption as well as what processes (atmospheric fluxes or
stock changes) are emphasized.
• Production approach: Accounts for the net changes in C stocks in forests and in the wood products pool,
but attributes both to the producing country.
• Stock change approach: Accounts for changes in the product pool within the boundaries of the consuming
country.
• Atmospheric flow approach: Accounts for net emissions or removals of C to and from the atmosphere
within national boundaries. Carbon removal due to forest growth is accounted for in the producing country
while C emissions to the atmosphere from oxidation of wood products are accounted for in the consuming
country.
• Default approach: Assumes no change in C stocks in HWP. IPCC (2006) requests that such an assumption
be justified if this is how a Party is choosing to report.
The United States uses the production accounting approach (as in previous years) to report HWP Contribution
(Table A-256). Though reported U.S. HWP estimates are based on the production approach, estimates resulting from use
of the two alternative approaches—the stock change and atmospheric flow approaches—are also presented for comparison
(see Table A-257). Annual estimates of change are calculated by tracking the additions to and removals from the pool of
products held in end uses (i.e., products in use such as housing or publications) and the pool of products held in solid
waste disposal sites (SWDS).
Estimates of five HWP variables that can be used to calculate HWP contribution for the stock change and
atmospheric flow approaches for imports and exports are provided in Table A-256. The HWP variables estimated are:
(1 A) annual change of C in wood and paper products in use in the United States,
(IB) annual change of C in wood and paper products in SWDS in the United States,
A-357
-------
(2A) annual change of C in wood and paper products in use in the United States and other countries where the
wood came from trees harvested in the United States,
(2B) annual change of C in wood and paper products in SWDS in the United States and other countries where the
wood came from trees harvested in the United States,
(3) Carbon in imports of wood, pulp, and paper to the United States,
(4) Carbon in exports of wood, pulp and paper from the United States, and
(5) Carbon in annual harvest of wood from forests in the United States.
A-358 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-253: Harvested Wood Products from Wood Harvested in United States—Annual Additions of Carbon to Stocks and Total Stocks Under the Production Approach
(Parentheses Indicate Het G Sequestration (i.e., a Het Removal of G from the Atmosphere)
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Net carbon additions per year (Tg C per year)
Products in use Products in SWDS
Total
(35.9)
(33.8)
(33.8)
(32.9)
(33.4)
(32.3)
(30.6)
(32.0)
(31.1)
(32.5)
(30.8)
(25.5)
(26.8)
(25.6)
(28.3)
(28.0)
(29.4)
(27.8)
(20.7)
(14.8)
(16.2)
(18.3)
(18.1)
Solid wood Paper Solid wood Paper
Total products products Total products products
(17.7) (14.4) (3.3) (18.3) (9.9) (8.3)
(14.9) (11.9) (3.1) (18.8) (11.1) (7.7)
(16.3) (12.6) (3.7) (17.4) (9.5) (7.9)
(15.0) (12.2) (2.8) (17.9) (9.7) (8.3)
(15.9) (12.1) (3.8) (17.5) (9.8) (7.7)
(15.1) (11.2) (3.8) (17.2) (10.7) (6.5)
(14.1) (11.5) (2.6) (16.5) (10.6) (6.0)
(14.7) (11.8) (3.0) (17.3) (10.3) (6.9)
(13.4) (11.4) (2.0) (17.7) (10.2) (7.5)
(14.1) (12.1) (2.0) (18.4) (10.6) (7.8)
(12.8) (11.9) (1.0) (18.0) (10.7) (7.3)
(8.7) (10.1) 1.4 (16.8) (10.7) (6.0)
(9.6) (10.7) 1.1 (17.2) (11.1) (6.1)
(9.4) (9.9) 0.5 (16.2) (11.0) (5.1)
(12.0) (11.3) (0.8) (16.3) (11.3) (5.0)
(11.7) (11.3) (0.4) (16.3) (11.5) (4.8)
(12.1) (10.5) (1.7) (17.3) (11.6) (5.7)
(10.4) (8.5) (1.9) (17.4) (11.6) (5.7)
(3.6) (2.9) (0.8) (17.0) (11.4) (5.7)
1.8 0.5 1.3 (16.6) (11.2) (5.4)
0.3 0.2 0.2 (16.5) (11.3) (5.2)
(1.6) (1.0) (0.6) (16.7) (11.4) (5.3)
(1.3) (1.4) 0.1 (16.8) (11.4) (5.4)
Total Carbon stocks (Tg C)
Products in Products in
Total use SWDS
1,859 1,231 628
1,895 1,249 646
1,929 1,264 665
1,963 1,280 683
1,996 1,295 701
2,029 1,311 718
2,061 1,326 735
2,092 1,340 752
2,124 1,355 769
2,155 1,368 787
2,188 1,382 805
2,218 1,395 823
2,244 1,404 840
2,271 1,414 857
2,296 1,423 873
2,325 1,435 890
2,353 1,447 906
2,382 1,459 923
2,410 1,469 940
2,430 1,473 958
2,445 1,471 974
2,461 1,471 991
2,480 1,472 1,007
2,498 1,474 1,024
Table A-254: Comparison of Het Annual Change in Harvested Wood Products Carbon Stocks Using Alternative Accounting Approaches
HWP Contribution to LULUCF Emissions/ removals (Tg C02 Eq.)
Inventory
Year
1990
1991
1992
1993
1994
Stock Change
Approach
(129.6)
(116.3)
(120.0)
(126.8)
(130.0)
Atmospheric Flow Production
Approach Approach
(138.4) (131.8)
(131.4) (123.8)
(131.6) (123.8)
(127.8) (120.7)
(129.9) (122.5)
A-359
-------
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
(126.0)
(122.3)
(131.4)
(137.2)
(147.1)
(141.2)
(125.0)
(130.7)
(125.8)
(143.2)
(142.1)
(138.1)
(115.1)
(73.1)
(42.3)
(50.5)
(52.9)
(57.7)
(128.0)
(122.5)
(127.4)
(122.8)
(127.4)
(120.4)
(100.4)
(103.3)
(98.7)
(108.5)
(107.3)
(113.9)
(111.5)
(88.4)
(69.8)
(79.4)
(90.9)
(90.8)
(118.4)
(112.2)
(117.3)
(114.2)
(119.2)
(113.0)
(93.5)
(98.2)
(93.8)
(103.8)
(102.8)
(107.8)
(101.8)
(75.8)
(54.1)
(59.3)
(67.1)
(66.5)
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
Table A-255:
Inventory
year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Harvested Wood Products Sectoral Background Data for LULUGF— United States (production approach)
1A
Annual Change
in stock of HWP
in use from
consumption
ACHWPIUDC
17,044
13,129
15,718
16,957
18,221
17,307
17,018
18,756
19,654
21,444
20,000
1B
Annual Change
in stock of HWP
inSWDSfrom
consumption
ACHWP SWDS DC
18,308
18,602
17,006
17,627
17,221
17,051
16,348
17,090
17,769
18,662
18,508
2A
Annual Change in
stock of HWP in
use produced
from domestic
harvest
AC HWP IU DH
17,659
14,940
16,334
14,971
15,930
15,065
14,092
14,740
13,404
14,146
12,840
2B
Annual
Change in
stock of HWP
in SWDS
produced
from
domestic
harvest
ACHWP SWDS DH
18,278
18,812
17,427
17,949
17,479
17,229
16,513
17,263
17,738
18,359
17,970
3
Annual
Imports of
wood, and
paper
products plus
wood fuel,
pulp,
recovered
paper,
roundwood/
chips
PlM
12,680
11,552
12,856
14,512
15,685
16,712
16,691
17,983
18,994
20,599
21,858
4
Annual
Exports of
wood, and
paper
products plus
wood fuel,
pulp,
recovered
paper,
roundwood/
chips
PEX
15,078
15,667
16,032
14,788
15,665
17,266
16,733
16,877
15,057
15,245
16,185
5
Annual
Domestic
Harvest
H
142,297
144,435
139,389
134,554
134,750
137,027
134,477
135,439
134,206
134,193
133,694
6
Annual release
of carbon to the
atmosphere
from HWP
consumption
(from fuelwood
and products in
use and
products in
SWDS)
fCHWPDC
104,547
108,588
103,489
99,694
99,328
102,115
101,069
100,699
100,720
99,440
100,859
7
Annual release
of carbon to the
atmosphere from
HWP (including
firewood) where
wood came from
domestic harvest
(from products in
use and products
in SWDS )
fCHWPDH
Gg C/yr
106,359
110,682
105,627
101,633
101,342
104,733
103,872
103,436
103,064
101,689
102,884
8
HWP
Contribution to
AFOLU CCfe
emissions/
removals
Gg C02/yr
(131,772)
(123,758)
(123,791)
(120,708)
(122,498)
(118,411)
(112,219)
(117,344)
(114,188)
(119,182)
(112,969)
A-360 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
16,491
17,414
16,986
21,409
20,992
19,085
13,085
2,414
(5,104)
(2,564)
(1,761)
(363)
17,610
18,235
17,326
17,643
17,765
18,587
18,308
17,510
16,641
16,324
16,184
16,113
8,713
9,566
9,415
12,045
11,746
12,135
10,405
3,627
(1,836)
(334)
1,595
1,345
16,781
17,213
16,171
16,268
16,290
17,269
17,364
17,043
16,600
16,501
16,700
16,787
22,051
23,210
23,707
26,428
26,793
25,443
21,648
16,980
13,114
14,161
13,922
13,950
15,336
15,744
16,303
16,953
17,313
18,836
20,656
21,157
20,616
22,052
24,302
22,961
127,896
126,866
126,477
131,738
132,482
129,529
123,640
106,096
96,032
97,555
100,848
103,019
100,510
98,683
99,569
102,160
103,205
98,464
93,239
81,996
76,994
75,904
76,045
78,258
102,402
100,087
100,891
103,425
104,446
100,125
95,871
85,426
81,269
81,388
82,553
84,887
(93,479)
(98,188)
(93,815)
(103,814)
(102,798)
(107,815)
(101,820)
(75,789)
(54,133)
(59,279)
(67,082)
(66,485)
Note: |C HWP DC = H + PIM - PEX - AC HWPIU DC - AC HWP SWDS DC AND |C HWP DH = H - AC HWP lu DH - AC HWP SWDS DH. Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
A-361
-------
Annual estimates of variables 1A, IB, 2A and 2B were calculated by tracking the additions to and removals from
the pool of products held in end uses (e.g., products in uses such as housing or publications) and the pool of products held
in SWDS. In the case of variables 2A and 2B, the pools include products exported and held in other countries and the
pools in the United States exclude products made from wood harvested in other countries. Solidwood products added to
pools include lumber and panels. End-use categories for solidwood include single and multifamily housing, alteration and
repair of housing, and other end uses. There is one product category and one end -use category for paper. Additions to and
removals from pools are tracked beginning in 1900, with the exception that additions of softwood lumber to housing
begins in 1800. Solidwood and paper product production and trade data are from USDA Forest Service and other sources
(Hair and Ulnch 1963; Hair 1958; USDC Bureau of Census 1976; Ulnch, 1985, 1989; Steer 1948; AF&PA 2006a, 2006b;
Howard 2003).
The rate of removals from products in use and the rate of decay of products in SWDS are specified by first order
(exponential) decay curves with given half -lives (time at which half of amount placed in use will have been discarded
from use). Half -lives for products in use, determined after calibration of the model to meet two criteria, are shown in
Table A-256. The first criterion is that the WOODCARB II model estimate of C in houses standing in 2001 needed to
match an independent estimate of C in housing based on U.S. Census and USDA Forest Service survey data. The second
criterion is that the WOODCARB II model estimate of wood and paper being discarded to SWDS needed to match EPA
estimates of discards over the period 1990 to 2000. This calibration strongly influences the estimate of variable 1A, and to
a lesser extent variable 2A. The calibration also determines the amounts going to SWDS. In addition ,WOODCARB II
landfill decay rates have been validated by making sure that estimates of methane emissions from landfills based on EPA
data are reasonable in comparison to methane estimates based on WOODCARB II landfill decay rates.
Decay parameters for products in SWDS are shown in Table A-257. Estimates of IB and 2B also reflect the
change over time in the fraction of products discarded to SWDS (versus burning or recycling) and the fraction of SWDS
that are sanitary landfills versus dumps.
Variables 2A and 2B are used to estimate HWP contribution under the production accounting approach. A key
assumption for estimating these variables is that products exported from the United States and held in pools in other
countries have the same half lives for products in use, the same percentage of discarded products going to SWDS, and the
same decay rates in SWDS. Summaries of net fluxes and stocks for harvested wood in products and SWDS are in Table
A-246 and Table A-247. The decline in net additions to HWP C stocks continued through 2009 from the recent high point
in 2006. This is due to sharp declines in U.S. production of solidwood and paper products in 2009 primarily due to the
decline in housing construction. The low level of gross additions to solidwood and paper products in use in 2009 was
exceeded by discards from uses. The result is a net reduction in the amount of HWP C that is held in products in use
during 2009. For 2009 additions to landfills still exceeded emissions from landfills and the net additions to landfills have
remained relatively stable. Overall, there were net C additions to HWP in use and in landfills combined.
Table A-256: Half life of Solidwood and Paper Products in End uses
Parameter Value Units
_
Half life of wood in single family housing 1920 and before 78.0 Years
Half life of wood in single family housing 1920-1939 78.0 Years
Half life of wood in single family housing 1940-1959 80.0 Years
Half life of wood in single family housing 1960-1979 81.9 Years
Half life of wood in single family housing 1980+ 83.9 Years
Ratio of multifamily half live to single family half life 0.61
Ratio of repair and alterations half life to single family half life 0.30
Half life for other solidwood product in end uses 38.0 Years
Half life of paper in end uses _ 2.54 Years
Source: Skog, K.E. (2008) "Sequestration of C in harvested wood products for the United States." Forest Products Journal 58:56-72.
Table A-257: Parameters Determining Decay of Wood and Paper in SWDS
Parameter _ Value Units
Percentage of wood and paper in dumps that is subject to decay 100 Percent
Percentage of wood in landfills that is subject to decay 23 Percent
Percentage of paper in landfills that is subject to decay 56 Percent
Half life of wood in landfills / dumps (portion subject to decay) 29 Years
Half life of paper in landfills/ dumps (portion subject to decay) _ 14.5 Years
Source: Skog, K.E. (2008) "Sequestration of C in harvested wood products for the United States." Forest Products Journal 58:56-72
A-362 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Uncertainty Analysis
The uncertainty analyses for total net flux of forest C (see uncertainty table in LULUCF chapter) are consistent
with the IPCC-recommended Tier 2 methodology (IPCC 2006). Separate analyses are produced for forest ecosystem and
HWP flux. The uncertainty estimates are from Monte Carlo simulations of the respective models and input data. Methods
generally follow those described in Heath and Smith (2000), Smith and Heath (2000), and Skog et al. (2004).
Uncertainties surrounding input data or model processes are quantified as probability distribution functions (PDFs), so that
a series of sample values can be randomly selected from the distributions. Model simulations are repeated a large number
of times to numerically simulate the effects of the random PDF selections on estimated total C flux. The separate results
from the ecosystem and HWP simulations are pooled for total uncertainty (see uncertainty table in LULUCF chapter).
Uncertainty surrounding current net C flux in forest ecosystems is based on the estimate for 2010 as obtained
from the Monte Carlo simulation. C stocks are based on forest condition level (plot-level) calculations, and, therefore,
uncertainty analysis starts probabilistic sampling at the plot level. Uncertainty surrounding C density (Mg/ha) is defined
for each of six C pools for each inventory plot. Live and standing dead tree C pools are generally assigned normal PDFs
that represent total uncertainty of all trees measured on the plot, which varies according to species, number of trees, and
per area representation. Error estimates for volume and the CRM for estimating biomass are not available, so an assumed
10 percent error on biomass from volume is applied to the volume portion of the estimate; error information in Jenkins et
al. (2003) is applied to uncertainty about the additional components (e.g., top, leaves, and roots). Uniform PDFs with a
range of ±90 percent of the average are used for those plots where C densities from similarly classified forest stands were
applied.
Distributions for the remaining C pools are triangular or uniform, which partly reflects the lower level of
information available about these estimates. The PDFs defined for these four pools were sampled as marginal
distributions. Downed dead wood, understory, and litter are assigned triangular distributions with the mean at the
expected value for each plot and the minimum and mode at 10 percent of the expected value. The use of these PDFs
skewed to the right reflects the assumption that a small proportion of plots will have relatively high C densities. Soil
organic C is defined as a uniform PDF at ±50 percent of the mean. Sub-state or state total C stocks associated with each
survey are the cumulative sum of random samples from the plot-level PDFs, which are then appropriately expanded to
population estimates. These expected values for each C pool include uncertainty associated with sampling, which is also
incorporated in the Monte Carlo simulation. Sampling errors are determined according to methods described for the
FIADB (Bechtold and Patterson 2005), are normally distributed, and are assigned a slight positive correlation between
successive surveys for Monte Carlo sampling. More recent annual inventories are assigned higher sampling correlation
between successive surveys based on the proportion of plot data jointly included in each. Errors for older inventory data
are not available, and these surveys are assigned values consistent with those obtained from the FIADB.
Uncertainty about net C flux in HWP is based on Skog et al. (2004) and Skog (2008). Latin hypercube sampling
is the basis for the HWP Monte Carlo simulation. Estimates of the HWP variables and HWP Contribution under the
production approach are subject to many sources of uncertainty. An estimate of uncertainty is provided that evaluated the
effect of uncertainty in 13 sources, including production and trade data and parameters used to make the estimate.
Uncertain data and parameters include data on production and trade and factors to convert them to C, the Census-based
estimate of C in housing in 2001, the EPA estimate of wood and paper discarded to S WDS for 1990 to 2000, the limits on
decay of wood and paper in SWDS, the decay rate (half-life) of wood and paper in SWDS, the proportion of products
produced in the United States made with wood harvested in the United States, and the rate of storage of wood and paper C
in other countries that came from United States harvest, compared to storage in the United States.
A total of ten thousand samples are drawn from the PDF input to separately determine uncertainties about forest
ecosystem and HWP flux before they are combined for a quantitative estimate of total forest C uncertainty (see uncertainty
table in LULUCF chapter). Again this year, true Monte Carlo sampling is used for the forest ecosystem estimates (in
contrast to Latin hypercube sampling, which was used in some previous estimates), and a part of the QA/QC process
includes verifying that the PDFs are adequately sampled.
Emissions from Fires
C02
As stated in other sections, the forest inventory approach implicitly accounts for emissions due to disturbances.
Net C stock change is estimated by subtracting consecutive C stock estimates. A disturbance removes C from the forest.
The inventory data, on which net C stock estimates are based, already reflects the C loss because only C remaining in the
forest is estimated. Estimating the CC>2 emissions from a disturbance such as fire and adding those emissions to the net
CC>2 change in forests would result in double-counting the loss from fire because the inventory data already reflect the
A-363
-------
loss. There is interest, however, in the size of the CC>2 emissions from disturbances such as fire. The IPCC (2003)
methodology and IPCC (2006) default combustion factor for wildfire were employed to estimate emissions from forest
fires. Using the methodology provided in IPCC (2003), C emissions from forest fires were calculated as:
C Emissions = Forest area burned (ha) x Carbon density (Mg per ha of dry matter)
x Combustion efficiency (45%) x Mg to Tg conversion factor (10"6)
where a default value of 0.45 from IPCC (2006) was assumed for the amount of biomass burned by wildfires as well as
prescribed fires (combustion factor value).
This methodology was used to estimate emissions from both wildfires and prescribed fires occurring in the lower
48 states. Wildfire area statistics are available, but they include non-forest land, such as shrublands and grasslands. It was
thus necessary to develop a rudimentary estimate of the percent of area burned in forest by multiplying the reported area
burned by a ratio of total forest land area to the total area considered to be under protection from fire. Data on total area of
forest land were obtained from FIA (USDA Forest Service 2013b). Data on "total area considered to be under protection
from fire" were available at the state level and obtained for the year 1990 from 1984-1990 Wildfire Statistics prepared by
the USDA Forest Service (USDA Forest Service 1992). Data for years 1998, 2002, 2004, 2006, and 2008 were obtained
from the National Association of State Foresters (NASF 2011, 2008, 2007a, 2007b, 2007c). For states where data were
available for all five years, the 1990 value was assumed for years 1990 to 1994, values for 1998 were assumed for years
1995 to 1998, values for 2002 were assumed for years 1999 to 2002, values for 2004 were assumed for years 2003 and
2004, values for 2006 were assumed for years 2005 and 2006, and values for 2008 were assumed for years 2008 to 2012.
For states where data were available for all years except 2002, 2004 data were assumed for years 1999 to 2004. For states
where data were available for all years except 2004, 2006 data were assumed for 2003 through 2008. For years where data
were available for all years except 2006, 2004 data were assumed for years 2003 to 2008. Since both the 1998 and 2006
values are missing from the NASF data for Alaska, the 1990 value was assumed for years 1990 to 1997, the 2002 value
was assumed for years 1998 to 2002, the 2004 value was assumed for years 2003 to 2006, and the 2008 value was
assumed for 2007 to 2012. Similarly, since the NASF data for New Mexico lacks values for 2002 and 2004, the 1990
value was assumed for years 1990-1995, while the 1998 value was assumed for year 1996 through 2001, the 2006 data
were assumed for 2002 to 2006, and the 2008 value was assumed for all remaining years. Illinois has not reported data on
wildland since 2002, so the 1990 value was assumed for years 1990-1995, while the 1998 value was assumed for years
1995 through 2001, and the 2002 value was assumed for all remaining years.
Total forestland area for the lower 48 states was divided by total area considered to be under protection from
wildfire for the lower 48 states across the 1990 to 2012 time series to create ratios that were then applied to reported area
burned to estimate the area of forestland burned for the lower 48 states. The ratio was applied to area burned from
wildland fires and prescribed fires occurring in the lower 48 states. Reported area burned data for prescribed fires was
available from 1998 to 2012 from the National Interagency Fire Center (NIFC 2013). Data for the year 1998 was assumed
for years 1990 to 1997.
Forest area burned data for Alaska are from the Alaska Department of Natural Resources (Alaska Department of
Natural Resources 2008) or the Alaska Interagency Coordination Center (Alaska Interagency Coordination Center 2013).
Data are acres of land which experienced fire activity on forest service land. The majority of wildfires in Alaska that
occur on lands protected by the USDA Forest Service occur in the coastal areas (Southeast and South Central); as this is
where the National Forest System land is located. According to expert judgment, the coastal area of Alaska included in
this Inventory is mostly temperate rainforest and, therefore, there is little call for prescribed burns (Smith 2008). It was,
thus, assumed that reported area burned for prescribed fires covers only prescribed fires in the lower 48 states.
The average C density in the lower 48 states for aboveground biomass C, dead wood C, and litter layer varied
between 68.8 and 76.5 Mg/ha, according to annual (1990-2012) data from FIA. In order to estimate these annual C
densities in the lower 48 states, the C contained in the aboveground, deadwood, and litter C pools was first summed for
each state and year. The methodology assumes that wildfires burn only those pools, and leaves the belowground C and
soil C un-burnt. The methodology estimates the C density value by taking a weighted average of these summed C pools in
each state and year. The states' C values are weighted according to area of forestland present in each state and year
compared with the total. A default value of 0.45 from IPCC (2006) was assumed for the amount of biomass burned by
wildfire (combustion factor value). According to the estimates, wildfires in the lower 48 states emit between 6.5 and 76.1
Tg C. For Alaska, the average C density reported by the USDA Forest Service varies between 140.2 and 145.0 Mg/ha,
based on data from FIA. In the case of wildfires in Alaska, Alaska's C pool values are used instead of a weighted average
for states. These values translate into 0 to 0.1 Tg C emitted. Based on data from the USDA Forest Service, the average C
density for prescribed fires varied between 24.8 and 25.7 Mg C/ha. For prescribed fires, the methodology assumes that
only the litter and deadwood C pools burn. The weighted average C densities estimated for prescribed fires therefore only
include the sum of these two pools, and excludes aboveground biomass. It is assumed that prescribed fires only occur in
A-364 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
the lower 48 states (Smith 2008). The default value of 0.45 from IPCC (2006) for wildfires was also assumed for the
amount of biomass burned by prescribed fires (combustion factor value). As a result, prescribed fires are estimated to emit
between 0.5 and 7.5 Tg C.
Carbon density estimates for Mg C/ha were multiplied by estimates of forest area burned by year; the resulting
estimates are displayed in Table A-258. C estimates were multiplied by 92.8 percent to account for the proportion of C
emitted as CC>2 and by 3.67 (i.e., 44/12) to yield CC>2 units. Total CC>2 emissions for wildfires and prescribed fires in the
lower 48 states and wildfires in Alaska in 2012 were estimated to be 242.7 Tg/yr.
Table A-258: Areas (Hectares) from Wildfire Statistics and Corresponding Estimates of Carbon and GO? (Tg/yr) Emissions
for Wildfires and Prescribed Fires in the Lower 48 states and Wildfires in Alaska1
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Lower 48 States
Wildfires
Reported
area
burned2
(ha)
579,589
486,807
785,892
438,865
1,540,987
727,051
2,212,309
335,914
489,246
1,869,918
2,685,981
1,356,830
2,023,976
1,358,986
637,258
1,629,067
3,888,011
3,512,122
2,099,842
1,201,996
929,687
3,406,788
3,658,098
Forest
area
burned3
(ha)
308,982
260,103
420,861
235,533
828,698
427,354
1,339,754
203,738
297,112
1,143,390
1,643,847
830,972
1,236,415
728,786
347,862
953,384
2,280,808
1,797,232
1,076,449
617,272
478,273
1,755,690
1,930,631
Carbon
emitted
(Tg/yr)
10
8
13
7
26
14
43
6
10
37
53
27
40
24
11
32
76
60
36
21
16
60
66
C02
emitted
(Tg/yr)
33
28
45
25
89
46
145
22
32
125
181
92
138
81
39
108
259
205
123
71
55
205
226
Prescribed Fires
Reported
area
burned2
(ha)
355,432
355,432
355,432
355,432
355,432
355,432
355,432
355,432
355,432
806,780
77,789
667,428
1,086,503
1,147,695
996,453
934,965
1,100,966
1,274,383
783,068
1,024,306
980,903
855,025
797,974
Forest
area
burned3
(ha)
189,483
189,909
190,341
190,755
191,141
208,920
215,246
215,576
215,848
493,318
47,607
408,757
663,728
615,476
543,936
547,172
645,855
652,131
401,427
526,021
504,620
440,638
421,146
Carbon
emitted
(Tg/yr)
2
2
2
2
2
2
2
2
2
6
1
5
7
7
6
6
7
7
5
6
6
5
5
C02
emitted
(Tg/yr)
7
7
7
7
7
8
8
8
8
19
2
16
25
24
21
21
25
25
16
20
20
17
17
Alaska
Wildfires
Forest
area
burned4
(acres)
8
557
47
110
23
7
103
33
2
7
1
2,078
28
17
23
353
8
2
1
22
12
5
2
Forest
area
burned
(ha)
3
225
19
45
9
3
42
13
1
3
1
841
11
7
9
143
3
1
0
9
5
2
1
Carbon
emitted
(Tg/yr)
0.000
0.014
0.001
0.003
0.001
0.000
0.003
0.001
0.000
0.000
0.000
0.054
0.001
0.000
0.001
0.009
0.000
0.000
0.000
0.001
0.000
0.000
0.000
C02
emitted
(Tg/yr)
0.001
0.048
0.004
0.010
0.002
0.001
0.009
0.003
0.000
0.001
0.000
0.184
0.002
0.002
0.002
0.031
0.001
0.000
0.000
0.002
0.001
0.000
0.000
1 Note that these emissions have already been accounted for in the estimates of net annual changes in C stocks, which accounts for the amount sequestered
minus any emissions, including the assumption that combusted wood may continue to decay through time.
2 National Interagency Fire Center (2013).
3 Ratios calculated using forest land area estimates from FIA (USDA Forest Service 2012b) and wildland area under protection estimates from USDA Forest
Service (1992) and the National Association of State Foresters (20011).
41990-2007 Alaskan forest fires data are from the Alaska Department of Natural Resources (2008). 2008-2011 data are from Alaska Interagency Coordination
Center (2013).
A/on-C02
Emissions of non-CC>2 gases from forest fires were estimated using the default IPCC (2003) methodology, IPCC
(2006) emission ratios, and default IPCC (2006) combustion factor for wildfires. The default IPCC (2003) methodology
and default IPCC (2006) combustion factor for wildfires were used to calculate the C emissions from forest fires as
discussed above. Carbon dioxide emissions were estimated by multiplying total C emitted by the C to CC>2 conversion
factor of 44/12 and by 92.8 percent, which is the estimated proportion of C emitted as CC>2 (Smith 2008). Emissions
estimates for CH4 and N2(D are calculated by multiplying the total estimated CC>2 emitted from forest burned by gas-
specific emissions ratios from IPCC (2006). The models used are:
CH4 Emissions = (C released) x 92.8% x (44/12) x (CH4 to CO2 emission ratio)
N2O Emissions = (C released) x 92.8% x (44/12) x (N2O to CO2 emission ratio)
Where the CH4 and N2O to CC>2 emission ratios were derived from IPCC (2006), in Table A-256 below.
A-365
-------
Table A- 259: Emission Factors for Extra Tropical Forest Burning and Emissions Ratios of CH* and H?0 to Clh
Emission Factor (g per kg dry _ . . _ ..
matter burned)' Em,ss,ons Ratios
cFu 470 CH4toC02 0~003
N20 0.26 N2OtoC02 0.0002
C02 1,569 C02 to C02 1.000
11PCC 2006
The resulting estimates are presented in Table A- 260.
Table A- 260: Estimated Carbon Released and Estimates of Non-Clh Emissions (Tg/yr) for U.S. forests1
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
C emitted (Tg/yr)
11.681
10.218
15.257
9.511
28.204
15.839
44.928
8.916
11.950
42.381
53.743
31.681
47.904
30.901
17.644
37.851
83.387
67.663
40.856
26.935
22.082
65.181
71.333
Cm emitted
(Tg/yr)
0.119
0.104
0.156
0.097
0.287
0.161
0.458
0.091
0.122
0.432
0.548
0.323
0.488
0.315
0.180
0.386
0.850
0.690
0.416
0.275
0.225
0.664
0.727
N20
(Tg/yr)
0.007
0.006
0.009
0.005
0.016
0.009
0.025
0.005
0.007
0.024
0.030
0.018
0.027
0.017
0.010
0.021
0.047
0.038
0.023
0.015
0.012
0.037
0.040
1 Calculated based on C emission estimates in Table A-258 and default factors in IPCC (2003, 2006)
A-366 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
3.14. Methodology for Estimating ChU Emissions from Landfills
Landfill gas is a mixture of substances generated when bacteria decompose the organic materials contained in
solid waste. By volume, landfill gas is about half CH4 and half CC>2.96 The amount and rate of CH4 generation depends
upon the quantity and composition of the landfilled material, as well as the surrounding landfill environment.
Not all CH4 generated within a landfill is emitted to the atmosphere. The CH4 can be extracted and either flared
or utilized for energy, thus oxidizing the CH4 to CC>2 during combustion. Of the remaining CH4, a portion oxidizes to CC>2
as it travels through the top layer of the landfill cover. In general, landfill-related CC>2 emissions are of biogenic origin
and primarily result from the decomposition, either aerobic or anaerobic, of organic matter such as food or yard wastes.97
To estimate the amount of CH4 produced in a landfill in a given year, information is needed on the type and quantity of
waste in the landfill, as well as the landfill characteristics (e.g., size, aridity, waste density). This information is not
available for the majority landfills in the United States. Consequently, to estimate CH4 generation, a methodology was
developed (i.e., the first order decay waste model) based on the quantity of waste placed in landfills nationwide each year
and model parameters from the analysis of measured CH4 generation rates for U.S. landfills with gas recovery systems.
From various studies and surveys of the generation and disposal of solid waste, estimates of the amount of waste
placed in MSW and industrial waste landfills were developed. A database of measured CH4 generation rates at MSW
landfills with gas recovery systems was compiled and analyzed. The results of this analysis and other studies were used to
develop an estimate of the CLL generation potential for use in the first order decay model. In addition, the analysis and
other studies provided estimates of the CLL generation rate constant as a function of precipitation. The first order decay
model was applied to annual waste disposal estimates for each year and for three ranges of precipitation to estimate CH4
generation rates nationwide for the years of interest. Based on the organic content of industrial wastes and the estimates of
the fraction of these wastes sent to industrial waste landfills, CH4 emissions from industrial waste landfills were also
estimated using the first order decay model. Total CH4 emissions were estimated by adding the CLL from MSW and
industrial landfills and subtracting the amounts recovered for energy or flaring at MSW landfills and the amount
oxidized in the soil at MSW and industrial landfills. The steps taken to estimate CH4 emissions from U.S. landfills for the
years 1990 through the current inventory year are discussed in greater detail below.
Figure A- 21 presents the CLL emissions process—from waste generation to emissions—in graphical format.
Step 1: Estimate Annual Quantities of Solid Waste Placed in Landfills
For 1989 to 2012, estimates of the annual quantity of waste placed in MSW landfills were developed from a
survey of State agencies as reported in BioCycle's State of Garbage (SOG) in America reports (BioCycle 2010), adjusted
to include U.S. territories." The SOG survey is the only continually updated nationwide survey of waste disposed in
landfills in the United States. Table A-261 shows estimates of waste quantities contributing to CLL emissions. The table
shows SOG estimates of total waste generated and total waste landfilled (adjusted for U.S. territories) for various years
over the 1990 to 2012 timeframe.
State-specific landfill waste generation data and a national average disposal factor for 1989 through 2008 were
obtained from the SOG survey for every two years (i.e., 2002, 2004, 2006, and 2008 as published in BioCycle 2006, 2008,
and 2010). A linear interpolation was used for the amount of waste generated in 2001, 2003, 2005, 2007, 2009, 2010,
2011, and 2012 because no BioCycle SOG surveys were published for those years. The most recent SOG survey was
published in December 2010 representing 2008 data. Upon publication of the next SOG survey, the waste landfilled for
2009 through 2012 will be updated. Estimates of the quantity of waste landfilled from 1989 to the current inventory year
are determined by applying a waste disposal factor to the total amount of waste generated (i.e., the SOG data). A waste
disposal factor is determined for each year a SOG survey is published and is the ratio of the total amount of waste
landfilled to the total amount of waste generated. The waste disposal factor is interpolated for the years in-between the
96 Typically, landfill gas also contains small amounts of nitrogen, oxygen, and hydrogen, less than 1 percent nonmethane volatile
organic compounds (NMVOCs), and trace amounts of inorganic compounds.
97 See Box 8-1 "Biogenic Emissions and Sinks of Carbon" in the Waste chapter for additional background on how biogenic emissions of
landfill CO2 are addressed in the U.S. Inventory.
Landfill gas recovery is only estimated for MSW landfills due to a lack of national data on industrial waste landfills. Approximately 1
percent of the industrial waste landfills reporting under the GHGRP have active landfill gas collection systems.
Since the SOG survey does not include U.S. territories, waste landfilled in U.S. territories was estimated using population data for the
U.S territories (U.S. Census Bureau 2013) and the per capita rate for waste landfilled from BioCycle (2010).
A-367
-------
SOG surveys. Methodological changes have occurred over the time that the SOG survey has been published, and this has
affected the fluctuating trends observed in the data (RTI 2013).
Figure A- 21: Methane Emissions Resulting from Landfilling Municipal and Industrial Waste
MSW and
Industrial
Waste
Generated"
MSW and
Industrial
Waste
Landfilled"
Total
Methane
at
Landfills'
Non-
recovered
Methane
Generated
at Landfills
no/,1"
Total
Methane
Emitted
a BioCycle 2010 for MSW and activity factors for industrial waste.
b 1960 through 1988 based on EPA 1988 and EPA 1993; 1989 through 2006 based on BioCycle 2010.
c 2006 IPCC Guidelines - First Order Decay Model
d EIA 2007 and flare vendor database
e EIA 2007 and EPA (LMOP) 2007.
f 2006 IPCC Guidelines; Mancinelli and McKay 1985, Czepiel et al 1996
A-368 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-261: Solid Waste in MSW Landfills Contributing to CHa Emissions tTg unless otherwise noted!
Description
Total Waste Generated3
Percent of Wastes Landfilled3
Total Wastes Landfilled3
Waste in Place (30 years)"
Waste Contributing to
1990
271
77%
205
4,671
1995
302
63%
187
5,054
2001 2002
416 455
63% 66%
259 294
5,562 5,562
2003 2004
459 462
65% 64%
293 291
5,709 5,852
2005
459
64%
290
5,991
2006
455
65%
289
6,126
2007
430
67%
283
6,257
2008
404
69%
275
6,378
2009
372
69%
278
6,488
2010
411
69%
280
6,598
2011 2012
414 417
69% 69%
282 284
6,707 6,814
Emissions' 6,808 7,772 9,340 9,340 9,632 9,924 10,214 10,503 10,786 11,061 11,339 11,619 11,901 12,185
a Source: BioCycle (2006, 2008, 2010), adjusted for missing data using U.S. Census Bureau (2009, 2013) population data and per capita disposal rate from
BioCycle. The data, originally reported in short tons, are converted to metric tons. Estimates shown for 2001, 2003, 2005, 2007, 2009, 2010, 2011, and 2012
are based on an interpolation between survey years and the increase in population because there were no surveys in these years.
b This estimate represents the waste that has been in place for 30 years or less, which contributes about 90 percent of the ChU generation. Values are based on
EPA (1993) for years 1940 to years 1988 (not presented in table) and BioCycle (2006, 2008, 2010) for years 1989 to 2012.
c This estimate represents the cumulative amount of waste that has been placed in landfills from 1940 to the year indicated and is the sum of the annual disposal
rates used in the first order decay model. Values are based on EPA (1993).
Estimates of the annual quantity of waste placed in landfills from 1960 through 1988 were developed from
EPA's 1993 Report to Congress (EPA 1993) and a 1986 survey of MSW landfills (EPA 1988). Based on the national
survey and estimates of the growth of commercial, residential and other wastes, the annual quantity of waste placed in
landfills averaged 127 million metric tons in the 1960s, 154 million metric tons in the 1970s, 190 million metric tons in the
1990s, and 285 million metric tons in the 2000's. Estimates of waste placed in landfills in the 1940s and 1950s were
developed based on U.S. population for each year and the per capital disposal rates from the 1960s.
Step 2: Estimate ChU Generation at Municipal Solid Waste Landfills
The CH4 generation was estimated from the integrated form of the first order decay (FOD) model using the
procedures and spreadsheets from IPCC (2006) for estimating CH4 emissions from solid waste disposal. The form of the
FOD model that was applied incorporates a time delay of 6 months after waste disposal before the generation of CH4
begins.
The input parameters needed for the FOD model equations are the mass of waste disposed each year, which was
discussed in the previous section, degradable organic carbon (DOC), and the decay rate constant (k). The DOC is
determined from the CFU generation potential (L0 in m3 CH4/Mg waste), which is discussed in more detail in subsequent
paragraphs, and the following equation:
DOC = [L0 x 6.74 x IQ-4] - [F x 16/12 x DOCf x MCF]
where,
DOC = degradable organic carbon (fraction, Gg C/Gg waste),
Lo = CH4 generation potential (m3 CH4/Mg waste),
6.74xlO'4 = CH4density (Mg/m3),
F = fraction of CH4 by volume in generated landfill gas (equal to 0.5)
16/12 = molecular weight ratio CH4/C,
DOCf = fraction of DOC that can decompose in the anaerobic conditions
in the landfill (fraction equal to 0.5 for MSW), and
MCF = methane correction factor for year of disposal (fraction equal to 1 for
anaerobic managed sites).
The DOC value used in the CFU generation estimates from MSW landfills is 0.203 based on the CFU generation
potential of 100 m3 CH4/Mg waste as described below. Data from a set of 52 representative landfills across the U.S. in
different precipitation ranges were chosen to evaluate L0, and ultimately the country-specific DOC value. The 2004
Chartwell Municipal Solid Waste Facility Directory confirmed that each of the 52 landfills chosen accepted or accepts
both MSW and construction and demolition (C&D) waste (Chartwell 2004; RTI 2009).
The methane generation potential (L0) varies with the amount of organic content of the waste material. A higher
Lo occurrs with a higher content of organic waste. Waste composition data are not collected for all landfills nationwide;
thus a default value must be used. Values for L0 were evaluated from landfill gas recovery data for this set of 52 landfills,
which resulted in a best fit value for L0 of 99 m3/Mg of waste (RTI 2004). This value compares favorably with a range of
50 to 162 (midrange of 106) m3/Mg presented by Peer, Thorneloe, and Epperson (1993); a range of 87 to 91 m3/Mg from a
detailed analysis of 18 landfills sponsored by the Solid Waste Association of North America (SWANA 1998); and a value
of 100 m3/Mg recommended in EPA's compilation of emission factors (EPA 1998; EPA 2008) based on data from 21
A-369
-------
landfills. Based on the results from these studies, a value of 100 m3/Mg appears to be a reasonable best estimate to use in
the FOD model for the national inventory.
The FOD model was applied to the gas recovery data for the 52 landfills to calculate the decay rate constant (k)
directly for Lo = 100 m3/Mg. The rate constant was found to increase with annual average precipitation; consequently,
average values of k were developed for three ranges of precipitation, shown in Table A- 262 and recommended in EPA's
compilation of emission factors (EPA 2008).
Table A- 262: Average Values for Rate Constant [hi by Precipitation Range [yr1]
Precipitation range (inches/year) k (yr-1)
<20 0.020
20-40 0.038
>40 0.057
These values for k show reasonable agreement with the results of other studies. For example, EPA's compilation
of emission factors (EPA 1998; EPA, 2008) recommends a value of 0.02 yr"1 for arid areas (less than 20 inches/year of
precipitation) and 0.04 yr"1 for non-arid areas. The SWANA study of 18 landfills reported a range in values of k from 0.03
to 0.06 yr"1 based on CFU recovery data collected generally in the time frame of 1986 to 1995.
Using data collected primarily for the year 2000, the distribution of waste in place versus precipitation was
developed from over 400 landfills (RTI 2004). A distribution was also developed for population vs. precipitation for
comparison. The two distributions were very similar and indicated that population in areas or regions with a given
precipitation range was a reasonable proxy for waste landfilled in regions with the same range of precipitation. Using U.S.
Census data and rainfall data, the distributions of population versus rainfall were developed for each Census decade from
1950 through 2000. The distributions showed that the U.S. population has shifted to more arid areas over the past several
decades. Consequently, the population distribution was used to apportion the waste landfilled in each decade according to
the precipitation ranges developed for k, as shown in Table A-263.
Table A-263: Percent of U.S. Population within Precipitation Ranges [%1
Precipitation Range (inches/year) 1950 1960 1970 1980 1990 2000
<20 11 13 14 16 19 20
20-40 40 39 38 36 34 33
>40 49 48 48 48 47 47
Source: RTI (2004) using population data from the U.S. Bureau of Census and precipitation data from the National Climatic Data Center's National Oceanic and
Atmospheric Administration.
In developing the Inventory, the proportion of waste disposed of in managed landfills versus open dumps prior to
1980 was re-evaluated. Based on the historical data presented by Mintz et al. (2003), a timeline was developed for the
transition from the use of open dumps for solid waste disposed to the use of managed landfills. Based on this timeline, it
was estimated that 6 percent of the waste that was land disposed in 1940 was disposed of in managed landfills and 94
percent was managed in open dumps. Between 1940 and 1980, the fraction of waste land disposed transitioned towards
managed landfills until 100 percent of the waste was disposed of in managed landfills in 1980. For wastes disposed of in
dumps, a methane correction factor (MCF) of 0.6 was used based on the recommended IPCC default value for
uncharacterized land disposal (IPCC 2006); this MCF is equivalent to assuming 50 percent of the open dumps are deep
and 50 percent are shallow. The recommended IPCC default value for the MCF for managed landfills of 1 was used for
the managed landfills (IPCC 2006).
Step 3: Estimate ChU Generation at Industrial Landfills
Industrial waste landfills receive waste from factories, processing plants, and other manufacturing activities. In
national inventories prior to the 1990 through 2005 inventory, CFU generation at industrial landfills was estimated as seven
percent of the total CtLt generation from MSW landfills, based on a study conducted by EPA (1993). For the 1990
through 2007 and current inventories, the methodology was updated and improved by using activity factors (industrial
production levels) to estimate the amount of industrial waste landfilled each year and by applying the FOD model to
estimate CFU generation. A nationwide survey of industrial waste landfills found that over 99 percent of the organic waste
placed in industrial landfills originated from two industries: food processing (meat, vegetables, fruits) and pulp and paper
(EPA 1993). Data for annual nationwide production for the food processing and pulp and paper industries were taken
from industry and government sources for recent years; estimates were developed for production for the earlier years for
which data were not available. For the pulp and paper industry, production data published by the Lockwood-Post's
Directory were used for years 1990 to 2001 and production data published by the U.S. Department of Agriculture were
used for years 2002 through 2011. An extrapolation based on U. S. real gross domestic product was used for years 1940
A-370 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
through 1964. For the food processing industry, production levels were obtained or developed from the U.S. Department
of Agriculture for the years 1990 through 2011 (ERG 2013). An extrapolation based on U.S. population was used for the
years 1940 through 1989.
In addition to production data for the pulp and paper and food processing industries, the following inputs were
needed to use the FOD model for estimating CFU generation from industrial landfills: 1) quantity of waste that is disposed
in industrial waste landfills (as a function of production), 2) CFU generation potential (Lo) or DOC, and 3) FOD decay
constant (k). Research into waste generation and disposal in landfills for the pulp and paper industry indicated that the
quantity of waste landfilled was about 0.050 Mg/Mg of product compared to 0.046 Mg/Mg product for the food
processing industry (RTI 2006). These factors were applied to estimates of annual production to estimate annual waste
disposal in landfills. Estimates for DOC were derived from available data (Kraft and Orender, 1993; NCASI 2008; Flores
et al. 1999). The DOC value for industrial pulp and paper waste is estimated at 0.20 (L0 of 99 m3/Mg); the DOC value for
industrial food waste is estimated as 0.26 (L0 of 128 m3/Mg) (RTI 2008). Estimates for k were taken from the default
values in the 2006 IPCC Guidelines; the value of k given for food waste with disposal in a wet temperate climate is 0.19
yr"1, and the value given for paper waste is 0.06 yr"1.
A literature review was conducted for the 1990 to 2010 inventory year with the intent of updating values for L0
and k in the pulp and paper industry. Where pulp and paper mill wastewater treatment residuals or sludge are the primary
constituents of pulp and paper waste landfilled, values for k range from 0.01/yr to 0.1/yr, while values for L0 range from
50 m3/Mg to 200 m3/Mg. 10° Values for these factors are highly variable and are dependent on the soil moisture content,
which is generally related to rainfall amounts. At this time, sufficient data were not obtained to warrant a change for the
current inventory year. EPA is considering an update to the L0 and k values for the pulp and paper sector and will work
with stakeholders to gather data and other feedback on potential changes to these values.
As with MSW landfills, a similar trend in disposal practices from open dumps to managed landfills was expected
for industrial waste landfills; therefore, the same time line that was developed for MSW landfills was applied to the
industrial landfills to estimate the average MCF. That is, between 1940 and 1980, the fraction of waste that was land
disposed transitioned from 6 percent managed landfills in 1940 and 94 percent open dumps to 100 percent managed
landfills in 1980 and on. For wastes disposed of in dumps, an MCF of 0.6 was used and for wastes disposed of in
managed landfills, an MCF of 1 was used, based on the recommended IPCC default values (IPCC 2006).
The parameters discussed above were used in the integrated form of the FOD model to estimate CFU generation
from industrial waste landfills.
Step 4: Estimate ChU Emissions Avoided
The estimate of CFL emissions avoided (e.g., combusted) was based on landfill-specific data on landfill gas-to-
energy (LFGTE) projects and flares. A destruction efficiency of 99 percent was applied to CFU recovered to estimate CFL
emissions avoided. The value for efficiency was selected based on the range of efficiencies (86 to 99+ percent)
recommended for flares in EPA's AP-42 Compilation of Air Pollutant Emission Factors, Draft Chapter 2.4, Table 2.4-3
(EPA 2008). A typical value of 97.7 percent was presented for the non-methane components (i.e., volatile organic
compounds and non-methane organic compounds) in test results (EPA 2008). An arithmetic average of 98.3 percent and a
median value of 99 percent are derived from the test results presented in EPA 2008. Thus, a value of 99 percent for the
destruction efficiency of flares has been used in Inventory methodology. Other data sources supporting a 99 percent
destruction efficiency include those used to establish new source performance standards (NSPS) for landfills and in
recommendations for closed flares used in the Landfill Methane Outreach Program (LMOP).
Step 4a: Estimate CH* Emissions Avoided Through Landfill Gas-to-Energy (LFGTE) Projects
The quantity of CFL avoided due to LFGTE systems was estimated based on information from two sources: (1)
a database developed by the Energy Information Administration (EIA) for the voluntary reporting of greenhouse gases
(EIA 2007) and (2) a database compiled by LMOP (EPA 2012). The EIA database included location information for
landfills with LFGTE projects, estimates of CFL reductions, descriptions of the projects, and information on the
methodology used to determine the CLU reductions. Generally the CFL reductions for each reporting year were based on
the measured amount of landfill gas collected and the percent CLU in the gas. For the LMOP database, data on landfill gas
flow and energy generation (i.e., MW capacity) were used to estimate the total direct CLU emissions avoided due to the
LFGTE project. Detailed information on the landfill name, owner or operator, city, and state were available for both the
100 Sources reviewed included Heath et al. 2010; Miner 2008; Skog 2008; Upton et al. 2008; Barlaz 2006; Sonne 2006; NCASI
2005; and Skog 2000.
A-371
-------
EIA and LMOP databases; consequently, it was straightforward to identify landfills that were in both databases. The EIA
database was given priority because reductions were reported for each year and were based on direct measurements.
Landfills in the LMOP database that were also in the EIA database were dropped to avoid double counting.
Step 4b: Estimate CM Emissions Avoided Through Flaring
The quantity of CFL flared was based on data from the EIA database and on information provided by flaring
equipment vendors. To avoid double-counting, flares associated with landfills in the EIA and LMOP databases were
excluded from the flare vendor database. As with the LFGTE projects, reductions from flaring landfill gas in the EIA
database were based on measuring the volume of gas collected and the percent of CLU in the gas. The information
provided by the flare vendors included information on the number of flares, flare design flow rates or flare dimensions,
year of installation, and generally the city and state location of the landfill. When a range of design flare flow rates was
provided by the flare vendor, the median landfill gas flow rate was used to estimate CLU recovered from each remaining
flare (i.e., for each flare not associated with a landfill in the EIA or LMOP databases). Several vendors provided
information on the size of the flare rather than the flare design gas flow rate. To estimate a median flare gas flow rate for
flares associated with these vendors, the size of the flare was matched with the size and corresponding flow rates provided
by other vendors. Some flare vendors reported the maximum capacity of the flare. An analysis of flare capacity versus
measured CLL flow rates from the EIA database showed that the flares operated at 51 percent of capacity when averaged
over the time series and at 72 percent of capacity for the highest flow rate for a given year. For those cases when the flare
vendor supplied maximum capacity, the actual flow was estimated as 50 percent of capacity. Total CFL avoided through
flaring from the flare vendor database was estimated by summing the estimates of CLU recovered by each flare for each
year.
Step 4c: Reduce CM Emissions Avoided Through Flaring
As mentioned in Step 4b, flares in the flare vendor database associated with landfills in the EIA and LMOP
databases were excluded from the flare reduction estimates in the flare vendor database. If comprehensive data on flares
were available, each LFGTE project in the EIA and LMOP databases would have an identified flare because it is assumed
that most LFGTE projects have flares. However, given that the flare vendor data only covers approximately 50 to 75
percent of the flare population, an associated flare was not identified for all LFGTE projects. These LFGTE projects
likely have flares, yet flares were unable to be identified for one of two reasons: 1) inadequate identifier information in the
flare vendor data; or 2) a lack of the flare in the flare vendor database. For those projects for which a flare was not
identified due to inadequate information, CFL avoided would be overestimated, as both the CFL avoided from flaring and
the LFGTE project would be counted. To avoid overestimating emissions avoided from flaring, the CFL avoided from
LFGTE projects with no identified flares was determined and the flaring estimate from the flare vendor database was
reduced by this quantity (referred to as a flare correction factor) on a state-by-state basis. This step likely underestimates
CLLi avoided due to flaring but was applied to be conservative in the estimates of CLU emissions avoided.
Additional effort was undertaken to improve the methodology behind the flare correction factor for the 1990-
2009 Inventory to reduce the total number of flares in the flare vendor database that were not matched (512) to landfills
and/or LFGTE projects in the EIA and LMOP databases. Each flare in the flare vendor database not associated with a
LFGTE project in the EIA or LMOP databases was investigated to determine if it could be matched to either a landfill in
the EIA database or a LFGTE project in the LMOP database. For some unmatched flares, the location information was
missing or incorrectly transferred to the flare vendor database. In other instances, the landfill names were slightly different
between what the flare vendor provided and the actual landfill name as listed in the EIA and LMOP databases.
It was found that a large majority of the unmatched flares are associated with landfills in LMOP that are
currently flaring, but are also considering LFGTE. These landfills projects considering a LFGTE project are labeled as
candidate, potential, or construction in the LMOP database. The flare vendor database was improved to match flares with
operational, shutdown as well as candidate, potential, and construction LFGTE projects, thereby reducing the total number
of unidentified flares in the flare vendor database, all of which are used in the flare correction factor. The results of this
effort significantly decreased the number of flares used in the flare correction factor, and consequently, increased
recovered flare emissions, and decreased net emissions from landfills for the 1990-2009 Inventory. The revised state-by -
state flare correction factors were applied to the entire Inventory time series.
Step 5: Estimate ChU Oxidation
A portion of the CFL escaping from a landfill oxidizes to CO2 in the top layer of the soil. The amount of
oxidation depends upon the characteristics of the soil and the environment. For purposes of this analysis, it was assumed
that of the CFL generated, minus the amount of gas recovered for flaring or LFGTE projects, 10 percent was oxidized in
the soil (Jensen and Pipatti 2002; Mancinelli and McKay 1985; Czepiel et al 1996). The factor of 10 percent is consistent
A-372 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
with the value recommended in the 2006 IPCC revised guidelines for managed and covered landfills, and was therefore
applied to the estimates of CH4 generation minus recovery for both MSW and industrial landfills
A literature review was conducted in 2011 (RTI 2011) to provide recommendations for the most appropriate
oxidation rate assumptions. It was found that oxidation values are highly variable and range from zero to over 100 percent
(i.e., the landfill is considered to be an atmospheric sink by virtue of the landfill gas extraction system pulling atmospheric
methane down through the cover). There is considerable uncertainty and variability surrounding estimates of the rate of
oxidation because oxidation is difficult to measure and varies considerably with the presence of a gas collection system,
thickness and type of the cover material, size and area of the landfill, climate, and the presence of cracks and/or fissures in
the cover material through which methane can escape. IPCC (2006) notes that test results from field and laboratory
studies may lead to over-estimations of oxidation in landfill cover soils because they largely determine oxidation using
uniform and homogeneous soil layers. In addition, a number of studies note that gas escapes more readily through the side
slopes of a landfill as compared to moving through the cover thus complicating the correlation between oxidation and
cover type or gas recovery.
Sites with landfill gas collection systems are generally designed and managed better to improve gas recovery.
More recent research (2006 to 2012) on landfill cover methane oxidation has relied on stable isotope techniques that may
provide a more reliable measure of oxidation. Results from this recent research consistently point to higher cover soil
methane oxidation rates than the IPCC (2006) default of 10 percent. A continued effort will be made to review the peer-
reviewed literature to better understand how climate, cover type, and gas recovery influence the rate of oxidation at active
and closed landfills. At this time, the IPCC recommended oxidation factor of 10 percent will continue to be used for all
landfills.
Step 6: Estimate Total ChU Emissions
Total CH4 emissions were calculated by adding emissions from MSW and industrial landfills, and subtracting
CH4 recovered and oxidized, as shown in Table A- 264.
A-373
-------
Table A- 264: CHa Emissions from Landfills tGgl
Activity
MSW CH4 Generation
Industrial Cm Generation
Potential Emissions
Landfill Gas-to-Energy
Flare
Emissions Avoided
Oxidation at MSW Landfills
Oxidation at Industrial Landfills
Net Emissions
1990
8,219
553
8,772
(634)
(321)
(954)
(726)
(55)
7,036
1995
9,132
617
9,749
(1,070)
(1,298)
(2,368)
(676)
(62)
6,643
2001
10,068
704
10,772
(2,565)
(2,505)
(5,070)
(500)
(70)
5,132
2002
10,367
711
11,078
(2,554)
(2,772)
(5,326)
(504)
(71)
5,177
2003
10,754
719
11,473
(2,520)
(2,920)
(5,440)
(531)
(72)
5,430
2004
11,120
724
11,844
(2,630)
(3,399)
(6,029)
(5109)
(72)
5,233
2005
11,466
732
12,198
(2,660)
(3,606)
(6,266)
(520)
(73)
5,339
2006
11,793
736
12,530
(2,766)
(3,880)
(6,646)
(515)
(74)
5,295
2007
12,103
741
12,844
(2,980)
(3,961)
(6,942)
(516)
(74)
5,312
2008
12,280
748
13,127
(3,189)
(3,880)
(7,079)
(530)
(75)
5,444
2009
12,623
753
13,377
(3,532)
(3,743)
(7,274)
(535)
(75)
5,492
2010
12,863
756
13,619
(3,927)
(3,876)
(7,803)
(506)
(76)
5,234
2011
13,099
758
13,857
(4,190)
(3,986)
(8,177)
(492)
(76)
5,112
2012
13,331
758
14,089
(4,608)
(4,040)
(8,648)
(468)
(76)
4,897
Note: Totals may not sum exactly to the last significant figure due to rounding.
Note: MSW generation in Table A-248 represents emissions before oxidation. In other tables throughout the text, MSW generation estimates account for oxidation
Note: Parentheses denote negative values.
A-374 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
References
Abdalla, M., Jones, J. Yeluripati, P. Smith, J. Burke and D M. Williams. 2010. Testing DayCent and DNDC model
simulations of N2(D fluxes and assessing the impacts of climate change on the gas flux and biomass production from a
humid pasture. Atmos. Environ. 44: 2961-2970.
AAPFCO (1995 through 2000b, 2002 through 2009) Commercial Fertilizers. Association of American Plant Food Control
Officials. University of Kentucky, Lexington, KY.
AAPFCO (2000a) 1999-2000 Commercial Fertilizers Data, ASCII files. Available from David Terry, Secretary,
Association of American Plant Food Control Officials.
AF&PA. 2006a (and earlier). Statistical roundup. (Monthly). Washington, DC: American Forest & Paper Association.
AF&PA. 2006b (and earlier). Statistics of paper, paperboard and wood pulp. Washington, DC: American Forest & Paper
Association.
Alaska Department of Natural Resources (2008) Division of Forestry. "Fire Statistics." Available online at
October 2008.
Alaska Interagency Coordination Center (AICC) (2013). "2012 Alaska Fire Season: Wildland Fire Summary and Statistics
Annual Report." Available online at < http://fire.ak.blm.gov/content/aicc/stats/firestats.pdf > November 2013.
Amichev, B. Y. and J. M. Galbraith (2004) "A Revised Methodology for Estimation of Forest Soil Carbon from Spatial
Soils and Forest Inventory Data Sets." Environmental Management 33 (Suppl. 1):S74-S86.
ArSova, Ljupka, Rob van Haaren, Nora Goldstein, Scott M. Kaufman, and Nickolas J. Themelis (2008). "16th Annual
BioCycle Nationwide Survey: The State of Garbage in America" Biocycle, JG Press, Emmaus, PA. December.
Astlett Rubber Inc. (n.d.) Material Safety Data Sheet, Carbon Black. Available online at:
. Accessed 5 November 2009.
Bahor, B (2009) Covanta Energy's public review comments re: Draft Inventory of U.S. Greenhouse Gas Emissions and
Sinks: 1990-2007. Submitted via email on April 9, 2009 to Leif Hockstad, U.S. EPA.
Barlaz, M.A. (2006) "Forest Products Decomposition in Municipal Solid Waste Landfills." Waste Management, 26(4):
321-333.
Bastian, R. (2007) Personal Communication. Robert Bastian, Office of Water, U.S. Environmental Protection Agency,
Washington, DC and Victoria Thompson, ICF International. July 20, 2007.
Bechtold, W.A.; Patterson, P.L. 2005. The enhanced forest inventory and analysis program—national sampling design and
estimation procedures. Gen. Tech. Rep. SRS-80. Asheville, NC: US Department of Agriculture Forest Service,
Southern Research Station. 85 p.
BioCycle (2006) "The State of Garbage in America" By L. Arsova, R. Van Haaren, N. Goldstein, S. Kaufman, and N.
Themelis. BioCycle. April 2006. Available online at
BioCycle (2008) "The State of Garbage in America" By L. Arsova, R. Van Haaren, N. Goldstein, S. Kaufman, and N.
Themelis. BioCycle. December 2008. Available online at < http://www.jgpress.com/archives/_free/001782.html>
BioCycle (2010) "The State of Garbage in America" By L. Arsova, R. Van Haaren, N. Goldstein, S. Kaufman, and N.
Themelis. BioCycle. December 2010. Available online at < http://www.jgpress.com/archives/_free/002191.html>
Birdsey, R.A., and L.S. Heath (1995) "Carbon Changes in U.S. Forests." In Productivity of America's Forests and
Climate Change. Gen. Tech. Rep. RM-271. Rocky Mountain Forest and Range Experiment Station, Forest Service,
U.S. Department of Agriculture. Fort Collins, CO, 56-70.
Birdsey, R. (1996) "Carbon Storage for Major Forest Types and Regions in the Conterminous United States." In R.N.
Sampson and D. Hair, (eds); Forest and Global Change, Volume 2: Forest Management Opportunities for Mitigating
Carbon Emissions. American Forests. Washington, DC, 1-26 and 261-379 (appendices 262 and 263).
Birdsey, R., and L. S. Heath (2001) "Forest Inventory Data, Models, and Assumptions for Monitoring Carbon Flux." In
5*o;7 Carbon Sequestration and the Greenhouse Effect. Soil Science Society of America. Madison, WI, 125-135.
Chartwell (2004) Municipal Solid Waste Directory. The Envirobiz Group.
A-375
-------
Cibrowski, P. (1996) Personal Communication. Peter Cibrowski, Minnesota Pollution Control Agency and Heike
Mainhardt, ICF Incorporated. July 29, 1996.
CTIC (2004) 2004 Crop Residue Management Survey. Conservation Technology Information Center. Available online at
.
Czepiel, P., B. Mosher, P. Grill, and R. Harriss (1996) "Quantifying the Effect of Oxidation on Landfill Methane
Emissions." Journal of Geophysical Research, 101(D11): 16721-16730.
Daly, C., G.H. Taylor, W.P. Gibson, T. Parzybok, G.L. Johnson, and P.A. Pasteris (1998) "Development of high-quality
spatial datasets for the United States." Proc., 1st International Conference on Geospatial Information in Agriculture and
Forestry, Lake Buena Vista, FL, I-512-I-519. June 1-3, 1998.
Daly, C., R.P. Neilson, and D.L. Phillips (1994) "A statistical-topographic model for mapping climatological precipitation
over mountainous terrain." Journal of Applied Meteorology, 33:140-158.
Dean, W. E., and E. Gorham (1998) Magnitude and significance of carbon burial in lakes, reservoirs, and peatlands.
Geology 26:535-538.
Delgado, J.A., S.J. Del Grosso, and S.M. Ogle (2009) "15N isotopic crop residue cycling studies and modeling suggest
that IPCC methodologies to assess residue contributions to N2O-N emissions should be reevaluated." Nutrient Cycling
in Agroecosystems, DOI 10.1007/sl0705-009-9300-9.
Del Grosso, S.J., S.M. Ogle, W.J. Parton. 2011. Soil Organic Matter Cycling and Greenhouse Gas Accounting
Methodologies, Chapter 1, pp 3-13 DOI: 10.1021/bk-2011-1072.chOOl. In: L. Guo, A. Gunasekara, L. McConnell
(Eds.) Understanding Greenhouse Gas Emissions from Agricultural Management, American Chemical Society,
Washington, D.C.
Del Grosso, S.J., W.J. Parton, C.A. Keough, andM. Reyes-Fox. (2011) Special features of the DayCent modeling package
and additional procedures for parameterization, calibration, validation, and applications, in Methods of Introducing
System Models into Agricultural Research, L.R. Ahuja and Liwang Ma, editors, p. 155-176, American Society of
Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, WI. USA.
Del Grosso, S.J., S.M. Ogle, W.J. Parton, and F.J. Breidt (2010) "Estimating Uncertainty in N2O Emissions from US
Cropland Soils." Global Biogeochemical Cycles, 24, GB1009, doi:10.1029/2009GB003544.
Del Grosso, S.J., T. Wirth, S.M. Ogle, W.J. Parton (2008) Estimating agricultural nitrous oxide emissions. EOS 89, 529-
530.
Del Grosso, S.J., A.R. Mosier, W.J. Parton, and D.S. Ojima (2005) "DAYCENT Model Analysis of Past and
Contemporary Soil N2O and Net Greenhouse Gas Flux for Major Crops in the USA." Soil Tillage and Research, 83: 9-
24. doi: 10.1016/j.still.2005.02.007.
Del Grosso, S.J., W.J. Parton, A.R. Mosier, M.D. Hartman, J. Brenner, D.S. Ojima, and D.S. Schimel (2001) "Simulated
Interaction of Carbon Dynamics and Nitrogen Trace Gas Fluxes Using the DAYCENT Model." In Schaffer, M., L. Ma,
S. Hansen, (eds.); Modeling Carbon and Nitrogen Dynamics for Soil Management. CRC Press. Boca Raton, Florida.
303-332.
Del Grosso, S.J., W.J. Parton, A.R. Mosier, D.S. Ojima, A.E. Kulmala and S. Phongpan (2000) General model for N2O
and N2 gas emissions from soils due to denitrification. Global Biogeochem. Cycles, 14:1045-1060.
DeZan, D.(2000) Personal Communication between Diane DeZan, Fiber Economics Bureau and Joe Casola, ICF
Consulting. 4 August 2000.
Domke, G.M., J.E. Smith, and C.W. Woodall. (2011) Accounting for density reduction and structural loss in standing
dead trees: Implications for forest biomass and carbon stock estimates in the United States. Carbon Balance and
Management. 6:14.
Domke, G.M., Woodall, C.W., Smith, J.E., Westfall, J.A., McRoberts, R.E. (2012) Consequences of alternative tree-level
biomass estimation procedures on U.S. forest carbon stock estimates. Forest Ecology and Management. 270: 108-116
Domke, G.M., Woodall, C.W., Walters, B.F., McRoberts, R.E., Hatfield, M.A. In Review. Strategies to compensate for
the effects of nonresponse on forest carbon baseline estimates from the national forest inventory of the United States.
Forest Ecology and Management.Domke, G.M., Woodall, C.W., Walters, B.F., Smith, J.E. (2013) From models to
measurements: comparing down dead wood carbon stock estimates in the U.S. forest inventory. PLoS ONE 8(3):
e59949.
A-376 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Easter, M. , S. Williams, and S. Ogle. (2008) Gap-filling NRI data for the Soil C Inventory. Natural Resource Ecology
Laboratory, Colorado State University, Fort Collins, CO. Report provided to the US Environmental Protection Agency,
Tom Wirth.
Edmonds, L., N. Gollehon, R.L. Kellogg, B. Kintzer, L. Knight, C. Lander, J. Lemunyon, D. Meyer, D.C. Moffitt, and J.
Schaeffer (2003) "Costs Associated with Development and Implementation of Comprehensive Nutrient Management
Plans." Part 1. Nutrient Management, Land Treatment, Manure and Wastewater Handling and Storage, and
Recordkeeping. Natural Resource Conservation Service, U.S. Department of Agriculture.
Energy Recovery Council (2009) "2007 Directory of Waste-to-Energy Plants in the United States," accessed September
29, 2009.
EIA (2007) Voluntary Greenhouse Gas Reports for EIA Form 1605B (Reporting Year 2006). Available online at
.
EPA (2005 through 2013) Municipal Solid Waste in the United States: Facts and Figures. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency. Washington, DC. Available online at <
http://www. epa. gov/osw/nonhaz/municipal/msw99.htm >.
EPA (2012) Landfill Gas-to-Energy Project Database. Landfill Methane and Outreach Program. July 2012.
EPA (2008). Compilation of Air Pollution Emission Factors, Publication AP-42, Draft Section 2.4 Municipal Solid Waste
Landfills. October 2008.
EPA (1999 through 2003) Characterization of Municipal Solid Waste in the United States: Source Data Update. Office of
Solid Waste, U.S. Environmental Protection Agency. Washington, DC.
EPA (1999) Biosolids Generation, Use and Disposal in the United States. Office of Solid Waste, U.S. Environmental
Protection Agency. Available online at .
EPA (1998) Compilation of Air Pollution Emission Factors, Publication AP-42, Section 2.4 Municipal Solid Waste
Landfills. November 1998.
EPA (1995) Compilation of Air Pollutant Emission Factors, AP-42. Fifth Edition, Vol. I: Stationary Point and Area
Sources, Introduction. Office of Air Quality Planning and Standards. Research Triangle Park, NC. October.
EPA (1993) Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress, U.S.
Environmental Protection Agency, Office of Air and Radiation. Washington, DC. EPA/430-R-93-003. April 1993.
EPA (1993) Federal Register. Part II. Standards for the Use and Disposal of Sewage Sludge; Final Rules. U.S.
Environmental Protection Agency, 40 CFR Parts 257, 403, and 503.
EPA (1988) National Survey of Solid Waste (Municipal) Landfill Facilities, U.S. Environmental Protection Agency.
Washington, DC. EPA/530-SW-88-011. September 1988.
ERG (2013). Production Data Supplied by ERG for 1990-2011 for Pulp and Paper, Fruits and Vegetables, and Meat.
October.
Euliss, N., and R. Gleason (2002) Personal communication regarding wetland restoration factor estimates and restoration
activity data. Ned Euliss and Robert Gleason of the U.S. Geological Survey, Jamestown, ND, to Stephen Ogle of the
National Resource Ecology Laboratory, Fort Collins, CO. August 2002.
FEE (2009) Fiber Economics Bureau, as cited in C&EN (2009) Chemical Output Slipped In Most Regions Chemical &
Engineering News, American Chemical Society, 6 July. Available online at .
Flores, R.A., C.W. Shanklin, M. Loza-Garay, S.H. Wie (1999) "Quantification and Characterization of Food Processing
Wastes/Residues." Compost Science & Utilization, 7(1): 63-71.
Prayer, W.E., and G.M. Furnival (1999) "Forest Survey Sampling Designs: A History." Journal of Forestry 97(12): 4-10.
Gold, M. A., W. J. Rietveld, H. E. Garrett, and R. F. Fisher. (2000) Agroforestry Nomenclature, Concepts, and Practices
for the United States. In H.E. Garrett, W.J. Rietveld, and R.F. Fisher, editors. North American Agroforestry: An
Integrated Science and Practice. ASA. Madison, Wisconsin, 63-77.
Gurung, R.B., F.J. Breidt, A. Dutin, and S.M. Ogle (2009) Predicting Enhanced Vegetation Index (EVI) for ecosystem
modeling applications. Remote Sensing of Environment 113:2186-2193.
Hair. D. and A.H. Ulrich (1963) The Demand and price situation for forest products - 1963. U.S. Department of
A-377
-------
Agriculture Forest Service, Misc Publication No. 953. Washington, DC.
Hair, D. (1958) "Historical forestry statistics of the United States." Statistical Bull. 228. U.S. Department of Agriculture
Forest Service, Washington, DC.
Halvorson, A.D., C.S. Snyder, A.D. Blaylock, and S.J. Del Grosso. 2013. Enhanced Efficiency Nitrogen Fertilizers:
Potential Role in Nitrous Oxide Emission Mitigation. Agronomy Journal, doi: 10.2134/agronj2013.0081
Hansen, M.H., T Frieswyk, J.F. Glover, J.F. Kelly (1992) The Eastwide Forest Inventory Data Base: Users Manual.
General Technical Report NC-151. U.S. Department of Agriculture, Forest Service, North Central Forest Experiment
Station. St. Paul, MN.
Harmon, M.E., C.W. Woodall, B. Fasth, J. Sexton, M. Yatkov. (2011) Differences between standing and downed dead
tree wood density reduction factors: A comparison across decay classes and tree species. Res. Paper. NRS-15.
Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 40 p
Heath, L.S., I.E., Smith, and R.A. Birdsey (2003) Carbon Trends in U. S. Forestlands: A Context for the Role of Soils in
Forest Carbon Sequestration. In J. M. Kimble, L. S. Heath, R. A. Birdsey, and R. Lai, editors. The Potential ofU. S.
Forest Soils to Sequester Carbon and Mitigate the Greenhouse Effect. Lewis Publishers (CRC Press). Boca Raton, FL,
35-45.
Heath, L. S., and I.E. Smith (2000) "Soil Carbon Accounting and Assumptions for Forestry and Forest-related Land Use
Change." In The Impact of Climate Change on America's Forests. Joyce, L.A., and Birdsey, R.A. Gen. Tech. Rep.
RMRS-59. Rocky Mountain Research Station, Forest Service, U.S. Department of Agriculture. Fort Collins, CO, 89-
101.
Heath, L.S., M.C. Nichols, I.E. Smith, and J.R. Mills. (2010) FORCARB2: An updated version of the U.S. Forest Carbon
Budget Model. Gen. Tech. Rep. NRS-67.USDA Forest Service, Northern Research Station, Newtown Square, PA. 52
p. [CD-ROM].
Heath, L.S., I.E. Smith, K.E. Skog, D.J. Nowak, and C.W. Woodall. (2011) Managed forest carbon estimates for the U.S.
Greenhouse Gas Inventory, 1990-2008. Journal of Forestry 109(3): 167-173.
Heath, L. S., J. E. Smith, C. W. Woodall, D. L. Azuma, and K. L. Waddell (2011). Carbon stocks on forestlands of the
United States, with emphasis on USDA Forest Service ownership. Ecosphere 2(1), article 6, 21 p.
Heath, L.S., V. Maltby, R. Miner, K.E. Skog, I.E. Smith, J. Unwin, B. Upton (2010) "Greenhouse Gas and Carbon Profile
of the U.S. Forest Products Industry Value Chain." Environ. Sci. Technol., 44: 3999-4005.
Hoover, C., and S. Rebain. 2008. The Kane Experimental Forest carbon inventory: Carbon reporting with FVS. Pages
17-22 in R.N. Havis and N.L. Crookston, comps. 2008. Third Forest Vegetation Simulator Conference; 2007 February
13-15; Fort Collins, CO. Proceedings RMRS-P-54. USDA Forest Service, Rocky Mountain Research Station, Fort
Collins, Colorado.
Howard, James L. (2003) U.S. timber production, trade, consumption, and price statistics 1965 to 2002. Res. Pap. FPL-
RP-615. Madison, WI: USDA, Forest Service, Forest Products Laboratory. Available online at
.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. The Intergovernmental Panel on
Climate Change, National Greenhouse Gas Inventories Programme, J. Penman, et al, eds. August 13, 2004. Available
online at .
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic Co-
Operation and Development, International Energy Agency. Paris, France.
Jenkins, J,C., D.C. Chojnacky, L.S. Heath, and R.A. Birdsey (2003) "National-scale biomass estimators for United States
tree species." Forest Science 49(1): 12-35.
Jensen, J.E.F., and R. Pipatti (2002) "CH4 Emissions from Solid Waste Disposal." Background paper for the Good
Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
A-378 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Johnson, D.M., and R. Mueller. 2010. The 2009 Cropland Data Layer. Photogrammetric engineering and remote sensing
76:1201-1205.
Kellogg R.L., C.H. Lander, B.C. Moffitt, and N. Gollehon (2000) Manure Nutrients Relative to Capacity of Cropland and
Pastureland to Assimilate Nutrients: Spatial and Temporal Trends for the United States. U.S. Department of Agriculture
Publication Number npsOO-0579.
Kraft, D.L. and H.C. Orender (1993) "Considerations for Using Sludge as a Fuel." Tappi Journal, 76(3): 175-183.
Li, Y., D. Chen, Y. Zhang, R. Edis and H. Ding. 2005. Comparison of three modeling approaches for simulating
denitrification and nitrous oxide emissions from loam-textured arable soils. Global Biogeochemical Cycles, 19,
GB3002.
Mancinelli, R. and C. McKay (1985) "Me thane-Oxidizing Bacteria in Sanitary Landfills." Proc. First Symposium on
Biotechnical Advances in Processing Municipal Wastes for Fuels and Chemicals, Minneapolis, MM, 437-450.
August.
McFarland, M.J. (2001) Biosolids Engineering, New York: McGraw-Hill, p. 2.12.
McGill, W.B., and C.V. Cole (1981) Comparative aspects of cycling of organic C, N, S and P through soil organic matter.
Geoderma 26:267-286.
Mesinger, F., G. DiMego, E. Kalnay, K. Mitchell, P. C. Shafran, W. Ebisuzaki, D. Jovic, J. Woollen, E. Rogers, E. H.
Berbery, M. B. Ek, Y. Fan, R. Grumbine, W. Higgins, H. Li, Y. Lin, G. Manikin, D. Parrish, and W. Shi (2006) North
American regional reanalysis. Bulletin of the American Meteorological Society 87:343-360.
Metherell, A.K., L.A. Harding, C.V. Cole, and W.J. Parton (1993) "CENTURY Soil Organic Matter Model Environment."
Agroecosystem version 4.0. Technical documentation, GPSR Tech. Report No. 4, USDA/ARS, Ft. Collins, CO.
Miner, R. (2008). "Calculations documenting the greenhouse gas emissions from the pulp and paper industry."
Memorandum from Reid Minor, National Council for Air and Stream Improvement, Inc. (NCASI) to Becky
Nicholson, RTI International, May 21, 2008.
Miner, C. (1998) Harvesting the High Plains: John Kriss and the business of wheat farming, 1920-1950. University Press
of Kansas, Lawrence, KS.
Mintz C., R. Freed, and M. Walsh (2003) "Timeline of Anaerobic Land Disposal of Solid Waste." Memorandum to T.
Wirth (EPA) and K. Skog (USDA), December 31, 2003.
Nair, P.K.R. and V.D. Nair. (2003) Carbon storage in North American Agroforestry systems. In Kimble J., Heath L.S.,
Birdsey R.A., Lai R., editors. The potential of U.S. forest soils to sequester carbon and mitigate the greenhouse effect.
CRC Press. Boca Raton, FL, 333-346.
NASS (2004) Agricultural Chemical Usage: 2003 Field Crops Summary. Report AgChl(04)a, National Agricultural
Statistics Service, U.S. Department of Agriculture. Available online at
.
NASS (1999) Agricultural Chemical Usage: 1998 Field Crops Summary. Report AgChl(99). National Agricultural
Statistics Service, U.S. Department of Agriculture. Available online at
.
NASS (1992) Agricultural Chemical Usage: 1991 Field Crops Summary. Report AgChl(92). National Agricultural
Statistics Service, U.S. Department of Agriculture. Available online at
.
National Council for Air and Stream Improvement, Inc. (NCASI) (2008) "Calculations Documenting the Greenhouse Gas
Emissions from the Pulp and Paper Industry." Memorandum to R. Nicholson (RTI).
National Council for Air and Stream Improvement, Inc. (NCASI) (2005) "Calculation Tools for Estimating Greenhouse
Gas Emissions from Pulp and Paper Mills, Version 1.1." July 8, 2005.
National Interagency Fire Center (NIFC) (2013). "Fire Information—Wildland Fire Statistics. Prescribed Fires (1998-
2012) and Wildland Fires and Acres (1960-2012)." Available online at
. November 2013.
National Association of State Foresters (2011). State Forestry Statistics Spreadsheet. Available online at
November 2011.
A-379
-------
National Association of State Foresters (2008) State Forestry Statistics 2006 Report. Available online at <
http://www.stateforesters.org/files/2006%20State%20Forestry%20Statistics-Web-Final.pdf> October 2008.
National Association of State Foresters (2007a) State Forestry Statistics 1998 Report. Available online at
March 2008.
National Association of State Foresters (2007b) State Forestry Statistics 2002 Report. Available online at
March 2008.
National Association of State Foresters (2007c) State Forestry Statistics 2004 Report. Available online at
March 2008.
NEBRA (2007) A National Biosolids Regulation, Quality, End Use & Disposal Survey. North East Biosolids and
Residuals Association, July 21, 2007
NRAES (1992) On-Farm Composting Handbook (NRAES-54). Natural Resource, Agriculture, and Engineering Service.
Available online at .
NRCS (1981) Land Resource Regions and Major Land Resource Areas of the United States, USDA Agriculture Handbook
296, United States Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Cente.,
Lincoln, NE, pp. 156.
NRCS (1997) "National Soil Survey Laboratory Characterization Data," Digital Data, Natural Resources Conservation
Service, U.S. Department of Agriculture. Lincoln, NE.
NRIAI (2003) Regional Budget and Cost Information. U.S. Department of Agriculture, Natural Resources Conservation
Service, Natural Resources Inventory and Analysis Institute. Available online at
http://www.economics.nrcs.usda.gov/care/budgets/index.html
Nusser, S.M., J.J. Goebel (1997) The national resources inventory: a long term monitoring programme. Environmental
and Ecological Statistics, 4, 181-204.
Nusser, S.M., F.J. Breidt, and. W.A. Fuller (1998) "Design and Estimation for Investigating the Dynamics of Natural
Resources, Ecological Applications, 8:234-245.
Ogle, S.M., F.J. Breidt, M. Easter, S. Williams, K. Killian, and K. Paustian (2010) "Scale and uncertainty in modeled soil
organic carbon stock changes for US croplands using a process-based model." Global Change Biology 16:810-822.
Ogle, S.M., F.J. Breidt, M. Easter, S. Williams and K. Paustian. (2007) "Empirically-Based Uncertainty Associated with
Modeling Carbon Sequestration Rates in Soils." Ecological Modeling 205:453-463.
Ogle, S.M., F.J. Breidt, and K. Paustian. (2006) "Bias and variance in model results due to spatial scaling of measurements
for parameterization in regional assessments." Global Change Biology 12:516-523.
Ogle, S.M., M.D. Eve, F.J. Breidt, and K. Paustian (2003) "Uncertainty in estimating land use and management impacts
on soil organic carbon storage for U.S. agroecosystems between 1982 and 1997." Global Change Biology 9:1521 -1542.
Parton, W.J., D.S. Schimel, C.V. Cole, D.S. Ojima (1987) "Analysis of factors controlling soil organic matter levels in
Great Plains grasslands." Soil Science Society of America Journal 51:1173-1179.
Parton, W. J., J. M. O. Scurlock, D. S. Ojima, T. G. Gilmanov, R. J. Scholes, D. S. Schimel, T. Kirchner, J.-C. Menaut, T.
Seastedt, E. G. Moya, A. Kamnalrut, and J. I. Kinyamario. 1993. Observations and modeling of biomass and soil
organic matter dynamics for grassland biomes worldwide. Global Biogeochemical Cycles 7:785-809.
Parton, W.J., D.S. Ojima, C.V. Cole, and D.S. Schimel (1994) "A General Model for Soil Organic Matter Dynamics:
Sensitivity to litter chemistry, texture and management," in Quantitative Modeling of Soil Forming Processes. Special
Publication 39, Soil Science Society of America, Madison, WI, 147-167.
Parton, W.J., M.D. Hartman, D.S. Ojima, and D.S. Schimel (1998) "DAYCENT: Its Land Surface Submodel: Description
and Testing". Glob. Planet. Chang. 19: 35-48.
Parton, W.J., E.A. Holland, S.J. Del Grosso, M.D. Hartman, R.E. Martin, A.R. Mosier, D.S. Ojima, and D.S. Schimel
(2001) Generalized model for NOX and N2O emissions from soils. Journal of Geophysical Research. 106 (D 15): 17403-
17420.
Perry, C.H., C. W. Woodall, and M. Schoeneberger (2005) Inventorying trees in agricultural landscapes: towards an
accounting of "working trees". In: "Moving Agroforestry into the Mainstream." Proc. 9th N. Am. Agroforestry Conf.,
Brooks, K.N. and Ffolliott, P.P. (eds). 12-15 June 2005, Rochester, MN [CD-ROM]. Dept. of Forest Resources, Univ.
A-380 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Minnesota, St. Paul, MN, 12 p. Available online at (verified 23 Sept 2006).
Potter, C. S., J.T. Randerson, C.B. Fields, P.A. Matson, P.M. Vitousek, H.A. Mooney, and S.A. Klooster. (1993)
"Terrestrial ecosystem production: a process model based on global satellite and surface data." Global Biogeochemical
Cycles 7:811-841.
Potter, C., S. Klooster, A. Huete, and V. Genovese (2007) Terrestrial carbon sinks for the United States predicted from
MODIS satellite data and ecosystem modeling. Earth Interactions 11, Article No. 13,DOI 10.1175/EI228.1.
Peer, R., S. Thorneloe, and D. Epperson (1993) "A Comparison of Methods for Estimating Global Methane Emissions
from Landfills." Chemosphere, 26(l-4):387-400.
Quam, V.C., J. Gardner, J.R. Brandle, and T.K. Boes (1992) Windbreaks in Sustainable Agricultural Systems. EC-91-
1772. University of Nebraska Extension. Lincoln, NE.
RMA (2012a) "Scrap Tire Markets: Facts and Figures - Scrap Tire Characteristics." Available online at:
Accessed 18 January 2012.
RMA (2012b) "Rubber FAQs." Rubber Manufacturers Association. Available online at:
. Accessed 18 January 2012.
RMA (2011) "U.S. Scrap Tire Management Summary 2005-2009." Rubber Manufacturers Association. October
2011. Available online at: .
RMA (2009) Scrap Tire Markets in the United States: 9th Biennial Report. Rubber Manufacturers Association.
Washington, DC. May 2009.
RMA (2002 through 2006) U.S. Scrap Tire Markets. Rubber Manufacturers Association. Washington, DC. Available
online at: .
RTI (2013) Review of State of Garbage Data Used in the U.S. Non-CC>2 Greenhouse Gas Inventory for Landfills.
Memorandum prepared by K. Weitz and K. Bronstein (RTI) for R. Schmeltz (EPA), November 25, 2013.
RTI (2011) Updated Research on Methane Oxidation in Landfills. Memorandum prepared by K. Weitz (RTI) for R.
Schmeltz (EPA), January 14, 2011.
RTI (2009) GHG Inventory Improvement - Construction & Demolition Waste DOC and L0 Value. Memorandum prepared
by J. Coburn and K. Bronstein (RTI) for R. Schmeltz, April 15, 2010.
RTI (2008) Analysis of DOC Values for Industrial Solid Waste for the Pulp and Paper Industry and the Food Industry.
Memorandum prepared by J. Coburn for M. Weitz (EPA), October 13, 2008.
RTI (2006) Methane Emissions for Industrial Landfills. Memorandum prepared by K. Weitz and M. Banner for M. Weitz
(EPA), September 5, 2006.
RTI (2004) Documentation for Changes to the Methodology for the Inventory of Methane Emissions from Landfills.
Memorandum prepared by M. Branscome and J. Coburn (RTI) E. Scheehle (EPA), August 26, 2004.
Ruddy B.C., D.L. Lorenz, and D.K. Mueller (2006) County-level estimates of nutrient inputs to the land surface of the
conterminous United States, 1982-2001. Scientific Investigations Report 2006-5012. US Department of the Interior.
Savitzky, A., and M. J. E. Golay. 1964. Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
Analytical Chemistry 36:1627-1639.
Saxton, K.E., W.J. Rawls, J.S. Romberger, and R.I. Papendick (1986) "Estimating Generalized Soil-Water Characteristics
From Texture." Soil Sci. Soc. Am. J. 50:1031-1036.
Schneider, S. (2007) E-mail communication Shelly Schneider, Franklin Associates to Sarah Shapiro, ICF International, A
Division of ERG. January 10, 2007.
Schoeneberger, P.J. (2006) Landscape Classification. U.S. Department of Agriculture. Lincoln, NE.
Skog, K.E., K. Pingoud, and J.E. Smith (2004) "A method countries can use to estimate changes in carbon stored in
harvested wood products and the uncertainty of such estimates." Environmental Management 33(Suppl. 1):S65-S73.
Skog, K.E. (2008) "Sequestration of Carbon in harvested wood products for the United States." Forest Products Journal,
58(6): 56-72.
A-381
-------
Skog, K. and G.A. Nicholson (2000) "Carbon Sequestration in Wood and Paper Products." USDA Forest Service Gen.
Tech. Rep. RMRS-GTR-59.
Smith, P., J. Brenner, K. Paustian, G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, K. Killian, D. Lamm, J. Schuler, and S.
Williams (2002) Quantifying the Change in Greenhouse Gas Emissions Due to Natural Resource Conservation Practice
Application in Indiana. Final Report to the Indiana Conservation Partnership, Colorado State University Natural
Resource Ecology Laboratory and U.S. Department of Agriculture Natural Resources Conservation Service, Fort
Collins, CO.
Smith, J. (2013a) Estimates of Forest Carbon Stocks and Flux: 1990-2013. E-mail correspondence between ICF and Jim
Smith, USDA Forest Service. September 18, 2013.
Smith, J. (2013b) Estimates of Forest Carbon Stocks by State: 1990-2013. E-mail correspondence between ICF and Jim
Smith, USDA Forest Service. November 7, 2013.
Smith, J. (2008) E-mail correspondence between Jean Kim, ICF, and Jim Smith, U.S. Forest Service, December 3, 2008.
Smith, J. and L.S. Heath (2000) Considerations for interpreting probabilistic estimates of uncertainty of forest carbon. In
Joyce, L.A., Birdsey, R., editors. The impact of climate change on America's forests: a technical document supporting
the 2000 USDA Forest Service RPA Assessment. Gen. Tech. Rep. RMRS-GTR-59. U.S. Department of Agriculture,
Forest Service, Rocky Mountain Research Station. Fort Collins, CO, 102-111.
Smith, J.E., and L.S. Heath (2002) "A model of forest floor carbon mass for United States forest types." Res. Paper NE-
722. USDA Forest Service, Northeastern Research Station, Newtown Square, PA.
Smith, W. B., J. S. Vissage, D. R. Darr, and R. M. Sheffield (2001) Forest Resources of the United States, 1997. General
Technical Report NC-219, U.S. Department of Agriculture Forest Service, North Central Research Station, St. Paul,
MN.
Smith, J. E., L. S. Heath, and J. C. Jenkins (2003) Forest Volume-to-Biomass Models and Estimates of Mass for Live and
Standing Dead Trees of U.S. Forests. General Technical Report NE-298, USDA Forest Service, Northeastern Research
Station, Newtown Square, PA.
Smith, J. E., L. S. Heath, and P. B. Woodbury (2004) "How to estimate forest carbon for large areas from inventory data."
Journal of Forestry 102:25-31.
Smith, J.E., L.S. Heath, K.E. Skog, R.A. Birdsey. (2006) Methods for calculating forest ecosystem and harvested carbon
with standard estimates for forest types of the United States. Gen. Tech. Rep. NE-343. U.S. Department of Agriculture,
Forest Service, Northeastern Research Station. Newtown Square, PA.
Smith, J.E., L.S. Heath, and M.C. Nichols (2010). U.S. Forest Carbon Calculation Tool User's Guide: Forestland Carbon
Stocks and Net Annual Stock Change. General Technical Report NRS-13 revised, U.S. Department of Agriculture
Forest Service, Northern Research Station, 34p.
Smith, W. B., P. D. Miles, C. H. Perry, and S. A. Pugh (2009) Forest Resources of the United States, 2007. General
Technical Report WO-78, U.S. Department of Agriculture Forest Service, Washington Office.
Smith, J.E., L.S. Heath, and M.C. Nichols (2010). U.S. Forest Carbon Calculation Tool User's Guide: Forestland Carbon
Stocks and Net Annual Stock Change. General Technical Report NRS-13 revised, U.S. Department of Agriculture
Forest Service, Northern Research Station.
Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. (2005) State Soil
Geographic (STATSGO) Database for State. Available online at
.
Solid Waste Association of North America (SWANA) (1998) Comparison of Models for Predicting Landfill Methane
Recovery. Publication No. GR-LG 0075. March 1998.
Sonne, E. (2006) "Greenhouse Gas Emissions from Forestry Operations: A Life Cycle Assessment." J. Environ. Qual.
35:1439-1450.
Spencer, S., S.M. Ogle, F.J. Breidt, J. Goebel, and K. Paustian. 2011. Designing a national soil carbon monitoring
network to support climate change policy: a case example for US agricultural lands. Greenhouse Gas Management &
Measurement 1:167-178.
Steer, Henry B. (1948) Lumber production in the United States. Misc. Pub. 669, U.S. Department of Agriculture Forest
Service. Washington, DC.
A-382 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Stehfest, E., and C. Milller (2004), Simulation of N2O emissions from a urine-affected pasture in New Zealand with the
ecosystem model DayCent, J. Geophys. Res., 109, DOS 109, doi: 10.1029/2003JD004261.
STMC (1990 through 1997) Scrap Tire Use/Disposal Study. Rubber Manufacturers Association: Scrap Tire Management
Council. Available online at: .
Towery, D. (2001) Personal Communication. Dan Towery regarding adjustments to the CTIC (1998) tillage data to reflect
long-term trends, Conservation Technology Information Center, West Lafayette, IN, and Marlen Eve, National
Resource Ecology Laboratory, Fort Collins, CO. February 2001.
TVA (1991 through 1992a, 1993 through 1994) Commercial Fertilizers. Tennessee Valley Authority, Muscle Shoals, AL.
TVA (1992b) Fertilizer Summary Data 1992. Tennessee Valley Authority, Muscle Shoals, AL.
Ulrich, Alice (1985) U.S. Timber Production, Trade, Consumption, and Price Statistics 1950-1985. Misc. Pub. 1453, U.S.
Department of Agriculture Forest Service. Washington, DC.
Ulrich, AH. (1989) U.S. Timber Production, Trade, Consumption, and Price Statistics, 1950-1987. USDA Miscellaneous
Publication No. 1471, U.S. Department of Agriculture Forest Service. Washington, DC, 77.
Upton, B., R. Miner, M. Spinney, L.S. Heath (2008) "The Greenhouse Gas and Energy Impacts of Using Wood Instead of
Alternatives in Residential Construction in the United States." Biomass andBioenergy, 32: 1-10.
U.S. Census Bureau (2013) Population Division. Table 1: Annual Estimates of the Resident Population for the United
States, Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2012 (NST-EST2012-01). September 2013.
Available online at < http://www.census.gov/popest/data/national/totals/2012/index.html >
U.S. Census Bureau (2009). Population Division. Table 1. Annual Estimates of the Resident Population for the United
States, Regions, States, and Puerto Rico: April 1, 2000 to July 1, 2009 (NST-EST2009-01). December 2009.
Available online at < http://www.census.gov/popest/data/historical/2000s/vintage_2009/index.html>.
USDA (2010a) Crop Production 2009 Summary, National Agricultural Statistics Service, Agricultural Statistics Board,
U.S. Department of Agriculture, Washington, DC. Available online at ,
USDA (2010b) Quick Stats: U.S. & All States Data - Crops. National Agricultural Statistics Service, U.S. Department of
Agriculture. Washington, DC. U.S. Department of Agriculture, National Agricultural Statistics Service. Washington,
D.C., Available online at
USDA (2003, 2005 through 2006, 2008 through 2009) Crop Production Summary, National Agricultural Statistics
Service, Agricultural Statistics Board, U.S. Department of Agriculture, Washington, DC. Available online at
,
USDA (1998) Field Crops Final Estimates 1992-1997. Statistical Bulletin Number 947a. National Agricultural Statistics
Service, U.S. Department of Agriculture. Washington, DC. Available online at .
Accessed July 2001.
USDA (1996) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651.
Natural Resources Conservation Service, U.S. Department of Agriculture. July 1996.
USDA (1994) Field Crops: Final Estimates, 1987-1992. Statistical Bulletin Number 896, National Agriculture Statistics
Service, U.S. Department of Agriculture. Washington, DC. Available online at .
USDC Bureau of Census (1976) Historical Statistics of the United States, Colonial Times to 1970, Vol. 1. Washington,
DC.
USDA Forest Service (1992) "1984-1990 Wildfire Statistics." Prepared by State and Private Forestry Fire and Aviation
Management Staff. Facsimile fromHelene Cleveland, USDA Forest Service, to ICF International. January 30, 2008.
USDA Forest Service (2013a) Forest Inventory and Analysis National Program: Program Features. U.S. Department of
Agriculture Forest Service. Washington, DC. Available online at < http://fia.fs.fed.us/program-features/>. Accessed 10
September 2013.
USDA Forest Service. (2013b) Forest Inventory and Analysis National Program: FIA Data Mart. U.S. Department of
Agriculture Forest Service. Washington, DC. Available online at .
Accessed 10 September 2013.
USDA Forest Service. (2013c) Forest Inventory and Analysis National Program, FIA library: Field Guides, Methods and
A-383
-------
Procedures. U.S. Department of Agriculture Forest Service. Washington, DC. Available online at
. Accessed 10 September 2013.
USDA Forest Service (2013d) Forest Inventory and Analysis National Program, FIA library: Database Documentation.
U.S. Department of Agriculture, Forest Service, Washington Office. Available online at <
http://www.fia.fs.fed.us/library/database-documentation/>. Accessed 10 September 2013.
USDA (1991) State Soil Geographic (STATSGO) Data Base Data use information. Miscellaneous Publication Number
1492, National Soil Survey Center, Natural Resources Conservation Service, U.S. Department of Agriculture, Fort
Worth, TX.
USDA-ERS (1997) Cropping Practices Survey Data—1995. Economic Research Service, United States Department of
Agriculture. Available online at .
USDA-ERS (2011) Agricultural Resource Management Survey (ARMS) Farm Financial and Crop Production Practices:
Tailored Reports. Online at: http://ers.usda.gov/Data/ARMS/CropOverview.htm.
USDA-FSA (2012) Conservation Reserve Program Monthly Summary - September 2012. U.S. Department of
Agriculture, Farm Service Agency, Washington, DC, Available online at
http://www. fsa. usda. go v/Internet/F S A_File/crpstatO 912. pdf.
USDA-NRCS (2009) Summary Report: 2007 National Resources Inventory, Natural Resources Conservation Service,
Washington, DC, and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa,
http://www.nrcs.usda.gov/technical/NRI/2007/2007_NRI_Summary.pdf.
van Haaren, Rob, Thermelis, N, and Goldstein, N. (2010) "The State of Garbage in America." BioCycle, October 2010.
Volume 51, Number 10, pg. 16-23.
Vogelman, I.E., S.M. Howard, L. Yang, C. R. Larson, B. K. Wylie, and J. N. Van Driel (2001) "Completion of the 1990's
National Land Cover Data Set for the conterminous United States." Photogrammetric Engineering and Remote Sensing,
67:650-662.
Waddell, K. L., D. D. Oswald, and D. S. Powell. (1989) Forest statistics of the United States, 1987. Resource Bulletin
PNW-168. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Portland, OR.
Waddell, K., and B. Hiserote. (2005) The PNW-FIA Integrated Database User Guide: A database of forest inventory
information for California, Oregon, and Washington. Forest Inventory and Analysis Program, Pacific Northwest
Research Station, Portland, Oregon, USA.
Williams, S.A. (2006) Data compiled for the Consortium for Agricultural Soils Mitigation of Greenhouse Gases
(CASMGS) from an unpublished manuscript. Natural Resource Ecology Laboratory, Colorado State University.
Williams, S. and K. Paustian (2005) Developing Regional Cropping Histories for Century Model U.S.-level Simulations.
Colorado State University, Natural Resources Ecology Laboratory, Fort Collins, CO.
Woodall, C.W., and V.J. Monleon (2008) Sampling protocol, estimation, and analysis procedures for the down woody
materials indicator of the FIA program. Gen. Tech. Rep. NRS-22. Newtown Square, PA: U.S. Department of
Agriculture, Forest Service, Northern Research StationF. 68 p.
Woodall, C.W., Walters, B.F., Oswalt, S.N., Domke, G.M., Toney, C., Gray, A.N. 2013. Biomass and carbon attributes
of downed woody materials in forests of the United States. Forest Ecology and Management 305: 48-59.
Woodall, C.W., B.L. Conklmg, M.C. Amacher, J.W. Coulston, S. Jovan, C.H. Perry, B. Schulz, G.C. Smith, S. Will Wolf.
(2010).. The Forest Inventory and Analysis Database Version 4.0: Database Description and Users Manual for Phase 3.
Gen. Tech. Rep. NRS-61. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research
Station. 180 p.
Woodall, C.W., L.S. Heath, G.M. Domke, and M.C. Nichols. (2011) Methods and equations for estimating aboveground
volume, biomass, and carbon for trees in the U.S. forest inventory, 2010. Gen. Tech. Rep. NRS-88. Newtown Square,
PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 30 p.
Woodall, C.W., Perry, C.H., Westfall, J.A. 2012. An empirical assessment of forest floor carbon stock components
across the United States. Forest Ecology andManagement 269: 1-9.
Woudenberg, S.W. and T.O. Farrenkopf (1995) The Westwide forest inventory data base: user's manual. General
Technical Report INT-GTR-317. U.S. Department of Agriculture Forest Service, Intermountain Research Station.
Ogden, UT.
A-384 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
ANNEX 4 IPCC Reference Approach for Estimating
C02 Emissions from Fossil Fuel Combustion
It is possible to estimate carbon dioxide (CC>2) emissions from fossil fuel consumption using alternative
methodologies and different data sources than those described in the Estimating Emissions from Fossil Fuel Combustion
Annex. For example, the UNFCCC reporting guidelines request that countries, in addition to their "bottom-up" sectoral
methodology, complete a "top-down" Reference Approach for estimating CC>2 emissions from fossil fuel combustion.
Section 1.3 of the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reporting Instructions states,
"If a detailed, Sectoral Approach for energy has been used for the estimation of CC>2 from fuel combustion you are still
asked to complete...the Reference Approach... for verification purposes" (IPCC/UNEP/OECD/TEA 1997). This reference
method estimates fossil fuel consumption by adjusting national aggregate fuel production data for imports, exports, and
stock changes rather than relying on end-user consumption surveys. The basic principle is that once C-based fuels are
brought into a national economy, they are either saved in some way (e.g., stored in products, kept in fuel stocks, or left
unoxidized in ash) or combusted, and therefore the C in them is oxidized and released into the atmosphere. Accounting
for actual consumption of fuels at the sectoral or sub-national level is not required. The following discussion provides the
detailed calculations for estimating CC>2 emissions from fossil fuel combustion from the United States using the IPCC-
recommended Reference Approach.
Step 1: Collect and Assemble Data in Proper Format
To ensure the comparability of national inventories, the IPCC has recommended that countries report energy data
using the International Energy Agency (IEA) reporting convention. National energy statistics were collected in physical
units from several EIA documents in order to obtain the necessary data on production, imports, exports, and stock
changes.
It was necessary to make a number of modifications to these data to generate more accurate apparent
consumption estimates of these fuels. The first modification adjusts for consumption of fossil fuel feedstocks accounted
for in the Industrial Processes chapter, which include the following: unspecified coal for coal coke used in iron and steel
production; natural gas, distillate fuel, and coal used in iron and steel production; natural gas used for ammonia
production; petroleum coke used in the production of aluminum, ferroalloys, titanium dioxide, ammonia, and silicon
carbide; and other oil and residual fuel oil used in the manufacture of C black. The second modification adjusts for the
fact that EIA energy statistics include synthetic natural gas in coal and natural gas data. The third modification adjusts for
the inclusion of ethanol in motor gasoline statistics. Ethanol is a biofuel, and net carbon fluxes from changes in biogenic
carbon reservoirs in croplands are accounted for in the estimates for Land Use, Land-Use Change, and Forestry (see
Chapter 7). The fourth modification adjusts for consumption of bunker fuels, which refer to quantities of fuels used for
international transportation estimated separately from U.S. totals. The fifth modification consists of the addition of U.S.
territories data that are typically excluded from the national aggregate energy statistics. The territories include Puerto
Rico, U.S. Virgin Islands, Guam, American Samoa, Wake Island, and U.S. Pacific Islands. These data, as well as the
production, import, export, and stock change statistics, are presented in Table A- 265.
The C content of fuel varies with the fuel's heat content. Therefore, for an accurate estimation of CC>2 emissions,
fuel statistics were provided on an energy content basis (e.g., Btu or joules). Because detailed fuel production statistics are
typically provided in physical units (as in Table A- 265 for 2012), they were converted to units of energy before CC>2
emissions were calculated. Fuel statistics were converted to their energy equivalents by using conversion factors provided
by EIA. These factors and their data sources are displayed in Table A- 266. The resulting fuel type-specific energy data
for 2012 are provided in Table A- 267.
Step 2: Estimate Apparent Fuel Consumption
The next step of the IPCC Reference Approach is to estimate "apparent consumption" of fuels within the country.
This requires a balance of primary fuels produced, plus imports, minus exports, and adjusting for stock changes. In this
way, C enters an economy through energy production and imports (and decreases in fuel stocks) and is transferred out of
the country through exports (and increases in fuel stocks). Thus, apparent consumption of primary fuels (including crude
oil, natural gas liquids, anthracite, bituminous, subbituminous and lignite coal, and natural gas) can be calculated as
follows:
A-385
-------
Apparent Consumption = Production + Imports - Exports - Stock Change
Flows of secondary fuels (e.g., gasoline, residual fuel, coke) should be added to primary apparent consumption.
The production of secondary fuels, however, should be ignored in the calculations of apparent consumption since the C
contained in these fuels is already accounted for in the supply of primary fuels from which they were derived (e.g., the
estimate for apparent consumption of crude oil already contains the C from which gasoline would be refined). Flows of
secondary fuels should therefore be calculated as follows:
Secondary Consumption = Imports - Exports - Stock Change
Note that this calculation can result in negative numbers for apparent consumption of secondary fuels. This
result is perfectly acceptable since it merely indicates a net export or stock increase in the country of that fuel when
domestic production is not considered.
Next, the apparent consumption and secondary consumption need to be adjusted for feedstock uses of fuels
accounted for in the Industrial Processes chapter, international bunker fuels, and U.S. territory fuel consumption. Bunker
fuels and feedstocks accounted for in the Industrial Processes chapter are subtracted from these estimates, while fuel
consumption in U.S. territories is added.
The IPCC Reference Approach calls for estimating apparent fuel consumption before converting to a common
energy unit. However, certain primary fuels in the United States (e.g., natural gas and steam coal) have separate
conversion factors for production, imports, exports, and stock changes. In these cases, it is not appropriate to multiply
apparent consumption by a single conversion factor since each of its components has different heat contents. Therefore,
United States fuel statistics were converted to their heat equivalents before estimating apparent consumption. Results are
provided in Table A- 266.
Step 3: Estimate Carbon Emissions
Once apparent consumption is estimated, the remaining calculations are similar to those for the "bottom-up"
Sectoral Approach (see Estimating Emissions from Fossil Fuel Combustion Annex). Potential CC>2 emissions were
estimated using fuel-specific C coefficients (see Table A- 267).101 The C in products from non-energy uses of fossil fuels
(e.g., plastics or asphalt) was then estimated and subtracted (see Table A-269). This step differs from the Sectoral
Approach in that emissions from both fuel combustion and non-energy uses are accounted for in this approach. Finally, to
obtain actual CC>2 emissions, net emissions were adjusted for any C that remained unoxidized as a result of incomplete
combustion (e.g., C contained in ash or soot). The fraction oxidized was assumed to be 100 percent for petroleum, coal,
and natural gas based on guidance in IPCC (2006) (see Estimating Emissions from Fossil Fuel Combustion Annex).
Step 4: Convert to C02 Emissions
Because the IPCC reporting guidelines recommend that countries report greenhouse gas emissions on a full
molecular weight basis, the final step in estimating CC>2 emissions from fossil fuel consumption was converting from units
of C to units of CO2. Actual C emissions were multiplied by the molecular-to-atomic weight ratio of CC>2 to C (44/12) to
obtain total CC>2 emitted from fossil fuel combustion in teragrams (Tg). The results are contained in Table A- 268.
Comparison Between Sectoral and Reference Approaches
These two alternative approaches can both produce reliable estimates that are comparable within a few percent.
Note that the reference approach includes emissions from non-energy uses. Therefore, these totals should be compared to
the aggregation of fuel use and emission totals from Emissions of CC>2 from Fossil Fuel Combustion and Carbon Emitted
from Non-Energy Uses of Fossil Fuels Annexes. These two sections together are henceforth referred to as the Sectoral
Approach. Other than this distinction, the major difference between methodologies employed by each approach lies in the
energy data used to derive C emissions (i.e., the actual surveyed consumption for the Sectoral Approach versus apparent
consumption derived for the Reference Approach). In theory, both approaches should yield identical results. In practice,
however, slight discrepancies occur. An examination of past CRF table submissions during UNFCCC reviews has
highlighted the need to further investigate these discrepancies. The investigation found that the most recent (two to three)
inventory years tend to have larger differences in consumption and emissions estimates occurring earlier in the time series.
This is a result of annual energy consumption data revisions in the EIA energy statistics, and the revisions have the
Carbon coefficients from EIA were used wherever possible. Because EIA did not provide coefficients for coal, the IPCC-
recommended emission factors were used in the top-down calculations for these fuels. See notes in Table A- 268 for more specific
source information.
A-386 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
greatest impact on the most recent few years of inventory estimates. As a result, the differences between the sectoral and
reference approach decrease and are resolved over time. For the United States, these differences are discussed below.
Differences in Total Amount of Energy Consumed
Table A-271 summarizes the differences between the Reference and Sectoral approaches in estimating total
energy consumption in the United States. Although theoretically the two methods should arrive at the same estimate for
U.S. energy consumption, the Reference Approach provides an energy consumption total that is 1.1 percent lower than the
Sectoral Approach for 2012. The greatest differences lie in lower estimates for petroleum and coal consumption for the
Reference Approach (1.9 percent and 1.6 percent, respectively) and higher estimates for natural gas consumption for the
Reference Approach (0.4 percent).
There are several potential sources for the discrepancies in consumption estimates:
• Product Definitions. The fuel categories in the Reference Approach are different from those used in the
Sectoral Approach, particularly for petroleum. For example, the Reference Approach estimates apparent
consumption for crude oil. Crude oil is not typically consumed directly, but refined into other products. As
a result, the United States does not focus on estimating the energy content of the various grades of crude oil,
but rather estimating the energy content of the various products resulting from crude oil refining. The
United States does not believe that estimating apparent consumption for crude oil, and the resulting energy
content of the crude oil, is the most reliable method for the United States to estimate its energy consumption.
Other differences in product definitions include using sector-specific coal statistics in the Sectoral Approach
(i.e., residential, commercial, industrial coking, industrial other, and transportation coal), while the
Reference Approach characterizes coal by rank (i.e. anthracite, bituminous, etc.). Also, the liquefied
petroleum gas (LPG) statistics used in the bottom-up calculations are actually a composite category
composed of natural gas liquids (NGL) and LPG.
• Heat Equivalents. It can be difficult to obtain heat equivalents for certain fuel types, particularly for
categories such as "crude oil" where the key statistics are derived from thousands of producers in the United
States and abroad.
• Possible inconsistencies in U.S. Energy Data. The United States has not focused its energy data collection
efforts on obtaining the type of aggregated information used in the Reference Approach. Rather, the United
States believes that its emphasis on collection of detailed energy consumption data is a more accurate
methodology for the United States to obtain reliable energy data. Therefore, top-down statistics used in the
Reference Approach may not be as accurately collected as bottom-up statistics applied to the Sectoral
Approach.
• Balancing Item. The Reference Approach uses apparent consumption estimates while the Sectoral Approach
uses reported consumption estimates. While these numbers should be equal, there always seems to be a
slight difference that is often accounted for in energy statistics as a "balancing item."
Differences in Estimated C02 Emissions
Given these differences in energy consumption data, the next step for each methodology involved estimating
emissions of CC>2. Table A-272 summarizes the differences between the two methods in estimated C emissions.
As mentioned above, for 2012, the Reference Approach resulted in a 1.1 percent lower estimate of energy
consumption in the United States than the Sectoral Approach. The resulting emissions estimate for the Reference
Approach was 0.6 percent lower. Estimates of natural gas emissions from the Reference Approach are higher (0.4
percent), and coal and petroleum emission estimates are lower (1.7 percent and 0.4 percent, respectively) than the Sectoral
Approach. Potential reasons for these differences may include:
• Product Definitions. Coal data is aggregated differently in each methodology, as noted above. The format
used for the Sectoral Approach likely results in more accurate estimates than in the Reference Approach.
Also, the Reference Approach relies on a "crude oil" category for determining petroleum-related emissions.
Given the many sources of crude oil in the United States, it is not an easy matter to track potential
differences in C content between many different sources of crude; particularly since information on the C
content of crude oil is not regularly collected.
• Carbon Coefficients. The Reference Approach relies on several default C coefficients by rank provided by
IPCC (IPCC/UNEP/OECD/iEA 1997), while the Sectoral Approach uses annually updated category-specific
coefficients by sector that are likely to be more accurate. Also, as noted above, the C coefficient for crude
oil is more uncertain than that for specific secondary petroleum products, given the many sources and grades
of crude oil consumed in the United States.
A-387
-------
Although the two approaches produce similar results, the United States believes that the "bottom-up" Sectoral
Approach provides a more accurate assessment of CC>2 emissions at the fuel level. This improvement in accuracy is
largely a result of the data collection techniques used in the United States, where there has been more emphasis on
obtaining the detailed products-based information used in the Sectoral Approach than obtaining the aggregated energy
flow data used in the Reference Approach. The United States believes that it is valuable to understand both methods.
References
EIA (2014) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, February 2014, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2013/12).
EIA (1995-2013) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington,
DC, Volume I. DOE/EIA-0340.
EIA (1992) Coal and lignite production. EIA State Energy Data Report 1992, Energy Information Administration, U.S.
Department of Energy, Washington, DC.
EPA (2010). Carbon Content Coefficients Developed for EPA's Mandatory Reporting Rule. Office of Air and Radiation,
Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
IPCC (2006), 2006IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas
Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T., and Tanabe K. (eds.). Published: IGES,
Japan.
IPCCAJNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, Paris:
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency.
SAIC (2004) Analysis prepared by Science Applications International Corporation for EPA, Office of Air and
Radiation, Market Policies Branch.
USGS (1998) CoalQualDatabase Version 2.0, U.S. Geological Survey.
A-388 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 265:2012 U.S. Energy Statistics [Physical Units!
Fuel Category (Units)
Solid Fuels (Thousand Short Tons)
Gas Fuels (Million Cubic Feet)
Liquid Fuels (Thousand Barrels)
Fuel Type
Anthracite Coal
Bituminous Coal
Sub-bituminous Coal
Lignite
Coke
Unspecified Coal
Natural Gas
Crude Oil
Nat Gas Liquids and LRGs
Other Liquids
Motor Gasoline
Aviation Gasoline
Kerosene
Jet Fuel
Distillate Fuel
Residual Fuel
Naphtha for petrochemical feedstocks
Petroleum Coke
Other Oil for petrochemical feedstocks
Special Naphthas
Lubricants
Waxes
Asphalt/Road Oil
Still Gas
Misc. Products
Production
2,133
465,091
474,378
75,219
24,023,327
2,374,021
881,306
38,449
Imports
[a]
[a]
[a]
[a]
1,418
9,159
3,137,789
3,120,755
62,192
459,916
16,147
29
464
19,993
46,179
93,672
11,588
4,419
14,504
4,496
11,391
1,837
11,196
74
Exports
[a]
[a]
[a]
[a]
974
125,746
1,618,828
24,693
115,054
78,359
149,657
1,994
48,458
368,633
142,167
184,167
17,847
27,326
1,982
11,152
1,474
Stock
Change
[a]
[a]
[a]
[a]
38
7,980
8,840
34,134
23,892
18,575
(5,420)
(88)
(690)
(1,863)
(14,403)
(238)
148
750
457
167
(253)
52
2,486
(17)
Adjustment
367
4,605
2,678
296,664
458
10,000
7,894
5,890
U.S.
Bunkers Territories
1,653
26,486
3,107
46,419
1,273
157,243 9,939
69,784 22,821
48 26,048
172
21,135
[a] Included in Unspecified Coal
Data Sources: Solid and Gas Fuels: EIA (2014);
Liquid Fuels: EIA (1995-2013).
A-389
-------
Table A- 266: Conversion Factors to Energy Units (Heat Equivalents]
Fuel Category (Units)
Solid Fuels (Million Btu/Short Ton)
Natural Gas (BTU/Cubic Foot)
Liquid Fuels (Million Btu/Barrel)
Fuel Type
Anthracite Coal
Bituminous Coal
Sub-bituminous Coal
Lignite
Coke
Unspecified
Crude Oil
Nat Gas Liquids and LRGs
Other Liquids
Motor Gasoline
Aviation Gasoline
Kerosene
Jet Fuel
Distillate Fuel
Residual Oil
Naphtha for petrochemical feedstocks
Petroleum Coke
Other Oil for petrochemical feedstocks
Special Naphthas
Lubricants
Waxes
Asphalt/Road Oil
Still Gas
Misc. Products
Production
22.57
23.89
17.14
12.87
1,022
5.80
3.68
5.83
5.22
Imports
23.13
25.00
1,025
6.17
3.68
5.83
5.22
5.05
5.67
5.67
5.83
6.29
5.25
6.02
5.83
5.25
6.07
5.54
6.64
6.00
5.80
Exports
24.55
25.97
1,009
5.58
3.68
5.83
5.22
5.05
5.67
5.67
5.83
6.29
5.25
6.02
5.83
5.25
6.07
5.54
6.64
6.00
5.80
Stock
Change
23.13
20.86
1,024
5.58
3.68
5.83
5.22
5.05
5.67
5.67
5.83
6.29
5.25
6.02
5.83
5.25
6.07
5.54
6.64
6.00
5.80
Adjustment
28.16
12.87
183.13
1,024
5.22
5.83
6.29
6.02
5.83
Bunkers
5.58
3.68
5.83
5.22
5.05
5.67
5.83
5.83
6.29
5.25
6.02
5.83
5.25
6.07
5.54
6.64
6.00
5.80
U.S.
Territories
25.14
1,024
5.58
3.68
5.83
5.22
5.05
5.67
5.67
5.83
6.29
5.25
6.02
5.83
5.25
6.07
5.54
6.64
6.00
5.80
Data Sources: Coal and lignite production: EIA (1992); Unspecified Solid Fuels, Coke, Natural Gas and Petroleum Products: EIA (2014).
A-390 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A- 267:2012 Apparent Consumption of Fossil Fuels (TBtu)
Fuel Category
Solid Fuels
Gas Fuels
Liquid Fuels
Total
Fuel Type
Anthracite Coal
Bituminous Coal
Sub-bituminous Coal
Lignite
Coke
Unspecified
Natural Gas
Crude Oil
Nat Gas Liquids and LRGs
Other Liquids
Motor Gasoline
Aviation Gasoline
Kerosene
Jet Fuel
Distillate Fuel
Residual Oil
Naphtha for petrochemical feedstocks
Petroleum Coke
Other Oil for petrochemical feedstocks
Special Naphthas
Lubricants
Waxes
Asphalt/Road Oil
Still Gas
Misc. Products
Production Imports
48.2
11,111.0
8,130.8
967.8
32.8
229.0
24,599.9 3,216.2
13,769.3 19,239.5
3,245.8 229.1
2,679.0
200.7 84.3
0.1
2.6
113.4
269.0
588.9
60.8
26.6
84.5
23.6
69.1
10.2
74.3
0.4
62,073.5 27,033.3
Exports Stock Change Adjustment Bunkers
23.9
3,265.9
1,633.4
137.9
423.7
456.4
781.1
11.3
274.8
2,147.3
893.8
1,109.4
93.7
165.7
11.0
74.0
8.5
11,511.8
10.3
59.2
0.9
166.5 490.4
9.1 303.7
190.6
88.0
108.2
(28.3)
(0.4)
(3.9)
(10.6) 916.3
(83.9) 2.7 406.5
(1.5) 62.9 0.3
0.8
4.5 47.6
2.7 34.3
0.9
(1.5)
0.3
16.5
(0.1)
458.6 1,011.1 1,323.0
U.S.
Territories
41.6
27.1
11.4
242.3
7.2
56.4
132.9
163.8
1.0
122.5
806.2
Apparent
Consumption
48.2
11,111.0
8,120.5
908.5
8.0
(3,652.2)
25,897.1
32,680.3
2,974.6
2,114.4
(225.6)
0.6
2.5
(1,010.7)
(2,070.6)
(202.8)
60.0
(1,134.9)
47.5
(70.9)
(94.1)
(1.1)
(16.2)
114.5
75,608.6
Note: Totals may not sum due to independent rounding.
A-391
-------
Table A-268:2012 Potential C02 Emissions
Fuel Category
Solid Fuels
Gas Fuels
Liquid Fuels
Total
Fuel Type
Anthracite Coal
Bituminous Coal
Sub-bituminous Coal
Lignite
Coke
Unspecified
Natural Gas
Crude Oil
Nat Gas Liquids and LRGs
Other Liquids
Motor Gasoline
Aviation Gasoline
Kerosene
Jet Fuel
Distillate Fuel
Residual Oil
Naphtha for petrochemical feedstocks
Petroleum Coke
Other Oil for petrochemical feedstocks
Special Naphthas
Lubricants
Waxes
Asphalt/Road Oil
Still Gas
Misc. Products
Apparent Consumption (QBtu)
0.05
11.11
8.12
0.91
0.01
(3.65)
25.90
32.68
2.97
2.11
(0.23)
0.00
0.00
(1.01)
(2.07)
(0.20)
0.06
(1.13)
0.05
(0.07)
(0.09)
(0.00)
(0.02)
0.11
Carbon Coefficients
(Tg Carbon/QBtu)
28.28
25.44
26.50
26.65
31.00
25.34
14.46
20.31
16.89
20.31
19.46
18.86
19.96
19.70
20.17
20.48
18.55
27.85
20.17
19.74
20.20
19.80
20.55
18.20
20.31
Potential Emissions
(Tg C02 Eq.)
5.0
1,036.5
789.1
88.8
0.9
(339.3)
1,372.6
2,433.2
184.2
157.4
(16.1)
0.0
0.2
(73.0)
(153.1)
(15.2)
4.1
(115.9)
3.5
(5.1)
(7.0)
(0.1)
(1.2)
8.5
5,358.0
Data Sources: C content coefficients by coal rank from USGS (1998) and SAIC (2004); Unspecified Solid Fuels, EIA (2014), Natural Gas and Liquid Fuels: EPA (2010).
Note: Totals may not sum due to independent rounding.
A-392 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Table A-269:2012 Non-Energy Carbon Stored in Products
Fuel Type
Coal
Natural Gas
Asphalt & Road Oil
LPG
Lubricants
Pentanes Plus
Petrochemical Feedstocks
Petroleum Coke
Special Naphtha
Waxes/Misc.
Misc. U.S. Territories Petroleum
Total
Consumption Carbon Carbon
for Non-Energy Coefficients Content Fraction Carbon Stored
Use (TBtu) (Tg Carbon/QBtu) (Tg Carbon) Sequestered (Tg C02 Eq.)
122.4
293.2
826.7
1,903.0
254.8
43.8
[a]
0.0
14.1
[a]
[a]
31.00
14.46
20.55
17.06
20.20
19.10
[a]
27.85
19.74
[a]
[a]
3.79
4.24
16.99
32.47
5.15
0.84
[a]
0.00
0.28
[a]
[a]
0.10
0.70
1.00
0.70
0.09
0.70
[a]
0.30
0.70
[a]
[a]
2.1
10.9
62.0
83.2
1.7
2.1
40.2
0.0
0.7
1.3
0.9
205.2
[a] Values for Misc. U.S. Territories Petroleum, Petrochemical Feedstocks and Waxes/Misc.
numerous smaller components.
Note: Totals may not sum due to independent rounding.
are not shown because these categories are aggregates of
Table A-270:2012 Reference Approach Clh Emissions from Fossil Fuel Consumption [Tg C0? Eq. unless otherwise noted!
Potential Carbon Net Fraction Total
Fuel Category Emissions Sequestered Emissions Oxidized Emissions
Coal
Petroleum
Natural Gas
1,581.0
2,404.4
1,372.6
2.1
192.2
10.9
1,578.9
2,212.1
1,316.7
100.0%
100.0%
100.0%
1,578.9
2,212.1
1,361.7
Total
5,358.0
205.2
5,152.8
5,152.8
Note: Totals may not sum due to independent rounding.
A-393
-------
Table A-271: Fuel Consumption in the United States by Estimating Approach tTBtul
Approach 1990 1995 1996
Sectoral 69,786 74,929 77,450
Coal 18,072 19,187 20,068
Natural Gas 19,184 22,170 22,589
Petroleum 32,530 33,573 34,793
Reference (Apparent) 68,928 74,190 76,549
Coal 17,573 18,567 19,425
Natural Gas 19,276 22,274 22,696
Petroleum 32,080 33,349 34,427
Difference -1.2% -1.0% -1.2%
Coal -2.8% -3.2% -3.2%
Natural Gas 0.5% 0.5% 0.5%
Petroleum -1.4% -0.7% -1.1%
1997 1998
78,465 78,963
20,529 20,823
22,723 22,323
35,213 35,817
78,062 78,203
20,105 19,981
22,828 22,403
35,129 35,819
-0.5% -1.0%
-2.1% -4.0%
0.5% 0.4%
-0.2% 0.0%
1999 2000
80,161 82,542
20,830 21,748
22,366 23,392
36,965 37,402
79,250 81,617
20,030 20,957
22,458 23,484
36,761 37,176
-1.1% -1.1%
-3.8% -3.6%
0.4% 0.4%
-0.6% -0.6%
2001 2002
81,118 81,919
21,121 21,192
22,466 23,163
37,531 37,564
80,762 81,526
20,710 20,797
22,535 23,238
37,517 37,491
-0.4% -0.5%
-1.9% -1.9%
0.3% 0.3%
0.0% -0.2%
2003 2004
82,339 84,031
21,625 21,893
22,561 22,623
38,152 39,515
81,892 83,700
21,081 21,735
22,630 22,690
38,181 39,275
-0.5% -0.4%
-2.5% -0.7%
0.3% 0.3%
0.1% -0.6%
2005
83,995
22,187
22,282
39,526
83,626
21,986
22,349
39,291
-0.4%
-0.9%
0.3%
-0.6%
2006
82,854
21,834
21,960
39,060
82,229
21,534
22,029
38,666
-0.8%
-1.4%
0.3%
-1.0%
2007
84,206
22,067
23,371
38,768
84,013
21,577
23,441
38,995
-0.2%
-2.2%
0.3%
0.6%
2008
81,607
21,753
23,594
36,260
80,450
21,391
23,666
35,394
-1.4%
-1.7%
0.3%
-2.4%
2009
76,891
19,231
23,193
34,467
76,623
19,243
23,277
34,103
-0.3%
0.1%
0.4%
-1.1%
2010
79,649
20,267
24,312
35,070
78,103
19,582
24,409
34,112
-1.9%
-3.4%
0.4%
-2.7%
2011 2012
78,227 76,431
19,071 16,819
24,679 25,805
34,477 33,806
76,904 75,609
18,914 16,544
24,778 25,897
33,212 33,167
-1.7% -1.1%
-0.8% -1.6%
0.4% 0.4%
-3.7% -1.9%
* Includes U.S. territories. Does not include international bunker fuels.
Note: Totals may not sum due to independent rounding.
Table A-272: G02 Emissions from Fossil Fuel Combustion by Estimating Approach ITg Clh Eq.)
Approach 1990 1995 1996
Sectoral 4,866 5,172 5,360
Coal 1,719 1,823 1,907
Natural Gas 1,007 1,164 1,185
Petroleum 2,140 2,184 2,268
Reference (Apparent) 4,811 5,149 5,324
Coal 1,654 1,756 1,836
Natural Gas 1,013 1,171 1,192
Petroleum 2,144 2,222 2,296
Difference -1.1% -0.4% -0.7%
Coal -3.8% -3.7% -3.7%
Natural Gas 0.6% 0.6% 0.6%
Petroleum 0.2% 1.7% 1.2%
1997 1998
5,432 5,485
1,950 1,980
1,192 1,169
2,290 2,336
5,440 5,456
1,901 1,894
1,199 1,174
2,340 2,388
0.2% -0.5%
-2.5% -4.3%
0.6% 0.4%
2.2% 2.2%
1999 2000
5,559 5,736
1,983 2,071
1,173 1,228
2,403 2,437
5,516 5,693
1,902 1,988
1,179 1,233
2,435 2,472
-0.8% -0.7%
-4.1% -4.0%
0.5% 0.5%
1.3% 1.4%
2001 2002
5,657 5,697
2,011 2,022
1,178 1,215
2,467 2,460
5,662 5,705
1,967 1,976
1,182 1,220
2,513 2,509
0.1% 0.1%
-2.2% -2.3%
0.3% 0.4%
1.8% 2.0%
2003 2004
5,751 5,864
2,066 2,093
1,183 1,189
2,502 2,582
5,752 5,893
2,002 2,065
1,188 1,194
2,562 2,634
0.0% 0.5%
-3.1% -1.3%
0.4% 0.4%
2.4% 2.0%
2005
5,893
2,121
1,172
2,600
5,903
2,087
1,176
2,640
0.2%
-1.6%
0.3%
1.5%
2006
5,812
2,083
1,157
2,572
5,799
2,049
1,161
2,589
-0.2%
-1.7%
0.3%
0.7%
2007
5,899
2,106
1,231
2,563
5,900
2,053
1,235
2,613
0.0%
-2.5%
0.3%
2.0%
2008
5,721
2,076
1,243
2,402
5,660
2,036
1,247
2,377
-1.1%
-1.9%
0.3%
-1.1%
2009
5,333
1,835
1,221
2,277
5,348
1,832
1,226
2,290
0.3%
-0.2%
0.4%
0.6%
2010
5,525
1,935
1,278
2,313
5,431
1,865
1,283
2,283
-1.7%
-3.6%
0.5%
-1.3%
2011 2012
5,388 5,182
1,820 1,606
1,297 1,356
2,271 2,221
5,320 5,153
1,803 1,579
1,303 1,362
2,214 2,212
-1.3% -0.6%
-1.0% -1.7%
0.5% 0.4%
-2.5% -0.4%
* Includes U.S. territories. Does not include international bunker fuels.
Note: Totals may not sum due to independent rounding.
A-394 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2011
-------
ANNEX 5 Assessment of the Sources and Sinks of
Greenhouse Gas Emissions Not Included
Although this report is intended to be a comprehensive assessment of anthropogenic102 sources and sinks of
greenhouse gas emissions for the United States, certain sources have been identified but not included in the estimates
presented for various reasons. Before discussing these sources, however, it is important to note that processes or activities
that are not anthropogenic in origin or do not result in a net source or sink of greenhouse gas emissions are intentionally
excluded from a national inventory of anthropogenic greenhouse gas emissions, in line with guidance from the IPCC in
their guidelines for national inventories.
Given a source category that is both anthropogenic and results in net greenhouse gas emissions, reasons for not
including a source related to an anthropogenic activity include one or more of the following:
• Though an estimating method has been developed, data were not adequately available to calculate
emissions.
• Emissions were implicitly accounted for within another source category (e.g., CC>2 from Fossil Fuel
Combustion).
It is also important to note that the United States believes that the sources discussed below are very low in
comparison with the overall estimate of total U.S. greenhouse gas emissions, and not including them introduces a very
minor bias. In general, the emission sources described in this annex are for source categories with methodologies
introduced in the 2006 IPCC Guidelines for which data collection has not been sufficient to pursue an initial estimation of
greenhouse gases.
N20 from Caprolactam Production
Caprolactam is a widely used chemical intermediate, primarily to produce nylon-6. All processes for producing
caprolactam involve the catalytic oxidation of ammonia, with N2O being produced as a byproduct. More research is
required to determine this source's significance because there is currently insufficient information available on
caprolactam production to estimate emissions in the United States.
C02 from Calcium Carbide Production
CC>2 is formed by the oxidation of petroleum coke in the production of calcium carbide. These CC>2 emissions
are implicitly accounted for in the storage factor calculation for the non-energy use of petroleum coke in the Energy
chapter. There is currently not sufficient data on coke consumption for calcium carbide production to estimate emissions
from this source.
C02 from Graphite Consumption in Ferroalloy and Steel Production
Emissions from "graphite," "wood," or "biomass" in calculating CC>2 emissions from ferroalloy production, iron
and steel production or other "Industrial Processes" included in Chapter 4 of the inventory are not explicitly calculated. It
is assumed that 100 percent of the C used in ferroalloy production is derived from petroleum coke and that all of the C
used in iron and steel production is derived from coal coke or petroleum coke. It is also assumed that all of the C used in
lead and zinc production is derived from coal coke. It is possible that some non-coke C is used in the production of
ferroalloys, lead, zinc, and iron and steel, but no data are available to conduct inventory calculations for sources of C other
than petroleum coke and coal coke used in these processes.
Non-fuel uses of coal coke and petroleum coke are accounted for in the Industrial Process chapter, either directly
for iron and steel, aluminum, ferroalloy, lead, zinc, and titanium dioxide production, or indirectly by applying a storage
factor to "uncharacterized" non-fuel uses of petroleum coke and coal coke. Non-fuel uses of wood and biomass are not
102 jjje term "anthropogenic," in this context, refers to greenhouse gas emissions and removals that are a direct result of human activities
or are the result of natural processes that have been affected by human activities ("2006 IPCC Guidelines for National Greenhouse Gas
Inventories").
A-395
-------
accounted for in the Energy or Industrial Process chapters, as all uses of wood and biomass are accounted for in the Land
Use, Land-Use Change, and Forestry chapter. It is assumed for the purposes of the CC>2 emission calculation that no wood
or other biogenic C is used in any of these industrial processes. Some biogenic C may be used in these industrial
processes but sufficient data to estimate emissions are not available.
Consumption of either natural or synthetic graphite is not explicitly accounted for in the Industrial Process
chapter. It is assumed that all of the C used in manufacturing C anodes for production of aluminum, ferroalloys, and
electric arc furnace (EAF) steel are derived directly from petroleum coke and coal tar pitch (a coal coke byproduct), not
from natural graphite or synthetic graphite sources. Some amount of C used in these industrial processes may be derived
from natural or synthetic graphite sources, but sufficient data to estimate emissions are not currently available.
Miscellaneous SFe Uses
Sulfur hexafluoride (SFe) is used in several applications for which estimates have not been provided in this
inventory. Sulfur hexafluoride may be emitted from the production, leakage, and dismantling of radar, tracer, and night
vision equipment. Emissions from this source are believed to be minor, and no data were available for estimating the
emissions. Sulfur hexafluoride may be used in foam insulation, for dry etching, in laser systems, for indoor air quality
testing, for laboratory hood testing, for chromatography, in tandem accelerators, in loudspeakers, in shock absorbers, and
for certain biomedical applications. Emissions from this source are believed to be minor, and no data were available for
estimating the emissions. Sulfur hexafluoride may be emitted from the production, breakage, or leakage of soundproof
double-glazed windows. Emissions from this source are believed to be minor, and no data were available for estimating
the emissions. Sulfur hexafluoride may be emitted from applications involving the production of sport shoes, tires, and
tennis balls. Emissions from this source are believed to be minor, and no data were available for estimating the emissions.
Sulfur hexafluoride may be emitted from applications involving tracer gasses to detect leakage from pressure vessels and
as a tracer gas in the open air. Emissions from this source are believed to be minor, and no data were available for
estimating the emissions.
C02 from Non-Hazardous Industrial Waste Incineration and Medical Waste Incineration
Waste incineration is incorporated in two sections of the energy chapter of the inventory: in the section on CC>2
emissions from waste incineration, and in the calculation of emissions and storage from non-energy uses of fossil fuels.
The former section addresses fossil-derived materials (such as plastics) that are discarded as part of the municipal
wastestream and combusted (generally for energy recovery). The latter addresses two types of combustion: hazardous
waste incineration of organic materials (assumed to be fossil-derived), in which regulated wastes are burned without
energy recovery, and burning of fossil-derived materials for energy recovery. There additional categories of waste
incineration that is not included in our calculus: industrial non-hazardous waste and medical waste incineration. Data are
not readily available for these sources. Further research is needed to estimate the magnitude of CC>2 emissions, though they
are believed to be very low in comparison with the overall emissions of waste incineration sources that are covered.
A-396 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2011
-------
ANNEX 6 Additional Information
6.1. Global Warming Potential Values
Global Warming Potential (GWP) is intended as a quantified measure of the globally averaged relative radiative
forcing impacts of a particular greenhouse gas. It is defined as the cumulative radiative forcing — both direct and indirect
effects — integrated over a period of time from the emission of a unit mass of gas relative to some reference gas (IPCC
1996). Carbon dioxide (CCh) was chosen as this reference gas. Direct effects occur when the gas itself is a greenhouse
gas. Indirect radiative forcing occurs when chemical transformations involving the original gas produce a gas or gases that
are greenhouse gases, or when a gas influences other radiatively important processes such as the atmospheric lifetimes of
other gases. The relationship between gigagrams (Gg) of a gas and teragrams of CC>2 equivalents (Tg CC>2 Eq.) can be
expressed as follows:
( T
TgCO,Eq. = (Ggofgas)x(GWP)
2 V \ & &- ; \ ,
1,000 Gg
where,
Tg CC>2 Eq. = Teragrams of Carbon Dioxide Equivalents
Gg = Gigagrams (equivalent to a thousand metric tons)
GWP = Global Warming Potential
Tg = Teragrams
GWP values allow policy makers to compare the impacts of emissions and reductions of different gases.
According to the IPCC, GWP values typically have an uncertainty of +35 percent, though some GWP values have larger
uncertainty than others, especially those in which lifetimes have not yet been ascertained. In the following decision, the
parties to the UNFCCC have agreed to use consistent GWP values from the IPCC Second Assessment Report (SAR),
based upon a 100 year time horizon, although other time horizon values are available (see Table A-273).
In addition to communicating emissions in units of mass, Parties may choose also to use global
warming potentials (GWPs) to reflect their inventories and projections in carbon dioxide-equivalent terms, using
information provided by the Intergovernmental Panel on Climate Change (IPCC) in its Second Assessment
Report. Any use of GWPs should be based on the effects of the greenhouse gases over a 100-year time horizon.
In addition, Parties may also use other time horizons. 103
Greenhouse gases with relatively long atmospheric lifetimes (e.g., CC>2, CH/i, N2(D, HFCs, PFCs, and SFe) tend to
be evenly distributed throughout the atmosphere, and consequently global average concentrations can be determined.
However, the short-lived gases such as water vapor, carbon monoxide, tropospheric ozone, other indirect greenhouse gases
(e.g., NOX and NMVOCs), and tropospheric aerosols (e.g., SC>2 products and black carbon) vary spatially, and
consequently it is difficult to quantify their global radiative forcing impacts. GWP values are generally not attributed to
these gases that are short-lived and spatially inhomogeneous in the atmosphere.
Table A-273: Global Warming Potentials 1GWP1 and Atmospheric Lifetimes [Years! of Gases Used in this Report
Gas _ Atmospheric Lifetime 100-year GWPa _ 20-year GWP _ 500-year GWP
Carbon dioxide (C02)
Methane (CH4)b
Nitrous oxide (N20)
HFC-23
HFC-32
See footnote0
12+3
120
264
5.6
1
21
310
11,700
650
1
56
280
9,100
2,100
1
6.5
170
9,800
200
103 Framework Convention on Climate Change; FCCC/CP/1996/15/Add.l; 29 October 1996; Report of the Conference of the Parties at
its second session; held at Geneva from 8 to 19 July 1996; Addendum; Part Two: Action taken by the Conference of the Parties at its
second session; Decision 9/CP.2; Communications from Parties included in Annex I to the Convention: guidelines, schedule and process
for consideration; Annex: Revised Guidelines for the Preparation of National Communications by Parties Included in Annex I to the
Convention; p. 18. FCCC (1996)
A-397
-------
HFC-125
HFC-134a
HFC-143a
HFC-152a
HFC-227ea
HFC-236fa
HFC-43-10mee
CF4
C2F6
C3F8
C4Fio
C-C4F8
CsFi2
C6Fi4
SF6
32.6
14.6
48.3
1.5
36.5
209
17.1
50,000
10,000
2,600
2,600
3,200
4,100
3,200
3,200
2,800
1,300
3,800
140
2,900
6,300
1,300
6,500
9,200
7,000
7,000
8,700
7,500
7,400
23,900
4,600
3,400
5,000
460
4,300
5,100
3,000
4,400
6,200
4,800
4,800
6,000
5,100
5,000
16,300
920
420
1,400
42
950
4,700
400
10,000
14,000
10,100
10,100
12,700
11,000
10,700
34,900
Source: IPCC(1996)
a GWP values used in this report are calculated over 100 year time horizon.
b The methane GWP includes the direct effects and those indirect effects due to the production of tropospheric ozone and stratospheric water vapor. The indirect
effect due to the production of C02 is not included.
c For a given amount of carbon dioxide emitted, some fraction of the atmospheric increase in concentration is quickly absorbed by the oceans and terrestrial
vegetation, some fraction of the atmospheric increase will only slowly decrease over a number of years, and a small portion of the increase will remain for many
centuries or more.
Table A-274 presents direct and net (i.e., direct and indirect) GWP values for ozone-depleting substances
(ODSs). Ozone-depleting substances directly absorb infrared radiation and contribute to positive radiative forcing;
however, their effect as ozone-depleters also leads to a negative radiative forcing because ozone itself is a potent
greenhouse gas. There is considerable uncertainty regarding this indirect effect; therefore, a range of net GWP values is
provided for ozone depleting substances. The IPCC Guidelines and the UNFCCC do not include reporting instructions for
estimating emissions of ODSs because their use is being phased-out under the Montreal Protocol (see note below Table A-
274). The effects of these compounds on radiative forcing are not addressed in this report
Table A-274: Net 100-year Global Warming Potentials for Select Ozone Depleting Substances
Gas
CFC-11
CFC-12
CFC-11 3
HCFC-22
HCFC-123
HCFC-124
HCFC-141b
HCFC-142b
CHCb
ecu
CHsBr
Halon-1211
Halon-1301
Direct
4,600
10,600
6,000
1,700
120
620
700
2,400
140
1,800
5
1,300
6,900
Netmin
(600)
7,300
2,200
1,400
20
480
(5)
1,900
(560)
(3,900)
(2,600)
(24,000)
(76,000)
Netmax
3,600
9,900
5,200
1,700
100
590
570
2,300
0
660
(500)
(3,600)
(9,300)
Source: IPCC (2001)
Parentheses indicate negative values.
Note: Because these compounds have been shown to deplete stratospheric ozone, they are typically referred to as ozone depleting substances (ODSs).
However, they are also potent greenhouse gases. Recognizing the harmful effects of these compounds on the ozone layer, in 1987 many governments signed
the Montreal Protocol on Substances that Deplete the Ozone Layer to limit the production and importation of a number of CFCs and other halogenated
compounds. The United States furthered its commitment to phase-out ODSs by signing and ratifying the Copenhagen Amendments to the Montreal Protocol in
1992. Under these amendments, the United States committed to ending the production and importation of halons by 1994, and CFCs by 1996.
The IPCC has published its Fifth Assessment Report (AR5), providing the most current and comprehensive
scientific assessment of climate change (IPCC 2013). Within this report, the GWP values of several gases were revised
relative to the SAR, the IPCC's Third Assessment Report (TAR) (IPCC 2001), and the IPCC's Fourth Assessment Report
(AR4) (IPCC 2007). Thus the GWP values used in this report have been updated three times by the IPCC; although the
SAR GWP values are used throughout this report, it is informative to review the changes to the GWP values and the
impact such improved understanding has on the total GWP-weighted emissions of the United States. All GWP values use
CO2 as a reference gas; a change in the radiative efficiency of CO2 thus impacts the GWP of all other greenhouse gases.
Since the SAR and TAR, the IPCC has applied an improved calculation of CO2 radiative forcing and an improved CO2
A-398 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2011
-------
response function. The GWP values are drawn from IPCC/TEAP (2005) and the TAR, with updates for those cases where
new laboratory or radiative transfer results have been published. Additionally, the atmospheric lifetimes of some gases
have been recalculated, and updated background concentrations were used. Because the revised radiative forcing of CC>2
is about 8 percent lower than that in the TAR, Table A- 275 shows how the GWP values of the other gases relative to CC>2
tend to be larger, taking into account revisions in lifetimes. Comparisons of GWP values are based on the 100-year time
horizon common to UNFCCC reporting. However, there were some instances in which other variables, such as the
radiative efficiency or the chemical lifetime, were altered that resulted in further increases or decreases in particular GWP
values. In addition, the values for radiative forcing and lifetimes have been calculated for a variety of halocarbons, which
were not presented in the SAR. Updates in some well-mixed HFC compounds (including HFC-23, HFC-32, HFC-134a,
and HFC-227ea) for AR4 result from investigation into radiative efficiencies in these compounds, with some GWP values
changing by up to 40 percent; with this change, the uncertainties associated with these well-mixed HFCs are thought to be
approximately 12 percent.
It should be noted that the official greenhouse gas emissions presented in this report using the SAR GWP values
are the final time the SAR GWP values will be used in the U.S. inventory. The United States and other developed
countries to the UNFCCC have agreed to submit annual inventories in 2015 and future years to the UNFCCC using GWP
values from the IPCC AR4, which will replace the current use of SAR GWP values in their annual greenhouse gas
inventories. The use of IPCC AR4 GWP values in future years will apply across the entire time series of the inventory
(i.e., from 1990 to 2013 in next year's national inventory report).
A-399
-------
Table A- 275: Comparison of GWP values and Lifetimes Used in the SAR,TAR, AIM, and AR5
Lifetime (years)
Gas SAR TAR AR4 AR5a
Carbon dioxide (C02> b c c c
Methane (CH4)d 12+3 8.4/126 8.7/126 12.4
Nitrous oxide (N20) 120 120/1146 120/1146 121
Hydrofluorocarbons
HFC-23 264 260 270 222
HFC-32 5.6 5.0 4.9 5.2
HFC-125 32.6 29 29 28.2
HFC-134a 14.6 13.8 14 13.4
HFC-143a 48.3 52 52 47.1
HFC-152a 1.5 1.4 1.4 1.5
HFC-227ea 36.5 33.0 34.2 38.9
HFC-236fa 209 220 240 242
HFC-245fa NA 7.2 7.6 7.7
HFC-365mfc NA 9.9 6.6 8.7
HFC-43-10mee 17.1 15 15.9 16.1
Fully Fluorinated
Species
SF6 3,200 3,200 3,200 3,200
CF4 50,000 50,000 50,000 50,000
C2F6 10,000 10,000 10,000 10,000
C3F8 2,600 2,600 2,600 2,600
C4Fio 2,600 2,600 2,600 2,600
c-C4F8 3,200 3,200 3,200 3,200
CsFi2 4,100 4,100 4,100 4,100
C6Fi4 3,200 3,200 3,200 3,100
Others'
NF3 NA 740 740 500
GWP (100 year)
SAR TAR AR4 AR5
1111
21 23 25 28
310 296 298 265
11,700 12,000 14,800 12,400
650 550 675 677
2,800 3,400 3,500 3,170
1,300 1,300 1,430 1,300
3,800 4,300 4,470 4,800
140 120 124 138
2,900 3,500 3,220 3,350
6,300 9,400 9,810 8,060
NA 950 1,030 858
NA 890 794 804
1,300 1,500 1,640 1,650
23,900 22,200 22,800 23,500
6,500 5,700 7,390 6,630
9,200 11,900 12,200 11,100
7,000 8,600 8,830 8,900
7,000 8,600 8,860 9,200
8,700 10,000 10,300 9,540
7,500 8,900 9,160 8,550
7,400 9,000 9,300 7,910
NA 10,800 17,200 16,100
Differ
TAR TAR (%)
NC NC
2 10%
(14) (5%)
300 3%
(100) (15%)
600 21%
NC NC
500 13%
(20) (14%)
600 21%
3,100 49%
NA NA
NA NA
200 15%
(1,700) (7%)
(800) (12%)
2,700 29%
1,600 23%
1,600 23%
1,300 15%
1,400 19%
1,600 22%
NA NA
ence in GWP (relative to
AR4 AR4 (%)
NC NC
4 19%
(12) (4%)
3,100 26%
25 4%
700 25%
130 10%
670 18%
(16) (11%)
320 11%
3,510 56%
NA NA
NA NA
340 26%
(1,100) (5%)
890 14%
3,000 33%
1 ,830 26%
1 ,860 27%
1,600 18%
1 ,660 22%
1 ,900 26%
NA NA
SAR)
AR5 AR5 (%)
NC NC
7 33%
(45) (15%)
700 6%
27 4%
370 13%
NC NC
1 ,000 26%
(2) (1%)
450 16%
1 ,760 28%
NA NA
NA NA
350 27%
(400) (2%)
130 2%
1,900 21%
1 ,900 27%
2,200 31%
840 10%
1,050 14%
510 7%
NA NA
NC (No Change)
NA (Not Applicable)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report. The AR5 report has also calculated GWP values (not shown here) where climate-carbon feedbacks have been
included for the non-C02 gases in order to be consistent with the approach used in calculating the C02 lifetime. Additionally, the AR5 reported separate values for fossil versus biogenic methane in order to account for the C02
oxidation product.
b For a given amount of carbon dioxide emitted, some fraction of the atmospheric increase in concentration is quickly absorbed by the oceans and terrestrial vegetation, some fraction of the atmospheric increase will only slowly
decrease over a number of years, and a small portion of the increase will remain for many centuries or more.
c No single lifetime can be determined for C02. (See IPCC 2001)
d The methane GWP includes the direct effects and those indirect effects due to the production of tropospheric ozone and stratospheric water vapor. The indirect effect due to the production of C02 is not included.
e Methane and nitrous oxide have chemical feedback systems that can alter the length of the atmospheric response, in these cases, global mean atmospheric lifetime (LT) is given first, followed by perturbation time (PT).
f Gases whose lifetime has been determined only via indirect means or for whom there is uncertainty over the loss process.
Note: Parentheses indicate negative values.
Source: IPCC (2013), IPCC (2007), IPCC (2001), IPCC (1996).
A-400 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2011
-------
The choice of GWP values between the SAR, TAR, AR4, and AR5 has an impact on both the overall emissions
estimated by the inventory, as well as the trend in emissions over time. To summarize, Table A-276 shows the overall
trend in U.S. greenhouse gas emissions, by gas, from 1990 through 2012 using the four GWP sets. The table also presents
the impact of TAR, AR4, and AR5 GWP values on the total emissions for 1990 and for 2012.
Table A-276: Effects on U.S. Greenhouse Gas Emissions Using SAR.TAR.AR4. and AR5 GWP values [Tg ClHq.l
Gas
C02
cm
N20
MFCs, PFCs, and SF6*
Total
Percent Change
Trend from 1990 to 201 2
SAR TAR AR4
274.5 274.5 274.5
(68.4) (74.9) (81.5)
11.5 11.0 11.0
74.8 80.9 80.4
292.4 291.4 284.5
4.7% 4.6% 4.5%
AR5
274.5
(91.2)
9.8
77.8
270.8
4.2%
Revisions to Annual Emissio
TAR AR4 AR5
1990
NC
60.5
(18.0)
(2.2)
40.3
0.6%
NC NC
121.1 211.9
(15.4) (57.9)
11.8 2.9
117.5 156.9
1.9% 2.5%
n Estimates (relative to SAR)
TAR AR4 AR5
2012
NC
54.0
(18.5)
3.9
39.4
0.6%
NC NC
108.1 189.1
(15.9) (59.5)
17.4 5.8
109.6 135.4
1.7% 2.1%
NC (No Change)
'Includes NFs
Note: Totals may not sum due to independent rounding. Excludes sinks. Parentheses indicate negative values.
When the GWP values from the AR4 are applied to the emission estimates presented in this report, total
emissions for the year 2012 are 6,635.2 Tg CC>2 Eq., as compared to 6,525.6 Tg CC>2 Eq. when the GWP values from the
SAR are used (a 1.7 percent difference). Table A-277 provides a detailed summary of U.S. greenhouse gas emissions and
sinks for 1990 through 2012, using the GWP values from the AR4. The percent change in emissions is equal to the percent
change in the GWP; however, in cases where multiple gases are emitted in varying amounts the percent change is variable
over the years, such as with substitutes for ozone depleting substances. Table A-278 summarizes the resulting change in
emissions from SAR to AR4 GWP values for 1990 through 2012 including the percent change for 2012.
Table A-277: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks using the AR4 GWP values [Tg Clh Eq.l
Gas/Source
C02
Fossil Fuel Combustion
Electricity Generation
Transportation
Industrial
Residential
Commercial
U.S. Territories
Non-Energy Use of Fuels
Iron and Steel Production &
Metallurgical Coke
Production
Natural Gas Systems
Cement Production
Lime Production
Incineration of Waste
Ammonia Production
Other Process Uses of
Carbonates
Cropland Remaining Cropland
Urea Consumption for Non-
Agricultural Purposes
Petrochemical Production
Aluminum Production
Soda Ash Production and
Consumption
Carbon Dioxide Consumption
Titanium Dioxide Production
Ferroalloy Production
Zinc Production
2008
5,936.9
5,593.4
2,360.9
1,816.5
804.1
346.2
224.7
41.0
128.0
2009
5,506.1
5,225.7
2,146.4
1,747.7
727.5
336.4
223.9
43.8
108.1
2010
5,722.3
5,404.9
2,259.2
1,765.0
775.6
334.8
220.7
49.6
120.8
2011
5,592.2
5,271.1
2,158.5
1,747.9
768.7
324.9
221.5
49.6
117.3
2012
5,383.2
5,072.3
2,022.7
1 ,739.5
774.2
288.9
197.4
49.6
110.3
66.8
32.7
41.2
14.0
11.9
8.4
5.9
8.6
4.1
3.6
4.5
2.9
1.8
1.8
1.6
1.2
43.0
32.2
29.4
10.9
11.7
8.5
7.6
7.2
3.4
2.8
3.0
2.5
1.8
1.6
1.5
0.9
55.7
32.4
31.3
12.8
12.0
9.2
9.6
8.6
4.7
3.5
2.7
2.6
2.3
1.8
1.7
1.2
60.0
35.1
32.0
13.5
12.1
9.4
9.3
7.9
4.0
3.5
3.3
2.6
1.8
1.7
1.7
1.3
54.3
35.2
35.1
13.3
12.2
9.4
8.0
7.4
5.2
3.5
3.4
2.7
1.8
1.7
1.7
1.4
A-401
-------
Glass Production 1.5> 1.9> 1.5 1.0 1.5 1.3 1.2
Phosphoric Acid Production 1.6l 1.4l 1.2 1.0 1.1 1.2 1.1
Wetlands Remaining Wetlands 1.ol 1.11 1.0 1.1 1.0 0.9 0.8
Lead Production O.sl 0.6• 0.5 0.5 0.5 0.5 0.5
Petroleum Systems 0.4l O.sl 0.3 0.3 0.3 0.3 0.4
Silicon Carbide Production and
Consumption 0.4l 0.2 • 0.2 0.1 0.2 0.2 0.2
Land Use, Land-Use Change,
and Forestry (Sink)' (831.1) (1,030.7)M (981.0) (961.6) (968.0) (980.3) (979.3)
Wood Biomass and Ethanol
Consumption- 218.6 228.6 253.7 249.5 264.0 267.0 266.9
International Bunker Fuels* 103.5 113.1 114.3 106.4 117.0 111.7 105.8
CH4 756.8 697.3 721.4 710.1 697.1 688.4 675.3
Enteric Fermentation 164.2 169.6 175.0 173.9 172.5 170.2 167.8
Natural Gas Systems 186.2 181.0 180.5 170.2 160.3 158.6 154.6
Landfills 175.9 133.5 136.1 137.3 130.8 127.8 122.4
Coal Mining 96.5 63.8 75.6 79.9 82.3 71.2 66.5
Manure Management 37.5B 56.6 61.3 60.1 61.7 62.0 63.0
Petroleum Systems 42.61 34.sl 34.3 34.7 35.2 36.3 37.8
Forest Land Remaining Forest
Land 3.0 9.6 • 10.4 6.9 5.6 16.6 18.2
Wastewater Treatment 15.71 15.91 15.9 15.6 15.5 15.3 15.2
Rice Cultivation 9.21 8.91 9.3 9.4 11.1 8.5 8.8
Stationary Combustion 8.91 7.91 7.9 7.9 7.6 7.6 6.8
Abandoned Underground Coal
Mines 7.2l 6.6 • 6.3 6.1 5.9 5.8 5.6
Petrochemical Production 2.71 3.71 3.4 3.4 3.7 3.7 3.7
Mobile Combustion 5.5 2.sl 2.3 2.2 2.1 2.1 2.0
Composting 0.41 1.9| 2.0 1.9 1.8 1.9 1.9
Iron and Steel Production &
Metallurgical Coke
Production 1.1| 0.9 • 0.8 0.4 0.6 0.7 0.7
Field Burning of Agricultural
Residues 0.3B 0.2 • 0.3 0.3 0.3 0.3 0.3
Ferroalloy Production +( +( + + + + +
Silicon Carbide Production and
Consumption
Incineration of Waste +• +• + + + + +
International Bunker Fuels* 0.2M 0.1U 0.1 0.1 0.1 0.1 0.1
N20 383.2 399.7 406.9 396.3 393.5 401.1 394.2
Agricultural Soil Management 271.2 285.8 306.7 304.1 298.1 295.8 294.7
Stationary Combustion 11.sl 19.sl 20.3 20.0 21.7 20.8 21.1
Manure Management 13.8I 16.41 17.1 17.0 17.1 17.3 17.3
Mobile Combustion 42.3 35.5 24.5 21.8 19.9 17.8 15.9
Nitric Acid Production 17.5B 16.3I 16.2 13.5 16.1 15.2 14.7
Forest Land Remaining Forest
Land 2.ol 6.7 • 7.2 4.9 4.1 11.3 12.3
Adipic Acid Production 15.21 7.11 2.5 2.7 4.2 10.2 5.5
Wastewater Treatment 3.3 • 4.3 • 4.6 4.6 4.7 4.8 4.8
N20 from Product Uses 4.2 • 4.2 • 4.2 4.2 4.2 4.2 4.2
Composting 0.3 • ul 1.8 1.7 1.6 1.7 1.7
Settlements Remaining
Settlements 0.9 1.4l 1.4 1.3 1.4 1.5 1.4
Incineration of Waste O.sl 0.4l 0.4 0.4 0.4 0.4 0.4
Field Burning of Agricultural
Residues 0.11 0.11 0.1 0.1 0.1 0.1 0.1
Wetlands Remaining Wetlands +B +B + + + + +
International Bunker Fuels* 0.9M 1.0M 1.0 0.9 1.0 1.0 0.9
MFCs 46.6 133.0 150.6 148.5 158.9 164.5 167.5
Substitution of Ozone Depleting
Substances^ rj.3| 112.8 133.1 141.5 150.6 155.5 161.9
A-402 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
HCFC-22 Production
Semiconductor Manufacture
PFCs
Semiconductor Manufacture
Aluminum Production
SF6
Electrical Transmission and
Distribution
Magnesium Production and
Processing
Semiconductor Manufacture
Total
Net Emissions (Sources and
Sinks)
46.1
0.2
24.3
2.9
21.5
31.1
25.4
5.2
0.5
6,350.7
5,519.6 |
20.0
0.2
7.1
3.7
3.4
14.0
10.5
2.7
0.7
7,363.3
6,332.6 |
17.2
0.2
6.6
3.4
3.2
10.2
8.0
1.8
0.4
7,232.7
6,251.7
6.8
0.2
4.4
2.5
1.9
9.2
7.2
1.6
0.3
6,774.6
5,813.0
8.0
0.2
4.9
3.1
1.9
9.4
6.9
2.1
0.4
6,986.1
6,018.1
8.8
0.2
7.5
4.1
3.5
10.3
6.9
2.8
0.7
6,864.0
5,883.7
5.5
0.2
6.9
3.9
2.9
8.0
5.7
1.6
0.6
6,635.2
5,655.9
+ Does not exceed 0.05 Tg C02 Eq.
a The net C02 flux total includes both emissions and sequestration, and constitutes a sink in the United States. Parentheses indicate negative values or
sequestration.
b Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals.
c Emissions from International Bunker Fuels are not included in totals.
d Small amounts of RFC emissions also result from this source.
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table A-278: Change in U.S. Greenhouse Gas Emissions and Sinks Using SAR us. AR4 GWP values [Tg Clh Eq.l
Percent
Change in
Gas/Source 1990 2005 2008 2009 2010 2011 2012 2012
C02
CH4
Enteric Fermentation
Natural Gas Systems
Landfills
Coal Mining
Manure Management
Petroleum Systems
Forest Land Remaining Forest
Land
Wastewater Treatment
Rice Cultivation
Stationary Combustion
Abandoned Underground Coal
Mines
Petrochemical Production
Mobile Combustion
Composting
Iron and Steel Production &
Metallurgical Coke Production
Field Burning of Agricultural
Residues
Ferroalloy Production
Silicon Carbide Production and
Consumption
Incineration of Waste
International Bunker Fuels3
N20
Agricultural Soil Management
Stationary Combustion
Manure Management
Mobile Combustion
Nitric Acid Production
NC
121.1
26.3
29.8
28.1
15.4
6.0
6.8
0.5
%
1.4
0.4
0.9
•
0.2
0.1
;
(15.4)
(10.9)
(0.5)
(0.6)
(1.7)
(0.7)
NC
111.6
27.1
29.0
21.4
10.2
9.1
5.5
Z
1.4
1.3
0.6
0.5
0.3
0.1
+1
;
(16.1)
(11. 5) •
(0.8)
(0.7)
(1.4)
(0.7)
NC
115.4
28.0
28.9
21.8
12.1
9.8
5.5
1.7
2.5
1.5
1.3
1.0
0.5
0.4
0.3
0.1
0.1
+
+
+
+
(16.4)
(12.4)
(0.8)
(0.7)
(1.0)
(0.7)
NC
113.6
27.8
27.2
22.0
12.8
9.6
5.6
1.1
2.5
1.5
1.3
1.0
0.6
0.3
0.3
0.1
+
+
+
+
+
(16.0)
(12.2)
(0.8)
(0.7)
(0.9)
(0.5)
NC
111.5
27.6
25.7
20.9
13.2
9.9
5.6
0.9
2.5
1.8
1.2
0.9
0.6
0.3
0.3
0.1
+
+
+
+
+
(15.8)
(12.0)
(0.9)
(0.7)
(0.8)
(0.6)
NC
110.2
27.2
25.4
20.4
11.4
9.9
5.8
2.7
2.4
1.4
1.2
0.9
0.6
0.3
0.3
0.1
+
+
+
+
+
(16.1)
(11.9)
(0.8)
(0.7)
(0.7)
(0.6)
NC
108.1
26.9
24.7
19.6
10.6
10.1
6.0
2.9
2.4
1.4
1.1
0.9
0.6
0.3
0.3
0.1
+
+
+
+
+
(15.9)
(11.9)
(0.9)
(0.7)
(0.6)
(0.6)
NC
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
A-403
-------
Forest Land Remaining Forest
Land
Adipic Acid Production
Wastewater Treatment
N20 from Product Uses
Composting
Settlements Remaining
Settlements
Incineration of Waste
Field Burning of Agricultural
Residues
Wetlands Remaining Wetlands
International Bunker Fuels*
MFCs
Substitution of Ozone Depleting
Substances
HCFC-22 Production
Semiconductor Manufacture
PFCs
Semiconductor Manufacture
Aluminum Production
SFe
Electrical Transmission and
Distribution
Magnesium Production and
Processing
Semiconductor Manufacture
Total
>---'•
(0.6)
(0.1)
(0.2)
(0.0)
(0.0)
(0.0)
(0.0)
(0.0)
(0.0)
9.7
M
9.7
0.045
3.7
0.6
3.0
(1.5)
(1.2)
(0.3)
(0.0)
117.5
>-'•
(0.3)
(0.2)
(0.2)
(0.1)
(0.1)
(0.0)
(0.0)
(0.0)
(0.0)
13.2
9.0
4.2
0.0
;i
0.5
(0.7)
(0.5)
(0.1)
(0.0)
109.6
(0.3)
(0.1)
(0.2)
(0.2)
(0.1)
(0.1)
(0.0)
(0.0)
(0.0)
(0.0)
14.6
10.9
3.6
0.0
1.5
1.0
0.5
(0.5)
(0.4)
(0.1)
(0.0)
114.6
(0.2)
(0.1)
(0.2)
(0.2)
(0.1)
(0.1)
(0.0)
(0.0)
(0.0)
(0.0)
13.3
11.9
1.4
0.0
1.1
0.9
0.3
(0.4)
(0.3)
(0.1)
(0.0)
111.7
(0.2)
(0.2)
(0.2)
(0.2)
(0.1)
(0.1)
(0.0)
(0.0)
(0.0)
(0.0)
14.9
13.2
1.7
0.0
1.2
0.9
0.3
(0.5)
(0.3)
(0.1)
(0.0)
111.3
(0.5)
(0.4)
(0.2)
(0.2)
(0.1)
(0.1)
(0.0)
(0.0)
(0.0)
(0.0)
15.9
14.0
1.8
0.0
1.6
1.1
0.5
(0.5)
(0.3)
(0.1)
(0.0)
111.0
(0.5)
(0.2)
(0.2)
(0.2)
(0.1)
(0.1)
(0.0)
W
W
W
16.3
15.1
1.1
0.0
1.5
1.0
0.4
(0.4)
(0.3)
(0.1)
(0.0)
109.6
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
11%
10%
26%
26%
27%
36%
18%
(5%)
(5%)
(5%)
(5%)
1.7%
NC (No change)
+ Does not exceed 0.05 Tg C02 Eq.
a Emissions from International Bunker Fuels are not included in totals.
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table A-279 below shows a comparison of total emissions estimates by sector using both the IPCC SAR and
AR4 GWP values. For most sectors, the change in emissions was minimal. The effect on emissions from waste was by
far the greatest (17.8 percent in 2012), due the predominance of CH4 emissions in this sector. Emissions from all other
sectors were comprised of mainly CC>2 or a mix of gases, which moderated the effect of the changes.
Table A-279: Comparison of Emissions by Sector using IPCC SAR and AR4 GWP Values [Tg C0? Eq.l
Sector 1990 2005 2008 2009 2010 2011 2012
Energy
SAR GWP, Used In Inventory 5,260.1 6,243.5 6,071.1 5,674.6 5,860.6 5,712.9 5,498.9
AR4 GWP, Updated 5,313.4 6,288.7 6,118.4 5,721.1 5,905.8 5,756.4 5,541.1
Difference (%) 1.0%B 0.7% 0.8% 0.8% 0.8% 0.8% 0.8%
Industrial Processes
SAR GWP, Used In Inventory 316.1 334.9 335.9 287.8 324.6 342.9 334.4
AR4 GWP, Updated 327.2 348.8 351.4 301.8 340.1 359.6 351.6
Difference (%) 3.5% 4.1% 4.6% 4.9% 4.8% 4.9% 5.2%
Solvent and Other Product Use
SAR GWP, Used In Inventory 4.4• 4.4> 4.4 4.4 4.4 4.4 4.4
AR4 GWP, Updated 4.2• 4.2! 4.2 4.2 4.2 4.2 4.2
Difference (%) (3.9%) (3.9%) (3.9%) (3.9%) (3.9%) (3.9%) (3.9%)
Agriculture
SAR GWP, Used In Inventory 473.9 512.2 543.4 538.9 534.2 528.3 526.3
AR4 GWP, Updated 496.2 537.7 569.7 565.0 560.7 554.2 552.1
Difference (%) 4.7% 5.0% 4.8% 4.8% 5.0% 4.9% 4.9%
LULUCF
SAR GWP, Used In Inventory (817.4) (1,005.2) • (953.7) (941.1) (948.0) (944.3) (941.5)
AR4 GWP, Updated (817.0) (1,004.0) (952.4) (940.2) (947.3) (942.2) (939.2)
A-404 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Difference (%)
Waste
SAR GWP, Used In Inventory
AR4 GWP, Updated
Difference (%)
Net Emissions (Sources and
Sinks)
SAR GWP (Used in Inventory)
AR4 GWP
Difference (%)
0.0%
165.0
195.6
18.5%
5,402.1
5,519.6
2.2%
(0.1 %)•
133.2
157.2
18.0%H
6,223.1
6,332.6
1.8%
(0.1%)
136.0
160.3
17.9%
6,137.1
6,251.7
1.9%
(0.1%)
136.5
161.0
17.9%
5,701.2
5,813.0
2.0%
(0.1%)
131.1
154.5
17.9%
5,906.7
6,018.1
1.9%
(0.2%)
128.5
151.4
17.8%
5,772.7
5,883.7
1.9%
(0.3%)
124.0
146.0
17.8%
5,546.3
5,655.9
2.0%
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Overall, these revisions to GWP values do not have a significant effect on U.S. emission trends, as shown in
Table A-280 and Table A-281.
Table A-280: Change in U.S. Greenhouse Gas Emissions and Sinks Using AR5 us. AR4 GWP values [Tg Clh Eq.l
Gas
1990
2005
2008
2009
2010
2011
2012
C02
CH4
N20
MFCs
PFCs*
SFe
Total
NC (No change)
'Includes NFs
NC
90.8
(42.4)
(7.5)
(2.4)
1.0
39.4
NC
83.7
(44.3)
(11. 4) •
(0.7)
0.4
27.7
NC
86.6
(45.1)
(12.3)
(0.6)
0.3
28.9
NC
85.2
(43.9)
(11.0)
(0.4)
0.3
30.2
NC
83.6
(43.6)
(11.6)
(0.5)
0.3
28.3
NC
82.6
(44.4)
(11.7)
(0.7)
0.3
26.2
NC
81.0
(43.7)
(11.2)
(0.6)
0.2
25.8
Note: Totals may not sum due to independent rounding.
Table A-281: Change in U.S. Greenhouse Gas Emissions Using AR5 vs. AR4 GWP values (Percent)
Gas/Source
C02
CH4
N20
MFCs
Substitution of Ozone
Depleting Substances
HCFC-22 Production"
Semiconductor Manufacture0
PFCs
Semiconductor Manufacture0
Aluminum Production3
SFe
Total
NC (No change)
a RFC emissions from CF4 and C2Fe
» HFC-23 emitted
c Emissions from HFC-23, CF4, C2Fe,
Note: Excludes Sinks.
1990
NC
12.0%
(11.1%)
(16.0%)
(16.2%)
(16.2%)
(10.0%)
(9.3%)
(10.1%)
3.1%
0.6%
1
2005
NC
12.0%
(11.1%)
(8.6%)
(7.2%) 1
(16.2%)
(16.2%)
(9.4%)
(8.8%)
(10.1%)
3.1%
0.4%
2008
NC
12.0%
(11.1%)
(8.2%)
(7.1%)
(16.2%)
(16.2%)
(9.3%)
(8.6%)
(10.0%)
3.1%
0.4%
2009
NC
12.0%
(11.1%)
(7.4%)
(7.0%)
(16.2%)
(16.2%)
(9.2%)
(8.6%)
(10.0%)
3.1%
0.4%
2010
NC
12.0%
(11.1%)
(7.3%)
(6.8%)
(16.2%)
(16.2%)
(9.2%)
(8.7%)
(10.0%)
3.1%
0.4%
2011
NC
12.0%
(11.1%)
(7.1%)
(6.6%)
(16.2%)
(16.2%)
(9.3%)
(8.8%)
(10.0%)
3.1%
0.4%
2012
NC
12.0%
(11.1%)
(6.7%)
(6.4%)
(16.2%)
(16.2%)
(9.4%)
(8.9%)
(10.0%)
3.1%
0.4%
CsFs, SFe, and the addition of NFs
A-405
-------
6.2. Ozone Depleting Substance Emissions
Ozone is present in both the stratosphere, where it shields the earth from harmful levels of ultraviolet
radiation, and at lower concentrations in the troposphere, where it is the main component of anthropogenic
photochemical "smog." Chlorofluorocarbons (CFCs), halons, carbon tetrachloride, methyl chloroform, and
hydrochlorofluorocarbons (HCFCs), along with certain other chlorine and bromine containing compounds, have been
found to deplete the ozone levels in the stratosphere. These compounds are commonly referred to as ozone depleting
substances (ODSs). If left unchecked, stratospheric ozone depletion could result in a dangerous increase of ultraviolet
radiation reaching the earth's surface. In 1987, nations around the world signed the Montreal Protocol on Substances that
Deplete the Ozone Layer. This landmark agreement created an international framework for limiting, and ultimately
eliminating, the production of most ozone depleting substances. ODSs have historically been used in a variety of
industrial applications, including refrigeration and air conditioning, foam blowing, fire extinguishing, sterilization, solvent
cleaning, and as an aerosol propellant.
In the United States, the Clean Air Act Amendments of 1990 provide the legal instrument for implementation of
the Montreal Protocol controls. The Clean Air Act classifies ozone depleting substances as either Class I or Class II,
depending upon the ozone depletion potential (ODP) of the compound. The production of CFCs, halons, carbon
tetrachloride, and methyl chloroform—all Class I substances—has already ended in the United States. However, large
amounts of these chemicals remain in existing equipment,107 and stockpiles of the ODSs, as well as material recovered
from equipment being decommissioned, are used for maintaining the existing equipment. As a result, emissions of Class I
compounds will continue, albeit in ever decreasing amounts, for many more years. Class II designated substances, all of
which are HCFCs, have been, or are being, phased out at later dates than Class I compounds because they have lower
ozone depletion potentials. These compounds served, and in some cases continue to serve, as interim replacements for
Class I compounds in many industrial applications. The use and emissions of HCFCs in the United States is anticipated to
continue for several decades as equipment that use Class I substances and Class II substances are retired from use. Under
current controls, however, the production for domestic use of all HCFCs in the United States will end by the year 2030.
In addition to contributing to ozone depletion, CFCs, halons, carbon tetrachloride, methyl chloroform, and
HCFCs are also potent greenhouse gases. However, the depletion of the ozone layer has a cooling effect on the climate
that counteracts the direct warming from tropospheric emissions of ODSs. Stratospheric ozone influences the earth's
radiative balance by absorption and emission of longwave radiation from the troposphere as well as absorption of
shortwave radiation from the sun; overall, stratospheric ozone has a warming effect.
The IPCC has prepared both direct GWP values and net (combined direct warming and indirect cooling) GWP
ranges for some of the most common ozone depleting substances (IPCC 1996). See Annex 6.1, Global Warming Potential
Values, for a listing of the net GWP values for ODS.
Although the IPCC emission inventory guidelines do not require the reporting of emissions of ozone depleting
substances, the United States believes that no inventory is complete without the inclusion of these compounds. Emission
estimates for several ozone depleting substances are provided in Table A- 282.
Table A- 282: Emissions of Ozone Depleting Substances tGgl
Compound 1990 2005 2008 2009 2010 2011 2012
Class I
104 jjjg stratosphere is the layer from the top of the troposphere up to about 50 kilometers. Approximately 90 percent of
atmospheric ozone is within the stratosphere. The greatest concentration of ozone occurs in the middle of the stratosphere, in a
region commonly called the ozone layer.
The troposphere is the layer from the ground up to about 11 kilometers near the poles and 16 kilometers in equatorial regions
(i.e., the lowest layer of the atmosphere, where humans live). It contains roughly 80 percent of the mass of all gases in the
atmosphere and is the site for weather processes including most of the water vapor and clouds.
Substances with an ozone depletion potential of 0.2 or greater are designated as Class I. All other designated substances that
deplete stratospheric ozone but which have an ODP of less than 0.2 are Class II.
107 Older refrigeration and air-conditioning equipment, fire extinguishing systems, meter-dose inhalers, and foam products
blown with CFCs/HCFCs may still contain ODS.
A-406 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
CFC-11
CFC-12
CFC-11 3
CFC-114
CFC-11 5
Carbon Tetrachloride
Methyl Chloroform
Halon-1211
Halon-1301
Class II
HCFC-22
HCFC-123
HCFC-124
HCFC-141b
HCFC-142b
HCFC-225ca/cb
29
126
59
5 •
d|
4I
223
2|
2!
49
+H
+H
1
2
+
+ Does not exceed 0.5 Gg.
1
1
87
1
2
7
4
1
1
88
1
2
8
2
1
1
84
1
1
9
1
1
1
83
1
1
9
1
1
1
78
1
1
9
1
Methodology and Data Sources
Emissions of ozone depleting substances were estimated using the EPA's Vintaging Model. The model, named
for its method of tracking the emissions of annual "vintages" of new equipment that enter into service, is a "bottom-up"
model. It models the consumption of chemicals based on estimates of the quantity of equipment or products sold,
serviced, and retired each year, and the amount of the chemical required to manufacture and/or maintain the equipment.
The Vintaging Model makes use of this market information to build an inventory of the in-use stocks of the equipment in
each of the end-uses. Emissions are estimated by applying annual leak rates, service emission rates, and disposal emission
rates to each population of equipment. By aggregating the emission and consumption output from the different end-uses,
the model produces estimates of total annual use and emissions of each chemical. Please see Annex 3.8, Methodology for
Estimating HFC and PFC Emissions from Substitution of Ozone Depleting Substances, of this Inventory for a more
detailed discussion of the Vintaging Model.
Uncertainties
Uncertainties exist with regard to the levels of chemical production, equipment sales, equipment characteristics,
and end-use emissions profiles that are used by these models. Please see the ODS Substitutes section of this report for a
more detailed description of the uncertainties that exist in the Vintaging Model.
6.3. Sulfur Dioxide Emissions
Sulfur dioxide (802), emitted into the atmosphere through natural and anthropogenic processes, affects the
Earth's radiative budget through photochemical transformation into sulfate aerosols that can (1) scatter sunlight back to
space, thereby reducing the radiation reaching the Earth's surface; (2) affect cloud formation; and (3) affect atmospheric
chemical composition (e.g., stratospheric ozone, by providing surfaces for heterogeneous chemical reactions). The overall
effect of SCVderived aerosols on radiative forcing is believed to be negative (IPCC 2007). However, because SC>2 is
short-lived and unevenly distributed through the atmosphere, its radiative forcing impacts are highly uncertain. Sulfur
dioxide emissions have been provided below in Table A-283.
The major source of SC>2 emissions in the United States is the burning of sulfur containing fuels, mainly coal.
Metal smelting and other industrial processes also release significant quantities of SC>2. The largest contributor to U.S.
emissions of SC>2 is electricity generation, accounting for 63.3 percent of total SC>2 emissions in 2012 (see Table A-284);
coal combustion accounted for approximately 92.0 percent of that total. The second largest source was industrial fuel
combustion, which produced 14.6 percent of 2012 SC>2 emissions. Overall, SO2 emissions in the United States decreased
by 77.4 percent from 1990 to 2012. The majority of this decline came from reductions from electricity generation,
primarily due to increased consumption of low sulfur coal from surface mines in western states.
Sulfur dioxide is important for reasons other than its effect on radiative forcing. It is a major contributor to the
formation of urban smog and acid rain. As a contributor to urban smog, high concentrations of SO2 can cause significant
A-407
-------
increases in acute and chronic respiratory diseases. In addition, once SC>2 is emitted, it is chemically transformed in the
atmosphere and returns to earth as the primary contributor to acid deposition, or acid rain. Acid rain has been found to
accelerate the decay of building materials and paints, and to cause the acidification of lakes and streams and damage trees.
As a result of these harmful effects, the United States has regulated the emissions of SC>2 under the Clean Air Act. The
EPA has also developed a strategy to control these emissions via four programs: (1) the National Ambient Air Quality
Standards program,108 (2) New Source Performance Standards,109 (3) the New Source Review/Prevention of Significant
Deterioration Program,110 and (4) the sulfur dioxide allowance program.111
Table A-283: SO Emissions [Ggl
Sector/Source
1990
2005
2008 2009 2010 2011
2012
Energy 19,628
Stationary Combustion 18,407
Oil and Gas Activities 390
Mobile Sources 793
Waste Combustion 38
Industrial Processes 1,307
Other Industrial Processes 362
Metals Processing 659
Chemical Manufacturing 269
Storage and Transport 6
Miscellaneous* 111
Solvent Use +1
Degreasing
Graphic Arts
Dry Cleaning
Surface Coating NA
Other Industrial
Non-industrial NA
Agriculture NA
Agricultural Burning NA
Waste
Landfills
Wastewater Treatment
Miscellaneous Waste
12,350
11,529
180
616
25
829
327
158
227
2
114
»
I
°
NA
NA
NA
0
8,659
8,289
135
217
18
690
229
161
168
6
126
1
0
0
0
0
1
NA
NA
NA
1
1
0
0
7,579
7,208
125
228
17
656
209
151
150
6
140
1
0
0
0
0
1
NA
NA
NA
1
1
0
0
6,406
6,128
115
147
16
622
189
141
132
7
153
+
0
0
0
0
+
NA
NA
NA
5,277
5,048
105
109
14
621
168
131
114
8
199
+
0
0
0
0
+
NA
NA
NA
4,118
3,895
105
103
14
621
168
131
114
8
199
+
0
0
0
0
+
NA
NA
NA
Total
20,935
13,180
9,350 8,236 7,029 5,898 4,739
Source: Data taken from EPA (2013) and disaggregated based on EPA (2003).
* Miscellaneous includes other combustion and fugitive dust categories.
+ Does not exceed 0.5 Gg
NA (Not Available)
Note: Totals may not sum due to independent rounding.
Table A-284: S02 Emissions from Electricity Generation (Gg)
Fuel Type
1990
2005
2008 2009
2010
2011
2012
Coal
Petroleum
Natural Gas
Misc. Internal Combustion
Other
Total
3,820 2,760
201 146
76 55
25 18
31 22
14,433
9,439
7,055 6,088 5,121 4,154 3,001
Source: Data taken from EPA (2013) and disaggregated based on EPA (2003).
Note: Totals may not sum due to independent rounding.
108
109
110
111
[42 U.S.C § 7409, CAA § 109]
[42U.S.C§7411,CAA§ 111]
[42 U.S.C §7473, CAA § 163]
[42 U.S.C § 7651, CAA § 401]
A-408 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
6.4. Complete List of Source Categories
Chapter/Source
Gas(es)
Energy
Fossil Fuel Combustion
Non-Energy Use of Fossil Fuels
Stationary Combustion (excluding C02)
Mobile Combustion (excluding C02)
Coal Mining
Abandoned Underground Coal Mines
Natural Gas Systems
Petroleum Systems
Incineration of Waste
Industrial Processes
Titanium Dioxide Production
Aluminum Production
Iron and Steel Production
Ferroalloy Production
Ammonia Production
Urea Consumption for Non-Agricultural Purposes
Cement Production
Lime Production
Other Process Uses of Carbonates
Soda Ash Production and Consumption
Glass Production
Carbon Dioxide Consumption
Phosphoric Acid Production
Petrochemical Production
Silicon Carbide Production and Consumption
Lead Production
Zinc Production
Adipic Acid Production
Nitric Acid Production
Substitution of Ozone Depleting Substances
HCFC-22 Production
Semiconductor Manufacture
Electrical Transmission and Distributing
Magnesium Production and Processing
Solvent and Other Product Use
N20 Product Usage
Agriculture
Enteric Fermentation
Manure Management
Rice Cultivation
Field Burning of Agricultural Residues
Agricultural Soil Management
Land Use, Land-Use Change, and Forestry
C02 Flux
Cropland Remaining Cropland
Settlements Remaining Settlements
Forestland Remaining Forestland
Wetlands Remaining Wetlands
Waste
Landfills
Wastewater Treatment
Composting
C02
C02
CH4, N20, CO, NOx, NMVOC
CH4, N20, CO, NOx, NMVOC
CH4
CH4
CH4
CH4
C02,CH4,N20
C02
C02, CF4, C2Fe
C02, CH4
C02, CH4
C02
C02
C02
C02
C02
C02
C02
C02
C02
CH4, C02
CH4, C02
C02
C02
N20
N20
MFCs, PFCsa
HFC-23
MFCs, PFCs, SF6b
SF6
SF6
CO, NOx, NMVOC
N20
CH4
CH4, N20
CH4
CH4, N20
N20, CO, NOx
C02 (sink)
C02
N20
CH4, N20
C02, N20
CH4
CH4, N20
CH4, N20
* Includes HFC-23, HFC-32, HFC-125, HFC-134a, HFC-143a,
b Includes such gases as HFC-23, CF4, C2F6, SF6.
HFC-236fa, CF4, HFC-152a, HFC-227ea, HFC-245fa, HFC-4310mee, and PFC/PFPEs.
A-409
-------
6.5. Constants, Units, and Conversions
Metric Prefixes
Although most activity data for the United States is gathered in customary U.S. units, these units are converted
into metric units per international reporting guidelines. Table A- 285 provides a guide for determining the magnitude of
metric units.
Table A- 285: Guide to Metric Unit Prefixes
Prefix/Symbol
atto (a)
femto (f)
pico (p)
nano (n)
micro (p )
milli (m)
centi (c)
deci (d)
deca (da)
hecto (h)
kilo (k)
mega (M)
giga (Q)
tera (T)
peta (P)
exa(E)
Factor
10-18
10-15
10-12
10-9
10-6
ID'3
ID'2
10-1
10
102
103
106
109
1012
1015
1018
Unit Conversions
1 kilogram
1 pound
1 short ton
1 metric ton
2.205 pounds
0.454 kilograms
2,000 pounds
1,000 kilograms
0.9072 metric tons
1.1023 short tons
1 cubic meter
1 cubic foot
1 U.S. gallon
1 barrel (bbl)
1 barrel (bbl)
1 liter
35.315 cubic feet
0.02832 cubic meters
3.785412 liters
0.159 cubic meters
42 U.S. gallons
0.001 cubic meters
1foot
1 meter
1 mile
1 kilometer
0.3048 meters
3.28 feet
1.609 kilometers
0.622 miles
1 acre = 43,560 square feet = 0.4047 hectares
1 square mile = 2.589988 square kilometers
4,047 square meters
To convert degrees Fahrenheit to degrees Celsius, subtract 32 and multiply by 5/9
To convert degrees Celsius to Kelvin, add 273.15 to the number of Celsius degrees
A-410 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Density Conversions112
Methane
Carbon dioxide
1 cubic meter
1 cubic meter
0.67606 kilograms
1.85387 kilograms
Natural gas liquids 1 metric ton = 11.6 barrels = 1,844.2 liters
Unfinished oils 1 metric ton = 7.46 barrels = 1,186.04 liters
Alcohol 1 metric ton = 7.94 barrels = 1,262.36 liters
Liquefied petroleum gas 1 metric ton = 11.6 barrels = 1,844.2 liters
Aviation gasoline 1 metric ton = 8.9 barrels = 1,415.0 liters
Naphtha jet fuel 1 metric ton = 8.27 barrels = 1,314.82 liters
Kerosene jet fuel 1 metric ton = 7.93 barrels = 1,260.72 liters
Motor gasoline 1 metric ton = 8.53 barrels = 1,356.16 liters
Kerosene 1 metric ton = 7.73 barrels = 1,228.97 liters
Naphtha 1 metric ton = 8.22 barrels = 1,306.87 liters
Distillate 1 metric ton = 7.46 barrels = 1,186.04 liters
Residual oil 1 metric ton = 6.66 barrels = 1,058.85 liters
Lubricants 1 metric ton = 7.06 barrels = 1,122.45 liters
Bitumen 1 metric ton = 6.06 barrels = 963.46 liters
Waxes 1 metric ton = 7.87 barrels = 1,251.23 liters
Petroleum coke 1 metric ton = 5.51 barrels = 876.02 liters
Petrochemical feedstocks 1 metric ton = 7.46 barrels = 1,186.04 liters
Special naphtha 1 metric ton = 8.53 barrels = 1,356.16 liters
Miscellaneous products 1 metric ton = 8.00 barrels = 1,271.90 liters
Energy Conversions
Converting Various Energy Units to Joules
The common energy unit used in international reports of greenhouse gas emissions is the joule. A joule is the
energy required to push with a force of one Newton for one meter. A terajoule (TJ) is one trillion (1012) joules. A British
thermal unit (Btu, the customary U. S. energy unit) is the quantity of heat required to raise the temperature of one pound of
water one degree Fahrenheit at or near 39.2 Fahrenheit.
1TJ =
2.388x1011 calories
23.88 metric tons of crude oil equivalent
947.8 million Btus
277,800 kilowatt-hours
Converting Various Physical Units to Energy Units
Data on the production and consumption of fuels are first gathered in physical units. These units must be
converted to their energy equivalents. The conversion factors in Table A-286 can be used as default factors, if local data
are not available. See Appendix A of EIA' s Annual Energy Review 2009 (EIA 2010) for more detailed information on the
energy content of various fuels.
112 Reference: EIA (2007)
A-411
-------
Table A-286: Conversion Factors to Energy Units (Heat Equivalents)
Fuel Type (Units) Factor
Solid Fuels (Million Btu/Short ton)
Anthracite coal 22.573
Bituminous coal 23.89
Sub-bituminous coal 17.14
Lignite 12.866
Coke 24.8
Natural Gas (Btu/Cubic foot) 1,026
Liquid Fuels (Million Btu/Barrel)
Motor gasoline 5,218
Aviation gasoline 5.048
Kerosene 5.670
Jet fuel, kerosene-type 5.670
Distillate fuel 5.825
Residual oil 6.287
Naphtha for petrochemicals 5.248
Petroleum coke 6.024
Other oil for petrochemicals 5.825
Special naphthas 5.248
Lubricants 6.065
Waxes 5.537
Asphalt 6.636
Still gas 6.000
Misc. products 5.796
Note: For petroleum and natural gas, Annual Energy Review 2009 (EIA 2010). For coal ranks, Sfafe Energy Data Report 1992 (EIA1993). All values are given
in higher heating values (gross calorific values).
6.6. Abbreviations
AAPFCO American Association of Plant Food Control Officials
ABS Acrylonitrile butadiene styrene
ACC American Chemistry Council
AEDT U.S. FAA Aviation Environmental Design Tool
AFEAS Alternative Fluorocarbon Environmental Acceptability Study
AFV Alternative fuel vehicle
AGA American Gas Association
AHEF Atmospheric and Health Effect Framework
AISI American Iron and Steel Institute
ANGA American Natural Gas Alliance
ANL Argonne National Laboratory
APC American Plastics Council
API American Petroleum Institute
APIA American Public Transportation Association
AR4 IPCC Fourth Assessment Report
AR5 IPCC Fifth Assessment Report
ARI Advanced Resources International
ASAE American Society of Agricultural Engineers
ASTM American Society for Testing and Materials
BCEF Biomass conversion and expansion factors
BEA Bureau of Economic Analysis, U.S. Department of Commerce
BLM Bureau of Land Management
BoC Bureau of Census
BOD Biological oxygen demand
BOD5 Biochemical oxygen demand over a 5-day period
BOEMRE Bureau of Ocean Energy Management, Regulation and Enforcement
BOF Basic oxygen furnace
A-412 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
BRS Biennial Reporting System
BTS Bureau of Transportation Statistics, U.S. Department of Transportation
Btu British thermal unit
C Carbon
C&EN Chemical and Engineering News
CAM Clean Air Act Amendments of 1990
CAPP Canadian Association of Petroleum Producers
GARB California Air Resources Board
CBI Confidential business information
CEFM Cattle Enteric Fermentation Model
CFC Chlorofluorocarbon
CFR Code of Federal Regulations
CGA Compressed Gas Association
Cm Methane
CHP Combined heat and power
CIGRE International Council on Large Electric Systems
CKD Cement kiln dust
CLE Crown Light Exposure
CMA Chemical Manufacturer's Association
CMOP Coalbed Methane Outreach Program
CMR Chemical Market Reporter
CNG Compressed natural gas
C02 Carbon dioxide
COD Chemical oxygen demand
COGCC Colorado Oil and Gas Conservation Commission
CRF Common Reporting Format
CRM Component ratio method
CRP Conservation Reserve Program
CTIC Conservation Technology Information Center
CVD Chemical vapor deposition
CWNS Clean Watershed Needs Survey
d.b.h Diameter breast height
DE Digestible energy
DESC Defense Energy Support Center-DoD's defense logistics agency
DFAMS Defense Fuels Automated Management System
DHS Department of Homeland Security
DM Dry matter
DOC Degradable organic carbon
DOC U.S. Department of Commerce
DoD U.S. Department of Defense
DOE U.S. Department of Energy
DOI U.S. Department of the Interior
DOT U.S. Department of Transportation
DRI Direct Reduced Iron
EAF Electric arc furnace
EOF Environmental Defense Fund
EF Emission factor
EFMA European Fertilizer Manufacturers Association
EJ Exajoule
EGR Exhaust gas recirculation
EIA Energy Information Administration, U.S. Department of Energy
EIIP Emissions Inventory Improvement Program
EOR Enhanced oil recovery
EPA U.S. Environmental Protection Agency
ERS Economic Research Service
ETMS Enhanced Traffic Management System
EVI Enhanced Vegetation Index
FAA Federal Aviation Administration
FAO Food and Agricultural Organization
A-413
-------
FCCC Framework Convention on Climate Change
FEB Fiber Economics Bureau
FERC Federal Energy Regulatory Commission
FGD Flue gas desulfurization
FHWA Federal Highway Administration
FIA Forest Inventory and Analysis
FIADB Forest Inventory and Analysis Database
FIPR Florida Institute of Phosphate Research
FQSV First-quarter of silicon volume
FTP Federal Test Procedure
g Gram
GCV Gross calorific value
GDP Gross domestic product
Gg Gigagram
GHG Greenhouse gas
GHGRP Greenhouse Gas Reporting Program
GRI Gas Research Institute
GPG Good Practice Guidance
Gg Gigajoule
GSAM Gas Systems Analysis Model
GWP Global warming potential
ha Hectare
HBFC Hydrobromofluorocarbon
HC Hydrocarbon
HCFC Hydrochlorofluorocarbon
HDDV Heavy duty diesel vehicle
HDGV Heavy duty gas vehicle
HOPE High density polyethylene
HFC Hydrofluorocarbon
HFE Hydrofluoroethers
HHV Higher Heating Value
HMA Hot Mix Asphalt
HTF Heat Transfer Fluid
HTS Harmonized Tariff Schedule
HWP Harvested wood product
IBF International bunker fuels
1C Integrated Circuit
ICAO International Civil Aviation Organization
IDB Integrated Database
IEA International Energy Association
IFO Intermediate Fuel Oil
IISRP International Institute of Synthetic Rubber Products
ILENR Illinois Department of Energy and Natural Resources
IMO International Maritime Organization
IPAA Independent Petroleum Association of America
IPCC Intergovernmental Panel on Climate Change
ITC U.S. International Trade Commission
ITRS International Technology Roadmap for Semiconductors
JWR Jim Walters Resources
KCA Key category analysis
kg Kilogram
kWh Kilowatt hour
LDDT Light duty diesel truck
LDDV Light duty diesel vehicle
LDGT Light duty gas truck
LDGV Light duty gas vehicle
LDPE Low density polyethylene
LEV Low emission vehicles
LFG Landfill gas
LFGTE Landfill gas-to-energy
A-414 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
LHV Lower Heating Value
LKD Lime kiln dust
LLDPE Linear low density polyethylene
LMOP EPA's Landfill Methane Outreach Program
LNG Liquefied natural gas
LPG Liquefied petroleum gas(es)
LTD Landing and take-off
LULUCF Land use, land-use change, and forestry
MC Motorcycle
MCF Methane conversion factor
MCL Maximum Contaminant Levels
MCFD Thousand cubic feet per day
MDI Metered dose inhalers
MEGS EIA Manufacturer's Energy Consumption Survey
MGO Marine gas oil
MJ Megajoule
MLRA Major Land Resource Area
mm Millimeter
MMBtu Million British thermal units
MMCF Million cubic feet
MMCFD Million cubic feet per day
MMS Minerals Management Service
MMT Million Metric Tons
MMTCE Million metric tons carbon equivalent
MODIS Moderate Resolution Imaging Spectroradiometer
MoU Memorandum of Understanding
MOVES U.S. EPA's Motor Vehicle Emission Simulator model
MPG Miles per gallon
MRLC Multi-Resolution Land Characteristics Consortium
MRV Monitoring, reporting, and verification
MSHA Mine Safety and Health Administration
MSW Municipal solid waste
MT Metric ton
MTBE Methyl Tertiary Butyl Ether
MTBS Monitoring Trends in Burn Severity
N20 Nitrous oxide
NA Not available
NACWA National Association of Clean Water Agencies
NAHMS National Animal Health Monitoring System
NAICS North American Industry Classification System
NAPAP National Acid Precipitation and Assessment Program
NARR North American Regional Reanalysis Product
NASA National Aeronautics and Space Administration
NASF National Association of State Foresters
NASS USDA's National Agriculture Statistics Service
NC No change
NCV Net calorific value
NE Not estimated
NEI National Emissions Inventory
NEMA National Electrical Manufacturers Association
NEMS National Energy Modeling System
NESHAP National Emission Standards for Hazardous Air Pollutants
NEU Non-Energy Use
NEV Neighborhood Electric Vehicle
NGHGI National Greenhouse Gas Inventory
NGL Natural gas liquids
NIR National Inventory Report
NLCD National Land Cover Dataset
NMOC Non-methane organic compounds
A-415
-------
NMVOC Non-methane volatile organic compound
NOX Nitrogen oxides
NOAA National Oceanic and Atmospheric Administration
NPRA National Petroleum and Refiners Association
NRC National Research Council
NRCS Natural Resources Conservation Service
NRI National Resources Inventory
NSCEP National Service Center for Environmental Publications
NSCR Non-selective catalytic reduction
NSPS New source performance standards
NWS National Weather Service
CAP EPA Office of Atmospheric Programs
OAQPS EPA Office of Air Quality Planning and Standards
OOP Ozone depleting potential
ODS Ozone depleting substances
OECD Organization of Economic Co-operation and Development
OEM Original equipment manufacturers
QMS EPA Office of Mobile Sources
ORNL Oak Ridge National Laboratory
OSHA Occupational Safety and Health Administration
OTA Office of Technology Assessment
OTAQ EPA Office of Transportation and Air Quality
PAH Polycyclic aromatic hydrocarbons
PCC Precipitate calcium carbonate
PDF Probability Density Function
PECVD Plasma enhanced chemical vapor deposition
PET Polyethylene terephthalate
PET Potential evapotranspiration
PEVM PFC Emissions Vintage Model
PFC Perfluorocarbon
PFPE Perfluoropolyether
POTW Publicly Owned Treatment Works
Ppbv Parts per billion (109) by volume
Ppm Parts per million
Ppmv Parts per million(106) by volume
Pptv Parts per trillion (1012) by volume
PRP Pasture/Range/Paddock
PS Polystyrene
PSU Primary Sample Unit
PU Polyurethane
PVC Polyvinyl chloride
PV Photovoltaic
QA/QC Quality Assurance and Quality Control
QBtu Quadrillion Btu
R&D Research and Development
RCRA Resource Conservation and Recovery Act
RMA Rubber Manufacturers' Association
RPA Resources Planning Act
RTO Regression-through-the-origin
SAE Society of Automotive Engineers
SAGE System for assessing Aviation's Global Emissions
SAN Styrene Acrylonitrile
SAR IPCC Second Assessment Report
SCR Selective catalytic reduction
SEC Securities and Exchange Commission
SEMI Semiconductor Equipment and Materials Industry
SFe Sulfur hexafluoride
SICAS Semiconductor International Capacity Statistics
SNAP Significant New Alternative Policy Program
SNG Synthetic natural gas
A-416 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
SOC Soil Organic Carbon
SOG State of Garbage survey
SSURGO Soil Survey Geographic Database
STMC Scrap Tire Management Council
SULEV Super Ultra Low Emissions Vehicle
SWANA Solid Waste Association of North America
SWDS Solid waste disposal sites
TA Treated anaerobically (wastewater)
TAM Typical animal mass
TAME Tertiary amyl methyl ether
TAR IPCC Third Assessment Report
TBtu Trillion Btu
TON Total digestible nutrients
TFI The Fertilizer Institute
Tg Teragrams
Tg C02 Eq. Teragrams carbon dioxide equivalent
TIGER Topologically Integrated Geographic Encoding and Referencing survey
TJ Terajoule
TLEV Traditional low emissions vehicle
TMLA Total Manufactured Layer Area
TRI Toxic Release Inventory
TSDF Hazardous waste treatment, storage, and disposal facility
TVA Tennessee Valley Authority
UAN Urea ammonium nitrate
UDI Utility Data Institute
UFORE U.S. Forest Service's Urban Forest Effects model
UG Underground (coal mining)
U.S. United States
U.S. ITC United States International Trade Commission
UEP United Egg Producers
ULEV Ultra low emission vehicle
UNEP United Nations Environmental Programme
UNFCCC United Nations Framework Convention on Climate Change
USAA U.S. Aluminum Association
USAF United States Air Force
USDA United States Department of Agriculture
USFS United States Forest Service
USGS United States Geological Survey
VAIP EPA's Voluntary Aluminum Industrial Partnership
VKT Vehicle kilometers traveled
VMT Vehicle miles traveled
VOCs Volatile organic compounds
VS Volatile solids
WERF Water Environment Research Federation
WFF World Fab Forecast (previously WFW, World Fab Watch)
WGC World Gas Conference
WIP Waste in place
WMO World Meteorological Organization
WM S Waste management systems
WW Wastewater
WWTP Wastewater treatment plant
ZEVs Zero emissions vehicles
6.7. Chemical Formulas
Table A-287: Guide to Chemical Formulas
Symbol
Name
Al
Aluminum
A-417
-------
AbOs
Br
C
CH4
C2H6
C3H8
CF4
C2F6
c-CsFe
C3F8
C-C4F8
CsFi2
CF3I
CFCb
CF2Cb
CFsCI
C2F3Cl3
CCbCFs
C2F4Cb
C2FsCI
CHCbF
CHF2CI
C2F3HCb
C2F4HCI
C2FH3Cb
C2H3F2CI
CF3CF2CHCb
CCIF2CF2CHCIF
ecu
CHCICCb
CCbCCb
CHsCI
ChbCCb
CKbCb
CHCb
CHF3
CH2F2
CH3F
C2HFs
C2H2F4
CH2FCF3
C2H3F3
C2H3F3
CKbFCKbF
C2H4F2
CH3CH2F
C3HF7
CF3CF2CH2F
CF3CHFCHF2
C3H2F6
C3H3Fs
CHF2CH2CF3
CF3CH2CF2CH3
CsKbFio
CF3OCHF2
CF2HOCF2H
CHsOCFs
CF3CHFOCF3
Aluminum Oxide
Bromine
Carbon
Methane
Ethane
Propane
Perfluoromethane
Perfluoroethane, hexafluoroethane
Perfluorocyclopropane
Perfluoropropane
Perfluorocyclobutone
Perfluorobutane
Perfluoropentane
Perfluorohexane
Trifluoroiodomethane
Trichlorofluoromethane (CFC-11)
Dichlorodifluoromethane (CFC-12)
Chlorotrifluoromethane (CFC-13)
Trichlorotrifluoroethane (CFC-113)*
CFC-113a*
Dichlorotetrafluoroethane (CFC-114)
Chloropentafluoroethane (CFC-115)
HCFC-21
Chlorodifluoromethane (HCFC-22)
HCFC-123
HCFC-124
HCFC-141b
HCFC-142b
HCFC-225ca
HCFC-225cb
Carbon tetrachloride
Trichloroethylene
Perchloroethylene, tetrachloroethene
Methylchloride
Methylchloroform
Methylenechloride
Chloroform, trichloromethane
HFC-23
HFC-32
HFC-41
HFC-125
HFC-134
HFC-134a
HFC-143*
HFC-143a*
HFC-152*
HFC-152a*
HFC-161
HFC-227ea
HFC-236cb
HFC-236ea
HFC-236fa
HFC-245ca
HFC-245fa
HFC-365mfc
HFC-43-10mee
HFE-125
HFE-134
HFE-143a
HFE-227ea
A-418 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
CF3CHCIOCHF2
CF3CHFOCHF2
CF3CH2OCF3
CF3CF2OCH3
CHF2CH2OCF3
CF3CH2OCHF2
CHF2CF2OCH3
CF3CH2OCH3
CF3CF2OCF2CHF2
CF3CF2OCH2CF3
CF3CF2CF2OCH3
CF3CF2OCH2CHF2
CF3CHFCF2OCH3
CHF2CF2CF2OCH3
CHF2CF2OCH2CHF2
CHF2CF2CH2OCHF2
CF3CF2CH2OCH3
CHF2CF2OCH2CH3
C4F9OCH3
C4F9OC2H5
CHF2OCF2OC2F4OCHF2
CHF2OCF2OCHF2
CHF2OCF2CF2OCHF2
CHsOCHs
CH2Br2
CH2BrCI
CHBrs
CHBrF2
CHsBr
CF2BrCI
CF3Br(CBrF3)
CF3I
CO
C02
CaCOs
CaMg(C03)2
CaO
Cl
F
Fe
Fe203
FeSi
H, H2
H20
H202
OH
N, N2
NH3
NH4+
HN03
NF3
N20
NO
N02
N03
Na
Na2C03
NasAIFe
0, 02
HCFE-235da2
HFE-236ea2
HFE-236fa
HFE-245cb2
HFE-245fa1
HFE-245fa2
HFE-254cb2
HFE-263fb2
HFE-329mcc2
HFE-338mcf2
HFE-347mcc3
HFE-347mcf2
HFE-356mec3
HFE-356pcc3
HFE-356pcf2
HFE-356pcf3
HFE-365mcf3
HFE-374pcf2
HFE-7100
HFE-7200
H-Galden 1040x
HG-10
HG-01
Dimethyl ether
Dibromomethane
Dibromochloromethane
Tribromomethane
Bromodifluoromethane
Methylbromide
Bromodichloromethane (Halon 1211)
Bromotrifluoromethane (Halon 1301)
FIC-1311
Carbon monoxide
Carbon dioxide
Calcium carbonate, Limestone
Dolomite
Calcium oxide, Lime
atomic Chlorine
Fluorine
Iron
Ferric oxide
Ferrosilicon
atomic Hydrogen, molecular Hydrogen
Water
Hydrogen peroxide
Hydroxyl
atomic Nitrogen, molecular Nitrogen
Ammonia
Ammonium ion
Nitric acid
Nitrogen trifluoride
Nitrous oxide
Nitric oxide
Nitrogen dioxide
Nitrate radical
Sodium
Sodium carbonate, soda ash
Synthetic cryolite
atomic Oxygen, molecular Oxygen
A-419
-------
Os Ozone
S atomic Sulfur
H2S04 Sulfuric acid
SFe Sulfur hexafluoride
SFsCFs Trifluoromethylsulphur pentafluoride
S02 Sulfur dioxide
Si Silicon
SiC Silicon carbide
SI02 Quartz
* Distinct isomers.
References
EIA (1993) State Energy Data Report 1992, DOE/EIA-0214(93), Energy Information Administration, U.S. Department of
Energy. Washington, DC. December.
EIA (1998) Emissions of Greenhouse Gases in the United States, DOE/EIA-0573(97), Energy Information
Administration, U.S. Department of Energy. Washington, DC. October.
EIA (2007) Emissions of Greenhouse Gases in the United States 2006, Draft Report. Office of Integrated Analysis and
Forecasting, Energy Information Administration, U.S. Department of Energy, Washington, DC. DOE-EIA-
0573(2006).
EIA (2010) Annual Energy Review 2009. Energy Information Administration, U.S. Department of Energy, Washington,
DC. DOE/EIA-0384(2009). August 2010.
EPA (2003) E-mail correspondence. Air pollutant data. Office of Air Pollution to the Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency (EPA). December 22, 2003.
EPA (2013). "1970 - 2013 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards, November 2013. Available
online at .
IPCC (1996) Climate Change 1995: The Science of Climate Change. Intergovernmental Panel on Climate Change, J.T.
Houghton, L.G. Meira Filho, B.A. Callander, N. Harris, A. Kattenberg, and K. Maskell. (eds.). Cambridge University
Press. Cambridge, United Kingdom.
IPCC (2001) Climate Change 2001: The Scientific Basis. Intergovernmental Panel on Climate Change, J.T. Houghton, Y.
Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, C.A. Johnson, and K. Maskell (eds.). Cambridge
University Press. Cambridge, United Kingdom.
IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon , D. Qin, M. Manning, Z. Chen,
M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United
Kingdom 996 pp.
IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change [Stacker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K.
Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge,
United Kingdom and New York, NY, USA, 1535 pp.
A-420 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
ANNEX 7 Uncertainty
The annual U.S. Inventory presents the best effort to produce estimates for greenhouse gas source and sink categories
in the United States. These estimates were generated according to the UNFCCC reporting guidelines, following the
recommendations set forth in the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC/UNEP/OECD/TEA 1997), the IPCC Good Practice Guidance (IPCC 2000), the Good Practice Guidance for Land
Use, Land-Use Change and Forestry (IPCC 2003), and the 2006 Guidelines for National Greenhouse Gas Inventories
(IPCC 2006). This Annex provides an overview of the uncertainty analysis conducted to support the U.S. Inventory,
describes the sources of uncertainty characterized throughout the Inventory associated with various source categories
(including emissions and sinks), and describes the methods through which uncertainty information was collected,
quantified, and presented.
7.1. Overview
The current inventory emission estimates for some source categories, such as for CC>2 Emissions from Fossil Fuel
Combustion, have relatively low level of uncertainty associated with them. However, for some other source categories, the
inventory emission estimates are considered less certain. The two major types of uncertainty associated with these
emission estimates are (1) model uncertainty, which arises when the emission and/or removal estimation models used in
developing the inventory estimates do not fully and accurately characterize the respective emission and/or removal
processes (due to a lack of technical details or other resources), resulting in the use of incorrect or incomplete estimation
methodologies and (2) parameter uncertainty, which arises due to a lack of precise input data such as emission factors and
activity data.
The model uncertainty can be partially analyzed by comparing the model results with those of other models
developed to characterize the same emission (or removal) process, after taking into account the differences in their
conceptual framework, capabilities, data and assumptions. However, it would be very difficult—if not impossible—to
quantify the model uncertainty associated with the emission estimates (primarily because, in most cases, only a single
model has been developed to estimate emissions from any one source). Therefore, model uncertainty was not quantified in
this report. Nonetheless, it has been discussed qualitatively, where appropriate, along with the individual source category
description and inventory estimation methodology.
Parameter uncertainty is, therefore, the principal type and source of uncertainty associated with the national
inventory emission estimates and is the main focus of the quantitative uncertainty analyses in this report. Parameter
uncertainty has been quantified for all of the emission sources and sinks in the U.S. Inventory, with the exception of one
very small emission source category, CH4 emissions from Incineration of Waste, which was included in the 1990-2008
National GHG Inventory for the first time, and two other source categories (International Bunker Fuels and biomass
energy consumption) whose emissions are not included in the Inventory totals.
The primary purpose of the uncertainty analysis conducted in support of the U.S. Inventory is (i) to determine the
quantitative uncertainty associated with the emission (and removal) estimates presented in the main body of this report
[based on the uncertainty associated with the input parameters used in the emission (and removal) estimation
methodologies] and (ii) to evaluate the relative importance of the input parameters in contributing to uncertainty in the
associated source category inventory estimate and in the overall inventory estimate. Thus, the U.S. Inventory uncertainty
analysis provides a strong foundation for developing future improvements and revisions to the Inventory estimation
process. For each source category, the analysis highlights opportunities for changes to data measurement, data collection,
and calculation methodologies. These are presented in the "Planned Improvements" sections of each source category's
discussion in the main body of the report.
7.2. Methodology and Results
The United States has developed a quality assurance and quality control (QA/QC) and uncertainty management
plan (EPA 2002) in accordance with the IPCC Good Practice Guidance (IPCC 2000). Like the QA/QC plan, the
uncertainty management plan is part of a continually evolving process. The uncertainty management plan provides for a
quantitative assessment of the inventory analysis itself, thereby contributing to continuing efforts to understand both what
causes uncertainty and how to improve inventory quality. Although the plan provides both general and specific guidelines
A-421
-------
for implementing quantitative uncertainty analysis, its components are intended to evolve over time, consistent with the
inventory estimation process. The U.S. plan includes procedures and guidelines, and forms and templates, for developing
quantitative assessments of uncertainty in the national Inventory estimates (EPA 2002).
The IPCC Good Practice Guidance recommends two approaches—Tier 1 and Tier 2—for developing
quantitative estimates of uncertainty in the inventory estimate of individual source categories and the overall
inventory. Of these, the Tier 2 approach is both more flexible and reliable than Tier 1; both methods are described in
the next section. The United States is in the process of implementing a multi-year strategy to develop quantitative
estimates of uncertainty for all source categories using the Tier 2 approach. For the current Inventory, a Tier 2
approach was implemented for all source categories with the exception of Composting and parts of Agricultural Soil
Management source categories.
The current Inventory reflects significant improvements over the previous publication in the extent to
which the Tier 2 approach to uncertainty analysis was adopted. Each of the new Tier 2 analyses reflects additional
detail and characterization of input parameters using statistical data collection, expert elicitation methods and more
informed judgment. In following the UNFCCC requirement under Article 4.1, emissions from International Bunker
Fuels and Indirect Greenhouse Gas Emissions are not included in the total emissions estimated for the U.S.
Inventory; therefore, no quantitative uncertainty estimates have been developed for these source categories.
Emissions from biomass combustion are accounted for implicitly in the LULUCF chapter through the calculation of
changes in carbon stocks. The Energy sector does provide an estimate of CCh emissions from bioenergy
consumption provided as a memo item for informational purposes in line with the UNFCCC reporting requirements.
Tier 1 and Tier 2 Approach
The Tier 1 method for estimating uncertainty is based on the error propagation equation. This equation combines
the uncertainty associated with the activity data and the uncertainty associated with the emission (or the other) factors.
The Tier 1 approach is applicable where emissions (or removals) are usually estimated as the product of an activity value
and an emission factor or as the sum of individual sub-source category values. Inherent in employing the Tier 1 method
are the assumptions that, for each source category, (i) both the activity data and the emission factor values are
approximately normally distributed, (ii) the coefficient of variation (i.e., the ratio of the standard deviation to the mean)
associated with each input variable is less than 30 percent, and (iii) the input variables within and across (sub-) source
categories are not correlated (i.e., value of each variable is independent of the values of other variables).
The Tier 2 method is preferred (i) if the uncertainty associated with the input variables is significantly large, (ii)
if the distributions underlying the input variables are not normal, (iii) if the estimates of uncertainty associated with the
input variables are correlated, and/or (iv) if a sophisticated estimation methodology and/or several input variables are used
to characterize the emission (or removal) process correctly. In practice, the Tier 2 is the preferred method of uncertainty
analysis for all source categories where sufficient and reliable data are available to characterize the uncertainty of the input
variables.
The Tier 2 method employs the Monte Carlo Stochastic Simulation technique (also referred to as the Monte
Carlo method). Under this method, estimates of emissions (or removals) for a particular source category are generated
many times (equal to the number of simulations specified) using an uncertainty model, which is an emission (or removal)
estimation equation that imitates or is the same as the inventory estimation model for a particular source category. These
estimates are generated using the respective, randomly-selected values for the constituent input variables using
commercially available simulation software such as @RISK or Crystal Ball.
Characterization of Uncertainty in Input Variables
Both Tier 1 and Tier 2 uncertainty analyses require that all the input variables are we 11-characterized in terms of
their Probability Density Functions (PDFs). In the absence of particularly convincing data measurements, sufficient data
samples, or expert judgments that determined otherwise, the PDFs incorporated in the current source category uncertainty
However, because the input variables that determine the emissions from the Fossil Fuel Combustion and the International
Bunker Fuels source categories are correlated, uncertainty associated with the activity variables in the International Bunker Fuels
was taken into account in estimating the uncertainty associated with the Fossil Fuel Combustion.
A-422 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
analyses were limited to normal, lognormal, uniform, triangular, and beta distributions. The choice among these five
PDFs depended largely on the observed or measured data and expert judgment.
Source Category Inventory Uncertainty Estimates
Discussion surrounding the input parameters and sources of uncertainty for each source category appears in the
body of this report. Table A-288 summarizes results based on assessments of source category-level uncertainty. The table
presents base year (1990 or 1995) and current year (2012) emissions for each source category. The combined uncertainty
(at the 95 percent confidence interval) for each source category is expressed as the percentage deviation above and below
the total 2012 emissions estimated for that source category. Source category trend uncertainty is described subsequently
in this Appendix.
Table A-288: Summary Results of Source Category Uncertainty Analyses
Source Category Base Year Emissions" 201 2 Emissions"
C02
Fossil Fuel Combustion0
Non-Energy Use of Fuels
Iron and Steel Production & Metallurgical Coke Production
Natural Gas Systems
Cement Production
Lime Production
Incineration of Waste
Ammonia Production
Other Process Uses of Carbonates
Cropland Remaining Cropland
Urea Consumption for Non-Agricultural Purposes
Petrochemical Production
Aluminum Production
Soda Ash Production and Consumption
Carbon Dioxide Consumption
Titanium Dioxide Production
Ferroalloy Production
Zinc Production
Glass Production
Phosphoric Acid Production
Wetlands Remaining Wetlands
Lead Production
Petroleum Systems
Silicon Carbide Production and Consumption
Land Use, Land-Use Change, and Forestry (Sink)d
Wood Biomassf
International Bunker Fuels'
Biomass - Ethanol6
CH4
Enteric Fermentation
Natural Gas Systems
Landfills
Coal Mining
Manure Management
Petroleum Systems
Forest Land Remaining Forest Land
Wastewater Treatment
Rice Cultivation
Stationary Combustion
Tg C02 Eq.
5,076.7
4,708.9
117.0
109.8
33.7
33.3
11.5
10.9
16.8
5.1
7.1
0.0
2.2
6.8
4.1
1.4
1.2
2.2
0.9
NA
1.5
1.0
0.3
0.4
0.4
-841.4
0.0
114.3
219.3
616.6
133.2
129.6
149.2
84.1
30.4
33.9
4.6
23.5
7.1
7.4
Tg CCfe Eq.
5,382.8
5,071.9
110.3
54.3
35.2
35.1
13.3
12.2
9.4
8.0
7.4
5.2
3.5
3.4
2.7
1.8
1.7
1.7
1.4
1.2
1.1
0.8
0.5
0.4
0.2
-979.3
194.1
105.8
72.9
567.3
141.0
129.9
102.8
55.8
52.9
31.7
15.3
12.8
7.4
5.7
201 2 Uncertainty"
Low
-2%
-2%
-21%
-16%
-19%
-6%
-3%
-10%
-6%
-14%
-57%
-9%
-25%
-2%
-6%
-38%
-12%
-12%
-16%
-5%
-19%
-26%
-14%
-24%
-9%
18%
NE
NE
NE
-10%
-11%
-19%
-56%
-4%
-18%
-24%
-82%
-27%
-52%
-36%
High
5%
5%
35%
17%
30%
6%
3%
13%
7%
21%
69%
10%
28%
2%
5%
43%
13%
12%
17%
4%
21%
30%
15%
149%
9%
-15%
NE
NE
NE
18%
18%
30%
49%
35%
20%
149%
176%
21%
96%
132%
A-423
-------
Abandoned Underground Coal Mines
Petrochemical Production
Mobile Combustion
Composting
Iron and Steel Production & Metallurgical Coke Production
Field Burning of Agricultural Residues
Ferroalloy Production
Silicon Carbide Production and Consumption
Incineration of Waste
International Bunker Fuels'
N20
Agricultural Soil Management
Stationary Combustion
Manure Management
Mobile Combustion
Nitric Acid Production
Forest Land Remaining Forest Land
Adipic Acid Production
Wastewater Treatment
N20 from Product Uses
Composting
Settlements Remaining Settlements
Incineration of Waste
Field Burning of Agricultural Residues
Wetlands Remaining Wetlands
International Bunker Fuels'
MFCs, PFCs, and SF6
Substitution of Ozone Depleting Substancess
Electrical Transmission and Distribution
HCFC-22 Production
Semiconductor Manufacture
Aluminum Production
Magnesium Production
Total"
Net Emissions (Sources and Sinks)11
6.0
0.9
4.2
0.3
1.0
0.7
0.0
0.0
0.0
0.2
315.0
200.3
12.8
12.1
40.4
20.0
0.5
15.3
3.7
4.4
0.4
1.0
0.5
0.4
0.0
1.1
90.5
28.5
26.8
36.4
2.9
18.5
5.4
6,098.7
5,257.3
4.7
3.1
1.7
1.6
0.6
0.3
0.0
0.0
0.0
0.1
410.1
306.6
22.0
18.0
16.5
15.3
12.8
5.8
5.0
4.4
1.8
1.5
0.4
0.1
0.0
1.0
161.9
143.6
6.0
4.3
3.7
2.5
1.7
6,522.0
5,542.7
-19%
-10%
-12%
-50%
-21%
-41%
-11%
-9%
NE
NE
-8%
NE
-20%
-16%
-3%
-37%
-66%
-4%
-75%
-24%
-50%
-49%
-48%
-30%
-73%
NE
0%
0%
-18%
-7%
-5%
-5%
-11%
-1%
-2%
26%
10%
16%
50%
22%
42%
11%
10%
NE
NE
32%
NE
51%
24%
27%
38%
146%
4%
100%
24%
50%
163%
322%
32%
38%
NE
13%
14%
25%
10%
5%
6%
12%
5%
7%
Notes:
Totals may not sum due to independent rounding.
NE: Not Estimated
+ Does not exceed 0.05 Tg C02 Eq.
a Emission estimates reported in this table correspond to emissions from only those source categories for which quantitative uncertainty was performed for the
current Inventory. Thus the totals reported for 2012 in this table exclude approximately 3.6 Tg C02 Eq. of emissions for which quantitative uncertainty was not
assessed. Hence, these emission estimates do not match the final total U.S. greenhouse gas emission estimates presented in this Inventory. All uncertainty
estimates correspond only to the totals reported in this table.
b The uncertainty estimates correspond to a 95 percent confidence interval, with the lower bound corresponding to 2.5th percentile and the upper bound
corresponding to 97.5th percentile.
'This source category's inventory estimates exclude C02 emissions from geothermal sources, as quantitative uncertainty analysis was not performed for that
sub-source category. Hence, for this source category, the emissions reported in this table do not match the emission estimates presented in the Energy chapter
of the Inventory.
d Sinks are only included in Net Emissions.
e Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals.
'Emissions from International Bunker Fuels are not included in the totals.
a This source category's estimate for 2012 excludes 3.8 Tg of C02 Eq. from several very small emission sources, as uncertainty associated with those sources
was not assessed. Hence, for this source category, the emissions reported in this table do not match the emission estimates presented in the Industrial
Processes chapter of the Inventory.
h Totals exclude emissions for which uncertainty was not quantified. .
1 Base Year is 1990 for all sources except Substitution of Ozone Depleting Substances, for which the United States has chosen 1995.
A-424 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Overall (Aggregate) Inventory Level Uncertainty Estimates
The overall level uncertainty estimate for the U.S. greenhouse gas emissions inventory was developed using the
IPCC Tier 2 uncertainty estimation methodology. The uncertainty models of all the emission source categories could not
be directly integrated to develop the overall uncertainty estimates due to software constraints in integrating multiple, large
uncertainty models. Therefore, an alternative approach was adopted to develop the overall uncertainty estimates. The
Monte Carlo simulation output data for each emission source category uncertainty analysis were combined by type of gas
and the probability distributions were fitted to the combined simulation output data, where such simulated output data
were available. If such detailed output data were not available for particular emissions sources, individual probability
distributions were assigned to those source category emission estimates based on the most detailed data available from the
quantitative uncertainty analysis performed.
For the Composting and for parts of Agricultural Soil Management source categories, Tier 1 uncertainty results
were used in the overall uncertainty analysis estimation. However, for all other emission sources (excluding international
bunker fuels, CC>2 from biomass combustion, and CH4 from incineration of waste), Tier 2 uncertainty results were used in
the overall uncertainty estimation.
The overall uncertainty model results indicate that the 2012 U.S. greenhouse gas emissions are estimated to be
within the range of approximately 6,448 to 6,873 Tg CC>2 Eq., reflecting a relative 95 percent confidence interval
uncertainty range of -1 percent to 5 percent with respect to the total U.S. greenhouse gas emission estimate of
approximately 6,522 Tg CC>2 Eq. The uncertainty interval associated with total CC>2 emissions, which constitute about 83
percent of the total U.S. greenhouse gas emissions in 2011, ranges from -2 percent to 5 percent of total CC>2 emissions
estimated. The results indicate that the uncertainty associated with the inventory estimate of the total CH4 emissions
ranges from -10 percent to 18 percent, uncertainty associated with the total inventory N2O emission estimate ranges from
-8 percent to 32 percent, and uncertainty associated with high GWP gas emissions ranges from 0 percent to 13 percent.
A summary of the overall quantitative uncertainty estimates is shown below.
Table A-289. Quantitative Uncertainty Assessment of Overall National Inventory Emissions ITg GO? Eq. and Percent)
201 2 Emission Standard
Estimate" Uncertainty Range Relative to Emission Estimate11 Mean0 Deviation0
Gas (TgCOzEq.) (Tg CO, Eq.) (%) (Tg CO, Eq.)
C02
CH4e
N20
RFC, HFC & SF6e
Total
Net Emissions (Sources and Sinks)
5,382.8
567.3
410.1
161.9
6,522.0
5,542.7
Lower
Bound"
5,265
513
378
161
6,448
5,420
Upper
Bound"
5,630
671
540
182
6,873
5,940
Lower
Bound
-2%
-10%
-8%
0%
-1%
-2%
Upper
Bound
5%
18%
32%
13%
5%
7%
5,448
586
452
172
6,658
5,681
93
40
41
5
109
134
Notes:
a Emission estimates reported in this table correspond to emissions from only those source categories for which quantitative uncertainty was performed this year.
Thus the totals reported in this table exclude approximately 3.6 Tg C02 Eq. of emissions for which quantitative uncertainty was not assessed. Hence, these
emission estimates do not match the final total U.S. greenhouse gas emission estimates presented in this Inventory.
b The lower and upper bounds for emission estimates correspond to a 95 percent confidence interval, with the lower bound corresponding to 2.5th percentile and
the upper bound corresponding to 97.5th percentile.
c Mean value indicates the arithmetic average of the simulated emission estimates; standard deviation indicates the extent of deviation of the simulated values
from the mean.
d The lower and upper bound emission estimates for the sub-source categories do not sum to total emissions because the low and high estimates for total
emissions were calculated separately through simulations.
e The overall uncertainty estimates did not take into account the uncertainty in the GWP values for ChU, N20 and high GWP gases used in the inventory emission
calculations for 2012.
Trend Uncertainty
In addition to the estimates of uncertainty associated with the current year's emission estimates, this Annex also
presents the estimates of trend uncertainty. The IPCC Good Practice Guidance defines trend as the difference in emissions
between the base year (i.e., 1990) and the current year (i.e., 2012) inventory estimates. However, for purposes of
understanding the concept of trend uncertainty, the emission trend is defined in this Inventory as the percentage change in
A-425
-------
the emissions (or removal) estimated for the current year, relative to the emission (or removal) estimated for the base year.
The uncertainty associated with this emission trend is referred to as trend uncertainty.
Under the Tier 1 approach, the trend uncertainty for a source category is estimated using the sensitivity of the
calculated difference between the base year and the current year (i.e., 2012) emissions to an incremental (i.e., 1 percent)
increase in one or both of these values for that source category. The two sensitivities are expressed as percentages: Type
A sensitivity highlights the effect on the difference between the base and the current year emissions caused by a 1 percent
change in both, while Type B sensitivity highlights the effect caused by a change to only the current year's emissions.
Both sensitivities are simplifications introduced in order to analyze the correlation between the base and the current year
estimates. Once calculated, the two sensitivities are combined using the error propagation equation to estimate the overall
trend uncertainty.
Under the Tier 2 approach, the trend uncertainty is estimated using Monte Carlo Stochastic Simulation technique.
The trend uncertainty analysis takes into account the fact that the base and the current year estimates often share input
variables. For purposes of the current Inventory, a simple approach has been adopted, under which the base year source
category emissions (or removals) are assumed to exhibit the same uncertainty characteristics as the current year emissions
(or removals). Source category-specific PDFs for base year estimates were developed using current year (i.e., 2012)
uncertainty output data. These were adjusted to account for differences in magnitude between the two years' inventory
estimates. Then, for each source category, a trend uncertainty estimate was developed using the Monte Carlo method.
The overall inventory trend uncertainty estimate was developed by combining all source category-specific trend
uncertainty estimates. These trend uncertainty estimates present the range of likely change from base year to 2012, and
are shown in Table A- 290.
Table A- 290. Quantitative Assessment of Trend Uncertainty tTg Clh Eg. and Percentl
Gas/Source
Base Year 2012
Emissions'." Emissions"
Emissions
Trend"
Trend Range"."
(Tg C02 Eq.) (%) (%)
C02
Fossil Fuel Combustion0
Non-Energy Use of Fuels
Iron and Steel Production & Metallurgical Coke Production
Natural Gas Systems
Cement Production
Ammonia Production
Lime Production
Incineration of Waste
Cropland Remaining Cropland
Aluminum Production
Other Process Uses of Carbonates
Urea Consumption for Non-Ag Purposes
Petrochemical Production
Soda Ash Production and Consumption
Ferroalloy Production
Phosphoric Acid Production
Glass Production
Carbon Dioxide Consumption
Titanium Dioxide Production
Wetlands Remaining Wetlands
Zinc Production
Lead Production
Petroleum Systems
Silicon Carbide Production and Consumption
Land Use, Land-Use Change, and Forestry (Sink)a
Biomass - Wood*
International Bunker Fuels'
Biomass - Eihanok
CH4
Natural Gas Systems
Landfills
5,108.3
4,744.7
120.8
99.8
37.7
33.3
13.0
11.4
8.0
7.1
6.8
4.9
3.8
3.4
2.7
2.2
1.6
1.5
1.4
1.2
1.0
0.6
0.5
0.4
0.4
-831.1
214.4
103.5
4.2
635.7
156.4
147.8
5,382.8
5,071.9
110.3
54.3
35.2
35.1
9.4
13.3
12.2
7.4
3.4
8.0
5.2
3.5
2.7
1.7
1.1
1.2
1.8
1.7
0.8
1.4
0.5
0.4
0.2
-979.3
194.1
105.8
72.9
567.3
129.9
102.8
5%
7%
-9%
-46%
-7%
5%
-28%
17%
53%
4%
-50%
63%
39%
2%
-3%
-23%
-31%
-19%
28%
46%
-20%
125%
2%
3%
-58%
18%
-9%
2%
1624%
-11%
-17%
-30%
Lower
Bound
1%
2%
-37%
-57%
-34%
-4%
-35%
12%
30%
-60%
-51%
27%
21%
-28%
-9%
-35%
-48%
-24%
-38%
13%
-47%
77%
-20%
-55%
-68%
1%
NE
NE
NE
-31%
-41%
-72%
Upper
Bound
11%
12%
35%
-31%
31%
15%
-21%
21%
81%
161%
-48%
108%
59%
59%
5%
-8%
-7%
-13%
71%
62%
21%
186%
23%
136%
-58%
38%
NE
NE
NE
3%
19%
65%
A-426 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Enteric Fermentation
Coal Mining
Petroleum Systems
Manure Management
Wastewater Treatment
Rice Cultivation
Stationary Combustion
Abandoned Underground Coal Mines
Mobile Combustion
Forest Land Remaining Forest Land
Petrochemical Production
Iron and Steel Production & Metallurgical Coke Production
Composting
Field Burning of Agricultural Residues
Ferroalloy Production
Silicon Carbide Production and Consumption
Incineration of Waste
International Bunker Fuels'
N20
Agricultural Soil Management
Mobile Combustion
Nitric Acid Production
AdipicAcid Production
Manure Management
Stationary Combustion
N20 from Product Uses
Wastewater Treatment
Forest Land Remaining Forest Land
Settlements Remaining Settlements
Incineration of Waste
Composting
Field Burning of Agricultural Residues
Wetlands Remaining Wetlands
International Bunker Fuels'
MFCs, PFCs, and SF6
HCFC-22 Production
Substitution of Ozone Depleting Substances
Electrical Transmission and Distribution
Aluminum Production
Magnesium Production and Processing
Semiconductor Manufacture
Total"
Net Emission (Sources and Sinks)
137.9
81.1
35.8
31.5
13.2
7.7
7.5
6.0
4.6
2.5
2.3
1.0
0.3
0.3
+
+
+
0.1
398.6
282.1
44.0
18.2
15.8
14.4
12.3
4.4
3.5
2.1
1.0
0.5
0.4
0.1
+
0.9
121.2
36.4
31.3
26.7
18.4
5.4
2.9
6,263.8
5,432.7
141.0
55.8
31.7
52.9
12.8
7.4
5.7
4.7
1.7
15.3
3.1
0.6
1.6
0.3
+
+
+
0.1
410.1
306.6
16.5
15.3
5.8
18.0
22.0
4.4
5.0
12.8
1.5
0.4
1.8
0.1
+
1.0
161.9
4.3
143.6
6.0
2.5
1.7
3.7
6,522.0
5,542.7
2%
-31%
-11%
68%
-3%
-4%
-24%
-22%
-63%
511%
36%
-36%
397%
-6%
-31%
-67%
-23%
-37%
3%
9%
-62%
-16%
-64%
25%
79%
0%
45%
509%
48%
-23%
397%
2%
-31%
8%
34%
-88%
358%
-77%
-86%
-69%
28%
4%
2%
-17%
-56%
-61%
13%
-73%
-64%
-72%
-51%
-70%
-14%
243%
-44%
120%
-49%
-42%
-71%
NE
NE
-26%
-33%
-69%
-58%
-35%
-5%
13%
-26%
-68%
29%
-53%
-83%
125%
-34%
-79%
NE
27%
-89%
317%
-83%
-87%
-73%
19%
-1%
-5%
25%
-33%
103%
134%
-7%
160%
106%
16%
-54%
4341%
554%
5%
1025%
80%
-19%
-62%
NE
NE
43%
79%
-54%
39%
-15%
64%
182%
28%
553%
2690%
378%
259%
1009%
62%
130%
NE
50%
-87%
404%
-70%
-85%
-63%
38%
9%
8%
Notes:
Totals may not sum due to independent rounding.
NE: Not Estimated
+ Does not exceed 0.05 Tg C02 Eq.
a Emission estimates reported in this table correspond to emissions from only those source categories for which quantitative uncertainty was performed for the
current Inventory. Thus the totals reported for 2011 in this table exclude approximately 3.6 Tg C02 Eq. of emissions for which quantitative uncertainty was not
assessed. Hence, these emission estimates do not match the final total U.S. greenhouse gas emission estimates presented in this Inventory. All uncertainty
estimates correspond only to the totals reported in this table.
b The trend range represents a 95 percent confidence interval for the emission trend, with the lower bound corresponding to 2.5th percentile value and the upper
bound corresponding to 97.5th percentile value.
'This source category's inventory estimates exclude C02 emissions from geothermal sources, as quantitative uncertainty analysis was not performed for that
sub-source category. Hence, for this source category, the emissions reported in this table do not match the emission estimates presented in the Energy chapter
of the Inventory.
d Sinks are only included in Net Emissions.
e Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals.
'Emissions from International Bunker Fuels are not included in the totals.
a This source category's estimate for 2011 excludes 3.8 Tg of C02 Eq. from several very small emission sources, as uncertainty associated with those sources
was not assessed. Hence, for this source category, the emissions reported in this table do not match the emission estimates presented in the Industrial
Processes chapter of the Inventory.
h Totals exclude emissions for which uncertainty was not quantified. .
A-427
-------
1 Base Year is 1990 for all sources except Substitution of Ozone Depleting Substances, for which the United States has chosen 1995.
7.3. Planned Improvements
Identifying the sources of uncertainty in the emission and sink estimates of the Inventory and quantifying the
magnitude of the associated uncertainty is the crucial first step towards improving those estimates. Quantitative
assessment of the parameter uncertainty may also provide information about the relative importance of input parameters
(such as activity data and emission factors), based on their relative contribution to the uncertainty within the source
category estimates. Such information can be used to prioritize resources with a goal of reducing uncertainty over time
within or among inventory source categories and their input parameters. In the current Inventory, potential sources of
model uncertainty have been identified for some emission source categories, and uncertainty estimates based on their
parameters' uncertainty have been developed for all the emission source categories, with the exception of CH4 from
incineration of waste, which is a minor emission source category newly added to the Inventory starting with the 2008
business year, and the international bunker fuels and wood biomass and ethanol combustion source categories, which are
not included in the energy sector totals. Emissions from biomass and ethanol combustion however are accounted for
implicitly in the LULUCF chapter through the calculation of changes in carbon stocks. The Energy sector does provide an
estimate of CC>2 emissions from bioenergy consumption provided as a memo item for informational purposes.
Specific areas that require further research include:
• Incorporating excluded emission sources. Quantitative estimates for some of the sources and sinks of
greenhouse gas emissions, such as from some land-use activities, industrial processes, and parts of mobile
sources, could not be developed at this time either because data are incomplete or because methodologies do not
exist for estimating emissions from these source categories. See Annex 5 of this report for a discussion of the
sources of greenhouse gas emissions and sinks excluded from this report. In the future, efforts will focus on
estimating emissions from excluded emission sources and developing uncertainty estimates for all source
categories for which emissions are estimated.
• Improving the accuracy of emission factors. Further research is needed in some cases to improve the accuracy of
emission factors used to calculate emissions from a variety of sources. For example, the accuracy of current
emission factors applied to CFU and N2O emissions from stationary and mobile combustion are highly uncertain.
• Collecting detailed activity data. Although methodologies exist for estimating emissions for some sources,
problems arise in obtaining activity data at a level of detail in which aggregate emission factors can be applied.
For example, the ability to estimate emissions of SFe from electrical transmission and distribution is limited due
to a lack of activity data regarding national SFe consumption or average equipment leak rates.
In improving the quality of uncertainty estimates the following include areas that deserve further attention:
• Refine Source Category and Overall Uncertainty Estimates. For many individual source categories, further
research is needed to more accurately characterize PDFs that surround emissions modeling input variables. This
might involve using measured or published statistics or implementing rigorous elicitation protocol to elicit expert
judgments, if published or measured data are not available.
• Include GWP uncertainty in the estimation of Overall level and trend uncertainty. The current year's Inventory
does not include the uncertainty associated with the GWP values in the estimation of the overall uncertainty for
the Inventory. Including this source would contribute to a better characterization of overall uncertainty and help
assess the level of attention that this source of uncertainty warrants in the future.
• Improve characterization of trend uncertainty associated with base year Inventory estimates. The
characterization of base year uncertainty estimates could be improved, by developing explicit uncertainty models
for the base year. This would then improve the analysis of trend uncertainty. However, not all of the simplifying
assumptions described in the "Trend Uncertainty" section above may be eliminated through this process due to a
lack of availability of more appropriate data.
A-428 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
7.4. Additional Information on Uncertainty Analyses by Source
The quantitative uncertainty estimates associated with each emission and sink source category are reported in
each chapter of this Inventory following the discussions of inventory estimates and their estimation methodology. This
section provides additional descriptions of the uncertainty analyses performed for some of the sources, including the
models and methods used to calculate the emission estimates and the potential sources of uncertainty surrounding them.
These sources are organized below in the same order as the sources in each chapter of the main section of this Inventory.
To avoid repetition, the following uncertainty analysis discussions of individual source categories do not include
descriptions of these source categories. Hence, to better understand the details provided below, refer to the respective
chapters and sections in the main section of this Inventory, as needed. All uncertainty estimates are reported relative to the
2012 Inventory estimates for the 95 percent confidence interval, unless otherwise specified.
Energy
The uncertainty analysis descriptions in this section correspond to source categories included in the Energy
Chapter of the Inventory.
CO2 from Fossil Fuel Combustion
For estimates of CCh from fossil fuel combustion, the amount of CC>2 emitted is directly related to the amount of
fuel consumed, the fraction of the fuel that is oxidized, and the carbon content of the fuel. Therefore, a careful accounting
of fossil fuel consumption by fuel type, average carbon contents of fossil fuels consumed, and production of fossil fuel-
based products with long-term carbon storage should yield an accurate estimate of CC>2 emissions.
Nevertheless, there are uncertainties in the consumption data, carbon content of fuels and products, and carbon
oxidation efficiencies. For example, given the same primary fuel type (e.g., coal, petroleum, or natural gas), the amount of
carbon contained in the fuel per unit of useful energy can vary. For the United States, however, the impact of these
uncertainties on overall CC>2 emission estimates is believed to be relatively small. See, for example, Marland and Pippin
(1990).
Although statistics of total fossil fuel and other energy consumption are relatively accurate, the allocation of this
consumption to individual end-use sectors (i.e., residential, commercial, industrial, and transportation) is less certain. For
example, for some fuels the sectoral allocations are based on price rates (i.e., tariffs), but a commercial establishment may
be able to negotiate an industrial rate or a small industrial establishment may end up paying an industrial rate, leading to a
misallocation of emissions. Also, the deregulation of the natural gas industry and the more recent deregulation of the
electric power industry have likely led to some minor problems in collecting accurate energy statistics as firms in these
industries have undergone significant restructuring.
To calculate the total CC>2 emission estimate from energy-related fossil fuel combustion, the amount of fuel used
in these non-energy production processes were subtracted from the total fossil fuel consumption. The amount of CC>2
emissions resulting from non-energy related fossil fuel use has been calculated separately and reported in the Carbon
Emitted from Non-Energy Uses of Fossil Fuels section of this report. These factors all contribute to the uncertainty in the
CC>2 estimates. Detailed discussions on the uncertainties associated with C emitted from Non-Energy Uses of Fossil Fuels
can be found within that section of this chapter.
Various sources of uncertainty surround the estimation of emissions from international bunker fuels, which are
subtracted from the U.S. totals (see the detailed discussions on these uncertainties provided in the International Bunker
Fuels section of this chapter). Another source of uncertainty is fuel consumption by U.S. territories. The United States
does not collect energy statistics for its territories at the same level of detail as for the fifty states and the District of
Columbia. Therefore, estimating both emissions and bunker fuel consumption by these territories is difficult.
Uncertainties in the emission estimates presented above also result from the data used to allocate CO 2 emissions
from the transportation end-use sector to individual vehicle types and transport modes. In many cases, bottom-up
estimates of fuel consumption by vehicle type do not match aggregate fuel-type estimates from EIA. Further research is
planned to improve the allocation into detailed transportation end-use sector emissions.
The uncertainty analysis was performed by primary fuel type for each end-use sector, using the IPCC-
recommended Tier 2 uncertainty estimation methodology, Monte Carlo Stochastic Simulation technique, with @RISK
software. For this uncertainty estimation, the inventory estimation model for CC>2 from fossil fuel combustion was
integrated with the relevant variables from the inventory estimation model for International Bunker Fuels, to realistically
A-429
-------
characterize the interaction (or endogenous correlation) between the variables of these two models. About 120 input
variables were modeled for CC>2 from energy-related Fossil Fuel Combustion (including about 10 for non-energy fuel
consumption and about 20 for International Bunker Fuels).
In developing the uncertainty estimation model, uniform distributions were assumed for all activity-related input
variables and emission factors, based on the SAIC/EIA (2001) report. Triangular distributions were assigned for the
oxidization factors (or combustion efficiencies). The uncertainty ranges were assigned to the input variables based on the
data reported in SAIC/EIA (2001) and on conversations with various agency personnel.
The uncertainty ranges for the activity-related input variables were typically asymmetric around their inventory
estimates; the uncertainty ranges for the emissions factors were symmetric. Bias (or systematic uncertainties) associated
with these variables accounted for much of the uncertainties associated with these variables (SAIC/EIA 2001). For
purposes of this uncertainty analysis, each input variable was simulated 10,000 times through Monte Carlo Sampling.
CH4 and N2O from Stationary Combustion
Methane emission estimates from stationary sources exhibit high uncertainty, primarily due to difficulties in
calculating emissions from wood combustion (i.e., fireplaces and wood stoves). The estimates of CFU and N2O emissions
presented are based on broad indicators of emissions (i.e., fuel use multiplied by an aggregate emission factor for different
sectors), rather than specific emission processes (i.e., by combustion technology and type of emission control).
An uncertainty analysis was performed by primary fuel type for each end-use sector, using the IPCC-
recommended Tier 2 uncertainty estimation methodology, Monte Carlo Stochastic Simulation technique, with @RISK
software.
The uncertainty estimation model for this source category was developed by integrating the CFU and N2O
stationary source inventory estimation models with the model for CC>2 from fossil fuel combustion to realistically
characterize the interaction (or endogenous correlation) between the variables of these three models. About 55 input
variables were simulated for the uncertainty analysis of this source category (about 20 from the CC>2 emissions from fossil
fuel combustion inventory estimation model and about 35 from the stationary source inventory models).
In developing the uncertainty estimation model, uniform distribution was assumed for all activity-related input
variables and N2(D emission factors, based on the SAIC/EIA (2001) report. For these variables, the uncertainty ranges
were assigned to the input variables based on the data reported in SAIC/EIA (2001). However, the CFU emission
factors differ from those used by EIA. Since these factors were obtained from IPCC/UNEP/OECD/IEA (1997),
uncertainty ranges were assigned based on IPCC default uncertainty estimates (IPCC 2000).
1!4 SAIC/EIA (2001) characterizes the underlying probability density function for the input variables as a combination of
uniform and normal distributions (the former to represent the bias component and the latter to represent the random component).
However, for purposes of the current uncertainty analysis, it was determined that uniform distribution was more appropriate to
characterize the probability density function underlying each of these variables.
H-5 In the SAIC/EIA (2001) report, the quantitative uncertainty estimates were developed for each of the three major fossil fuels
used within each end-use sector; the variations within the sub-fuel types within each end-use sector were not modeled. However,
for purposes of assigning uncertainty estimates to the sub-fuel type categories within each end-use sector in the current
uncertainty analysis, SAIC/EIA (2001)-reported uncertainty estimates were extrapolated.
116 Although, in general, random uncertainties are the main focus of statistical uncertainty analysis, when the uncertainty
estimates are elicited from experts, their estimates include both random and systematic uncertainties. Hence, both these types of
uncertainties are represented in this uncertainty analysis.
1!7 SAIC/EIA (2001) characterizes the underlying probability density function for the input variables as a combination of
uniform and normal distributions (the former distribution to represent the bias component and the latter to represent the random
component). However, for purposes of the current uncertainty analysis, it was determined that uniform distribution was more
appropriate to characterize the probability density function underlying each of these variables.
11^ In the SAIC/EIA (2001) report, the quantitative uncertainty estimates were developed for each of the three major fossil fuels
used within each end-use sector; the variations within the sub-fuel types within each end-use sector were not modeled. However,
for purposes of assigning uncertainty estimates to the sub-fuel type categories within each end-use sector in the current
uncertainty analysis, SAIC/EIA (2001)-reported uncertainty estimates were extrapolated.
A-430 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
CH4 and N2O from Mobile Combustion
A quantitative uncertainty analysis was conducted for the mobile source sector using the IPCC-recommended
Tier 2 uncertainty estimation methodology, Monte Carlo Stochastic Simulation technique, using @RISK software. The
uncertainty analysis was performed on 2012 estimates of CH4 and N2O emissions, incorporating probability distribution
functions associated with the major input variables. For the purposes of this analysis, the uncertainty was modeled for the
following four major sets of input variables: (1) vehicle miles traveled (VMT) data, by on-road vehicle and fuel type and
(2) emission factor data, by on-road vehicle, fuel, and control technology type, (3) fuel consumption, data, by non-road
vehicle and equipment type, and (4) emission factor data, by non-road vehicle and equipment type.
Uncertainty analyses were not conducted for NOX, CO, or NMVOC emissions. Emission factors for these gases
have been extensively researched since emissions of these gases from motor vehicles are regulated in the United States,
and the uncertainty in these emission estimates is believed to be relatively low. For more information, see Section 3.8.
However, a much higher level of uncertainty is associated with CFU and N2O emission factors due to limited emission test
data, and because, unlike CCh emissions, the emission pathways of CFU and N2O are highly complex.
Carbon Emitted from Non-Energy Uses of Fossil Fuels
An uncertainty analysis was conducted to quantify the uncertainty surrounding the estimates of emissions and
storage factors from non-energy uses. This analysis, performed using @RISK software and the IPCC-recommended Tier
2 methodology (Monte Carlo Stochastic Simulation technique), provides for the specification of probability density
functions for key variables within a computational structure that mirrors the calculation of the inventory estimate. The
results presented below provide the 95 percent confidence interval, the range of values within which emissions are likely
to fall, for this source category.
As noted above, the non-energy use analysis is based on U.S.-specific storage factors for (1) feedstock materials (natural
gas, LPG, pentanes plus, naphthas, other oils, still gas, special naphthas, and other industrial coal), (2) asphalt, (3)
lubricants, and (4) waxes. For the remaining fuel types (the "other" category in Table 3-20 and Table 3-21 of the main
Inventory document), the storage factors were taken directly from the IPCC Guidelines for National Greenhouse Gas
Inventories, where available, and otherwise assumptions were made based on the potential fate of carbon in the respective
NEU products. To characterize uncertainty, five separate analyses were conducted, corresponding to each of the five
categories. In all cases, statistical analyses or expert judgments of uncertainty were not available directly from the
information sources for all the activity variables; thus, uncertainty estimates were determined using assumptions based on
source category knowledge.
Incineration of Waste
A Tier 2 Monte Carlo analysis was performed to determine the level of uncertainty surrounding the estimates of
CC>2 emissions and N2O emissions from the incineration of waste (given the very low emissions for CFU, no uncertainty
estimate was derived). IPCC Tier 2 analysis allows the specification of probability density functions for key variables
within a computational structure that mirrors the calculation of the inventory estimate. Uncertainty estimates and
distributions for waste generation variables (i.e., plastics, synthetic rubber, and textiles generation) were obtained through
a conversation with one of the authors of the Municipal Solid Waste in the United States reports. Statistical analyses or
expert judgments of uncertainty were not available directly from the information sources for the other variables; thus,
uncertainty estimates for these variables were determined using assumptions based on source category knowledge and the
known uncertainty estimates for the waste generation variables.
The uncertainties in the waste incineration emission estimates arise from both the assumptions applied to the data
and from the quality of the data. Key factors include MSW incineration rate; fraction oxidized; missing data on waste
composition; average C content of waste components; assumptions on the synthetic/biogenic C ratio; and combustion
conditions affecting N2O emissions. The highest levels of uncertainty surround the variables that are based on assumptions
(e.g., percent of clothing and footwear composed of synthetic rubber); the lowest levels of uncertainty surround variables
that were determined by quantitative measurements (e.g., combustion efficiency, C content of C black).
Coal Mining
A quantitative uncertainty analysis was conducted for the coal mining source category using the IPCC-
recommended Tier 2 uncertainty estimation methodology. Because emission estimates from underground ventilation
systems were based on actual measurement data, uncertainty is relatively low. A degree of imprecision was introduced
because the measurements used were not continuous but rather an average of quarterly instantaneous readings.
A-431
-------
Additionally, the measurement equipment used can be expected to have resulted in an average of 10 percent
overestimation of annual CH4 emissions (Mutmansky & Wang 2000).
Estimates of CH4 recovered by degasification systems are relatively certain for utilized CH4 because of the
availability of gas sales information. In addition, many coal mine operators provided information on mined-through dates
for pre-drainage wells. Many of the recovery estimates use data on wells within 100 feet of a mined area. However,
uncertainty exists concerning the radius of influence of each well. The number of wells counted, and thus the avoided
emissions, may vary if the drainage area is found to be larger or smaller than estimated. The 2012 GHGRP data (EPA
2013) used for determining CH4 emissions from vented degasification wells are based on weekly measurements, an
improvement over the previous year's estimates, thus lowering the uncertainty of that subsource.
Abandoned Underground Coal Mines
A quantitative uncertainty analysis was conducted to estimate the uncertainty surrounding the estimates of
emissions from abandoned underground coal mines. The uncertainty analysis described below provides for the
specification of probability density functions for key variables within a computational structure that mirrors the calculation
of the inventory estimate. The results provide the range within which, with 95 percent certainty, emissions from this
source category are likely to fall.
As discussed above, the parameters for which values must be estimated for each mine in order to predict its
decline curve are: 1) the coal's adsorption isotherm; 2) CH4 flow capacity as expressed by permeability; and 3) pressure at
abandonment. Because these parameters are not available for each mine, a methodological approach to estimating
emissions was used that generates a probability distribution of potential outcomes based on the most likely value and the
probable range of values for each parameter. The range of values is not meant to capture the extreme values, but rather
values that represent the highest and lowest quartile of the cumulative probability density function of each parameter.
Once the low, mid, and high values are selected, they are applied to a probability density function.
Petroleum Systems
A quantitative uncertainty analysis was conducted for previous Inventories to determine the level of uncertainty
surrounding estimates of emissions from petroleum systems using the recommended methodology from IPCC. Performed
using @RISK software and the IPCC-recommended Tier 2 methodology (Monte Carlo Simulation technique), this
analysis provides for the specification of probability density functions for key variables within a computational structure
that mirrors the calculation of the inventory estimate. The IPCC guidance notes that in using this method, "some
uncertainties that are not addressed by statistical means may exist, including those arising from omissions or double
counting, or other conceptual errors, or from incomplete understanding of the processes that may lead to inaccuracies in
estimates developed from models." As a result, the understanding of the uncertainty of emissions estimates for this
category will evolve and will improve as the underlying methodologies and datasets improve.
Performed using @RISK software and the IPCC-recommended Tier 2 methodology (Monte Carlo Stochastic
Simulation technique), the method employed provides for the specification of probability density functions for key
variables within a computational structure that mirrors the calculation of the inventory estimate. The results provide the
range within which, with 95 percent certainty, emissions from this source category are likely to fall.
The detailed, bottom-up inventory analysis used to evaluate U.S. petroleum systems reduces the uncertainty
related to the CH4 emission estimates in comparison to a top-down approach. However, some uncertainty still remains.
Emission factors and activity factors are based on a combination of measurements, equipment design data, engineering
calculations and studies, surveys of selected facilities and statistical reporting. Statistical uncertainties arise from natural
variation in measurements, equipment types, operational variability and survey and statistical methodologies. Published
activity factors are not available every year for all 64 activities analyzed for petroleum systems; therefore, some are
estimated. Because of the dominance of the seven major sources, which account for 92 percent of the total methane
emissions, the uncertainty surrounding these seven sources has been estimated most rigorously, and serves as the basis for
determining the overall uncertainty of petroleum systems emission estimates.
Natural Gas Systems
A quantitative uncertainty analysis was conducted for previous Inventories to determine the level of uncertainty
surrounding estimates of emissions from natural gas systems using the recommended methodology from IPCC.
Performed using @RISK software and the IPCC-recommended Tier 2 methodology (Monte Carlo Simulation technique),
this analysis provides for the specification of probability density functions for key variables within a computational
structure that mirrors the calculation of the inventory estimate. The IPCC guidance notes that in using this method, "some
A-432 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
uncertainties that are not addressed by statistical means may exist, including those arising from omissions or double
counting, or other conceptual errors, or from incomplete understanding of the processes that may lead to inaccuracies in
estimates developed from models." As a result, the understanding of the uncertainty of emissions estimates for this
category will evolve and will improve as the underlying methodologies and datasets improve.
The @RISK model was used to quantify the uncertainty associated with the emissions estimates using the top
twelve emission sources for the year 2009. The uncertainty analysis has not yet been updated for the 1990 through 2012
Inventory; instead, the uncertainty ranges calculated previously were applied to 2012 emissions estimates. The majority of
sources in the current inventory were calculated using the same emission factors and activity data for which PDFs were
developed in the 1990 through 2009 uncertainty analysis. Several emissions sources have been updated with the current
Inventory, and the 2009 uncertainty ranges will not reflect the uncertainty associated with the recently updated emission
factors and activity data sources. EPA plans to revise this uncertainty analysis.
International Bunker Fuels
Emission estimates related to the consumption of international bunker fuels are subject to the same uncertainties
as those from domestic aviation and marine mobile combustion emissions; however, additional uncertainties result from
the difficulty in collecting accurate fuel consumption activity data for international transport activities separate from
domestic transport activities. ^^ For example, smaller aircraft on shorter routes often carry sufficient fuel to complete
several flight segments without refueling in order to minimize time spent at the airport gate or take advantage of lower fuel
prices at particular airports. This practice, called tankering, when done on international flights, complicates the use of fuel
sales data for estimating bunker fuel emissions. Tankering is less common with the type of large, long-range aircraft that
make many international flights from the United States, however. Similar practices occur in the marine shipping industry
where fuel costs represent a significant portion of overall operating costs and fuel prices vary from port to port, leading to
some tankering from ports with low fuel costs.
Uncertainties exist with regard to the total fuel used by military aircraft and ships, and in the activity data on
military operations and training that were used to estimate percentages of total fuel use reported as bunker fuel emissions.
Total aircraft and ship fuel use estimates were developed from DoD records, which document fuel sold to the Navy and
Air Force from the Defense Logistics Agency. These data may slightly over or under estimate actual total fuel use in
aircraft and ships because each Service may have procured fuel from, and/or may have sold to, traded with, and/or given
fuel to other ships, aircraft, governments, or other entities. There are uncertainties in aircraft operations and training
activity data. Estimates for the quantity of fuel actually used in Navy and Air Force flying activities reported as bunker
fuel emissions had to be estimated based on a combination of available data and expert judgment. Estimates of marine
bunker fuel emissions were based on Navy vessel steaming hour data, which reports fuel used while underway and fuel
used while not underway. This approach does not capture some voyages that would be classified as domestic for a
commercial vessel. Conversely, emissions from fuel used while not underway preceding an international voyage are
reported as domestic rather than international as would be done for a commercial vessel. There is uncertainty associated
with ground fuel estimates for 1997 through 2001. Small fuel quantities may have been used in vehicles or equipment
other than that which was assumed for each fuel type.
There are also uncertainties in fuel end-uses by fuel-type, emissions factors, fuel densities, diesel fuel sulfur
content, aircraft and vessel engine characteristics and fuel efficiencies, and the methodology used to back-calculate the
data set to 1990 using the original set from 1995. The data were adjusted for trends in fuel use based on a closely
correlating, but not matching, data set. All assumptions used to develop the estimate were based on process knowledge,
Department and military Service data, and expert judgments. The magnitude of the potential errors related to the various
uncertainties has not been calculated, but is believed to be small. The uncertainties associated with future military bunker
fuel emission estimates could be reduced through additional data collection.
Although aggregate fuel consumption data have been used to estimate emissions from aviation, the
recommended method for estimating emissions of gases other than CCh in the 2006 IPCC Guidelines is to use data by
specific aircraft type, number of individual flights and, ideally, movement data to better differentiate between domestic
and international aviation and to facilitate estimating the effects of changes in technologies. The IPCC also recommends
119
See uncertainty discussions under Carbon Dioxide Emissions from Fossil Fuel Combustion.
A-433
-------
that cruise altitude emissions be estimated separately using fuel consumption data, while landing and take-off (LTO) cycle
data be used to estimate near-ground level emissions of gases other than CC^.^"
There is also concern regarding the reliability of the existing DOC (2011) data on marine vessel fuel
consumption reported at U. S. customs stations due to the significant degree of inter-annual variation.
Wood Biomass and Ethanol Consumption
It is assumed that the combustion efficiency for woody biomass is 100 percent, which is believed to be an
overestimate of the efficiency of wood combustion processes in the United States. Decreasing the combustion efficiency
would decrease emission estimates. Additionally, the heat content applied to the consumption of woody biomass in the
residential, commercial, and electric power sectors is unlikely to be a completely accurate representation of the heat
content for all the different types of woody biomass consumed within these sectors. Emission estimates from ethanol
production are more certain than estimates from woody biomass consumption due to better activity data collection
methods and uniform combustion techniques.
Industrial Processes
The uncertainty analysis descriptions in this section correspond to source categories included in the Industrial
Processes Chapter of the Inventory.
Cement Production
The uncertainties contained in these estimates are primarily due to uncertainties in the lime content of clinker and
in the percentage of CKD recycled inside the cement kiln. Uncertainty is also associated with the assumption that all
calcium-containing raw materials are CaCOs, when a small percentage likely consists of other carbonate and non-
carbonate raw materials. The lime content of clinker varies from 60 to 67 percent; 65 percent is used as a representative
value (van Oss 2013a). CKD loss can range from 1.5 to 8 percent depending upon plant specifications. Additionally,
some amount of CC>2 is reabsorbed when the cement is used for construction. As cement reacts with water, alkaline
substances such as calcium hydroxide are formed. During this curing process, these compounds may react with CCh in the
atmosphere to create calcium carbonate. This reaction only occurs in roughly the outer 0.2 inches of surface area.
Because the amount of CC>2 reabsorbed is thought to be minimal, it was not estimated.
Lime Production
The uncertainties contained in these estimates can be attributed to slight differences in the chemical composition
of lime products and recovery rates for sugar refineries and PCC manufacturers located at lime plants. Although the
methodology accounts for various formulations of lime, it does not account for the trace impurities found in lime, such as
iron oxide, alumina, and silica. Due to differences in the limestone used as a raw material, a rigid specification of lime
material is impossible. As a result, few plants produce lime with exactly the same properties.
In addition, a portion of the CC>2 emitted during lime production will actually be reabsorbed when the lime is
consumed, especially at captive lime production facilities. As noted above, lime has many different chemical, industrial,
environmental, and construction applications. In many processes, CCh reacts with the lime to create calcium carbonate
(e.g., water softening). Carbon dioxide reabsorption rates vary, however, depending on the application. For example, 100
percent of the lime used to produce precipitated calcium carbonate reacts with CC>2; whereas most of the lime used in steel
making reacts with impurities such as silica, sulfur, and aluminum compounds. Quantifying the amount of CC>2 that is
reabsorbed would require a detailed accounting of lime use in the United States and additional information about the
associated processes where both the lime and byproduct CCh are "reused" are required to quantify the amount of CCh that
U.S. aviation emission estimates for CO, NOX, and NMVOCs are reported by EPA's National Emission Inventory (NEI) Air
Pollutant Emission Trends web site, and reported under the Mobile Combustion section. It should be noted that these estimates
are based solely upon LTO cycles and consequently only capture near ground-level emissions, which are more relevant for air
quality evaluations. These estimates also include both domestic and international flights. Therefore, estimates reported under the
Mobile Combustion section overestimate IPCC-defmed domestic CO, NOX, and NMVOC emissions by including landing and
take-off (LTO) cycles by aircraft on international flights, but underestimate because they do not include emissions from aircraft
on domestic flight segments at cruising altitudes. The estimates in Mobile Combustion are also likely to include emissions from
ocean-going vessels departing from U.S. ports on international voyages.
A-434 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
is reabsorbed. Research conducted thus far has not yielded the necessary information to quantify CO 2 reabsorbtion
rates.
121
In some cases, lime is generated from calcium carbonate byproducts at pulp mills and water treatment plants.122
The lime generated by these processes is included in the USGS data for commercial lime consumption. In the pulping
industry, mostly using the Kraft (sulfate) pulping process, lime is consumed in order to causticize a process liquor (green
liquor) composed of sodium carbonate and sodium sulfide. The green liquor results from the dilution of the smelt created
by combustion of the black liquor where biogenic C is present from the wood. Kraft mills recover the calcium carbonate
"mud" after the causticizing operation and calcine it back into lime—thereby generating CC>2—for reuse in the pulping
process. Although this re-generation of lime could be considered a lime manufacturing process, the CC>2 emitted during
this process is mostly biogenic in origin, and therefore is not included in the industrial processes totals (Miner and Upton
2002). In accordance with IPCC methodological guidelines, any such emissions are calculated by accounting for net
carbon (C) fluxes from changes in biogenic C reservoirs in wooded or crop lands (see Chapter 7).
In the case of water treatment plants, lime is used in the softening process. Some large water treatment plants
may recover their waste calcium carbonate and calcine it into quicklime for reuse in the softening process. Further
research is necessary to determine the degree to which lime recycling is practiced by water treatment plants in the United
States.
Uncertainties also remain surrounding recovery rates used for sugar refining and PCC production. The recovery
rate for sugar refineries is based on consultation with USGS commodity expert (Miller 2013) and two sugar beet
processing and refining facilities located in California that use 100 percent recovered CC>2 from lime plants (Lutter 2009).
This analysis assumes that all sugar refineries located on-site at lime plants also use 100 percent recovered CC>2. The
recovery rate for PCC producers located on-site at lime plants is based on the 2012 value for PCC manufactured at
commercial lime plants, given by USGS (Miller 2012). However, most PCC production occurs at non-commercial lime
facilities, such as paper mills. Satellite PCC plants at paper mills tend to use CCh produced from the paper mill (potentially
biomass based). This could introduce additional uncertainty in the CC>2 estimates, because CC>2 recovered from pulp and
paper facilities is mostly biogenic in origin.
Another uncertainty is the assumption that calcination emissions for LKD are around 2 percent. The National
Lime association has commented that the estimates of emissions from LKD in the US could be closer to 6 percent. In
addition, they note emissions may also be generated through production of other byproducts/wastes at lime plants (Seeger
2013). There is limited data publicly available on LKD generation rates and also quantities, types of other
byproducts/wastes produced at lime facilities. Further research is needed to improve understanding of additional
calcination emissions to consider revising the current assumptions based on the IPCC Guidelines
Other Process Uses of Carbonates
The uncertainty levels presented in this section account for uncertainty associated with activity data. Data on
limestone and dolomite consumption are collected by USGS through voluntary national surveys. USGS contacts the mines
(i.e., producers of various types of crushed stone) for annual sales data. Data on other carbonate consumption are not
readily available. The producers report the annual quantity sold to various end-users/industry types. USGS estimates the
historical response rate for the crushed stone survey to be approximately 70 percent, the rest is estimated by USGS. Large
fluctuations in reported consumption exist, reflecting year-to-year changes in the number of survey responders. The
uncertainty resulting from a shifting survey population is exacerbated by the gaps in the time series of reports. The
accuracy of distribution by end use is also uncertain because this value is reported by the producer/mines and not the end
user. Additionally, there is significant inherent uncertainty associated with estimating withheld data points for specific
end uses of limestone and dolomite. Lastly, much of the limestone consumed in the United States is reported as "other
unspecified uses;" therefore, it is difficult to accurately allocate this unspecified quantity to the correct end-uses.
Representatives of the National Lime Association estimate that CO2 reabsorption that occurs from the use of lime may offset
as much as a quarter of the CO2 emissions from calcination (Males 2003).
122 Some carbide producers may also regenerate lime from their calcium hydroxide byproducts, which does not result in
emissions of CO2. In making calcium carbide, quicklime is mixed with coke and heated in electric furnaces. The regeneration of
lime in this process is done using a waste calcium hydroxide (hydrated lime) [CaC2 + 2H2O —> C2H2 + Ca(OH) 2], not calcium
carbonate [CaCOs]. Thus, the calcium hydroxide is heated in the kiln to simply expel the water [Ca(OH)2 + heat -> CaO + FtO]
and no CO2 is released.
A-435
-------
Uncertainty in the estimates also arises in part due to variations in the chemical composition of limestone. In
addition to calcium carbonate, limestone may contain smaller amounts of magnesia, silica, and sulfur, among other
minerals. The exact specifications for limestone or dolomite used as flux stone vary with the pyrometallurgical process
and the kind of ore processed.
Soda Ash Production and Consumption
Emission estimates from soda ash production have relatively low associated uncertainty levels in that reliable and
accurate data sources are available for the emission factor and activity data. Soda ash production data was collected by the
USGS from voluntary surveys. A survey request was sent to each of the five soda ash producers, all of which responded,
representing 100 percent of the total production data (USGS 2013). One source of uncertainty is the purity of the trona ore
used for manufacturing soda ash. The emission factor used for this estimate assumes the ore is 100 percent pure, and
likely overestimates the emissions from soda ash manufacture. The average water-soluble sodium carbonate-bicarbonate
content for ore mined in Wyoming ranges from 85.5 to 93.8 percent (USGS 1995a).The primary source of uncertainty,
however, results from the fact that emissions from soda ash consumption are dependent upon the type of processing
employed by each end-use. Specific emission factors for each end-use are not available, so a Tier 1 default emission
factor is used for all end uses. Therefore, there is uncertainty surrounding the emission factors from the consumption of
soda ash.
Glass Production
The uncertainty levels presented in this section arise in part due to variations in the chemical composition of
limestone used in glass production. In addition to calcium carbonate, limestone may contain smaller amounts of
magnesia, silica, and sulfur, among other minerals (potassium carbonate, strontium carbonate and barium carbonate, and
dead burned dolomite). Similarly, the quality of the limestone (and mix of carbonates) used for glass manufacturing will
depend on the type of glass being manufactured.
The estimates below also account for uncertainty associated with activity data. Large fluctuations in reported
consumption exist, reflecting year-to-year changes in the number of survey responders. The uncertainty resulting from a
shifting survey population is exacerbated by the gaps in the time series of reports. The accuracy of distribution by end use
is also uncertain because this value is reported by the manufacturer of the input carbonates (limestone, dolomite & soda
ash) and not the end user. For 2012, there has been no reported consumption of dolomite for glass manufacturing. This
data has been reported to USGS by dolomite manufacturers and not end-users (i.e., glass manufacturers). There is a high
uncertainty associated with this estimate, as dolomite is a major raw material consumed in glass production. Additionally,
there is significant inherent uncertainty associated with estimating withheld data points for specific end uses of limestone
and dolomite. The uncertainty of the estimates for limestone and dolomite used in glass making is especially high;
however, since glass making accounts for a small percent of consumption, its contribution to the overall emissions
estimate is low. Lastly, much of the limestone consumed in the United States is reported as "other unspecified uses;"
therefore, it is difficult to accurately allocate this unspecified quantity to the correct end-uses. Further research is needed
into alternate and more complete sources of data on carbonate-based raw material consumption by the glass industry.
Ammonia Production
The uncertainties presented in this section are primarily due to how accurately the emission factor used
represents an average across all ammonia plants using natural gas feedstock. Uncertainties are also associated with
ammonia production estimates and the assumption that all ammonia production and subsequent urea production was from
the same process—conventional catalytic reforming of natural gas feedstock, with the exception of one ammonia
production plant located in Kansas that is manufacturing ammonia from petroleum coke feedstock. Uncertainty is also
associated with the representativeness of the emission factor used for the petroleum coke-based ammonia process. It is
also assumed that ammonia and urea are produced at collocated plants from the same natural gas raw material.
Recovery of CC>2 from ammonia production plants for purposes other than urea production (e.g., commercial
sale, etc.) has not been considered in estimating the CC>2 emissions from ammonia production, as data concerning the
disposition of recovered CC>2 are not available. Such recovery may or may not affect the overall estimate of CC>2 emissions
depending upon the end use to which the recovered CC>2 is applied. Further research is required to determine whether
byproduct CC>2 is being recovered from other ammonia production plants for application to end uses that are not accounted
for elsewhere.
Urea Consumption for Non-Agricultural Purposes
There is limited publicly available data on the quantities of urea produced and consumed for non-agricultural
purposes. Therefore, the amount of urea used for non-agricultural purposes is estimated based on a balance that relies on
A-436 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
estimates of urea production, urea imports, urea exports, and the amount of urea used as fertilizer. The primary
uncertainties associated with this source category are associated with the accuracy of these estimates as well as the fact
that each estimate is obtained from a different data source. Because urea production estimates are no longer available from
the USGS, there is additional uncertainty associated with urea produced beginning in 2011. There is also uncertainty
associated with the assumption that all of the carbon in urea is released into the environment as CC>2 during use.
Nitric Acid Production
Uncertainty associated with the parameters used to estimate N2O emissions includes that of production data, the
share of U.S. nitric acid production attributable to each emission abatement technology over the time series, and the
emission factors applied to each abatement technology type. While some information has been obtained through outreach
with industry associations, limited information is available over the time series for a variety of facility level variables,
including plant specific production levels, plant production technology (e.g., low, high pressure, etc.) and abatement
technology type, installation date of abatement technology, and accurate destruction and removal efficiency rates.
Adipic Acid Production
Uncertainty associated with N2(D emission estimates included that of the methods used by companies to monitor
and estimate emissions.
Silicon Carbide Production and Consumption
There is uncertainty associated with the emission factors used because they are based on stoichiometry as
opposed to monitoring of actual SiC production plants. An alternative would be to calculate emissions based on the
quantity of petroleum coke used during the production process rather than on the amount of silicon carbide produced.
However, these data were not available. For CH4, there is also uncertainty associated with the hydrogen-containing
volatile compounds in the petroleum coke (IPCC 2006). There is also uncertainty associated with the use or destruction of
methane generated from the process in addition to uncertainty associated with levels of production, net imports,
consumption levels, and the percent of total consumption that is attributed to metallurgical and other non-abrasive uses.
Petrochemical Production
The CH4 emission factors used for petrochemical production are based on a limited number of studies. Using
plant-specific factors instead of default or average factors could increase the accuracy of the emission estimates; however,
such data were not available for the current publication. There may also be other significant sources of CH4 arising from
petrochemical production activities that have not been included in these estimates.
The results of the quantitative uncertainty analysis for the CC>2 emissions from carbon black production
calculation are based on feedstock consumption, import and export data, and carbon black production data. The
composition of carbon black feedstock varies depending upon the specific refinery production process, and therefore the
assumption that carbon black feedstock is 90 percent C gives rise to uncertainty. Also, no data are available concerning
the consumption of coal-derived carbon black feedstock, so CC>2 emissions from the utilization of coal-based feedstock are
not included in the emission estimate. In addition, other data sources indicate that the amount of petroleum-based
feedstock used in carbon black production may be underreported by the U.S. Census Bureau. Finally, the amount of
carbon black produced from the acetylene black, thermal black, and lamp black processes, although estimated to be a
small percentage of the total production, is not known. Therefore, there is some uncertainty associated with the
assumption that all of the carbon black is produced using the furnace black process.
Titanium Dioxide Production
Each year, USGS collects titanium industry data for titanium mineral and pigment production operations. If TiC>2
pigment plants do not respond, production from the operations is estimated on the basis of prior year production levels and
industry trends. Variability in response rates varies from 67 to 100 percent of TiC>2 pigment plants over the time series.
Although some TiC>2 may be produced using graphite or other carbon inputs, information and data regarding these
practices were not available. Titanium dioxide produced using graphite inputs, for example, may generate differing
amounts of CC>2 per unit of TiC>2 produced as compared to that generated through the use of petroleum coke in production.
While the most accurate method to estimate emissions would be to base calculations on the amount of reducing agent used
in each process rather than on the amount of TiC>2 produced, sufficient data were not available to do so.
As of 2004, the last remaining sulfate-process plant in the United States closed. Since annual TiC>2 production
was not reported by USGS by the type of production process used (chloride or sulfate) prior to 2004 and only the
percentage of total production capacity by process was reported, the percent of total TiC>2 production capacity that was
A-437
-------
attributed to the chloride process was multiplied by total TiCh production to estimate the amount of TiCh produced using
the chloride process. Finally, the emission factor was applied uniformly to all chloride-process production, and no data
were available to account for differences in production efficiency among chloride-process plants. In calculating the
amount of petroleum coke consumed in chloride-process TiC>2 production, literature data were used for petroleum coke
composition. Certain grades of petroleum coke are manufactured specifically for use in the TiCh chloride process;
however, this composition information was not available.
Carbon Dioxide Production
Uncertainty is associated with the number of facilities that are currently producing CC>2 from naturally occurring
CQz reservoirs for commercial uses other than EOR, and for which the CCh emissions are not accounted for elsewhere.
Research indicates that there are only two such facilities, which are in New Mexico and Mississippi; however, additional
facilities may exist that have not been identified. In addition, it is possible that CC>2 recovery exists in particular
production and end-use sectors that are not accounted for elsewhere. Such recovery may or may not affect the overall
estimate of CCh emissions from that sector depending upon the end use to which the recovered CC>2 is applied. Further
research is required to determine whether CC>2 is being recovered from other facilities for application to end uses that are
not accounted for elsewhere.
Phosphoric Acid Production
Phosphate rock production data used in the emission calculations were developed by the USGS through monthly
and semiannual voluntary surveys of the active phosphate rock mines during 2012. For previous years in the time series,
USGS provided the data disaggregated regionally; however, beginning in 2006, only total U.S. phosphate rock production
was reported. Regional production for 2012 was estimated based on regional production data from previous years and
multiplied by regionally-specific emission factors. There is uncertainty associated with the degree to which the estimated
2012 regional production data represents actual production in those regions. Total U.S. phosphate rock production data
are not considered to be a significant source of uncertainty because all the domestic phosphate rock producers report their
annual production to the USGS. Data for exports of phosphate rock used in the emission calculation are reported by
phosphate rock producers and are not considered to be a significant source of uncertainty. Data for imports for
consumption are based on international trade data collected by the U.S. Census Bureau. These U.S. government economic
data are not considered to be a significant source of uncertainty.
An additional source of uncertainty in the calculation of CC>2 emissions from phosphoric acid production is the
carbonate composition of phosphate rock, the composition of phosphate rock varies depending upon where the material is
mined, and may also vary over time. The inventory relies on one study (FIPR 2003) of chemical composition of the
phosphate rock; limited data is available beyond this study. Another source of uncertainty is the disposition of the organic
carbon content of the phosphate rock. A representative of the FIPR indicated that in the phosphoric acid production
process, the organic carbon content of the mined phosphate rock generally remains in the phosphoric acid product, which
is what produces the color of the phosphoric acid product (FIPR 2003a). Organic carbon is therefore not included in the
calculation of CC>2 emissions from phosphoric acid production.
A third source of uncertainty is the assumption that all domestically-produced phosphate rock is used in
phosphoric acid production and used without first being calcined. Calcination of the phosphate rock would result in
conversion of some of the organic carbon in the phosphate rock into CC>2. However, according to air permit information
available to the public, at least one facility has calcining units permitted for operation (NCDENR 2013).
Finally, USGS indicated that approximately 7 percent of domestically-produced phosphate rock is used to
manufacture elemental phosphorus and other phosphorus-based chemicals, rather than phosphoric acid (USGS 2006).
According to USGS, there is only one domestic producer of elemental phosphorus, in Idaho, and no data were available
concerning the annual production of this single producer. Elemental phosphorus is produced by reducing phosphate rock
with coal coke, and it is therefore assumed that 100 percent of the carbonate content of the phosphate rock will be
converted to CC>2 in the elemental phosphorus production process. The calculation for CC>2 emissions is based on the
assumption that phosphate rock consumption, for purposes other than phosphoric acid production, results in CC>2 emissions
from 100 percent of the inorganic carbon content in phosphate rock, but none from the organic carbon content.
Iron and Steel Production and Metallurgical Coke Production
The estimates of CC>2 and CFU emissions from metallurgical coke production are based on material production
and consumption data and average carbon contents. Uncertainty is associated with the total U.S. coking coal consumption,
total U.S. coke production and materials consumed during this process. Data for coking coal consumption and
metallurgical coke production are from different data sources (EIA) than data for other carbonaceous materials consumed
A-438 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
at coke plants (AISI), which does not include data for merchant coke plants. There is uncertainty associated with the fact
that coal tar and coke breeze production were estimated based on coke production because coal tar and coke breeze
production data were not available. Since merchant coke plant data is not included in the estimate of other carbonaceous
materials consumed at coke plants, the mass balance equation for CC>2 from metallurgical coke production cannot be
reasonably completed. Therefore, for the purpose of this analysis, uncertainty parameters are applied to primary data
inputs to the calculation (i.e, coking coal consumption and metallurgical coke production) only.
The estimates of CC>2 emissions from iron and steel production are based on material production and
consumption data and average carbon contents. There is uncertainty associated with the assumption that direct reduced
iron and sinter consumption are equal to production. There is uncertainty associated with the assumption that all coal used
for purposes other than coking coal is for direct injection coal; some of this coal may be used for electricity generation.
There is also uncertainty associated with the carbon contents for pellets, sinter, and natural ore, which are assumed to
equal the carbon contents of direct reduced iron. For EAF steel production, there is uncertainty associated with the
amount of EAF anode and charge carbon consumed due to inconsistent data throughout the time series. Also for EAF steel
production, there is uncertainty associated with the assumption that 100 percent of the natural gas attributed to
"steelmaking furnaces" by AISI is process-related and nothing is combusted for energy purposes. Uncertainty is also
associated with the use of process gases such as blast furnace gas and coke oven gas. Data are not available to
differentiate between the use of these gases for processes at the steel mill versus for energy generation (i.e., electricity and
steam generation); therefore, all consumption is attributed to iron and steel production. These data and carbon contents
produce a relatively accurate estimate of CC>2 emissions. However, there are uncertainties associated with each.
For the purposes of the CFU calculation from iron and steel production it is assumed that all of the CFU escapes
as fugitive emissions and that none of the CFU is captured in stacks or vents. Additionally, the CC>2 emissions calculation
is not corrected by subtracting the carbon content of the CH/i, which means there may be a slight double counting of
carbon as both CCh and CH/i.
Ferroalloy Production
Annual ferroalloy production is currently reported by the USGS in three broad categories: ferroalloys containing
25 to 55 percent silicon (including miscellaneous alloys), ferroalloys containing 56 to 95 percent silicon, and silicon metal
(through 2005 only). Silicon metal production values for 2006 through 2012 are assumed to be equal to the 2005 value
reported by USGS (USGS did not report silicon metal production for 2006 through 2012). Ferrosilicon production values
for 2011 and 2012 are assumed to be equal to the 2010 value reported by USGS (USGS did not report ferrosilicon
production for 2011 and 2012). It was assumed that the IPCC emission factors apply to all of the ferroalloy production
processes, including miscellaneous alloys. Finally, production data for silvery pig iron (alloys containing less than 25
percent silicon) are not reported by the USGS to avoid disclosing proprietary company data. Emissions from this
production category, therefore, were not estimated.
Also, some ferroalloys may be produced using wood or other biomass as a primary or secondary carbon source
(carbonaceous reductants), information and data regarding these practices were not available. Emissions from ferroalloys
produced with wood or other biomass would not be counted under this source because wood-based carbon is of biogenic
origin.123 Even though emissions from ferroalloys produced with coking coal or graphite inputs would be counted in
national trends, they may be generated with varying amounts of CC>2 per unit of ferroalloy produced. The most accurate
method for these estimates would be to base calculations on the amount of reducing agent used in the process, rather than
the amount of ferroalloys produced. These data, however, were not available, and are also often considered confidential
business information.
Emissions of CFU from ferroalloy production will vary depending on furnace specifics, such as type, operation
technique, and control technology. Higher heating temperatures and techniques such as sprinkle charging will reduce CHi
emissions; however, specific furnace information was not available or included in the CH4 emission estimates.
Aluminum Production
Uncertainty was assigned to the CC>2, CF4, and C2pe emission values reported by each individual facility to
EPA's GHGRP. As previously mentioned, the methods for estimating emissions for EPA's GHGRP and this report are
the same, and follow the IPCC (2006) methodology. As a result, it was possible to assign uncertainty bounds (and
123 Emissions and sinks of biogenic carbon are accounted for in the Land Use, Land-Use Change, and Forestry chapter.
A-439
-------
distributions) based on an analysis of the uncertainty associated with the facility-specific emissions estimated for previous
inventory years. Uncertainty surrounding the reported CO2, CF4, and C2pe emission values were determined to have a
normal distribution with uncertainty ranges of ±6, ±16, and ±20 percent, respectively. A Monte Carlo analysis was
applied to estimate the overall uncertainty of the CC>2, CF/i, and C2pe emission estimates for the U. S. aluminum industry as
a whole, and the results are provided below.
Magnesium Production
To estimate the uncertainty surrounding the estimated 2012 SFe emissions from magnesium production and
processing, the uncertainties associated with three variables were estimated: (1) emissions reported by magnesium
producers and processors for 2012 through EPA's GHGRP, (2) emissions estimated for magnesium producers and
processors that reported via the Partnership in prior years but did not report 2012 emissions through EPA's GHGRP, and
(3) emissions estimated for magnesium producers and processors that did not participate in the Partnership or report
through EPA's GHGRP. An uncertainty of 5 percent was assigned to the SFe emissions (usage) data reported by each
GHGRP reporter (per the 2006 IPCC Guidelines). If facilities did not report emissions data during the current reporting
year through EPA's GHGRP reporting program, SFg emissions data were held constant at the most recent available value
reported through the Partnership. The uncertainty associated with these values was estimated to be 30 percent for each
year of extrapolation. One known sand caster (the lone Partner) has not reported since 2007 and its activity and emission
factor were held constant at 2005 levels due to a reporting anomaly in 2006 because of malfunctions at the facility. The
uncertainty associated with the SFe usage for the sand casting Partner was 74 percent. For those industry processes that are
not represented in Partnership, such as permanent mold and wrought casting, SFe emissions were estimated using
production and consumption statistics reported by USGS and estimated process-specific emission factors (see Table 4-81
in the main Inventory document). The uncertainties associated with the emission factors and USGS-reported statistics
were assumed to be 75 percent and 25 percent, respectively. Emissions associated with die casting and sand casting
activities utilized emission factors based on Partner reported data with an uncertainties of 75 percent. In general, where
precise quantitative information was not available on the uncertainty of a parameter, a conservative (upper-bound) value
was used.
Additional uncertainties exist in these estimates that are not addressed in this methodology, such as the basic
assumption that SFe neither reacts nor decomposes during use. The melt surface reactions and high temperatures
associated with molten magnesium could potentially cause some gas degradation. Previous measurement studies have
identified SFe cover gas degradation in die casting applications on the order of 20 percent (Bartos et al. 2007). Sulfur
hexafluoride may also be used as a cover gas for the casting of molten aluminum with high magnesium content; however,
the extent to which this technique is used in the United States is unknown.
Zinc Production
The uncertainties contained in these estimates are two-fold, relating to activity data and emission factors used.
First, there is uncertainty associated with the amount of EAF dust consumed in the United States to produce secondary
zinc using emission-intensive Waelz kilns. The estimate for the total amount of EAF dust consumed in Waelz kilns is
based on (1) an EAF dust consumption value reported annually by Horsehead Corporation as part of its financial reporting
to the Securities and Exchange Commission (SEC), and (2) an EAF dust consumption value obtained from the Waelz kiln
facility operated in Alabama by Steel Dust Recycling LLC. Since actual EAF dust consumption information is not
available for PIZO's facility (2009-2010) and SDR's facility (2008-2010), the amount is estimated by multiplying the EAF
dust recycling capacity of the facility (available from the company's Web site) by the capacity utilization factor for
Horsehead Corporation (which is available from Horsehead's financial reports). Also, the EAF dust consumption for
PIZO's facility in 2011 was estimated by multiplying the average capacity utilization factor developed from Horsehead
Corp. and SDR's annual capacity utilization rates by PIZO's EAF dust recycling capacity. Therefore, there is uncertainty
associated with the assumption used to estimate PIZO and SDR's annual EAF dust consumption values (except SDR's
EAF dust consumption in 2011 which was obtained from SDR's recycling facility in Alabama).
Second, there are uncertainties associated with the emission factors used to estimate CO2 emissions from
secondary zinc production processes. The Waelz kiln emission factors are based on materials balances for metallurgical
coke and EAF dust consumed as provided by Viklund-White (2000). Therefore, the accuracy of these emission factors
depend upon the accuracy of these materials balances. Data limitations prevented the development of emission factors for
the electrothermic process. Therefore, emission factors for the Waelz kiln process were applied to both electrothermic and
Waelz kiln production processes.
A-440 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Lead Production
Uncertainty associated with lead production relates to the emission factors and activity data used. The direct
smelting emission factor used in primary production is taken from Sjardin (2003) who averaged the values provided by
three other studies (Dutrizac et al. 2000, Morris et al. 1983, Ullman 1997). For secondary production, Sjardin (2003)
added a CC>2 emission factor associated with battery treatment. The applicability of these emission factors to plants in the
United States is uncertain. There is also a smaller level of uncertainty associated with the accuracy of primary and
secondary production data provided by the USGS.
HCFC-22 Production
The uncertainty analysis presented in this section was based on a plant-level Monte Carlo Stochastic Simulation
for 2006. The Monte Carlo analysis used estimates of the uncertainties in the individual variables in each plant's
estimating procedure. This analysis was based on the generation of 10,000 random samples of model inputs from the
probability density functions for each input. A normal probability density function was assumed for all measurements and
biases except the equipment leak estimates for one plant; a log-normal probability density function was used for this
plant's equipment leak estimates. The simulation for 2006 yielded a 95-percent confidence interval for U.S. emissions of
6.8 percent below to 9.6 percent above the reported total.
The relative errors yielded by the Monte Carlo Stochastic Simulation for 2006 were applied to the U.S. emission
estimate for 2012. The resulting estimates of absolute uncertainty are likely to be reasonably accurate because (1) the
methods used by the three plants to estimate their emissions are not believed to have changed significantly since 2006, and
(2) although the distribution of emissions among the plants may have changed between 2006 and 2012 (because both
HCFC-22 production and the FfFC-23 emission rate declined significantly), the two plants that contribute significantly to
emissions were estimated to have similar relative uncertainties in their 2006 (as well as 2005) emission estimates. Thus,
changes in the relative contributions of these two plants to total emissions are not likely to have a large impact on the
uncertainty of the national emission estimate.
Substitution of Ozone Depleting Substances
Given that emissions of ODS substitutes occur from thousands of different kinds of equipment and from millions
of point and mobile sources throughout the United States, emission estimates must be made using analytical tools such as
the Vintaging Model or the methods outlined in IPCC (2006). Though the model is more comprehensive than the IPCC
default methodology, significant uncertainties still exist with regard to the levels of equipment sales, equipment
characteristics, and end-use emissions profiles that were used to estimate annual emissions for the various compounds.
The Vintaging Model estimates emissions from 60 end-uses. The uncertainty analysis, however, quantifies the
level of uncertainty associated with the aggregate emissions resulting from the top 21 end-uses, comprising over 95
percent of the total emissions, and 6 other end-uses. These 27 end-uses comprise 97 percent of the total emissions,
equivalent to 143.6 Tg CC>2 Eq. In an effort to improve the uncertainty analysis, additional end-uses are added annually,
with the intention that over time uncertainty for all emissions from the Vintaging Model will be fully characterized. Any
end-uses included in previous years' uncertainty analysis were included in the current uncertainty analysis, whether or not
those end-uses were included in the top 95 percent of emissions from ODS Substitutes.
In order to calculate uncertainty, functional forms were developed to simplify some of the complex "vintaging"
aspects of some end-use sectors, especially with respect to refrigeration and air-conditioning, and to a lesser degree, fire
extinguishing. These sectors calculate emissions based on the entire lifetime of equipment, not just equipment put into
commission in the current year, thereby necessitating simplifying equations. The functional forms used variables that
included growth rates, emission factors, transition from ODSs, change in charge size as a result of the transition, disposal
quantities, disposal emission rates, and either stock for the current year or original ODS consumption. Uncertainty was
estimated around each variable within the functional forms based on expert judgment, and a Monte Carlo analysis was
performed. The most significant sources of uncertainty for this source category include the emission factors for
refrigerated transport, as well as the percent of non-MDI aerosol propellant that is HFC-152a.
Semiconductor Manufacture
A quantitative uncertainty analysis of this source category was performed using the IPCC-recommended Tier 2
uncertainty estimation methodology, the Monte Carlo Stochastic Simulation technique. The equation used to estimate
uncertainty is:
Total Emissions (Ex) = GHGRP Reported Emissions (£R) + Non-GHGRP Reporters Emissions (£NR)
A-441
-------
where ER and ENR denote totals for the indicated subcategories of emissions.
The uncertainty in ET presented in Table 4-97 in the main Inventory document below results from the
convolution of two distributions of emissions, each reflecting separate estimates of possible values of ER and ENR. The
approach and methods for estimating each distribution and combining them to arrive at the reported 95 percent CI are
described in the remainder of this section.
The uncertainty estimate of ER, or GHGRP reported emissions, is developed based on gas-specific uncertainty
estimates of emissions for two representative model facilities, one processing 200 mm wafers and one processing 300 mm
wafers. Uncertainties in emissions for each gas and model facility were developed during the assessment of emission
estimation methods for the subpart I GHGRP rulemaking in 2012 (see Technical Support for Modifications to the
Fluorinated Greenhouse Gas Emission Estimation Method Option for Semiconductor Facilities under Subpart I, docket
EPA-HQ-OAR-2011-0028). This analysis did not take into account the use of abatement. For the model facility that
processed 200 mm wafers, estimates of uncertainties at a 95 percent CI ranged from ±29 percent for CsFs to ±10 percent
for CF/i. For the corresponding model 300 mm facility, estimates of the 95 percent CI ranged from ±36 percent for C4p8 to
±16 percent for CF/i. These gas and wafer-specific uncertainty estimates are applied for facilities that did not abate
emissions as reported under EPA's GHGRP.
For those facilities reporting abatement of emissions under EPA's GHGRP, estimates of uncertainties for the no
abatement model facilities are modified to reflect the use of full abatement (abatement of all gases from all cleaning and
etching equipment) and partial abatement. These assumptions used to develop uncertainties for the partial and full
abatement facilities are identical for 200 mm and 300 mm wafer processing facilities. For all facilities reporting gas
abatement, a triangular distribution of destruction or removal efficiency is assumed for each gas. For facilities reporting
partial abatement, the distribution of destruction efficiencies, for each gas, is assumed to be right triangularly distributed.
Consideration of abatement then resulted in four additional model facilities, two (model) 200 mm wafer-processing
facilities (one fully and one partially abating each gas) and two (model) 300 mm wafer-processing facilities (one fully and
the other partially abating each gas). Gas-specific emission uncertainties were estimated by convolving the distributions of
unabated emissions with the appropriate distribution of abatement efficiency for fully and partially abated facilities using a
Montel Carlo simulation.
The uncertainty in ER is obtained by mapping GHGRP-reported gas and wafer-specific emissions to one of the
six described model facilities, and then running a Monte Carlo simulation which results in the 95 percent CI for GHGRP
reporting facilities (£R).
The estimate of uncertainty in ENR entailed developing estimates of uncertainties for the emissions factors for
each non-reporting sub-category and the corresponding estimates of TMLA.
The uncertainty in TMLA depends on the uncertainty of two variables - an estimate of the uncertainty in the
average annual capacity utilization for each level of production of fabs (e.g., full scale or R&D production) and a
corresponding estimate of the uncertainty in the number of layers manufactured. For both variables, the distributions of
capacity utilizations and number of manufactured layers are assumed triangular for all categories of non-reporting fabs.
For production fabs the most probable utilization is assumed to be 89 percent, with the highest and lowest utilization
assumed to be 95 percent and 70 percent, respectively. The corresponding values for facilities that manufacture discrete
devices are, 84 percent, 95 percent, and 73 percent, respectively, while the values for utilization for R&D facilities, are
assumed to be 20 percent, 30 percent, and 10 percent, respectively. For the triangular distributions that govern the number
of possible layers manufactured, it is assumed the most probable value is one layer less than reported in the ITRS; the
On November 13, 2013, EPA published a final rule revising subpart I (Electronics Manufacturing) of the GHGRP (78 FR
68162). The revised rule includes updated default emission factors and updated default destruction and removal efficiencies that
are slightly different from those that semiconductor manufacturers were required to use to report their 2012 emissions. The
uncertainty analyses that were performed during the development of the revised rule focused on these updated defaults, but are
expected to be reasonably representative of the uncertainties associated with the older defaults, particularly for estimates at the
country level. (They may somewhat underestimate the uncertainties associated with the older defaults at the facility level.) For
simplicity, the 2012 estimates are assumed to be unbiased although in some cases, the updated (and therefore more
representative) defaults are higher or lower than the older defaults. Multiple models and sensitivity scenarios were run for the
subpart I analysis. The uncertainty analysis presented here made use of the Input gas and wafer size model (Model 1) under the
following conditions: Year = 2010, f = 20, n = SIA3.
A-442 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
smallest number varied by technology generation between one and two layers less than given in the ITRS and largest
number of layers corresponded to the figure given in the ITRS.
The uncertainty bounds for the average capacity utilization and the number of layers manufactured are used as
inputs in a separate Monte Carlo simulation to estimate the uncertainty around the TMLA of both individual facilities as
well as the total non-reporting TMLA of each sub-population.
The uncertainty around the emission factors for each non-reporting category of facilities is dependent on the
uncertainty of the total emissions (MMTCCbe units) and the TMLA of each reporting facility in that category. For each
subpopulation of reporting facilities, total emissions were regressed on TMLA (with an intercept forced to zero) for 10,000
emissions and 10,000 TMLA values in a Monte Carlo simulation, which results in 10,000 total regression coefficients
(emission factors). The 2.5th and the 97.5th percentile of these emission factors are determined and the bounds are
assigned as the percent difference from the estimated emission factor.
For simplicity, the results of the Monte Carlo simulations on the bounds of the gas- and wafer size-specific
emissions as well as the TMLA and emission factors are assumed to be normally distributed and the uncertainty bounds
are assigned at 1.96 standard deviations around the estimated mean. The departures from normality were observed to be
small.
The final step in estimating the uncertainty in emissions of non-reporting facilities is convolving the distribution
of emission factors with the distribution of TMLA using Monte Carlo simulation.
Electrical Transmission and Distribution
To estimate the uncertainty associated with emissions of SFe from Electrical Transmission and Distribution,
uncertainties associated with four quantities were estimated: (1) emissions from Partners, (2) emissions from GHGRP-
Only Reporters, (3) emissions from Non-Reporters, and (4) emissions from manufacturers of electrical equipment. A
Monte Carlo analysis was then applied to estimate the overall uncertainty of the emissions estimate.
Total emissions from the SFe Emission Reduction Partnership include emissions from both reporting (through
the Partnership or GHGRP) and non-reporting Partners. For reporting Partners, individual Partner-reported SFe data was
assumed to have an uncertainty of 10 percent. Based on a Monte Carlo analysis, the cumulative uncertainty of all Partner-
reported data was estimated to be 2.5 percent. The uncertainty associated with extrapolated or interpolated emissions from
non-reporting Partners was assumed to be 20 percent.
For GHGRP-Only Reporters, reported SFe data was assumed to have an uncertainty of 20 percent. Based on a
Monte Carlo analysis, the cumulative uncertainty of all GHGRP-Only reported data was estimated to be 5.2 percent.
There are two sources of uncertainty associated with the regression equations used to estimate emissions in 2012
from Non-Reporters: (1) uncertainty in the coefficients (as defined by the regression standard error estimate), and (2) the
uncertainty in total transmission miles for Non-Reporters. Uncertainties were also estimated regarding (1) the quantity of
SFe supplied with equipment by equipment manufacturers, which is projected from Partner provided nameplate capacity
data and industry SFe nameplate capacity estimates, and (2) the manufacturers' SFe emissions rate.
Solvent and Other Product Use
The uncertainty analysis descriptions in this section correspond to source categories included in the Solvent and
Other Product Use Chapter of the Inventory.
Nitrous Oxide from Product Uses
The overall uncertainty associated with the 2012 N2O emission estimate from N2O product usage was calculated
using the IPCC Guidelines for National Greenhouse Gas Inventories (2006) Tier 2 methodology. Uncertainty associated
with the parameters used to estimate N2O emissions include production data, total market share of each end use, and the
emission factors applied to each end use, respectively.
Uncertainty is assumed to be higher for the GHGRP-Only category, because 2011 is the first year that those utilities have
reported to EPA.
A-443
-------
Agriculture
The uncertainty analysis descriptions in this section correspond to some source categories included in the
Agriculture Chapter of the Inventory.
Enteric Fermentation
A quantitative uncertainty analysis for this source category was performed using the IPCC-recommended Tier 2
uncertainty estimation methodology based on a Monte Carlo Stochastic Simulation technique as described in ICF (2003).
These uncertainty estimates were developed for the 1990 through 2001 Inventory report (i.e., 2003 submission to the
UNFCCC). There have been no significant changes to the methodology since that time; consequently, these uncertainty
estimates were directly applied to the 2012 emission estimates in this report.
A total of 185 primary input variables (177 for cattle and 8 for non-cattle) were identified as key input variables
for the uncertainty analysis. A normal distribution was assumed for almost all activity- and emission factor-related input
variables. Triangular distributions were assigned to three input variables (specifically, cow-birth ratios for the three most
recent years included in the 2001 model run) to ensure only positive values would be simulated. For some key input
variables, the uncertainty ranges around their estimates (used for inventory estimation) were collected from published
documents and other public sources; others were based on expert opinion and best estimates. In addition, both
endogenous and exogenous correlations between selected primary input variables were modeled. The exogenous
correlation coefficients between the probability distributions of selected activity-related variables were developed through
expert judgment.
Manure Management
An analysis (ERG 2003a) was conducted for the manure management emission estimates presented in the 1990
through 2001 Inventory report (i.e., 2003 submission to the UNFCCC) to determine the uncertainty associated with
estimating CFU and N2O emissions from livestock manure management. The quantitative uncertainty analysis for this
source category was performed in 2002 through the IPCC-recommended Tier 2 uncertainty estimation methodology, the
Monte Carlo Stochastic Simulation technique. The uncertainty analysis was developed based on the methods used to
estimate CFU and N2(D emissions from manure management systems. A normal probability distribution was assumed for
each source data category. The series of equations used were condensed into a single equation for each animal type and
state. The equations for each animal group contained four to five variables around which the uncertainty analysis was
performed for each state. These uncertainty estimates were directly applied to the 2012 emission estimates as there have
not been significant changes in the methodology since that time.
Rice Cultivation
The largest uncertainty in the calculation of CtLt emissions from rice cultivation is associated with the emission
factors. Seasonal emissions, derived from field measurements in the United States, vary by more than one order of
magnitude. This inherent variability is due to differences in cultivation practices, particularly fertilizer type, amount, and
mode of application; differences in cultivar type; and differences in soil and climatic conditions. A portion of this
variability is accounted for by separating primary from ratooned areas. However, even within a cropping season or a
given management regime, measured emissions may vary significantly. Of the experiments used to derive the emission
factors applied here, primary emissions ranged from 61 to 500 kg CH/i/hectare-season and ratoon emissions ranged from
481 to 1,490 kg CFU/hectare-season. The uncertainty distributions around the California winter flooding, California non-
winter flooding, non-California primary, and ratoon emission factors were derived using the distributions of the relevant
emission factors available in the literature and described above. Variability around the rice emission factor means was not
normally distributed for any crops, but rather skewed, with a tail trailing to the right of the mean. A lognormal statistical
distribution was, therefore, applied in the Tier 2 Monte Carlo analysis.
Other sources of uncertainty include the primary rice-cropped area for each state, percent of rice-cropped area
that is ratooned, the length of the growing season, and the extent to which flooding outside of the normal rice season is
practiced. Expert judgment was used to estimate the uncertainty associated with primary rice-cropped area for each state
at 1 to 5 percent, and a normal distribution was assumed. Uncertainties were applied to ratooned area by state, based on
the level of reporting performed by the state. Within California, the uncertainty associated with the percentage of rice
fields that are winter flooded was estimated at plus and minus 20 percent. No uncertainty estimates were calculated for the
practice of flooding outside of the normal rice season outside of California because CFU flux measurements have not been
undertaken over a sufficient geographic range or under a broad enough range of representative conditions to account for
this source in the emission estimates or its associated uncertainty.
A-444 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
Agricultural Soil Management
Uncertainty was estimated for each of the following five components of N22 gases emitted from forest fires depend on several variables, including: forest area for Alaska and the
lower 48 states; average C densities for wildfires in Alaska, wildfires in the lower 48 states, and prescribed fires in the
lower 48 states; emission ratios; and combustion factor values (proportion of biomass consumed by fire). To quantify the
uncertainties for emissions from forest fires, a Monte Carlo (Tier 2) uncertainty analysis was performed using information
about the uncertainty surrounding each of these variables.
Direct N2O fluxes from Forest Soils
The amount of N2(D emitted from forests depends not only on N inputs and fertilized area, but also on a large
number of variables, including organic C availability, oxygen gas partial pressure, soil moisture content, pH, temperature,
and tree planting/harvesting cycles. The effect of the combined interaction of these variables on N2O flux is complex and
highly uncertain. IPCC (2006) does not incorporate any of these variables into the default methodology, except variation
in estimated fertilizer application rates and estimated areas of forested land receiving N fertilizer. All forest soils are
treated equivalently under this methodology. Furthermore, only synthetic N fertilizers are captured, so applications of
organic N fertilizers are not estimated. However, the total quantity of organic N inputs to soils is included in the
Agricultural Soil Management and Settlements Remaining Settlements sections.
Uncertainties exist in the fertilization rates, annual area of forest lands receiving fertilizer, and the emission
factors. Fertilization rates were assigned a default level126 of uncertainty at ±50 percent, and area receiving fertilizer was
assigned a ±20 percent according to expert knowledge (Binkley 2004). IPCC (2006) provided estimates for the
uncertainty associated with direct N2(D emission factor for synthetic N fertilizer application to soils. Quantitative
uncertainty of this source category was estimated through the IPCC-recommended Tier 2 uncertainty estimation
126 Uncertainty is unknown for the fertilization rates so a conservative value of ±50 percent was used in the analysis.
A-445
-------
methodology. The uncertainty ranges around the 2005 activity data and emission factor input variables were directly
applied to the 2012 emissions estimates.
Cropland Remaining Cropland
The uncertainty analysis descriptions in this section correspond to source categories included in the Cropland
Remaining Cropland sub-chapter of Land Use, Land-Use Change, and Forestry Chapter of the Inventory.
Agricultural Soil Carbon Stock Change
Uncertainty associated with the Cropland Remaining Cropland land-use category was addressed for changes in
agricultural soil C stocks (including both mineral and organic soils). Uncertainty estimates are presented in Table 7-22 in
the main Inventory document for each subsource (mineral soil C stocks and organic soil C stocks) and method that was
used in the inventory analysis (i.e., Tier 2 and Tier 3). Uncertainty for the portions of the Inventory estimated with Tier 2
and 3 approaches was derived using a Monte Carlo approach (see Annex 3.12 for further discussion). Uncertainty
estimates from each approach were combined using the error propagation equation in accordance with IPCC (2006). The
combined uncertainty was calculated by taking the square root of the sum of the squares of the standard deviations of the
uncertain quantities.
CO2 Emissions from Agricultural Liming
Uncertainty regarding limestone and dolomite activity data inputs was estimated at ±15 percent and assumed to
be uniformly distributed around the inventory estimate (Tepordei 2003b, Willett 2013b). Analysis of the uncertainty
associated with the emission factors included the following: the fraction of agricultural lime dissolved by nitric acid versus
the fraction that reacts with carbonic acid, and the portion of bicarbonate that leaches through the soil and is transported to
the ocean. Uncertainty regarding the time associated with leaching and transport was not accounted for, but should not
change the uncertainty associated with CC>2 emissions (West 2005). The uncertainties associated with the fraction of
agricultural lime dissolved by nitric acid and the portion of bicarbonate that leaches through the soil were each modeled as
a smoothed triangular distribution between ranges of zero percent to 100 percent. The uncertainty surrounding these two
components largely drives the overall uncertainty estimates reported below. More information on the uncertainty
estimates for Liming of Agricultural Soils is contained within the Uncertainty Annex.
CO2 Emissions from Urea Fertilization
A Tier 2 Monte Carlo analysis was completed. The largest source of uncertainty was the default emission factor,
which assumes that 100 percent of the C in CO(NH2)2 applied to soils is ultimately emitted into the environment as CC>2.
This factor does not incorporate the possibility that some of the C may be retained in the soil. The emission estimate is,
therefore, likely to be high. In addition, each urea consumption data point has an associated uncertainty. Urea for non-
fertilizer use, such as aircraft deicing, may be included in consumption totals; it was determined through personal
communication with Fertilizer Regulatory Program Coordinator David L. Terry (2007), however, that this amount is most
likely very small. Research into aircraft deicing practices also confirmed that urea is used minimally in the industry; a
1992 survey found a known annual usage of approximately 2,000 tons of urea for deicing; this would constitute 0.06
percent of the 1992 consumption of urea (EPA 2000). Similarly, surveys conducted from 2002 to 2005 indicate that total
urea use for deicing at U.S. airports is estimated to be 3,740 MT per year, or less than 0.07 percent of the fertilizer total for
2007 (Me 2009). Lastly, there is uncertainty surrounding the assumptions behind the calculation that converts fertilizer
years to calendar years.
Land Converted to Cropland
Uncertainty analysis for mineral soil C stock changes using the Tier 3 and Tier 2 approaches were based on the
same method described for Cropland Remaining Cropland. The uncertainty for annual C emission estimates from drained
organic soils in Land Converted to Cropland was estimated using the Tier 2 approach, as described in the Cropland
Remaining Cropland section.
Uncertainty for the portions of the Inventory estimated with Tier 2 and 3 approaches was derived using a Monte
Carlo approach (see Annex 3.12 for further discussion). Uncertainty estimates from each approach were combined using
the error propagation equation in accordance with IPCC (2006), i.e., by taking the square root of the sum of the squares of
the standard deviations of the uncertain quantities.
Grassland Remaining Grassland
Uncertainty for the portions of the Inventory estimated with Tier 2 and 3 approaches was derived using a Monte
Carlo approach (see Annex 3.12 for further discussion). Uncertainty estimates from each approach were combined using
A-446 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
the error propagation equation in accordance with IPCC (2006), i.e., by taking the square root of the sum of the squares of
the standard deviations of the uncertain quantities.
Land Converted to Grassland
Uncertainty for the portions of the Inventory estimated with Tier 2 and 3 approaches was derived using a Monte
Carlo approach (see Annex 3.12 for further discussion). Uncertainty estimates from each approach were combined using
the error propagation equation in accordance with IPCC (2006) (i.e., by taking the square root of the sum of the squares of
the standard deviations of the uncertain quantities).
Wetlands Remaining Wetlands
The uncertainty analysis descriptions in this section correspond to source categories included in the Wetlands
Remaining Wetlands sub-chapter of Land Use, Land-Use Change, and Forestry Chapter of the Inventory.
Peatlands Remaining Peatlands
The uncertainty associated with peat production data was estimated to be ± 25 percent (Apodaca 2008) and
assumed to be normally distributed. The uncertainty associated with peat production data stems from the fact that the
USGS receives data from the smaller peat producers but estimates production from some larger peat distributors. The peat
type production percentages were assumed to have the same uncertainty values and distribution as the peat production data
(i.e., ± 25 percent with a normal distribution). The uncertainty associated with the Alaskan reported production data was
assumed to be the same as the lower 48 states, or ± 25 percent with a normal distribution. It should be noted that the
Alaska Department of Natural Resources estimates that around half of producers do not respond to their survey with peat
production data; therefore, the production numbers reported are likely to underestimate Alaska peat production (Szumigala
2008). The uncertainty associated with the average bulk density values was estimated to be ± 25 percent with a normal
distribution (Apodaca 2008). IPCC (2006) gives uncertainty values for the emissions factors for the area of peat deposits
managed for peat extraction based on the range of underlying data used to determine the emission factors. The uncertainty
associated with the emission factors was assumed to be triangularly distributed. The uncertainty values surrounding the C
fractions were based on IPCC (2006) and the uncertainty was assumed to be uniformly distributed. Based on these values
and distributions, a Monte Carlo (Tier 2) uncertainty analysis was applied to estimate the uncertainty of CCh and N2(D
emissions from Peatlands Remaining Peatlands.
Settlements Remaining Settlements
The uncertainty analysis descriptions in this section correspond to source categories included in the Settlements
Remaining Settlements sub-chapter of Land Use, Land-Use Change, and Forestry Chapter of the Inventory.
Changes in Carbon Stocks in Urban Trees
Uncertainty associated with changes in C stocks in urban trees includes the uncertainty associated with urban
area, percent urban tree coverage, and estimates of gross and net C sequestration for each of the 50 states and the District
of Columbia. A 10 percent uncertainty was associated with urban area estimates based on expert judgment. Uncertainty
associated with estimates of percent urban tree coverage for each of the 50 states was based on standard error estimates
reported by Nowak and Greenfield (2012). Uncertainty associated with estimate of percent urban tree coverage for the
District of Columbia was based on the standard error estimate reported by Nowak et al. (2013). Uncertainty associated
with estimates of gross and net C sequestration for each of the 50 states and the District of Columbia was based on
standard error estimates for each of the state-level sequestration estimates reported by Nowak et al. (2013). These
estimates are based on field data collected in each of the 50 states and the District of Columbia, and uncertainty in these
estimates increases as they are scaled up to the national level.
Additional uncertainty is associated with the biomass equations, conversion factors, and decomposition
assumptions used to calculate C sequestration and emission estimates (Nowak et al. 2002). These results also exclude
changes in soil C stocks, and there may be some overlap between the urban tree C estimates and the forest tree C
estimates. Due to data limitations, urban soil flux is not quantified as part of this analysis, while reconciliation of urban
tree and forest tree estimates will be addressed through the land-representation effort described in the Planned
Improvements section of this chapter.
Direct N2O Fluxes from Settlement Soils
The amount of N2O emitted from settlements depends not only on N inputs and fertilized area, but also on a large
number of variables, including organic C availability, oxygen gas partial pressure, soil moisture content, pH, temperature,
and irrigation/watering practices. The effect of the combined interaction of these variables on N2O flux is complex and
A-447
-------
highly uncertain. The IPCC default methodology does not explicitly incorporate any of these variables, except variations
in fertilizer N and sewage sludge application rates. All settlement soils are treated equivalently under this methodology.
Uncertainties exist in both the fertilizer N and sewage sludge application rates in addition to the emission factors.
Uncertainty in fertilizer N application was assigned a default level of ±50 percent. ^ Uncertainty in the amounts of
sewage sludge applied to non-agricultural lands and used in surface disposal was derived from variability in several
factors, including: (1) N content of sewage sludge; (2) total sludge applied in 2000; (3) wastewater existing flow in 1996
and 2000; and (4) the sewage sludge disposal practice distributions to non-agricultural land application and surface
disposal. Uncertainty in the emission factors was provided by the IPCC (2006).
Other
The uncertainty analysis descriptions in this section correspond to source categories included in the Other sub-
chapter of Land Use, Land-Use Change, and Forestry Chapter of the Inventory.
Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills
The uncertainty analysis for landfilled yard trimmings and food scraps includes an evaluation of the effects of
uncertainty for the following data and factors: disposal in landfills per year (tons of C), initial C content, moisture content,
decay rate, and proportion of C stored. The C storage landfill estimates are also a function of the composition of the yard
trimmings (i.e., the proportions of grass, leaves and branches in the yard trimmings mixture). There are respective
uncertainties associated with each of these factors.
Waste
The uncertainty analysis descriptions in this section correspond to source categories included in the Waste
Chapter of the Inventory.
Landfills
Several types of uncertainty are associated with the estimates of CLU emissions from MSW and industrial waste
landfills. The primary uncertainty concerns the characterization of landfills. Information is not available on two
fundamental factors affecting CLL production: the amount and composition of waste placed in every MSW and industrial
waste landfill for each year of its operation. The SOG survey is the only nationwide data source that compiles the amount
of MSW disposed at the state-level. The surveys do not include information on waste composition and there are no
comprehensive data sets that compile quantities of waste disposed or waste composition by landfill. Some MSW landfills
have conducted detailed waste composition studies, but landfills in the United States are not required to perform these
types of studies. The approach used here assumes that the CLU generation potential and the rate of decay that produces
CLLi, as determined from several studies of CLL recovery at MSW landfills, are representative of conditions at U.S.
landfills. When this top-down approach is applied at the nationwide level, the uncertainties are assumed to be less than
when applying this approach to individual landfills and then aggregating the results to the national level. In other words,
this approach may over- and under-estimate CLLi generation at some landfills if used at the facility-level, but the end result
is expected to balance out because it is being applied nationwide. There is also a high degree of uncertainty and variability
associated with the first order decay model, particularly when a homogeneous waste composition and hypothetical
decomposition rates are applied to heterogeneous landfills (IPCC 2006).
Additionally, there is a lack of landfill-specific information regarding the number and type of industrial waste
landfills in the United States. The approach used here assumes that the majority (99 percent) of industrial waste disposed
of in industrial waste landfills consists of waste from the pulp and paper and food and beverage industries. However,
because waste generation and disposal data are not available in an existing data source for all U.S. industrial waste
landfills, we apply a straight disposal factor over the entire time series to the amount of waste generated to determine the
amounts disposed.
Aside from the uncertainty in estimating CLL generation potential, uncertainty exists in the estimates of the
landfill gas oxidized. A constant oxidation factor of 10 percent as recommended by the Intergovernmental Panel on
Climate Change (IPCC) for managed landfills is used for both MSW and industrial waste landfills regardless of climate,
the type of cover material, and/or presence of a gas collection system. The number of field studies measuring the rate of
127 No uncertainty is provided with the USGS fertilizer consumption data (Ruddy et al. 2006) so a conservative ±50% was used
in the analysis.
A-448 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
oxidation has increased substantially since the IPCC 2006 Guidelines were published and, as discussed in the Potential
Improvements section, efforts are being made to review the literature and revise this value based on recent, peer-reviewed
studies.
Another significant source of uncertainty lies with the estimates of CH4 that are recovered by flaring and gas-to-
energy projects at MSW landfills. Three separate databases containing recovery information are used to determine the total
amount of CH4 recovered and there are uncertainties associated with each. The LMOP database and the flare vendor
databases are updated annually, while the EIA database has not been updated since 2005 and will essentially be replaced
by GHGRP data for a portion of landfills (i.e., those meeting the GHGRP thresholds). To avoid double counting and to use
the most relevant estimate of CH4 recovery for a given landfill, a hierarchical approach is used among the three databases.
The EIA data are given precedence because CH4 recovery was directly reported by landfills, the LMOP data are given
second priority because CH4 recovery is estimated from facility-reported LFGTE system characteristics, and the flare data
are given third priority because this database contains minimal information about the flare and no site-specific operating
characteristics (Bronstein et al, 2012). The IPCC default value of 10 percent for uncertainty in recovery estimates was
used in the uncertainty analysis when metering of landfill gas was in place (for about 64 percent of the CH4 estimated to be
recovered). This 10 percent uncertainty factor applies to 2 of the 3 databases (EIA and LMOP). For flaring without
metered recovery data (approximately 34 percent of the CLU estimated to be recovered), a much higher uncertainty of
approximately 50 percent was used (e.g., when recovery was estimated as 50 percent of the flare's design capacity).
Wastewater Treatment
The overall uncertainty associated with both the 2012 CLU and N2O emission estimates from wastewater
treatment and discharge was calculated using the IPCC Good Practice Guidance Tier 2 methodology (2000). Uncertainty
associated with the parameters used to estimate CLL emissions include that of numerous input variables used to model
emissions from domestic wastewater, and wastewater from pulp and paper manufacture, meat and poultry processing,
fruits and vegetable processing, ethanol production, and petroleum refining. Uncertainty associated with the parameters
used to estimate N2O emissions include that of sewage sludge disposal, total U.S. population, average protein consumed
per person, fraction of N in protein, non-consumption nitrogen factor, emission factors per capita and per mass of sewage-
N, and for the percentage of total population using centralized wastewater treatment plants.
References
EPA (2002) Quality Assurance/Quality Control and Uncertainty Management Plan for the U.S. Greenhouse Gas
Inventory: Procedures Manual for Quality Assurance/Quality Control and Uncertainty Analysis, U.S. Greenhouse
Gas Inventory Program, U.S. Environmental Protection Agency, Office of Atmospheric Programs, EPA 430-R-02-
007B, June 2002.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, Paris:
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories,
Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories Programme, Montreal, IPCC-
XVI/Doc. 10 (1.IV.2000), May 2000.
IPCCAJNEP/OECD/IEA (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Paris:
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency.
USGS (2011) 2010Mineral Yearbook; Aluminum [AdvancedRelease]. U.S. Geological Survey, Reston, VA.
A-449
-------
ANNEX 8 QA/QC Procedures
8.1. Background
The purpose of this annex is to describe the QA/QC procedures and information quality considerations that are
used throughout the process of creating and compiling the U.S. Greenhouse Gas Inventory. This includes evaluation of the
quality and relevance of data and models used as inputs into the Inventory; proper management, incorporation, and
aggregation of data; and review of the numbers and estimates to ensure that they are as accurate and transparent as
possible. Quality control—in the form of both good practices (such as documentation procedures) and checks on whether
good practices and procedures are being followed—is applied at every stage of inventory development and document
preparation. In addition, quality assurance occurs at two stages—an expert review and a public review. While both phases
can significantly contribute to inventory quality, the public review phase is also essential for promoting the openness of
the inventory development process and the transparency of the inventory data and methods.
8.2. Purpose
The Quality Assurance/Quality Control and Uncertainty Management Plan for the U.S. Greenhouse Gas
Inventory (QA/QC Management Plan) guides the process of ensuring inventory quality by describing data and
methodology checks, developing processes governing peer review and public comments, and developing guidance on
conducting an analysis of the uncertainty surrounding the emission estimates. The QA/QC Management Plan procedures
also stress continual improvement, providing for corrective actions that are designed to improve the inventory estimates
over time.
Key attributes of the QA/QC Management Plan are summarized in Figure A- 22. These attributes include:
• Procedures and Forms: detailed and specific systems that serve to standardize the process of documenting and
archiving information, as well as to guide the implementation of QA/QC and the analysis of uncertainty
• Implementation of Procedures: application of QA/QC procedures throughout the whole inventory development
process from initial data collection, through preparation of the emission estimates, to publication of the Inventory
• Quality Assurance: expert and public reviews for both the inventory estimates and the Inventory report (which is
the primary vehicle for disseminating the results of the inventory development process)
• Quality Control, consideration of secondary data and source-specific checks (Tier 2 QC) in parallel and
coordination with the uncertainty assessment; the development of protocols and templates, which provides for
more structured communication and integration with the suppliers of secondary information
• Tier 1 (general) and Tier 2 (source-specific) Checks: quality controls and checks, as recommended by IPCC
Good Practice Guidance
• Record Keeping: provisions to track which procedures have been followed, the results of the QA/QC, uncertainty
analysis, and feedback mechanisms for corrective action based on the results of the investigations which provide
for continual data quality improvement and guided research efforts
• Multi-Year Implementation: a schedule for coordinating the application of QA/QC procedures across multiple
years
• Interaction and Coordination: promoting communication within the EPA, across Federal agencies and
departments, state government programs, and research institutions and consulting firms involved in supplying
data or preparing estimates for the Inventory. The QA/QC Management Plan itself is intended to be revised and
reflect new information that becomes available as the program develops, methods are improved, or additional
supporting documents become necessary.
In addition, based on the national QA/QC Management Plan for the Inventory, source-specific QA/QC plans
have been developed for a number of sources. These plans follow the procedures outlined in the national QA/QC plan,
tailoring the procedures to the specific text and spreadsheets of the individual sources. For each greenhouse gas emissions
A-450 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
source or sink included in this Inventory, a minimum of a Tier
activities for a particular source go beyond the minimum Tier
source category text.
Figure A- 22: U.S. QA/QG Plan Summary
1 QA/QC analysis has been undertaken. Where QA/QC
1 level, further explanation is provided within the respective
•I-1
V)
IQ
C
<
>-
l_
4-1
nl
>
c
4-1
t/1
_>
ro
c
<
(J
a
S
• Obtain data in electronic
format (if possible}
• Review spreadsheet
construction
Avoid hardwiring
• Use data validation
Protect cells
• Develop automatic
checkers for:
• Outliers, negative
values, or missing
data
Variabletvpes
match values
• Time series
consistency
• Contact reports for non-
electroniccommunications
1 Provide cell references for
primary data elements
• Obtain copies of all data
sources
• List and location of any
working/external
spreadsheets
• Document assumptions
• Clearly label parameters,
units, and conversion
factors
• Review spreadsheet
integrity
• Equations
• Units
• inputs and output
• Develop automated
checkers for:
• Input ranges
• Calculations
• Emission aggregation
Maintaintrackingtabfor II
status of gathering
efforts Uk UL \
• Check input data for
transcription errors
• Inspectautomatic
checkers
• Identify spreadsheet
modificationsthat could
provide additional
QA/QC checks
* Check citations in
spreadsheet and text for
accuracy and style
• Check reference docket for
new citations
1 Review documentation for
any data/ methodology
changes
• Reproduce calculations
• Reviewtimeseries
consistency
• Review changes in
data/consist en cy with IPCC
methodology
*
Data Gathering
Data Documentation CalculatingEmissions
Common starting
versions for each
inventory year
Utilize unalterable
summary tab for each
source spreadsheet for
linkingtoa master
summary spreadsheet
Follow strictversion
control procedures
Document QA/QC
procedures
Cross-Cutting
Coordination
8.3. Assessment Factors
The U.S. Greenhouse Gas Inventory development process follows guidance outlined in EPA's Guidelines for
Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity, of Information Disseminated by the
~ 128
Environmental Protection Agency and A Summary of General Assessment Factors for Evaluating the Quality of
Scientific and Technical Information™ This includes evaluating the data and models used as inputs into the U.S.
Greenhouse Gas Inventory against the five general assessment factors: soundness, applicability and utility, clarity and
completeness, uncertainty and variability, evaluation and review. Table A- 291 defines each factor and explains how it was
considered during the process of creating the current Inventory.
128 EPA report #260R-02-008, October 2002, available at www.epa.gov/quality/informationguidelines
129 EPA report #100/8-03/001, June 2003, available at www.epa.gov/stpc/assess.htm
A-451
-------
Table A- 291: Assessment Factors and Definitions
General
Assessment
Factor
Definition
How the Factor was Considered
Soundness (API)
The extent to which the
scientific and technical
procedures, measures, methods
or models employed to
generate the information are
reasonable for, and consistent
with, the intended application.
The intended application is to provide information regarding
all sources and sinks of greenhouse gases in the United States
for the Inventory year, as required per UNFCCC Annex I
country reporting requirements. The underlying data,
methodology, and models used to generate the U.S.
Greenhouse Gas Inventory are reasonable for and consistent
with their intended application. The U.S. emissions
calculations follow IPCC Guidelines developed specifically
for UNFCCC inventory reporting. They are based on the best
available, peer-reviewed scientific information, and have
been used by the international community for over 20 years.
When possible, Tier 2 and Tier 3 methodologies from the
IPCC Guidelines are applied to calculate more accurate
United States emissions.
Applicability and
Utility (AF2)
The extent to which the
information is relevant for the
Agency's intended use.
The Inventory's underlying data, methodology, and models
are relevant for their intended application because they
generate the sector-specific greenhouse gas emissions trends
necessary for assessing and understanding all sources and
sinks of greenhouse gases in the United States for the
Inventory year. They are relevant for communicating U.S.
emissions information to domestic audiences, and they are
consistent with IPCC Guidelines developed specifically for
UNFCCC reporting purposes of international greenhouse gas
inventories.
Clarity and
Completeness (AF3)
The degree of clarity and
completeness with which the
data, assumptions, methods,
quality assurance, sponsoring
organizations and analyses
employed to generate the
information are documented.
The methodological and calculation approaches applied to
generate the U.S. Greenhouse Gas Inventory are extensively
documented in the IPCC Guidelines. The U.S. Greenhouse
Gas Inventory report describes its adherence to the IPCC
Guidelines, and the U.S. Government agencies providing data
to implement the IPCC Guidelines approaches. Any changes
made to calculations, due to updated data and methods, are
explained and documented in the report consistent with
UNFCCC reporting guidelines.
Uncertainty and
Variability (AF4)
The extent to which the
variability and uncertainty
(quantitative and qualitative) in
the information or in the
procedures, measures, methods
or models are evaluated and
characterized.
In accordance with IPCC Guidelines, the uncertainty
associated with the Inventory's underlying data,
methodology, and models was evaluated by running a Monte-
Carlo uncertainty analysis on source category emissions data
to produce a 95 percent confidence interval for the annual
greenhouse gas emissions for that source. To develop overall
uncertainty estimates, the Monte Carlo simulation output data
for each emission source category uncertainty analysis were
combined by type of gas, and the probability distributions
A-452 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012
-------
were fitted to the combined simulation output data where
such simulated output data were available.
The evaluation of uncertainties for the underlying data is
documented in an Uncertainty section of the Annex to the
U.S. Greenhouse Gas Inventory.
Evaluation and Review
(AF5)
The extent of independent
verification, validation and
peer review of the information
or of the procedures, measures,
methods or models.
The majority of the underlying methodology, calculations,
and models used to generate the U.S. Greenhouse Gas
Inventory have been independently verified and peer
reviewed as part of their publication in the IPCC Guidelines.
In cases where the methodology differs slightly from the
IPCC Guidelines, these were independently verified and
validated by technical experts during an annual expert review
phase of the Inventory report.
For the data used in calculating greenhouse gas emissions for
each source, multiple levels of evaluation and review occur.
Data are compared to results from previous years, and
calculations and equations are continually evaluated and
updated as appropriate. Throughout the process, inventory
data and methodological improvements are planned and
incorporated.
The Inventory undergoes annual cycles of expert and public
review before publication. This process ensures that both
experts and the general public can review each source of
emissions and have an extended opportunity to provide
feedback on the methodologies used, calculations, data
sources, and presentation of information.
A-453
------- |