EPA 430-R-15-004
Inventory of U.S. Greenhouse Gas
Emissions and Sinks:
1990-2013
APRIL 15,2015
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 2013, 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) 343-9897, 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, 2015
-------
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. Sarah Fro man, Susan Burke and Amy Bunker
directed the work on mobile combustion and transportation. Work on industrial processes and product use 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 the Land Use, Land-Use Change, and Forestry chapters. Work on emissions of HFCs, PFCs,
SF6, and NF3 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 Hedger, Lauren Pederson, Mollie Averyt, Mark Flugge, Larry
O'Rourke, Deborah Harris, Leslie Chinery, Dean Gouveia, Jonathan Cohen, Alexander Lataille, Andrew Pettit,
Sabrina Andrews, Marybeth Riley-Gilbert, Sarah Kolansky, Greg Carlock, Ben Eskin, Jessica Kuna, David Towle,
Bikash Acharya, Bobby Renz, Rebecca Ferenchiak, Nikita Pavlenko, Jay Creech, Kirsten Jaglo, Kasey Knoell, Cory
Jemison, Kevin Kurkul, and Matt Lichtash 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.
-------
-------
Preface
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 ES-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 National Inventory Arrangements 1-10
1.3 Inventory Process 1-13
1.4 Methodology and Data Sources 1-14
1.5 Key Categories 1-15
1.6 Quality Assurance and Quality Control (QA/QC) 1-19
1.7 Uncertainty Analysis of Emission Estimates 1-21
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
2.1 Emissions by Economic Sector 2-22
2.2 Indirect Greenhouse Gas Emissions (CO, NOX, NMVOCs, and SO2) 2-33
3. ENERGY 3-1
3.1 Fossil Fuel Combustion (IPCC Source Category 1A) 3-4
3.2 Carbon Emitted from Non-Energy Uses of Fossil Fuels (IPCC Source Category 1A) 3-38
3.3 Incineration of Waste (IPCC Source Category lAla) 3-45
3.4 Coal Mining (IPCC Source Category IB la) 3-49
-------
3.5 Abandoned Underground Coal Mines (IPCC Source Category IBla) 3-53
3.6 Petroleum Systems (IPCC Source Category lB2a) 3-57
3.7 Natural Gas Systems (IPCC Source Category lB2b) 3-68
3.8 Energy Sources of Indirect Greenhouse Gas Emissions 3-80
3.9 International Bunker Fuels (IPCC Source Category 1: Memo Items) 3-81
3.10 WoodBiomass andEthanol Consumption (IPCC Source Category 1A) 3-86
4. INDUSTRIAL PROCESSES AND PRODUCT USE 4-1
4.1 Cement Production (IPCC Source Category 2A1) 4-6
4.2 Lime Production (IPCC Source Category 2A2) 4-9
4.3 Glass Production (IPCC Source Category 2A3) 4-14
4.4 Other Process Uses of Carbonates (IPCC Source Category 2 A4) 4-17
4.5 Ammonia Production (IPCC Source Category 2B1) 4-20
4.6 Urea Consumption for Non-Agricultural Purposes 4-24
4.7 Nitric Acid Production (IPCC Source Category 2B2) 4-27
4.8 Adipic Acid Production (IPCC Source Category 2B3) 4-30
4.9 Silicon Carbide Production and Consumption (IPCC Source Category 2B5) 4-34
4.10 Titanium Dioxide Production (IPCC Source Category 2B6) 4-37
4.11 Soda Ash Production and Consumption (IPCC Source Category 2B7) 4-40
4.12 Petrochemical Production (IPCC Source Category 2B8) 4-43
4.13 HCFC-22 Production (IPCC Source Category 2B9a) 4-49
4.14 Carbon Dioxide Consumption (IPCC Source Category 2B10) 4-52
4.15 Phosphoric Acid Production (IPCC Source Category 2B10) 4-55
4.16 Iron and Steel Production (IPCC Source Category 2C1) and Metallurgical Coke Production 4-59
4.17 Ferroalloy Production (IPCC Source Category 2C2) 4-67
4.18 Aluminum Production (IPCC Source Category 2C3) 4-71
4.19 Magnesium Production and Processing (IPCC Source Category 2C4) 4-76
4.20 Lead Production (IPCC Source Category 2C5) 4-81
4.21 Zinc Production (IPCC Source Category 2C6) 4-84
4.22 Semiconductor Manufacture (IPCC Source Category 2E1) 4-87
4.23 Substitution of Ozone Depleting Substances (IPCC Source Category 2F) 4-97
4.24 Electrical Transmission and Distribution (IPCC Source Category 2G1) 4-102
4.25 Nitrous Oxide from Product Uses (IPCC Source Category 2G3) 4-110
4.26 Industrial Processes and Product Use Sources of Indirect Greenhouse Gases 4-113
5. AGRICULTURE 5-1
5.1 Enteric Fermentation (IPCC Source Category 3A) 5-2
5.2 Manure Management (IPCC Source Category 3B) 5-8
5.3 Rice Cultivation (IPCC Source Category 3C) 5-15
vi Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
5.4 Agricultural Soil Management (IPCC Source Category 3D) 5-22
5.5 Field Burning of Agricultural Residues (IPCC Source Category 3F) 5-38
6. LAND USE, LAND-USE CHANGE, AND FORESTRY 6-1
6.1 Representation of the U.S. Land Base 6-5
6.2 Forest Land Remaining Forest Land 6-18
6.3 Land Converted to Forest Land (IPCC Source Category 4A2) 6-39
6.4 Cropland Remaining Cropland (IPCC Source Category 4B1) 6-40
6.5 Land Converted to Cropland (IPCC Source Category 4B2) 6-54
6.6 Grassland Remaining Grassland (IPCC Source Category 4C1) 6-60
6.7 Land Converted to Grassland (IPCC Source Category 4C2) 6-66
6.8 Wetlands Remaining Wetlands (IPCC Source Category 4D1) 6-73
6.9 Settlements Remaining Settlements 6-79
6.10 Land Converted to Settlements (IPCC Source Category 4E2) 6-86
6.11 Other (IPCC Source Category 4H) 6-86
7. WASTE 7-1
7.1 Landfills (IPCC Source Category 5A1) 7-3
7.2 Wastewater Treatment (IPCC Source Category 5D) 7-16
7.3 Composting (IPCC Source Category 5B1) 7-30
7.4 Waste Incineration (IPCC Source Category 5C1) 7-33
7.5 Waste Sources of Indirect Greenhouse Gases 7-33
8. OTHER 8-1
9. RECALCULATIONS AND IMPROVEMENTS 9-1
10. REFERENCES 10-1
vn
-------
List of Tables, Figures, and Boxes
Tables
Table ES-1: Global Wanning Potentials (100-Year Time Horizon) Used in this Report ES-3
Table ES-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (MMT CO2 Eq.) ES-5
Table ES-3: CO2 Emissions from Fossil Fuel Combustion by Fuel Consuming End-Use Sector (MMT CO2 Eq.).ES-
11
Table ES-4: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector (MMT CO2 Eq.)
ES-17
Table ES-5: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry (MMT CO2 Eq.) ...ES-
20
Table ES-6: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (MMT CO2 Eq.) ES-22
Table ES-7: U.S Greenhouse Gas Emissions by Economic Sector with Electricity-Related Emissions Distributed
(MMTCO2Eq.) ES-23
Table ES-8: 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 (1990-2013) 1-16
Table 1 -5: Estimated Overall Inventory Quantitative Uncertainty (MMT CO2 Eq. and Percent) 1-21
Table 1-6: IPCC Sector Descriptions 1-22
Table 1-7: List of Annexes 1-24
Table 2-1: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (MMT CO2 Eq.) 2-4
Table 2-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (kt) 2-7
Table 2-3: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector (MMT CO2 Eq.) 2-
9
Table 2-4: Emissions from Energy (MMT CO2 Eq.) 2-12
Table 2-5: CO2 Emissions from Fossil Fuel Combustion by End-Use Sector (MMT CO2 Eq.) 2-13
Table 2-6: Emissions from Industrial Processes and Product Use (MMT CO2Eq.) 2-16
Table 2-7: Emissions from Agriculture (MMT CO2 Eq.) 2-18
Table 2-8: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry (MMT CO2 Eq.)... 2-20
Table 2-9: Emissions from Waste (MMT CO2 Eq.) 2-22
Table 2-10: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (MMT CO2 Eq. and Percent of Total in
2013) 2-23
Table 2-11: Electricity Generation-Related Greenhouse Gas Emissions (MMT CO2 Eq.) 2-25
Table 2-12: U.S. Greenhouse Gas Emissions by Economic Sector and Gas with Electricity-Related Emissions
Distributed (MMT CO2Eq.) and Percent of Total in 2013 2-27
Table 2-13: Transportation-Related Greenhouse Gas Emissions (MMT CO2Eq.) 2-29
viii Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 2-14: Recent Trends in Various U.S. Data (Index 1990 = 100) 2-32
Table 2-15: Emissions of NOX, CO, NMVOCs, and SO2 (kt) 2-34
Table 3-1: CO2, CH4, andN2O Emissions fromEnergy (MMT CO2 Eq.) 3-2
Table 3-2: CO2, CH4, and N2O Emissions from Energy (kt) 3-3
Table 3-3: CO2, CH4, and N2O Emissions from Fossil Fuel Combustion (MMT CO2 Eq.) 3-5
Table 3-4: CO2, CH4, and N2O Emissions from Fossil Fuel Combustion (kt) 3-5
Table 3-5: CO2 Emissions from Fossil Fuel Combustion by Fuel Type and Sector (MMT CO2 Eq.) 3-5
Table 3-6: Annual Change in CO2 Emissions and Total 2013 Emissions from Fossil Fuel Combustion for Selected
Fuels and Sectors (MMT CO2 Eq. and Percent) 3-6
Table 3-7: CO2, CH4, andN2O Emissions from Fossil Fuel Combustion by Sector (MMT CO2 Eq.) 3-10
Table 3-8: CO2, CH4, and N2O Emissions from Fossil Fuel Combustion by End-Use Sector (MMT CO2Eq.).... 3-11
Table 3-9: CO2 Emissions from Stationary Fossil Fuel Combustion (MMT CO2 Eq.) 3-12
Table 3-10: CH4 Emissions from Stationary Combustion (MMT CO2 Eq.) 3-13
Table 3-11: N2O Emissions from Stationary Combustion (MMT CO2 Eq.) 3-13
Table 3-12: CO2 Emissions from Fossil Fuel Combustion in Transportation End-Use Sector (MMT CO2 Eq.)... 3-19
Table 3-13: CH4 Emissions fromMobile Combustion (MMT CO2 Eq.) 3-22
Table 3-14: N2O Emissions fromMobile Combustion (MMT CO2 Eq.) 3-22
Table 3-15: Carbon Intensity from Direct Fossil Fuel Combustion by Sector (MMT CO2 Eq./QBtu) 3-28
Table 3-16: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Energy-related Fossil Fuel
Combustion by Fuel Type and Sector (MMT CO2 Eq. and Percent) 3-30
Table 3-17: Approach 2 Quantitative Uncertainty Estimates for CH4 and N2O Emissions from Energy-Related
Stationary Combustion, Including Biomass (MMT CO2Eq. and Percent) 3-34
Table 3-18: Approach 2 Quantitative Uncertainty Estimates for CH4 and N2O Emissions from Mobile Sources
(MMT CO2 Eq. and Percent) 3-37
Table 3-19: CO2 Emissions from Non-Energy Use Fossil Fuel Consumption (MMT CO2 Eq. and percent) 3-39
Table 3-20: Adjusted Consumption of Fossil Fuels for Non-Energy Uses (TBtu) 3-40
Table 3-21: 2013 Adjusted Non-Energy Use Fossil Fuel Consumption, Storage, and Emissions 3-40
Table 3-22: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Non-Energy Uses of Fossil
Fuels (MMT CO2Eq. and Percent) 3-42
Table 3-23: Approach 2 Quantitative Uncertainty Estimates for Storage Factors of Non-Energy Uses of Fossil Fuels
(Percent) 3-42
Table 3 -24: CO2, CH4, and N2O Emissions from the Incineration of Waste (MMT CO2 Eq.) 3-46
Table 3-25: CO2, CH4, andN2O Emissions from the Incineration of Waste (kt) 3-46
Table 3 -26: Municipal Solid Waste Generation (Metric Tons) and Percent Combusted 3-47
Table 3-27: Approach 2 Quantitative Uncertainty Estimates for CO2 and N2O from the Incineration of Waste (MMT
CO2 Eq. and Percent) 3-48
Table 3-28: CH4 Emissions from Coal Mining (MMT CO2 Eq.) 3-50
Table 3-29: CH4 Emissions from Coal Mining (kt) 3-50
Table 3-30: Coal Production (kt) 3-52
IX
-------
Table 3-31: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Coal Mining (MMT CO2 Eq.
and Percent) 3-52
Table 3-32: CH4 Emissions from Abandoned Coal Mines (MMT CO2 Eq.) 3-54
Table 3-33: CH4 Emissions from Abandoned Coal Mines (kt) 3-54
Table 3-34: Number of Gassy Abandoned Mines Present in U.S. Basins, grouped by Class according to Post-
Abandonment State 3-56
Table 3-35: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Abandoned Underground Coal
Mines (MMT CO2 Eq. and Percent) 3-57
Table 3-36: CH4 Emissions from Petroleum Systems (MMT CO2 Eq.) 3-58
Table 3-37: CH4 Emissions from Petroleum Systems (kt) 3-59
Table 3-38: CO2 Emissions from Petroleum Systems (MMT CO2Eq.) 3-59
Table 3-39: CO2 Emissions from Petroleum Systems (kt) 3-60
Table 3-40: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Petroleum Systems (MMT
CO2 Eq. and Percent) 3-62
Table 3-41: Potential Emissions from CO2 Capture and Transport (MMT CO2 Eq.) 3-68
Table 3-42: Potential Emissions from CO2 Capture and Transport (kt) 3-68
Table 3-43: CH4 Emissions from Natural Gas Systems (MMT CO2 Eq.)a 3-70
Table 3-44: CH4 Emissions from Natural Gas Systems (kt)a 3-70
Table 3-45: Calculated Potential CH4 and Captured/Combusted CH4 from Natural Gas Systems (MMT CO2 Eq.). 3-
70
Table 3 -46: Non-combustion CO2 Emissions from Natural Gas Systems (MMT CO2 Eq.) 3-70
Table 3-47: Non-combustion CO2 Emissions from Natural Gas Systems (kt) 3-71
Table 3-48: Approach 2 Quantitative Uncertainty Estimates for CH4 and Non-energy CO2 Emissions from Natural
Gas Systems (MMT CO2 Eq. and Percent) 3-74
Table 3-49: NOX, CO, and NMVOC Emissions from Energy-Related Activities (kt) 3-80
Table 3-50: CO2, CH4, and N2O Emissions from International Bunker Fuels (MMT CO2Eq.) 3-82
Table 3-51: CO2, CH4 and N2O Emissions from International Bunker Fuels (kt) 3-83
Table 3 -52: Aviation CO2 and N2O Emissions for International Transport (MMT CO2 Eq.) 3-83
Table 3-53: Aviation Jet Fuel Consumption for International Transport (Million Gallons) 3-84
Table 3-54: Marine Fuel Consumption for International Transport (Million Gallons) 3-84
Table 3-55: CO2 Emissions from Wood Consumption by End-Use Sector (MMT CO2 Eq.) 3-87
Table 3-56: CO2 Emissions from Wood Consumption by End-Use Sector (kt) 3-87
Table 3-57: CO2 Emissions fromEthanol Consumption (MMT CO2 Eq.) 3-87
Table 3-58: CO2 Emissions fromEthanol Consumption (kt) 3-87
Table 3-59: Woody Biomass Consumption by Sector (Trillion Btu) 3-88
Table 3-60: Ethanol Consumption by Sector (Trillion Btu) 3-88
Table 4-1: Emissions from Industrial Processes and Product Use (MMT CO2Eq.) 4-3
Table 4-2: Emissions from Industrial Processes and Product Use (kt) 4-4
Table 4-3: CO2 Emissions from Cement Production (MMT CO2Eq. andkt) 4-7
x Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 4-4: Clinker Production (kt) 4-8
Table 4-5: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Cement Production (MMT CO2
Eq. and Percent) 4-9
Table 4-6: CO2 Emissions from Lime Production (MMT CO2 Eq. and kt) 4-10
Table 4-7: Potential, Recovered, and Net CO2 Emissions from Lime Production (kt) 4-10
Table 4-8: High-Calcium- and Dolomitic-Quicklime, High-Calcium- and Dolomitic-Hydrated, and Dead-Burned-
Dolomite Lime Production (kt) 4-11
Table 4-9: Adjusted Lime Production (kt) 4-12
Table 4-10: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Lime Production (MMT CO2
Eq. and Percent) 4-13
Table 4-11: CO2 Emissions from Glass Production (MMT CO2 Eq. and kt) 4-15
Table 4-12: Limestone, Dolomite, and Soda Ash Consumption Used in Glass Production (kt) 4-16
Table 4-13: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Glass Production (MMT CO2
Eq. and Percent) 4-17
Table 4-14: CO2 Emissions from Other Process Uses of Carbonates (MMT CO2 Eq.) 4-18
Table 4-15: CO2 Emissions from Other Process Uses of Carbonates (kt) 4-18
Table 4-16: Limestone and Dolomite Consumption (kt) 4-19
Table 4-17: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Other Process Uses of
Carbonates (MMT CO2 Eq. and Percent) 4-20
Table 4-18: CO2 Emissions from Ammonia Production (MMT CO2 Eq.) 4-21
Table 4-19: CO2 Emissions from Ammonia Production (kt) 4-21
Table 4-20: Ammonia Production and Urea Production (kt) 4-23
Table 4-21: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Ammonia Production (MMT
CO2 Eq. and Percent) 4-23
Table 4-22: CO2 Emissions from Urea Consumption for Non-Agricultural Purposes (MMT CO2 Eq.) 4-25
Table 4-23: CO2 Emissions from Urea Consumption for Non-Agricultural Purposes (kt) 4-25
Table 4-24: Urea Production, Urea Applied as Fertilizer, Urea Imports, and Urea Exports (kt) 4-26
Table 4-25: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Urea Consumption for Non-
Agricultural Purposes (MMT CO2Eq. and Percent) 4-26
Table 4-26: N2O Emissions from Nitric Acid Production (MMT CO2 Eq. and kt N2O) 4-27
Table 4-27: Nitric Acid Production (kt) 4-29
Table 4-28: Approach 2 Quantitative Uncertainty Estimates for N2O Emissions from Nitric Acid Production (MMT
CO2 Eq. and Percent) 4-29
Table 4-29: N2O Emissions from Adipic Acid Production (MMT CO2 Eq. and kt N2O) 4-31
Table 4-30: Adipic Acid Production (kt) 4-33
Table 4-31: Approach 2 Quantitative Uncertainty Estimates for N2O Emissions from Adipic Acid Production
(MMT CO2 Eq. and Percent) 4-33
Table 4-32: CO2 and CH4 Emissions from Silicon Carbide Production and Consumption (MMT CO2 Eq.) 4-35
Table 4-33: CO2 and CH4 Emissions from Silicon Carbide Production and Consumption (kt) 4-35
Table 4-34: Production and Consumption of Silicon Carbide (Metric Tons) 4-36
XI
-------
Table 4-35: Approach 2 Quantitative Uncertainty Estimates for CH4 and CO2 Emissions from Silicon Carbide
Production and Consumption (MMT CO2Eq. and Percent) 4-36
Table 4-36: CO2 Emissions from Titanium Dioxide (MMT CO2 Eq. and kt) 4-38
Table 4-37: Titanium Dioxide Production (kt) 4-38
Table 4-38: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Titanium Dioxide Production
(MMT CO2 Eq. and Percent) 4-39
Table 4-39: CO2 Emissions from Soda Ash Production and Consumption Not Associated with Glass Manufacturing
(MMTCO2Eq.) 4-41
Table 4-40: CO2 Emissions from Soda Ash Production and Consumption Not Associated with Glass Manufacturing
(kt) 4-41
Table 4-41: Soda Ash Production and Consumption Not Associated with Glass Manufacturing (kt) 4-42
Table 4-42: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Soda Ash Production and
Consumption (MMT CO2Eq. and Percent) 4-43
Table 4-43: CO2 and CH4 Emissions from Petrochemical Production (MMT CO2 Eq.) 4-45
Table 4-44: CO2 and CH4 Emissions from Petrochemical Production (kt) 4-45
Table 4-45: Production of Selected Petrochemicals (kt) 4-48
Table 4-46: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Petrochemical Production and
CO2 Emissions from Carbon Black Production (MMT CO2Eq. and Percent) 4-48
Table 4-47: HFC-23 Emissions fromHCFC-22 Production (MMT CO2 Eq. and kt HFC-23) 4-50
Table 4-48: HCFC-22 Production (kt) 4-51
Table 4-49: Approach 2 Quantitative Uncertainty Estimates for HFC-23 Emissions from HCFC-22 Production
(MMT CO2 Eq. and Percent) 4-52
Table 4-50: CO2 Emissions from CO2 Consumption (MMT CO2 Eq. and kt) 4-53
Table 4-51: CO2 Production (kt CO2) and the Percent Used for Non-EOR Applications 4-54
Table 4-52: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from CO2 Consumption (MMT CO2
Eq. and Percent) 4-54
Table 4-53: CO2 Emissions from Phosphoric Acid Production (MMT CO2Eq. andkt) 4-56
Table 4-54: Phosphate Rock Domestic Consumption, Exports, and Imports (kt) 4-57
Table 4-55: Chemical Composition of Phosphate Rock (Percent by weight) 4-57
Table 4-56: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Phosphoric Acid Production
(MMT CO2 Eq. and Percent) 4-58
Table 4-57: CO2 and CH4 Emissions from Metallurgical Coke Production (MMT CO2 Eq.) 4-60
Table 4-58: CO2 and CH4 Emissions from Metallurgical Coke Production (kt) 4-60
Table 4-59: CO2 Emissions from Iron and Steel Production (MMT CO2 Eq.) 4-60
Table 4-60: CO2 Emissions from Iron and Steel Production (kt) 4-60
Table 4-61: CH4 Emissions from Iron and Steel Production (MMT CO2 Eq.) 4-61
Table 4-62: CH4 Emissions from Iron and Steel Production (kt) 4-61
Table 4-63: Material Carbon Contents for Metallurgical Coke Production 4-62
Table 4-64: Production and Consumption Data for the Calculation of CO2 and CH4 Emissions from Metallurgical
Coke Production (Thousand Metric Tons) 4-63
xii Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 4-65: Production and Consumption Data for the Calculation of CO2 Emissions from Metallurgical Coke
Production (million ft3) 4-63
Table 4-66: CO2 Emission Factors for Sinter Production and Direct Reduced Iron Production 4-63
Table 4-67: Material Carbon Contents for Iron and Steel Production 4-64
Table 4-68: CH4 Emission Factors for Sinter and Pig Iron Production 4-64
Table 4-69: Production and Consumption Data for the Calculation of CO2 and CH4 Emissions from Iron and Steel
Production (Thousand Metric Tons) 4-65
Table 4-70: Production and Consumption Data for the Calculation of CO2 Emissions from Iron and Steel Production
(million ft3 unless otherwise specified) 4-65
Table 4-71: Approach 2 Quantitative Uncertainty Estimates for CO2 and CH4 Emissions from Iron and Steel
Production and Metallurgical Coke Production (MMT CO2Eq. and Percent) 4-67
Table 4-72: CO2 and CH4 Emissions from Ferroalloy Production (MMT CO2 Eq.) 4-68
Table 4-73: CO2 and CH4 Emissions from Ferroalloy Production (kt) 4-68
Table 4-74: Production of Ferroalloys (Metric Tons) 4-69
Table 4-75: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Ferroalloy Production (MMT
CO2 Eq. and Percent) 4-70
Table 4-76: CO2 Emissions from Aluminum Production (MMT CO2 Eq. and kt) 4-71
Table 4-77: PFC Emissions from Aluminum Production (MMT CO2Eq.) 4-72
Table 4-78: PFC Emissions from Aluminum Production (kt) 4-72
Table 4-79: Production of Primary Aluminum (kt) 4-75
Table 4-80: Approach 2 Quantitative Uncertainty Estimates for CO2 and PFC Emissions from Aluminum
Production (MMT CO2 Eq. and Percent) 4-75
Table 4-81: SF6, HFC-134a, FK 5-1-12 and CO2 Emissions from Magnesium Production and Processing (MMT
CO2Eq.) 4-76
Table 4-82: SF6, HFC-134a, FK 5-1-12 and CO2 Emissions from Magnesium Production and Processing (kt)... 4-77
Table 4-83: SF6 Emission Factors (kg SF6 per metric ton of magnesium) 4-79
Table 4-84: Approach 2 Quantitative Uncertainty Estimates for SF6, HFC-134a and CO2 Emissions from
Magnesium Production and Processing (MMT CO2Eq. and Percent) 4-80
Table 4-85: CO2 Emissions from Lead Production (MMT CO2 Eq. and kt) 4-82
Table 4-86: Lead Production (Metric Tons) 4-82
Table 4-87: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Lead Production (MMT CO2
Eq. and Percent) 4-83
Table 4-88: Zinc Production (Metric Tons) 4-85
Table 4-89: CO2 Emissions from Zinc Production (MMT CO2 Eq. and kt) 4-85
Table 4-90: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Zinc Production (MMT CO2
Eq. and Percent) 4-87
Table 4-91: PFC, HFC, SF6, NF3, and N2O Emissions from Semiconductor Manufacture (MMT CO2 Eq.) 4-88
Table 4-92: PFC, HFC, SF6, NF3, and N2O Emissions from Semiconductor Manufacture (kt) 4-89
Table 4-93: Approach 2 Quantitative Uncertainty Estimates for HFC, PFC, SF6, NF3 and N2O Emissions from
Semiconductor Manufacture (MMT CO2Eq. andPercent) 4-96
Table 4-94: Emissions of HFCs and PFCs from ODS Substitutes (MMT CO2Eq.) 4-97
xiii
-------
Table 4-95: Emissions of HFCs and PFCs from ODS Substitution (MT) 4-98
Table 4-96: Emissions of HFCs and PFCs from ODS Substitutes (MMT CO2Eq.) by Sector 4-98
Table 4-97: Approach 2 Quantitative Uncertainty Estimates for HFC and PFC Emissions from ODS Substitutes
(MMT CO2 Eq. and Percent) 4-101
Table 4-98: SF6 Emissions from Electric Power Systems and Electrical Equipment Manufacturers (MMT CO2 Eq.)
4-102
Table 4-99: SF6 Emissions from Electric Power Systems and Electrical Equipment Manufacturers (kt) 4-102
Table 4-100: Transmission Mile Coverage and Regression Coefficients for Large and Non-Large Utilities, Percent
4-106
Table 4-101: Approach 2 Quantitative Uncertainty Estimates for SF6 Emissions from Electrical Transmission and
Distribution (MMT CO2 Eq. and Percent) 4-107
Table 4-102: 2013 Potential and Actual Emissions of HFCs, PFCs, SF6, and NF3 from Selected Sources (MMT CO2
Eq.) 4-109
Table 4-103: N2O Production (kt) 4-111
Table 4-104: N2O Emissions from N2O Product Usage (MMT CO2 Eq. and kt) 4-111
Table 4-105: Approach 2 Quantitative Uncertainty Estimates for N2O Emissions from N2O Product Usage (MMT
CO2 Eq. and Percent) 4-113
Table 4-106: NOX, CO, and NMVOC Emissions from Industrial Processes and Product Use (kt) 4-114
Table 5-1: Emissions from Agriculture (MMT CO2Eq.) 5-2
Table 5-2: Emissions from Agriculture (kt) 5-2
Table 5-3: CH4 Emissions from Enteric Fermentation (MMT CO2 Eq.) 5-3
Table 5-4: CH4 Emissions from Enteric Fermentation (kt) 5-3
Table 5-5: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Enteric Fermentation (MMT
CO2 Eq. and Percent) 5-6
Table 5-6: CH4 and N2O Emissions from Manure Management (MMT CO2Eq.) 5-9
Table 5-7: CH4 and N2O Emissions fromManure Management (kt) 5-10
Table 5-8: Approach 2 Quantitative Uncertainty Estimates for CH4 and N2O (Direct and Indirect) Emissions from
Manure Management (MMT CO2Eq. and Percent) 5-13
Table 5-9: 2006 IPCC Implied Emission Factor Default Values Compared with Calculated Values for CH4 from
Manure Management (kg/head/year) 5-14
Table 5-10: CH4 Emissions from Rice Cultivation (MMT CO2 Eq.) 5-16
Table 5-11: CH4 Emissions from Rice Cultivation (kt) 5-17
Table 5-12: Rice Area Harvested (Hectare) 5-18
Table 5-13: Ratooned Area as Percent of Primary Growth Area 5-19
Table 5-14: Non-USDAData Sources for Rice Harvest Information 5-19
Table 5-15: Non-California Seasonal Emission Factors (kg CHVhectare/season) 5-20
Table 5-16: California Emission Factors (kg CHVhectare/year or season) 5-20
Table 5-17: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Rice Cultivation (MMT CO2
Eq. and Percent) 5-21
Table 5-18: N2O Emissions from Agricultural Soils (MMT CO2 Eq.) 5-24
xiv Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 5-19: N2O Emissions from Agricultural Soils (kt) 5-24
Table 5-20: Direct N2O Emissions from Agricultural Soils by Land Use Type and N Input Type (MMT CO2 Eq.) 5-
24
Table 5-21: Indirect N2O Emissions from Agricultural Soils (MMT CO2Eq.) 5-25
Table 5-22: Quantitative Uncertainty Estimates of N2O Emissions from Agricultural Soil Management in 2013
(MMT CO2 Eq. and Percent) 5-36
Table 5-23: CH4 and N2O Emissions from Field Burning of Agricultural Residues (MMT CO2 Eq.) 5-39
Table 5-24: CH4, N2O, CO, and NOX Emissions from Field Burning of Agricultural Residues (kt) 5-39
Table 5-25: Agricultural Crop Production (kt of Product) 5-42
Table 5-26: U.S. Average Percent Crop Area Burned by Crop (Percent) 5-42
Table 5-27: Key Assumptions for Estimating Emissions from Field Burning of Agricultural Residues 5-42
Table 5-28: Greenhouse Gas Emission Ratios and Conversion Factors 5-42
Table 5-29: Approach 2 Quantitative Uncertainty Estimates for CH4 and N2O Emissions from Field Burning of
Agricultural Residues (MMT CO2Eq. and Percent) 5-43
Table 6-1: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry by Land-Use Change
Category (MMT CO2 Eq.) 6-2
Table 6-2: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry (MMT CO2 Eq.) 6-3
Table 6-3: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry (kt) 6-4
Table 6-4: Managed and Unmanaged Land Area by Land-Use Categories for All 50 States (Thousands of Hectares)
6-6
Table 6-5: Land Use and Land-Use Change for the U.S. Managed Land Base for All 50 States (Thousands of
Hectares) 6-7
Table 6-6: Data Sources Used to Determine Land Use and Land Area for the Conterminous United States, Hawaii,
and Alaska 6-12
Table 6-7: Total Land Area (Hectares) by Land-Use Category for U.S. Territories 6-18
Table 6-8: Estimated Net Annual Changes in C Stocks (MMT CO^yr) in Forest and Harvested Wood Pools 6-22
Table 6-9: Estimated Net Annual Changes in C Stocks (MMT C/yr) in Forest and Harvested Wood Pools 6-22
Table 6-10: Estimated Forest area (1,000 ha) and C Stocks (MMT C) in Forest and Harvested Wood Pools 6-23
Table 6-11: Estimates of CO2 (MMT/yr) Emissions from Forest Fires for the Lower 48 States and Alaska 6-26
Table 6-12: Approach 2 Quantitative Uncertainty Estimates for Net CO2 Flux from Forest Land Remaining Forest
Land: Changes in Forest C Stocks (MMT CO2 Eq. and Percent) 6-30
Table 6-13: Estimated Non-CO2 Emissions from Forest Fires (MMT CO2Eq.) for U.S. Forests 6-35
Table 6-14: Estimated Non-CO2 Emissions from Forest Fires (kt) for U.S. Forests 6-35
Table 6-15: Estimated C Released from Forest Fires for U.S. Forests (MMT/yr) 6-36
Table 6-16: Approach 2 Quantitative Uncertainty Estimates of Non-CO2 Emissions from Forest Fires in Forest
Land Remaining Forest Land (MMT CO2Eq. and Percent) 6-36
Table 6-17: N2O Fluxes from Soils in Forest Land Remaining Forest Land (MMT CO2 Eq. and kt N2O) 6-37
Table 6-18: Quantitative Uncertainty Estimates of N2O Fluxes from Soils in Forest Land Remaining Forest Land
(MMT CO2 Eq. and Percent) 6-39
Table 6-19: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland (MMT CO2 Eq.) 6-41
xv
-------
Table 6-20: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland (MMT C) 6-41
Table 6-21: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Cropland
Remaining Cropland (MMT CO2Eq. and Percent) 6-47
Table 6-22: Emissions from Liming of Agricultural Soils (MMT CO2 Eq.) 6-48
Table 6-23: Emissions from Liming of Agricultural Soils (MMT C) 6-49
Table 6-24: Applied Minerals (MMT) 6-50
Table 6-25: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Liming of Agricultural Soils
(MMT CO2 Eq. and Percent) 6-51
Table 6-26: CO2 Emissions from Urea Fertilization (MMT CO2 Eq.) 6-51
Table 6-27: CO2 Emissions from Urea Fertilization (MMT C) 6-51
Table 6-28: Applied Urea (MMT) 6-52
Table 6-29: Quantitative Uncertainty Estimates for CO2 Emissions from Urea Fertilization (MMT CO2 Eq. and
Percent) 6-53
Table 6-30: Net CO2 Flux from Soil C Stock Changes in Land Converted to Cropland by Land Use Change
Category (MMT CO2 Eq.) 6-54
Table 6-31: Net CO2 Flux from Soil C Stock Changes in Land Converted to Cropland (MMT C) 6-55
Table 6-32: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Land
Converted to Cropland (MMT CO2Eq. and Percent) 6-59
Table 6-33: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland (MMT CO2 Eq.) 6-61
Table 6-34: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland (MMT C) 6-61
Table 6-35: Approach 2 Quantitative Uncertainty Estimates for C Stock Changes Occurring Within Grassland
Remaining Grassland (MMT CO2Eq. and Percent) 6-65
Table 6-36: Net CO2 Flux from Soil C Stock Changes for Land Converted to Grassland (MMT CO2 Eq.) 6-67
Table 6-37: Net CO2 Flux from Soil C Stock Changes for Land Converted to Grassland (MMT C) 6-67
Table 6-38: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Land
Converted to Grassland (MMT CO2Eq. and Percent) 6-72
Table 6-39: Emissions from PeatlandsRemaining Peatlands (MMT CO2 Eq.) 6-74
Table 6-40: Emissions from Peatlands Remaining Peatlands (kt) 6-75
Table 6-41: Peat Production of Lower 48 States (kt) 6-76
Table 6-42: Peat Production of Alaska (Thousand Cubic Meters) 6-76
Table 6-43: Approach 2 Quantitative Uncertainty Estimates for CO2, CH4, and N2O Emissions from Peatlands
Remaining Peatlands (MMT CO2Eq. and Percent) 6-77
Table 6-44: Net C Flux from Urban Trees (MMT CO2 Eq. and MMT C) 6-79
Table 6-45: 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 6-82
Table 6-46: Approach 2 Quantitative Uncertainty Estimates for Net C Flux from Changes in C Stocks in Urban
Trees (MMT CO2 Eq. and Percent) 6-83
Table 6-47: N2O Fluxes from Soils in Settlements Remaining Settlements (MMT CO2 Eq. and kt N2O) 6-84
Table 6-48: Quantitative Uncertainty Estimates of N2O Emissions from Soils in Settlements Remaining Settlements
(MMT CO2 Eq. and Percent) 6-85
Table 6-49: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills (MMT CO2 Eq.) 6-87
xvi Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 6-50: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills (MMT C) 6-87
Table 6-51: Moisture Contents, C Storage Factors (Proportions of Initial C Sequestered), Initial C Contents, and
Decay Rates for Yard Trimmings and Food Scraps in Landfills 6-90
Table 6-52: C Stocks in Yard Trimmings and Food Scraps in Landfills (MMT C) 6-90
Table 6-53: Approach 2 Quantitative Uncertainty Estimates for CO2 Flux from Yard Trimmings and Food Scraps in
Landfills (MMT CO2 Eq. and Percent) 6-90
Table 7-1: Emissions from Waste (MMT CO2 Eq.) 7-2
Table 7-2: Emissions from Waste (kt) 7-2
Table 7-3: CH4 Emissions from Landfills (MMT CO2 Eq.) 7-5
Table 7-4: CH4 Emissions from Landfills (kt) 7-5
Table 7-5: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Landfills (MMT CO2 Eq. and
Percent) 7-10
Table 7-6: Materials Discarded in the Municipal Waste Stream by Waste Type (Percent) 7-15
Table 7-7: CH4 and N2O Emissions from Domestic and Industrial Wastewater Treatment (MMT CO2 Eq.) 7-17
Table 7-8: CH4 and N2O Emissions from Domestic and Industrial Wastewater Treatment (kt) 7-17
Table 7-9: U.S. Population (Millions) and Domestic Wastewater BOD5 Produced (kt) 7-19
Table 7-10: Domestic Wastewater CH4 Emissions from Septic and Centralized Systems (2013) 7-20
Table 7-11: Industrial Wastewater CH4 Emissions by Sector (2013) 7-20
Table 7-12: U.S. Pulp and Paper, Meat, Poultry, Vegetables, Fruits and Juices, Ethanol, and Petroleum Refining
Production (MMT) 7-20
Table 7-13: Variables Used to Calculate Percent Wastewater Treated Anaerobically by Industry (percent) 7-22
Table 7-14: Wastewater Flow (m3/ton) and BOD Production (g/L) for U.S. Vegetables, Fruits, and Juices Production
7-23
Table 7-15: U.S. Population (Millions), Population Served by Biological Denitrification (Millions), Fraction of
Population Served by Wastewater Treatment (percent), Available Protein (kg/person-year), Protein Consumed
(kg/person-year), and Nitrogen Removed with Sludge (kt-N/year) 7-26
Table 7-16: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Wastewater Treatment (MMT
CO2 Eq. and Percent) 7-27
Table 7-17: CH4 and N2O Emissions from Composting (MMT CO2 Eq.) 7-31
Table 7-18: CH4 and N2O Emissions from Composting (kt) 7-31
Table 7-19: U.S. Waste Composted (kt) 7-32
Table 7-20: Approach 1 Quantitative Uncertainty Estimates for Emissions from Composting (MMT CO2 Eq. and
Percent) 7-32
Table 7-21: Emissions of NOX, CO, and NMVOC from Waste (kt) 7-34
Table 9-1: Revisions to U.S. Greenhouse Gas Emissions, Including Quantitative Change Related to Use of AR4
GWP values (MMT CO2Eq.) 9-5
Table 9-2: Revisions to U.S. Greenhouse Gas Emissions due only to Methodology and Data Changes, with the AR4
GWP values applied across the time series (MMT CO2 Eq.) 9-7
Table 9-3: Revisions to Annual Sinks (C Sequestration) from Land Use, Land-Use Change, and Forestry (MMT
CO2Eq.) 9-9
XVll
-------
Figures
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: 2013 Greenhouse Gas Emissions by Gas (Percentages based on MMT €62 Eq.) ES-8
Figure ES-5: 2013 Sources of CO2 Emissions ES-9
Figure ES-6: 2013 CC>2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type ES-10
Figure ES-7: 2013 End-Use Sector Emissions of CCh from Fossil Fuel Combustion ES-10
Figure ES-8: 2013 Sources of CH4 Emissions ES-13
Figure ES-9: 2013 Sources of N2O Emissions ES-15
Figure ES-10: 2013 Sources of HFCs, PFCs, SF6, and NF3 Emissions ES-16
Figure ES-11: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector ES-17
Figure ES-12: 2013 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: 2013 Key Categories ES-26
Figure 1-1: National Inventory Arrangements Diagram 1-12
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 2-9
Figure 2-5: 2013 Energy Chapter Greenhouse Gas Sources 2-11
Figure 2-6: 2013 U.S. Fossil CarbonFlows (MMT CO2 Eq.) 2-12
Figure 2-7: 2013 CCh Emissions from Fossil Fuel Combustion by Sector and Fuel Type 2-14
Figure 2-8: 2013 End-Use Sector Emissions of CC>2 from Fossil Fuel Combustion 2-14
Figure 2-9: 2013 Industrial Processes and Product Use Chapter Greenhouse Gas Sources 2-16
Figure 2-10: 2013 Agriculture Chapter Greenhouse Gas Sources 2-18
Figure 2-11: 2013 Waste Chapter Greenhouse Gas Sources 2-21
Figure 2-12: Emissions Allocated to Economic Sectors 2-23
Figure 2-13: Emissions with Electricity Distributed to Economic Sectors 2-26
Figure 2-14: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic Product 2-33
Figure 3-1: 2013 Energy Chapter Greenhouse Gas Sources 3-1
Figure 3-2: 2013 U.S. Fossil CarbonFlows (MMT CO2 Eq.) 3-2
Figure 3-3: 2013 U.S. Energy Consumption by Energy Source (percent) 3-7
xviii Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 3-4: U.S. Energy Consumption (Quadrillion Btu) 3-7
Figure 3 -5: 2013 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type (MMT CO2 Eq.) 3-8
Figure 3-6: Annual Deviations from Normal Heating Degree Days for the United States (1950-2013) 3-9
Figure 3-7: Annual Deviations from Normal Cooling Degree Days for the United States (1950-2013) 3-9
Figure 3-8: Nuclear, Hydroelectric, and Wind Power Plant Capacity Factors in the United States (1990-2013).. 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, 1990-2013
(miles/gallon) 3-19
Figure 3-12: Sales of New Passenger Cars and Light-Duty Trucks, 1990-2013 (percent) 3-19
Figure 3-13: Mobile Source CH4 and N2O Emissions (MMT CO2 Eq.) 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: 2013 Industrial Processes and Product Use Chapter Greenhouse Gas Sources 4-2
Figure 5-1: 2013 Agriculture Chapter Greenhouse Gas Emission Sources 5-1
Figure 5-2: Sources and Pathways of N that Result in N2O Emissions from Agricultural Soil Management 5-23
Figure 5-3: Crops, Annual Direct N2O Emissions Estimated Using the Tier 3 DAYCENT Model, 1990-2013 (MMT
CO2Eq./year) 5-26
Figure 5-4: Grasslands, Annual Direct N2O Emissions Estimated Using the Tier 3 DAYCENT Model, 1990-2013
(MMT CO2 Eq./year) 5-27
Figure 5-5: Crops, Average Annual N Losses Leading to Indirect N2O Emissions Estimated Using the Tier 3
DAYCENT Model, 1990-2013 (ktN/year) 5-28
Figure 5-6: Grasslands, Average Annual N Losses Leading to Indirect N2O Emissions Estimated Using the Tier 3
DAYCENT Model, 1990-2013 (ktN/year) 5-29
Figure 5-7: Comparison of Measured Emissions at Field Sites and Modeled Emissions Using the DAYCENT
Simulation Model and IPCC Tier 1 Approach 5-37
Figure 6-1: Percent of Total Land Area for Each State in the General Land-Use Categories for 2013 6-9
Figure 6-2: Forest SectorCPools and Flows 6-20
Figure 6-3: Forest Ecosystem Carbon (All Pools) Stocks and Stock Change (1990-2013) 6-21
Figure 6-4: Estimates of Net Annual Changes in C Stocks for Major C Pools 6-24
Figure 6-5: Forest Ecosystem C Density Imputed from Forest Inventory Plots, Conterminous United States, 2001-
2009 6-25
Figure 6-6: The Size of Alaska Compared to European Countries 6-33
Figure 6-7: Delineations between Forest, Non-forest, Managed Land, and Inventoried Areas of Alaska 6-34
Figure 6-8: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2013,
Cropland Remaining Cropland 6-42
Figure 6-9: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2013,
Cropland Remaining Cropland 6-43
Figure 6-10: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2013,
Land Converted to Cropland 6-56
Figure 6-11: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2013,
Land Converted to Cropland 6-57
xix
-------
Figure 6-12: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2013,
Grassland Remaining Grassland 6-62
Figure 6-13: Total Net Annual CC>2 Flux for Organic Soils under Agricultural Management within States, 2013,
Grassland Remaining Grassland 6-63
Figure 6-14: Total Net Annual CC>2 Flux for Mineral Soils under Agricultural Management within States, 2013,
Land Converted to Grassland 6-69
Figure 6-15: Total Net Annual CC>2 Flux for Organic Soils under Agricultural Management within States, 2013,
Land Converted to Grassland 6-70
Figure 7-1: 2013 Waste Chapter Greenhouse Gas Sources 7-1
Figure 7-2: Management of Municipal Solid Waste in the United States, 2011 7-13
Figure 7-3: MSW Management Trends from 1990 to 2012 7-14
Figure 7-4: Percent of Recovered Degradable Materials from 1990 to 2012 (Percent) 7-15
Boxes
BoxES-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks ES-1
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 WCC Fifth Assessment Report and Global Warming Potentials 1-9
Box 1-3 :IPCC Reference Approach 1-15
Box 2-1: Methodology for Aggregating Emissions by Economic Sector 2-31
Box 2-2: Recent Trends in Various U.S. Greenhouse Gas Emissions-Related Data 2-32
Box2-3: Sources and Effects of Sulfur Dioxide 2-34
Box 3-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks 3-3
Box 3-2: Energy Data from the Greenhouse Gas Reporting Program 3-4
Box 3 -3: Weather and Non-Fossil Energy Effects on CO2 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 3-26
Box 3-5: Carbon Intensity of U.S. Energy Consumption 3-27
Box 3-6: Reporting of Lubricants, Waxes, and Asphalt and Road Oil Product Use in Energy Sector 3-44
Box 3-7: Carbon Dioxide Transport, Injection, and Geological Storage 3-67
Box 4-1: Industrial Processes Data from EPA's Greenhouse Gas Reporting Program 4-6
Box 4-2: Potential Emission Estimates of HFCs, PFCs, SF6, and NF3 4-109
Box 5-1: Comparison of the U.S. Inventory Seasonal Emission Factors and IPCC (1996) Default Emission Factors
5-19
Box 5-2: Tier 1 vs. Tier 3 Approach for Estimating N2O Emissions 5-30
Box 5-3: Comparison of Tier 2 U.S. Inventory Approach and IPCC (2006) Default Approach 5-40
Box 6-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks 6-4
Box 6-2: Preliminary Estimates of Land Use in U.S. Territories 6-17
xx Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Box 6-3: CO2 Emissions from Forest Fires 6-26
Box 6-4: Tier 3 Approach for Soil C Stocks Compared to Tier 1 or 2 Approaches 6-45
Box 6-5: Comparison of the Tier 2 U.S. Inventory Approach and IPCC (2006) Default Approach 6-49
Box 7-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks 7-1
Box 7-2: Waste Data from the Greenhouse Gas Reporting Program 7-3
Box 7-3: Nationwide Municipal Solid Waste Data Sources 7-12
Box 7-4: Overview of the Waste Sector 7-13
Box 7-5: Description of a Modern, Managed Landfill 7-15
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 2013. 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 2006
Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories (IPCC
2006). The structure of this report is consistent with the UNFCCC guidelines for inventory reporting.4
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
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 2006).
2 Article 2 of the Framework Convention on Climate Change published by the UNEP/WMO Information Unit on Climate
Change. See .
3 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
.
4 See < http://unfccc.int/resource/docs/2013/copl9/eng/10a03.pdf>.
5 See < http://www.ipcc-nggip.iges.or.jp/public/index.html>.
Executive Summary ES-1
-------
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. The manner that emissions and sinks are provided in
this Inventory is one of many ways U.S. emissions and sinks could be examined; 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 2013, concentrations of these
greenhouse gases have increased globally by 43, 152, and 20 percent, respectively (IPCC 2007 and NOAA/ESRL
2015). 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 climate change 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 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 2013). 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 million metric tons of CO2 equivalent (MMT CO2 Eq.).9'10 All gases in this
6 See < http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/8108.php >.
7 See and .
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.
ES-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Executive Summary are presented in units of MMT CO2 Eq. Emissions by gas in unweighted mass tons are provided
in the Trends chapter of this report.
Revised UNFCCC reporting guidelines for national inventories now require the use of GWP values from the IPCC
Fourth Assessment Report (AR4) (IPCC 2007).11 Therefore, to comply with international reporting standards under
the UNFCCC, official emission estimates are reported by the United States using AR4 GWP values, which have
replaced the previously required use of SAR GWP values in the U.S. Inventory. All estimates are provided
throughout the report in both CC>2 equivalents and unweighted units. A comparison of emission values using the
AR4 GWP values versus the IPCC Second Assessment Report (SAR) (IPCC 1996), IPCC Third Assessment Report
(TAR) (IPCC 2001), and the IPCC Fifth Assessment Report (AR5) (IPCC 2013) 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 use of IPCC AR4 GWP values in this and in future year inventories will apply across the entire
time series of the Inventory (i.e., from 1990 to 2013 in this 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
C6Fi4
SF6
NF3
GWP
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
17,200
Source: IPCC (2007)
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.
11 See < http://unfccc.int/resource/docs/2013/copl9/eng/10a03.pdf:
Executive Summary ES-3
-------
ES.2. Recent Trends in U.S. Greenhouse Gas
Emissions and Sinks
In 2013, total U.S. greenhouse gas emissions were 6,673.0 MMT, or million metric tons, CCh Eq. Total U.S.
emissions have increased by 5.9 percent from 1990 to 2013, and emissions increased from 2012 to 2013 by 2.0
percent (127.9 MMT CCh Eq.). The increase from 2012 to 2013 was due to an increase in the carbon intensity of
fuels consumed to generate electricity due to an increase in coal consumption, with decreased natural gas
consumption. Additionally, relatively cool winter conditions led to an increase in fuels for the residential and
commercial sectors for heating. In 2013 there also was an increase in industrial production across multiple sectors
resulting in increases in industrial sector emissions. Lastly, transportation emissions increased as a result of a small
increase in vehicle miles traveled (VMT) and fuel use across on-road transportation modes. Since 1990, U.S.
emissions have increased at an average annual rate of 0.3 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 2013.
Figure ES-1: U.S. Greenhouse Gas Emissions by Gas
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
i MFCs, PFCs,SFandNF Nitrous Oxide
6 3
i Methane
• Carbon Dioxide
6,673
cr
LU
O
u
ES-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure ES-2: Annual Percent Change in U.S. Greenhouse Gas Emissions
4% -,
2%
3.3%
2.6%
2.0%
(1Z!/00.4%(±/0| n.6%,,.4%11 0.5%
-3.4%
-6.5%
1991 1992 1993 1994 1995 1996 1997 1998 1999 201K) 2(101 211112 20(13 20(14 2005 2006 2007 2008 2009 2010 2011 2012 2013
Figure ES-3: Annual Greenhouse Gas Emissions Relative to 1990 (1990=0)
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
8
,200 n
,100
,000
900
800
700 -
600
500 -
400 -
300 -
200 -
10(1 -
II
-11)11
-2011
912
810
1,049 1,099
1,014 981
878 M • • 891
617
664 692
372
200
r-t (N
SO*
&>
8888
rN rM rsl rj
f
rj
88888
Table ES-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (MMT COz Eq.)
Gas/Source
1990
2005
2009
2010
2011
2012
2013
C02
Fossil Fuel Combustion
Electricity Generation
Transportation
Industrial
Residential
Commercial
U.S. Territories
Non-Energy Use of Fuels
5
,123.7
4,740.7
1,820.8
1,493.8
842.5
338.3
217.4
27.9
117.7
6,134.0
5,747.7
2,400.9
1,887.8
827.8
357.8
223.5
49.9
138.9
5,500.6
5,197.1
2,145.7
1,720.3
727.7
336.4
223.5
43.5
106.0
5
5
2
1
,704.5
,367.1
,258.4
,732.0
775.7
334.7
220.2
46.2
114.6
5,568.9
5,231.3
2,157.7
1,711.5
774.1
327.2
221.0
39.8
108.4
5,358.3
5,026.0
2,022.2
1,700.8
784.2
283.1
197.1
38.6
104.9
5,505.2
5,157.7
2,039.8
1,718.4
817.3
329.6
220.7
32.0
119.8
Iron and Steel Production &
Metallurgical Coke Production 99.8 66.7 43.0 55.7 60.0 54.3 52.3
Natural Gas Systems 37.6 30.0 32.2 32.3 35.6 34.8 37.8
Cement Production 33.3 45.9 29.4 31.3 32.0 35.1 36.1
Petrochemical Production 21.6 28.1 23.7 27.4 26.4 26.5 26.5
Lime Production 11.7 14.6 11.4 13.4 14.0 13.7 14.1
Ammonia Production 13.0 9.2 8.5 9.2 9.3 9.4 10.2
Incineration of Waste 8.0 12.5 11.3 11.0 10.5 10.4 10.1
Petroleum Systems 4.41 4.9 4.7 4.2 4.5 5.1 6.0
Liming of Agricultural Soils 4.7 4.3 3.7 4.8 3.9 5.8 5.9
Urea Consumption for Non-
Agricultural Purposes 3.s| 3.?| 3.4 4.7 4.0 4.4 4.7
Executive Summary ES-5
-------
Other Process Uses of Carbonates 4.91 6.3
Urea Fertilization 2.41 3.5
Aluminum Production 6.81 4.1
Soda Ash Production and
Consumption 2.?B 2.9
Ferroalloy Production 2.21 1.4
Titanium Dioxide Production 1-^1 ^
Zinc Production 0.61 1.0
Phosphoric Acid Production 1.61 1.4
Glass Production l.sB 1.9
Carbon Dioxide Consumption l-^B
Peatlands Remaining Peatlands 1.11 1.1
Lead Production 0.5 B 0.6
Silicon Carbide Production and
Consumption 0.4 B 0.2
Magnesium Production and
Processing +1
Land Use, Land-Use Change, and
Forestry (Sink)" (775.8) (911.9)
WoodBiomass and Ethanol
Consumption11 219.4 229.8
International Bunker Fuelsc 103.5 113.1
CH4 745.5 707.8
Enteric Fermentation 164.2 168.9
Natural Gas Systems 179.1 176.3
Landfills 186.2 165.5
Coal Mining 96.5 64.1
Manure Management 37.2 56.3
Petroleum Systems 31.5 23.5
Wastewater Treatment 15.7 15.9
Rice Cultivation 9.2M 8.9
Stationary Combustion 8.5H 7.4
Abandoned Underground Coal
Mines 7.2 B 6.6
Forest Fires 2.sB 8.3
Mobile Combustion 5.6B 3.0
Composting 0.4 B 1.9
Iron and Steel Production &
Metallurgical Coke Production 1.11 0.9
Field Burning of Agricultural
Residues O.sB 0.2
Petrochemical Production 0.2 B 0.1
Ferroalloy Production + B +1
Silicon Carbide Production and
Consumption + B +1
Peatlands Remaining Peatlands +B +1
Incineration of Waste +B +1
International Bunker Fuels0 0.2U 0.1
N2O 329.9 355.9
Agricultural Soil Management 224.0 243.6
Stationary Combustion 11.9 20.2
Mobile Combustion 41.2 38.1
Manure Management 13.8H 16.4
Nitric Acid Production 12.1 11.3
Wastewater Treatment 3.4H 4.3
N2O from Product Uses 4.2 B 4.2
Adipic Acid Production 15.2 7.1
Forest Fires l.?B 5.5
7.6
3.6
3.0
2.5
1.5
1.6
0.9
1.0
1.0
1.8
1.0
0.5
9.6
3.8
2.7
2.6
1.7
1.8
1.2
1.1
1.5
1.2
1.0
0.5
9.3
4.1
3.3
2.6
1.7
1.7
1.3
1.2
1.3
0.8
0.9
0.5
8.0
4.2
3.4
2.7
1.9
1.5
1.5
1.1
1.2
0.8
0.8
0.5
4.4
4.0
3.3
2.7
1.8
1.6
1.4
1.2
1.2
0.9
0.8
0.5
0.1
250.5
106.4
709.5
172.7
168.0
158.1
79.9
59.7
21.5
15.6
9.4
7.4
6.4
5.8
2.3
1.9
0.4
0.3
0.1
356.1
264.1
20.4
24.6
17.0
9.6
4.6
4.2
2.7
3.8
0.2
0.2
0.2
265.1
117.0
667.2
171.1
159.6
121.8
82.3
60.9
21.3
15.5
11.1
7.1
6.6
4.7
2.3
1.8
0.3
0.1
0.1
360.1
264.3
22.2
23.7
17.1
11.5
4.7
4.2
4.2
3.1
268.1
111.7
660.9
168.7
159.3
121.3
71.2
61.4
22.0
15.3
8.5
7.1
6.4
14.6
2.3
1.9
0.7
0.3
0.1
371.9
265.8
21.3
22.5
17.3
10.9
4.8
4.2
10.2
9.6
267.7
105.8
647.6
166.3
154.4
115.3
66.5
63.7
23.3
15.2
9.3
6.6
6.2
15.7
2.2
1.9
0.7
0.3
0.1
0.1
365.6
266.0
21.4
20.2
17.3
10.5
4.9
4.2
5.5
10.3
0.2
(870.9) (871.6) (881.0) (880.4) (881.7)
283.3
99.8
636.3
164.5
157.4
114.6
64.6
61.4
25.2
15.0
8.3
8.0
6.2
5.8
2.1
2.0
0.7
0.3
0.1
0.1
355.2
263.7
22.9
18.4
17.3
10.7
4.9
4.2
4.0
3.8
ES-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Settlement Soils 1.4l 2.3• 2.2 2.4 2.5 2.5 2.4
Composting 0.3 B l.vB 1.7 1.6 1.7 1.7 1.8
Forest Soils 0.1 B O.sB 0.5 0.5 0.5 0.5 0.5
Incineration of Waste O.sB 0.4B 0.3 0.3 0.3 0.3 0.3
Semiconductor Manufacture + B O.lB 0.1 0.1 0.2 0.2 0.2
Field Burning of Agricultural
Residues O.lB O.lB 0.1 0.1 0.1 0.1 0.1
Peatlands Remaining Peatlands + B +B + + + + +
International Bunker Fuelsb 0.9M l.OM 0.9 1.0 1.0 0.9 0.9
HFCs 46.6 131.4 B 142.9 152.6 157.4 159.2 163.0
Substitution of Ozone Depleting
Substances'1 O.sB 111.1 136.0 144.4 148.4 153.5 158.6
HCFC-22 Production 46.1 20.0 6.8 8.0 8.8 5.5 4.1
Semiconductor Manufacture 0.2 B 0.2 B 0.2 0.2 0.2 0.2 0.2
Magnesium Production and
Processing O.oB 0.0 B + + + + 0.1
PFCs 24.3 6.6 B 3.9 4.4 6.9 6.0 5.8
Aluminum Production 21.5 3.4B 1.9 1.9 3.5 2.9 3.0
Semiconductor Manufacture 2.sB 3.2B 2.0 2.6 3.4 3.0 2.9
SF6 31.1 14.0 9.3 9.5 10.0 7.7 6.9
Electrical Transmission and
Distribution 25.4 lO.eB 7.3 7.0 6.8 5.7 5.1
Magnesium Production and
Processing
Semiconductor Manufacture
NF3
Semiconductor Manufacture
Total Emissions
Total Sinks3
Net Emissions (Sources and Sinks)
5.2
0.5
+B
+
6,301.1
(775.8)
5,525.2
2.7B
().?B
o.sB
0.5
7,350.2
(911.9)
6,438.3
1.6
0.3
0.4
0.4
6,722.7
(870.9)
5,851.9
2.1
0.4
0.5
0.5
6,898.8
(871.6)
6,027.2
2.8
0.4
0.7
0.7
6,776.6
(881.0)
5,895.6
1.6
0.4
0.6
0.6
6,545.1
(880.4)
5,664.7
1.4
0.4
0.6
0.6
6,673.0
(881.7)
5,791.2
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
+ Does not exceed 0.05 MMT CO2 Eq.
a Parentheses indicate negative values or sequestration. Sinks (i.e., CCh removals) are only included in the Net Emissions
total. Refer to Table ES-5 for a breakout of emissions and removals for Land Use, Land-Use Change, and Forestry by
gas and source category.
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 2013. The
primary greenhouse gas emitted by human activities in the United States was CCh, representing approximately 82.5
percent of total greenhouse gas emissions. The largest source of €62, and of overall greenhouse gas emissions, was
fossil fuel combustion. CH4 emissions, which have decreased by 14.6 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 byproduct 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: 2013 Greenhouse Gas Emissions by Gas (Percentages based on MMT COz Eq.)
MFCs, PFCs,
SF6 and NF3
Subtotal
2.6%
Overall, from 1990 to 2013, total emissions of CO2 increased by 381.5 MMT CO2 Eq. (7.4 percent), while total
emissions of CH4 decreased by 109.2 MMT CO2 Eq. (14.6 percent), and N2O increased by 25.3 MMT CO2 Eq. (7.7
percent). During the same period, aggregate weighted emissions of HFCs, PFCs, SF6 and NF3 rose by 74.3 MMT
CO2 Eq. (72.9 percent). From 1990 to 2013, HFCs increased by 116.4 MMT CO2 Eq. (249.8 percent), PFCs
decreased by 18.4 MMT CO2 Eq. (76.0 percent), SF6 decreased by 24.1 MMT CO2 Eq. (77.7 percent), and NF3
increased by 0.5 MMT CO2 Eq. (1,070.1 percent). Despite being emitted in smaller quantities relative to the other
principal greenhouse gases, emissions of HFCs, PFCs, SF6 andNF3 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 13.2 percent of
total emissions in 2013. 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.12 Since the Industrial Revolution (i.e., about 1750), global atmospheric concentrations of CO2 have risen
approximately 43 percent (IPCC 2007 and NOAA/ESRL 2015), principally due to the combustion of fossil fuels.
Within the United States, fossil fuel combustion accounted for 93.7 percent of CO2 emissions in 2013. Globally,
approximately 32,310 MMT of CO2 were added to the atmosphere through the combustion of fossil fuels in 2012, of
which the United States accounted for about 16 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). Although fossil fuel combustion is the greatest source of CO2 emissions, there are
25 additional sources of CO2 emissions (Figure ES-5).
The term "flux" is used to describe the net emissions of greenhouse gases to the atmosphere accounting for both the emissions
of CO2 to and the removals of CCh from the atmosphere. Removal of CCh from the atmosphere is also referred to as "carbon
sequestration."
Global CO2 emissions from fossil fuel combustion were taken from Energy Information Administration International Energy
Statistics 2013 < http://tonto.eia.doe.gov/cfapps/ipdbproject/IEDIndex3.cfm> EIA (2013).
ES-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure ES-5: 2013 Sources of COz Emissions
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Fossil Fuel Combustion
Non-Enerqy Use of Fuels
Iron and Steel Prod. & Metallurgical Coke Prod.
Natural Gas Systems
Cement Production
Petrochemical Production
Lime Production
Ammonia Production
Incineration of Waste
Petroleum Systems
Liming of Agricultural Soils
Urea Consumption for Non-Agricultural Purposes
Other Process Uses of Carbonates
Urea Fertilization
Aluminum Production
Soda Ash Production and Consumption
Ferroalloy Production
Titanium Dioxide Production
Zinc Production
Phosphoric Acid Production
Glass Production
Carbon Dioxide Consumption
Peatiands Remaining Peatlands
Lead Production
Silicon Carbide Production and Consumption
Magnesium Production and Processing
5,158
C02 as a Portion
of all Emissions
25 5(1 75 100
MMT CO2 Eq.
125
150
Note: Electricity generation also includes emissions of less than 0.05 MMT CCh Eq. from geothermal-based generation.
As the largest source of U.S. greenhouse gas emissions, CCh from fossil fuel combustion has accounted for
approximately 77 percent of GWP-weighted emissions since 1990, and is approximately 77 percent of total GWP-
weighted emissions in 2013. Emissions of CCh from fossil fuel combustion increased at an average annual rate of
0.4 percent from 1990 to 2013. The fundamental factors influencing this trend include (1) a generally growing
domestic economy over the last 24 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 2013, CO2 emissions from fossil fuel combustion
increased from 4,740.7 MMT CCh Eq. to 5,157.7 MMT CCh Eq., an 8.8 percent total increase over the twenty-four-
year period. From 2012 to 2013, these emissions increased by 131.7 MMT CCh Eq. (2.6 percent).
Historically, changes in emissions from fossil fuel combustion have been the dominant factor affecting U.S.
emission trends. Changes in CCh 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
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: 2013 COz Emissions from Fossil Fuel Combustion by Sector and Fuel Type
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Relative Contribution
by Fuel Type
Figure ES-7: 2013 End-Use Sector Emissions of COz from Fossil Fuel Combustion
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
*
2,fMX)
1,500 •
1,(K)()
5(X)
f] I—
I From Direct Fossil Fuel Combustion
From Electricity Consumption
933
1,722
1,4(111
1,070
The five major fuel consuming sectors contributing to CCh emissions from fossil fuel combustion are electricity
generation, transportation, industrial, residential, and commercial. €62 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: 1990-2013
-------
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
(MMT COz Eq.)
End-Use Sector
Transportation
Combustion
Electricity
Industrial
Combustion
Electricity
Residential
Combustion
Electricity
Commercial
Combustion
Electricity
U.S. Territories3
Total
Electricity Generation
1990
1,496.8
1,493.8
3.o!
1,529.2
842.5
686.vl
931.41
338.3 1
593.o!
755.4 1
217.4!
538.0 1
27.9
4,740.7
1,820.8
2005
1,892.5
1,887.8
4.7l
1,564.4
827. 8 1
736.6 1
1,214.1
357.8 1
856.31
1,026.7
223.5
803.31
49.9
5,747.7
2,400.9
2009
1,724.8
1,720.3
4.5
1,329.5
727.7
601.8
1,122.6
336.4
786.2
976.7
223.5
753.2
43.5
5,197.1
2,145.7
2010
1,736.5
1,732.0
4.5
1,416.5
775.7
640.8
1,174.8
334.7
840.1
993.2
220.2
773.0
46.2
5,367.1
2,258.4
2011
1,715.8
1,711.5
4.3
1,398.8
774.1
624.7
1,117.9
327.2
790.7
959.1
221.0
738.0
39.8
5,231.3
2,157.7
2012
1,704.6
1,700.8
3.9
1,377.0
784.2
592.8
1,008.4
283.1
725.3
897.4
197.1
700.3
38.6
5,026.0
2,022.2
2013
1,722.4
1,718.4
4.0
1,399.8
817.3
582.5
1,070.2
329.6
740.6
933.3
220.7
712.6
32.0
5,157.7
2,039.8
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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 33.4 percent of U.S. €62 emissions from fossil fuel combustion in 2013. The
largest sources of transportation CCh emissions in 2013 were passenger cars (42.7 percent), freight trucks (22.8
percent), light duty trucks, which include sport utility vehicles, pickup trucks, and minivans (17.0 percent),
commercial aircraft (6.6 percent), pipelines (2.8 percent), rail (2.6 percent), and ships and boats (2.3 percent).
Annex 3.2 presents the total emissions from all transportation and mobile sources, including €62, CH4, N2O, and
MFCs.
In terms of the overall trend, from 1990 to 2013, total transportation €62 emissions rose by 15 percent due, in large
part, to increased demand for travel as fleet wide light-duty vehicle fuel economy was relatively stable (average new
vehicle fuel economy declined slowly from 1990 through 2004 and then increased more rapidly from 2005 through
2013). The number of vehicle miles traveled by light-duty motor vehicles (passenger cars and light-duty trucks)
increased 35 percent from 1990 to 2013, 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.
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 €62 from
fossil fuel combustion in 2013. Approximately 58 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.
Residential and Commercial End-Use Sectors. The residential and commercial end-use sectors accounted for 21
and 18 percent, respectively, of €62 emissions from fossil fuel combustion in 2013. Both sectors relied heavily on
Executive Summary ES-11
-------
electricity for meeting energy demands, with 69 and 76 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 15 percent and 24 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 34 percent of total U.S. energy uses from fossil fuels and emitted 40 percent of the
CO2 from fossil fuel combustion in 2013. 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 of their total energy requirements in 2013.14 In addition, the coal used by electricity
generators accounted for 93 percent of all coal consumed for energy in the United States in 2013 ^5 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 27 percent of their total energy
requirements in 2013.16. Across the time series, changes in electricity demand and the carbon intensity of fuels used
for electricity generation have a significant impact on CC>2 emissions.
Other significant CCh trends included the following:
• CO2 emissions from non-energy use of fossil fuels have increased by 2.2 MMT CO2 Eq. (1.9 percent) from
1990 through 2013. Emissions from non-energy uses of fossil fuels were 119.8 MMT CChEq. in 2013,
which constituted 2.2 percent of total national €62 emissions, approximately the same proportion as in
1990.
• CO2 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. In 2013,
emissions from cement production increased by 3.1 percent from the 2012 levels.
• CO2 sequestration from Land Use, Land-Use Change, and Forestry increased by 105.9 MMT €62 Eq. (13.6
percent) from 1990 through 2013. 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.17 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
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
See < http://www.eia.gov/energyexplained/index.cfm?page=electricity_in_the_united_states >.
15 See Table 6.2 Coal Consumption by Sector of EIA 2015a.
16 See < http://www.eia.gov/energyexplained/index.cfm?page=electricity_in_the_united_states >.
17 See < http://www.ipcc-nggip.iges.or.jp/public/index.html>.
ES-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.18
Methane Emissions
Methane (CH4) is 25 times as effective as CCh at trapping heat in the atmosphere (IPCC 2007). Over the last two
hundred and fifty years, the concentration of CH4 in the atmosphere increased by 152 percent (IPCC 2007 and
NOAA/ESRL 2015). 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: 2013 Sources of CHU Emissions
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Enteric Fermentation
Natural Gas Systems
Landfills
Coal Mining
Manure Management
Petroleum Systems
Wastewater Treatment
Rice Cultvalien
Stationary Combustion
#>andoned Underground Coal Mines
Forest Fires
Mobile Combustien
Composting
Iron and Steel Prod. & Metallurgical Coke Prod,
Field Burning of Agricultural Residues
Petrochemica' Production
Ferroc/loy Production
Silicon Carbide Production and Consumption
Peatiands Remaning Peattands
Incineration of Waste
75 100
MMTCO;Eq.
Some significant trends in U.S. emissions of CH4 include the following:
Enteric fermentation is the largest anthropogenic source of CH4 emissions in the United States. In 2013,
enteric fermentation CH4 emissions were 164.5 MMT CCh Eq. (25.9 percent of total CH4 emissions),
which represents an increase of 0.4 MMT CC>2 Eq. (0.2 percent) since 1990. This increase in emissions
from 1990 to 2013 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
18
See.
Executive Summary ES-13
-------
the literature for dairy cow diets indicated a trend toward a decrease in feed digestibility for those years.
Emissions decreased again from 2008 to 2013 as beef cattle populations again decreased.
• Natural gas systems were the second largest anthropogenic source category of CH4 emissions in the United
States in 2013 with 157.4 MMT CO2 Eq. of CH4 emitted into the atmosphere. Those emissions have
decreased by 21.8 MMT CO2 Eq. (12.2 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 use of plunger lifts for liquids unloading, from regulatory reductions such as reductions from
hydraulically fractured gas well completions and workovers resulting from the 2012 New Source
Performance Standards (NSPS) for oil and gas, and from a variety of voluntary reduction activities. The
decrease in distribution emissions is due to a decrease in unprotected steel and cast iron pipelines and their
replacement with plastic pipelines. Emissions from field production account for 30 percent of CH4
emissions and 42 percent of non-combustion CO2 emissions from natural gas systems in 2013. CH4
emissions from field production decreased by 21 percent from 1990 to 2013; however, the trend was not
stable over the time series - emissions from production generally increased through 2006 due primarily to
increases in emissions from pneumatic controllers and hydraulically fractured gas well completions and
workovers, and then declined from 2007 to 2013. Reasons for the 2007 to 2013 trend include an increase in
plunger lift use for liquids unloading, increased voluntary reductions over that time period (including those
associated with pneumatic controllers), and increased 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 (114.6 MMT
CO2 Eq.), accounting for 18.0 percent of total CH4 emissions in 2013. From 1990 to 2013, CH4 emissions
from landfills decreased by 71.6 MMT CO2 Eq. (38.4 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 (i.e., used for energy or flared),19 which has more than offset the additional CH4 emissions
resulting from an increase in the amount of municipal solid waste landfilled.
• Methane emissions from manure management increased by 65.2 percent since 1990, from 37.2 MMT CO2
Eq. in 1990 to 61.4 MMT CO2 Eq. in 2013. 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 2007). Since 1750, the global atmospheric concentration of N2O has risen by approximately 20
percent (IPCC 2007 and NOAA/ESRL 2015). The main anthropogenic activities producing N2O in the United
States are agricultural soil management, stationary fuel combustion, fuel combustion in motor vehicles, manure
management and nitric acid production (see Figure ES-9).
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: 1990-2013
-------
N;O as a Portion
of all Emissions
Figure ES-9: 2013 Sources of N2O Emissions
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Agricultural Soil Management ^^^^^^^^^^^^^^^^^^^^^^^^^^^d ^| 264
Stationary Combustion
Mobile Combustion
Manure Management
Nitric tad Production
Wastewater Treatment
N 20 from Product Uses
Adi pic Add Product on
Forest Fires
Settlement Soils
Composting
Forest Soils
Incineration of Waste
Semiconductor Manufacture
Field Burning of Agricultural Residues
Peatiands Remaining Peatlands
0 5 10 15 20 25
MMTC02Eq.
Some significant trends in U.S. emissions of N2O include the following:
• Agricultural soils accounted for approximately 74.2 percent of N2O emissions and 4.0 percent of total
emissions in the United States in 2013. Estimated emissions from this source in 2013 were 263.7 MMT
CO2 Eq. Annual N2O emissions from agricultural soils fluctuated between 1990 and 2013, although overall
emissions were 17.7 percent higher in 2013 than in 1990. Year-to-year fluctuations are largely a reflection
of annual variation in weather patterns, synthetic fertilizer use, and crop production.
• N2O emissions from stationary combustion increased 11.0 MMT CO2 Eq. (91.9 percent) from 1990 through
2013. 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 2013, total N2O emissions from manure management were estimated to be 17.3 MMT CO2 Eq.;
emissions were 13.8 MMT CO2 Eq. in 1990. 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 25.4 percent increase from 1990 to
2013 and a 0.1 percent decrease from 2012 through 2013. Overall shifts toward liquid systems have driven
down the emissions per unit of nitrogen excreted.
• N2O emissions from adipic acid production were 4.0 MMT CO2 Eq. in 2013, 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 73.8
percent since 1990 and by 76.4 percent since a peak in 1995.
HFC, RFC, SF6, and NF3 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.
Executive Summary ES-15
-------
These compounds, however, along with SF6 and NF3, 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 2013).
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: 2013 Sources of MFCs, PFCs, SFe, and NFs Emissions
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Substitution of Ozone Depleting Substances
Electrical Transmission and Distribution
HCFC-22 Production
Semiconductor Manufacture
Aluminum Production
Magnesium Production and Processing
MFCs, PFCs, SF6 and NF3 as a Portion
of all Emissions
2.6%
159
f
Id
MMT CO, Eq.
20
Some significant trends in U.S. HFC, PFC, SF6, and NF3 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 158.6 MMT CO2 Eq. in 2013. 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.
• GWP-weighted PFC, HFC, SF6, and NF3 emissions from semiconductor manufacture have increased by 12
percent from 1990 to 2013, due to industrial growth and the adoption of emissions reduction technologies.
Within that time span, emissions peaked in 1999, the initial year of the EP A's PFC Reduction / Climate
Partnership for the Semiconductor Industry, but have since declined to 4.0 MMT CO2 Eq. in 2013 (a 56
percent decrease relative to 1999).
• SF6 emissions from electric power transmission and distribution systems decreased by 79.9 percent (20.3
MMT CO2 Eq.) from 1990 to 2013. There are two potential causes for this decrease: (1) a sharp increase in
the price of SF6 during the 1990s and (2) a growing awareness of the environmental impact of SF6
emissions through programs such as EPA's SF6 Emission Reduction Partnership for Electric Power
Systems.
• PFC emissions from aluminum production decreased by 86.2 percent (18.5 MMT CO2 Eq.) from 1990 to
2013. This 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.
ES-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
ES.3. Overview of Sector Emissions and Trends
In accordance with the UNFCCC decision to set the 2006IPCC Guidelines for National Greenhouse Gas
Inventories (IPCC 2006) as the standard for Annex I countries at the Nineteenth Conference of the Parties
(UNFCCC 2014), 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-four-year period of 1990 to
2013, total emissions in the Energy, Industrial Processes and Product Use, and Agriculture sectors grew by 346.2
MMT CO2 Eq. (6.5 percent), 17.0 MMT CO2 Eq. (5.0 percent), and 67.0 MMT CO2 Eq. (14.9 percent), respectively.
Emissions from the Waste sector decreased by 67.7 MMT CO2 Eq. (32.9 percent). 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 removals from all LULUCF source categories) increased by 96.4 MMT CO2 Eq. (12.7 percent).
Figure ES-11: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
u
7,51)1)
4,000
iff 3,500
3,000
2,500
2,000
1,500
1,000
501)
0
(5(10)
(1,000)
(1,500)
Industrial Processes and
Product Uses
Waste
LULUCF (sources)
Land-Use Change and Forestry (sinks)
Table ES-4: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC
Sector (MMT COz Eq.)
Chapter/IPCC Sector
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 and Product Use
Substitution of Ozone Depleting
Substances
Iron and Steel Production &
Metallurgical Coke Production
Cement Production
Petrochemical Production
Lime Production
1990
5,290.5
4,740.7
216.8
117,7 •
96. 5l
36.0
20.4
46.9
8.4
7.2
342.1
0.3
100.9
33.3
21.9
11.7
2005
6,273.6
5,747.7
206.3
138.9
64.1
28.4
27.6
41.1
12. sl
6.6
367.4
111.1
67.5
45.9
28.3
14.6|
2009
5,682.1
5,197.1
200.2
106.0
79.9
26.2
27.8
26.9
11.6
6.4
314.9
136.0
43.5
29.4
23.8
11.4
2010
5,854.6
5,367.1
191.9
114.6
82.3
25.5
29.3
26.0
11.4
6.6
353.6
144.4
56.4
31.3
27.4
13.4
2011
5,702.6
5,231.3
194.8
108.4
71.2
26.4
28.4
24.8
10.9
6.4
371.0
148.4
60.7
32.0
26.4
14.0
2012
5,482.2
5,026.0
189.2
104.9
66.5
28.3
28.0
22.4
10.7
6.2
361.2
153.5
55.1
35.1
26.5
13.7
2013
5,636.6
5,157.7
195.2
119.8
64.6
31.2
30.8
20.6
10.4
6.2
359.1
158.6
53.0
36.1
26.6
14.1
Executive Summary ES-17
-------
Nitric Acid Production
Ammonia Production
Aluminum Production
Electrical Transmission and
Distribution
Urea Consumption for Non-
Agricultural Purposes
Other Process Uses of Carbonates
N2O from Product Uses
Semiconductor Manufacture
HCFC-22 Production
Adipic Acid Production
Soda Ash Production and
Consumption
Ferroalloy Production
Titanium Dioxide Production
Magnesium Production and
Processing
Zinc Production
Phosphoric Acid Production
Glass Production
Carbon Dioxide Consumption
Lead Production
Silicon Carbide Production and
Consumption
Agriculture
Agricultural Soil Management
Enteric Fermentation
Manure Management
Rice Cultivation
Field Burning of Agricultural
Residues
Land Use, Land-Use Change, and
Forestry
Forest Fires
Liming of Agricultural Soils
Urea Fertilization
Settlement Soils
Peatlands Remaining Peatlands
Forest Soils
Waste
Landfills
Wastewater Treatment
Composting
12.1
13.0
28.3
25.4
3.8
4.9
4.2
3.6
46.1
15.2
2.7
2.2
1.2
5.2
0.6
1.6
1.5
1.5
0.5
0.4
448.7
224.0 B
164.2 1
1"
0.7
11.3
9.2
7.6
10.6
3.7
6.3
4.2
4.7
20.0
7.1
2.9
1.4
1.8
2.7
1.0
1.4
1.9
1.4
0.6
0.2
494.5
9.6
8.5
4.9
7.3
3.4
7.6
4.2
3.1
6.8
2.7
2.5
1.5
1.6
1.6
0.9
1.0
1.0
1.8
0.5
0.2
523.3
243.6 B 264.1
168.9B 172.7
72.8 76.7
8.9 9.4
O.sl 0.4
25.5 20.6
13.8 9.7
4.3 3.7
3.5 3.6
2.3 2.2
i.iB i.o
o.sB 0.5
189.2 181.8
165. 5B 158.1
20.2 B 20.2
3.5 3.6
11.5
9.2
4.6
7.0
4.7
9.6
4.2
3.8
8.0
4.2
2.6
1.7
1.8
2.1
1.2
1.1
1.5
1.2
0.5
0.2
524.8
264.3
171.1
78.0
11.1
0.4
20.3
7.9
4.8
3.8
2.4
1.0
0.5
145.5
121.8
20.2
3.5
10.9
9.3
6.8
6.8
4.0
9.3
4.2
4.9
8.8
10.2
2.6
1.7
1.7
2.8
1.3
1.2
1.3
0.8
0.5
0.2
522.1
265.8
168.7
78.7
8.5
0.4
36.1
24.2
3.9
4.1
2.5
0.9
0.5
144.9
121.3
20.1
3.5
10.5
9.4
6.4
5.7
4.4
8.0
4.2
4.5
5.5
5.5
2.7
1.9
1.5
1.7
1.5
1.1
1.2
0.8
0.5
0.2
523.0
266.0
166.3
81.0
9.3
0.4
39.8
26.0
5.8
4.2
2.5
0.8
0.5
138.9
115.3
20.0
3.7
10.7
10.2
6.2
5.1
4.7
4.4
4.2
4.2
4.1
4.0
2.7
1.8
1.6
1.5
1.4
1.2
1.2
0.9
0.5
0.2
515.7
263.7
164.5
78.7
8.3
0.4
23.3
9.7
5.9
4.0
2.4
0.8
0.5
138.3
114.6
20.0
3.7
Total Emissions
6,301.1
7,350.2
6,722.7 6,898.8 6,776.6 6,545.1 6,673.0
Total Sinksa
(775.8)
(911.9)
(870.9) (871.6) (881.0) (880.4) (881.7)
Net Emissions (Sources and Sinks)
5,525.2
6,438.3
5,851.9 6,027.2 5,895.6 5,664.7 5,791.2
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
a Sinks (i.e., CCh removals) are only included in Net Emissions total. Refer to Table ES-5 for a breakout of emissions and
removals for Land Use, Land-Use Change, and Forestry by gas and source category.
Note: Totals may not sum due to independent rounding. 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. CCh emissions for the period of 1990 through 2013. In 2013,
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
ES-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
emissions (41 percent and 12 percent of total U.S. emissions of each gas, respectively). Overall, emission sources in
the Energy chapter account for a combined 84.5 percent of total U.S. greenhouse gas emissions in 2013.
Figure ES-12: 2013 U.S. Energy Consumption by Energy Source
Renewable
Energy
9.6%
Nuclear Electric
tower
8.5%
Industrial Processes and Product Use
The Industrial Processes and Product Use (IPPU) section includes greenhouse gas emissions occurring from
industrial processes and from the use of greenhouse gases in products. This section includes sources of emissions
formerly represented in the "Industrial Processes" and "Solvent and Other Product Use" sectors in prior versions of
this report.
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 CO2,
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, zinc production, and N2O from product uses. Industrial processes also release HFCs, PFCs, SF6, and
NF3. In addition to their use as ODS substitutes, HFCs, PFCs, SF6, NF3, 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. Overall, emission sources in the Industrial Process
and Product Use chapter account for 5.4 percent of U.S. greenhouse gas emissions in 2013.
Agriculture
The Agriculture 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 25.9 percent and 9.6 percent of total CH4 emissions from
anthropogenic activities, respectively, in 2013. Agricultural soil management activities such as fertilizer application
Executive Summary ES-19
-------
and other cropping practices were the largest source of U.S. N2O emissions in 2013, accounting for 74.2 percent. In
2013, emission sources accounted for in the Agricultural chapters were responsible for 7.7 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 removal of CO2 (sequestration of C) in the United States. Forests (including
vegetation, soils, and harvested wood) accounted for 88 percent of total 2013 CO2 removals, urban trees accounted
for 10 percent, mineral and organic soil carbon stock changes accounted for less than 0.5 percent, and landfilled yard
trimmings and food scraps accounted for 1.4 percent of the total CO2 removals in 2013. 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 2.4 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 2013 resulted in a C sequestration (i.e., total sinks) of 881.7
MMT CO2 Eq. (Table ES-5).20 This represents an offset of 13.2 percent of total (i.e., gross) greenhouse gas
emissions in 2013. Emissions from land use, land-use change and forestry activities in 2013 represent 0.3 percent of
total greenhouse gas emissions.21 Between 1990 and 2013, total land use, land-use change, and forestry C
sequestration increased by 13.6 percent, 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.
CO2 removals are presented in Table ES-5 along with CO2, CH4, and N2O emissions for Land Use, Land-Use
Change, and Forestry source categories. Liming of agricultural soils and urea fertilization in 2013 resulted in CO2
emissions of 9.9 MMT CO2 Eq. (9,936 kt). Lands undergoing peat extraction (i.e., Peatlands Remaining Peatlands)
resulted in CO2 emissions of 0.8 MMT CO2 Eq. (770 kt) and CH4 and N2O emissions of less than 0.05 MMT CO2
Eq. each. The application of synthetic fertilizers to forest soils in 2013 resulted in N2O emissions of 0.5 MMT CO2
Eq. (2 kt). 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, N2O emissions from fertilizer
application to settlement soils in 2013 accounted for 2.4 MMT CO2Eq. (8kt). This represents an increase of 77
percent since 1990. Forest fires in 2013 resulted in CH4 emissions of 5.8 MMT CO2 Eq. (233 kt), and in N2O
emissions of 3.8 MMT CO2 Eq. (13 kt).
Table ES-5: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry
(MMT COz Eq.)
Gas/Land-Use Category
C02
Forest Land Remaining Forest Land:
Changes in Forest Carbon Stocka
1990
(767.7)
(639.4)B
1 2005
(903.0)
(807.1)
2009
(862.6)
(764.9)
2010
(862.0)
(765.4)
2011
(872.1)
(773.8)
2012
(869.6)
(773.1)
2013
(871.0)
(775.7)
The total sinks value includes the positive C sequestration reported for Forest Land Remaining Forest Land, Cropland
Remaining Cropland, Land Converted to Grassland, Settlements Remaining Settlements, and Other Land plus the loss in C
sequestration reported for Land Converted to Cropland and Grassland Remaining Grassland.
The emissions value includes the CCh, CH4, andN2O emissions reported for Forest Fires, Forest Soils, Liming of Agricultural
Soils, Urea Fertilization, Settlement Soils, and Peatlands Remaining Peatlands.
ES-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Cropland Remaining Cropland:
Changes in Agricultural Soil Carbon
Stock (65.2) (28.0) (27.5) (25.9) (25.8) (25.0) (23.4)
Cropland Remaining Cropland:
Liming of Agricultural Soils 4.?B 4.sB 3.7 4.8 3.9 5.8 5.9
Cropland Remaining Cropland:
Urea Fertilization 2AM 3.sB 3.6 3.8 4.1 4.2 4.0
Land Converted to Cropland 24.5 19.sB 16.2 16.2 16.2 16.1 16.1
Grassland Remaining Grassland (1.9) 4.2M 11.7 11.7 11.7 11.5 12.1
Land Converted to Grassland (7.4) (9.0)B (8.9) (8.9) (8.9) (8.8) (8.8)
Settlements Remaining Settlements:
Changes in Urban Tree Carbon Stockb (60.4) (80.5) (85.0) (86.1) (87.3) (88.4)
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands 1.1| 1.1| 1.0 1.0 0.9 0.8
Other:
Landfilled Yard Trimmings and Food
Scraps (26.0) (11.4) (12.5) (13.2) (13.2) (12.8) (12.6)
CH4 2.5B 8.3l 5.8 4.8 14.6 15.7 5.8
Forest Land Remaining Forest Land:
ForestFires 2.sB S.sB 5.8 4.7 14.6 15.7 5.8
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands +H +H + + + + +
N2O 3.lB 8.3B 6.5 6.0 12.6 13.3 6.7
Forest Land Remaining Forest Land:
ForestFires l.?B S.sB 3.8 3.1 9.6 10.3 3.8
Forest Land Remaining Forest Land:
Forest Soils0 O.lB O.sB 0.5 0.5 0.5 0.5 0.5
Settlements Remaining Settlements:
Settlement Soils'1 1.4B 2.3B 2.2 2.4 2.5 2.5 2.4
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands + + + + + + +
Total Flux6 (762.1) (886.4) (850.2) (851.3) (844.9) (840.6) (858.5)
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
+ Less than 0.05 MMT CO2 Eq.
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.
c 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.
d Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements, and Land
Converted to Settlements, but not from land-use conversion.
e "Total Flux" is defined as the sum of positive emissions (i.e., sources) of greenhouse gases to the atmosphere plus
removals of CCh (i.e., sinks or negative emissions) from the atmosphere.
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values or sequestration.
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 18.0 percent of total U.S. CH4
emissions.22 Additionally, wastewater treatment accounts for 14.4 percent of Waste emissions, 2.4 percent of U.S.
CH4 emissions, and 1.4 percent of U.S. N2O emissions. Emissions of CH4 and N2O from composting are also
accounted for in this chapter, generating emissions of 2.0 MMT €62 Eq. and 1.8 MMT €62 Eq., respectively.
Overall, emission sources accounted for in the Waste chapter generated 2.1 percent of total U.S. greenhouse gas
emissions in 2013.
Landfills also store carbon, due to incomplete degradation of organic materials such as harvest wood products, yard
trimmings, and food scraps, as described in the Land-Use, Land-Use Change, and Forestry chapter of the Inventory report.
Executive Summary ES-21
-------
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
five sectors (i.e., chapters) defined by the IPCC: Energy; Industrial Processes and 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 economic sectors: Residential, Commercial, Industry,
Transportation, Electricity Generation, Agriculture, and U.S. Territories.
Table ES-6 summarizes emissions from each of these sectors, and Figure ES-13 shows the trend in emissions by
sector from 1990 to 2013.
Figure ES-13: Emissions Allocated to Economic Sectors
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
2,51X1 -,
2,01X1
1,(KX)
500
Electric
Power Industry
Transportation
"" Industry
Agriculture
i Commercial (Red)
Residential (Blue)
en o
O *-<
Table ES-6: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (MMT COz Eq.)
Implied Sectors
Electric Power Industry
Transportation
Industry
Agriculture
Commercial
Residential
U.S. Territories
Total Emissions
Total Sinks3
Net Emissions (Sources and Sinks)
1990
1,864.8
1,551.3
1,587.7
492.5
424.8
346.3
33.7
6,301.1
(775.8)
5,525.2
2005
2,443.9
2,017.7
1,462.8
565.0
429.8
372.8
58.2
7,350.2
(911.9)
6,438.3
2009
2,185.7
1,835.3
1,272.5
588.8
431.9
360.9
47.6
6,722.7
(870.9)
5,851.9
2010
2,300.5
1,843.5
1,353.3
590.8
396.4
363.7
50.6
6,898.8
(871.6)
6,027.2
2011
2,198.1
1,815.4
1,353.0
605.5
400.7
360.5
43.5
6,776.6
(881.0)
5,895.6
2012
2,060.8
1,795.9
1,338.9
611.6
374.3
321.5
42.1
6,545.1
(880.4)
5,664.7
2013
2,077.0
1,806.2
1,392.1
586.8
401.1
375.0
34.8
6,673.0
(881.7)
5,791.2
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
a Sinks (i.e., CCh removals) are only included in the Net Emissions total. Refer to Table ES-5 for a breakout of emissions
and removals for Land Use, Land-Use Change, and Forestry by gas and source category.
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values or sequestration.
ES-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Using this categorization, emissions from electricity generation accounted for the largest portion (31 percent) of
U.S. greenhouse gas emissions in 2013. Transportation activities, in aggregate, accounted for the second largest
portion (27 percent), while emissions from industry accounted for the third largest portion (21 percent) of U.S.
greenhouse gas emissions in 2013. 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 6 percent of
emissions and U.S. Territories accounted for 1 percent of emissions; emissions from these sectors primarily
consisted of CCh emissions from fossil fuel combustion. CCh 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-7 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.23 These source categories include CCh from
fossil fuel combustion and the use of limestone and dolomite for flue gas desulfurization, €62 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 (29 percent and 27 percent, respectively) in 2013. The
residential and commercial sectors contributed the next largest shares of total U.S. greenhouse gas emissions in
2013. 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 2013.
Table ES-7: U.S Greenhouse Gas Emissions by Economic Sector with Electricity-Related
Emissions Distributed (MMT COz Eq.)
Implied Sectors
Industry
Transportation
Residential
Commercial
Agriculture
U.S. Territories
Total Emissions
Total Sinks3
Net Emissions (Sources and
Sinks)
1990
2,229.7
1,554.4
953.6B
975.8
553. (>M
33.7 I
6,301.1
(775.8)
5,525.2 |
2005
2,148.5
2,022.5
1,244 .4H
1,247.5
629. ll
58.2
7,350.2
(911.9)
6,438.3 |
2009
1,817.7
1,839.9
1,161.8
1,199.2
656.6
47.6
6,722.7
(870.9)
5,851.9
2010
1,937.7
1,848.1
1,219.5
1,183.8
659.2
50.6
6,898.8
(871.6)
6,027.2
2011
1,923.9
1,819.7
1,166.0
1,152.6
670.9
43.5
6,776.6
(881.0)
5,895.6
2012
1,880.9
1,799.8
1,060.6
1,088.0
673.7
42.1
6,545.1
(880.4)
5,664.7
2013
1,922.6
1,810.3
1,129.1
1,126.7
649.4
34.8
6,673.0
(881.7)
5,791.2
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Emissions from electricity generation are allocated based on aggregate electricity consumption in each end-use
sector.
a Sinks (i.e., CCh removals) are only included in the Net Emissions total. Refer to Table ES-5 for a breakout of emissions
and removals for Land Use, Land-Use Change, and Forestry by gas and source category.
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
-------
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values or sequestration.
See Table 2-12 for more detailed data.
Figure ES-14: Emissions with Electricity Distributed to Economic Sectors
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
2,500
2,000
d- 1,500
u
Ł 1,000
z
500
Industry (Green)
Transportation
(Purple)
Residential (Blue)
Commercial (Red)
' Agriculture
o ~H
8 8!
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 non-utilities combined—was the largest source of U.S. greenhouse gas
emissions in 2013; (4) emissions per unit of total gross domestic product as a measure of national economic activity;
and (5) emissions per capita.
Table ES-8 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 0.3 percent
since 1990. 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-8: Recent Trends in Various U.S. Data (Index 1990 = 100)
Variable
1990
2005
Avg. Annual
2009 2010 2011 2012 2013 Growth Rate
Greenhouse Gas Emissions*
Energy Consumption15
Fossil Fuel Consumption15
Electricity Consumption15
GDPC
Population"1
100
100
100
100
100
100
117
118
119
134
159
118
107
112
108
131
161
123
109
116
112
137
165
124
108
115
110
137
168
125
104
112
107
135
172
125
106
115
110
135
175
126
0.3%
0.6%
0.4%
1.3%
2.5%
1.0%
a GWP-weighted values
b Energy content-weighted values (EIA 2015a)
c Gross Domestic Product in chained 2009 dollars (BEA 2014)
d U.S. Census Bureau (2014)
ES-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure ES-15: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic
Product
I
175
165
155
145
135
125
115
1115
95
85
75
65
55
Real GDP
Population
Emissions
per capita
Emissions
'per $GDP
Source: BEA (2014), U.S. Census Bureau (2014), and emission estimates in this report.
Key Categories
The 2006IPCC Guidelines (IPCC 2006) defines 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
greenhouse gases in terms of the absolute level, the trend, or the uncertainty in emissions and removals."24 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 2013 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: Key Categories and Annex 1.
See Chapter 4 "Methodological Choice and Identification of Key Categories" in IPCC (2006).
Executive Summary ES-25
-------
Figure ES-16: 2013 Key Categories
002 Emissions frcm Stationery Combustion - Coal -Elec. Gen.
CD2 Emissions from Mobile Combustion: Road
CO2 Emissions from Stationary Combustion - Gas - Industrial
COZEmissions frcm Stationary Combustion - Gas - Elec, Gen.
CO2 Emissions from Stationary Combustion - Ql - Industrial
CO2 Emissions from Stationary Combustion - Gas - Residential
DirectNZO Emissions from Agricultural Soil Management
C02 Emissions frcm Stationary Combustion - Gas - Commercial
OH4 Emissions from Enteric Fermentation
Emissions from Substitutes for Ozone Depleting Substances
CH4Emissions from Natural Gas Systems
CO2 Emissions from Mobile Combustion: Aviation
CO2 Emissions frcm Non-Energy Use of Fuels
CH4Emissions from Landfills
CO2 Emissions from Mobile Combustion: Other
CO2Emissions frcm Stationary Combustion - Coal -Industrial
Fugitive Emissions from Coal Mining
CO2 Emissions from Stationary Combustion - Oil - Residential
CH4 Emissions from Manure Management
CO2 Emissions from Iron and Steel Prod. & Metallurgical Coke Prod,
Indirect N2O Emisgons from Applied Nitrogen
COZ Emissions from Mobile Combustion: Marine
C02 Emissions frcm Stationary Combustion - Oil - Commercial
CO2 Emissions from Natural Gas Systems
C02 Emissions from Cement Prodjction
CH4 Emissions from Petroleum Systems
Non-CO2 Emissions from Stationary Combustion -Elec. Gen.
Non-C02 Emissions from Stationary Combustion -Residential
Key Categories as a Portion of All Emissions
200 400 600
800 1,000 1,200
MMTCO^Eq.
1,400 1,600 1300
Note: For a complete discussion of the key category analysis, see Annex 1. Blue bars indicate either an Approach 1, or Approach
1 and Approach 2 level assessment key category. Gray bars indicate solely an Approach 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 CC>2 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 2006
IPCC Guidelines (IPCC 2006) and require that countries provide single estimates of uncertainty for source and sink
categories.
ES-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 2006IPCC
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 2013) has been
recalculated to reflect the change, per the 2006IPCC 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 2013. 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 basisl 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."2'3
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.. ."4 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 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 further expanded upon the
methodologies in the Revised 1996 IPCC Guidelines. In 2006, the IPCC accepted the 2006 Guidelines for National
Greenhouse Gas Inventories at its Twenty-Fifth Session (Mauritius, April 2006). The 2006 IPCC Guidelines built
1 More information provided in "Global Warming Potentials" section of this chapter on the use of IPCC Fourth Assessment
Report (AR4) GWP values.
2 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 2006).
3 Article 2 of the Framework Convention on Climate Change published by the UNEP/WMO Information Unit on Climate
Change. See . (UNEP/WMO 2000)
4 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
-------
upon the previous bodies of work and include 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. The UNFCCC adopted the 2006IPCC Guidelines as the standard methodological approach for Annex I
countries at the Nineteenth Conference of the Parties (Warsaw, November 11-23, 2013). This report presents
information in accordance with these guidelines.
Overall, this Inventory of anthropogenic greenhouse gas emissions and sinks 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.5 The structure of this report is consistent with
the current UNFCCC Guidelines on Annual Inventories (UNFCCC 2014).
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 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.6 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.7 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. The manner that emissions and sinks are provided in
this Inventory is one of many ways U.S. emissions and sinks could be examined; this Inventory report presents
emissions and sinks in a common format consistent with how countries are to report inventories under the
UNFCCC. 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.8 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, land-use changes, and
other sources have caused the concentrations of heat-trapping "greenhouse gases" to increase significantly in our
U.S. Territories include American Samoa, Guam, Puerto Rico, U.S. Virgin Islands, Wake Island, and other U.S. Pacific Islands.
See .
7 See .
8 See and .
1-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
atmosphere (NOAA 2014). These gases in the atmosphere absorb some of the energy being radiated from the
surface of the Earth and then re-radiate this energy with some returning to the Earth's surface, 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, with a portion of these gases occurring naturally from such
sources as respiration and volcanic eruptions, without natural concentrations of greenhouse gases the planet's
surface would be about 60 °F cooler than present (EPA 2009). But, as the concentrations of these gases continue to
increase in the atmosphere from man-made sources, the Earth's temperature is climbing above past levels. The
Earth's averaged land and ocean surface temperature has increased by about 1.2 to 1.9 °F since 1880. The last three
decades have each been the warmest decade successively at the Earth's surface since 1850 (IPCC 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
is likely to increase from 0.5 to 8.6 °F above 1986 through 2005 levels by the end of this century (IPCC 2013).
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.9
Greenhouse Gases
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), methane (CH4), nitrous
oxide (N2O), and other trace gases in the atmosphere that absorb the terrestrial radiation leaving the surface of the
Earth (IPCC 2013). Changes in the atmospheric concentrations of these greenhouse gases can alter the balance of
energy transfers between the atmosphere, space, land, and the oceans.10 A gauge of these changes is called radiative
forcing, which is a measure of the influence a perturbation has in altering the balance of incoming and outgoing
energy in the Earth-atmosphere system (IPCC 2013). 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).
Human activities are continuing to affect the Earth's energy budget by changing the emissions and
resulting atmospheric concentrations ofradiatively important gases and aerosols and by changing land
surface properties (IPCC 2013).
Naturally occurring greenhouse gases include water vapor, CO2, CH4, 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.11 Some other fluorine-containing halogenated substances—hydrofluorocarbons (HFCs),
perfluorocarbons (PFCs), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3)—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 other substances that influence the global radiation budget but are short-lived and therefore
not well-mixed. These substances include carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and
9 For more information see .
10 For more on the science of climate change, see NRC (2001).
Emissions estimates of CFCs, HCFCs, halons and other ozone-depleting substances are included in this document for
informational purposes.
Introduction 1-3
-------
tropospheric (ground level) ozone (O3). 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 suspended in the Earth's atmosphere that are often
composed of sulfur compounds, carbonaceous combustion products (e.g., black carbon), crustal materials (e.g., dust)
and other human induced pollutants. They can affect the absorptive characteristics of the atmosphere (e.g.,
scattering incoming sunlight away from the Earth's surface) and can play a role in affecting cloud formation and
lifetime affecting the radiative forcing of clouds and precipitation patterns. Comparatively, however, while the
understanding of aerosols has increased in recent years, they still account for the largest contribution to uncertainty
estimates in global energy budgets (IPCC 2013).
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)
CO2
280 ppm
399 ppm
1 .4 ppm/yr
See footnote*
CH4
0.700 ppm
1.762-1. 893 ppma
0.005 ppm/yrb
12e
N2O
0.270 ppm
0.324-0.326 ppma
0.26%/yr
114e
SF6
Oppt
7.39-7.79 ppta
Linear0
3,200
CF4
40ppt
79 pptf
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 (2015).
a The range is the annual arithmetic averages from a mid-latitude Northern-Hemisphere site and a mid-latitude Southern-
Hemisphere site for 2012 (CDIAC 2014).
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.
0 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.
f The 2011 CF4 global mean atmospheric concentration is from the Advanced Global Atmospheric Gases Experiment (IPCC
2013).
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). Water vapor is the largest contributor to the natural greenhouse effect. Water vapor is
fundamentally different from other greenhouse gases in that it can condense and rain out when it reaches high
concentrations, and the total amount of water vapor in the atmosphere is a function of the Earth's temperature.
While some human activities such as evaporation from irrigated crops or power plant cooling release water vapor
into the air, this has been determined to have a negligible effect on climate (IPCC 2013). The lifetime of water vapor
in the troposphere is on the order of 10 days. Water vapor can also contribute to cloud formation, and clouds can
have both warming and cooling effects by either trapping or reflecting heat. Because of the relationship between
water vapor levels and temperature, water vapor and clouds serve as a feedback to climate change, such that for any
given increase in other greenhouse gases, the total warming is greater than would happen in the absence of water
1-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
vapor. Aircraft contrails, which consist of water vapor and other substances, are aviation-induced clouds with the
same radiative forcing effects as high-altitude cirrus clouds (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 398ppmvin2013, a 42.4 percent increase (IPCC 2007
and NOAA/ESRL 2015).12'13 The IPCC definitively states that "the increase of CO2 ... is caused by anthropogenic
emissions from the use of fossil fuel as a source of energy and from land use and land use changes, in particular
agriculture" (IPCC 2013). 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 152
percent since 1750, from a pre-industrial value of about 700 ppb to 1,762- 1,893 ppb in 2012,14 although the rate of
increase decreased to near zero in the early 2000s, and has recently increased again to about 5 ppb/year. 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 primarily 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
increases the atmospheric lifetime of CH4 (IPCC 2013).
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 324-326 ppb in2012,15 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).
The pre-industrial period is considered as the time preceding the year 1750 (IPCC 2001).
13 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).
14 The range is the annual arithmetic averages from a mid-latitude Northern-Hemisphere site and a mid-latitude Southern-
Hemisphere site for October 2012 through September 2013 (CDIAC 2014).
The range is the annual arithmetic averages from a mid-latitude Northern-Hemisphere site and a mid-latitude Southern-
Hemisphere site for October 2012 through September 2013 (CDIAC 2014).
Introduction 1-5
-------
Ozone (Os). Ozone is present in both the upper stratosphere,16 where it shields the Earth from harmful levels of
ultraviolet radiation, and at lower concentrations in the troposphere,17 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 2013). 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 CCh and CH4. Tropospheric ozone is
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 2013).
Halocarbons, Perfluorocarbons, Sulfur Hexafluoride, and Nitrogen Triflouride. 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 518 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, SF6, and NF3 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 2013). PFCs, SF6, and NF3 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, SF6, and NF3 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 2013).
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.
16 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.
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.
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.
1-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Nitrogen Oxides (NOX). 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, are a precursor to nitrate particles (i.e.,
aerosols) and, to a lesser degree, lower stratosphere, where they have positive radiative forcing effects.19
Additionally, NOX emissions are also likely to decrease CH4 concentrations, thus having a negative radiative forcing
effect (IPCC 2013). 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 that are either directly
emitted into or are created through chemical reactions in the Earth's atmosphere. Aerosols or their chemical
precursors can be emitted by natural events such as dust storms and volcanic activity, or by anthropogenic processes
such as fuel combustion and biomass burning. Various categories of aerosols exist, including naturally produced
aerosols such as soil dust, sea salt, biogenic aerosols, sulfates, nitrates, and volcanic aerosols, and anthropogenically
manufactured aerosols such as industrial dust and carbonaceous20 aerosols (e.g., black carbon, organic carbon) from
transportation, coal combustion, cement manufacturing, waste incineration, and biomass burning. Aerosols can be
removed from the atmosphere relatively rapidly by precipitation or through more complex processes under dry
conditions.
Aerosols affect radiative forcing differently than greenhouse gases. Their radiative effects occur through direct and
indirect mechanisms: directly by scattering and absorbing solar radiation (and to a lesser extent scattering,
absorption, and emission of terrestrial radiation); and indirectly by increasing cloud droplets and ice crystals that
modify the formation, precipitation efficiency, and radiative properties of clouds (IPCC 2013). Despite advances in
understanding of cloud-aerosol interactions, the contribution of aerosols to radiative forcing are difficult to quantify
because aerosols generally have short atmospheric lifetimes, and have number concentrations, size distributions, and
compositions that vary regionally, spatially, and temporally (IPCC 2013).
The net effect of aerosols on the Earth's radiative forcing is believed to be negative (i.e., net cooling effect on the
climate). In fact, "despite the large uncertainty ranges on aerosol forcing, there is high confidence that aerosols have
offset a substantial portion of GHG forcing" (IPCC 2013).21 Although because they remain in the atmosphere for
only days to weeks, their concentrations respond rapidly to changes in emissions.22 Not all aerosols have a cooling
effect. Current research suggests that another constituent of aerosols, black carbon, has a positive radiative forcing
by heating the Earth's atmosphere and causing surface warming when deposited on ice and snow (IPCC 2013).
Black carbon also influences cloud development, but the direction and magnitude of this forcing is an area of active
research.
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 2007).
Direct radiative effects occur when the gas itself absorbs radiation. Indirect radiative forcing occurs when chemical
19 NOX emissions injected higher in the stratosphere, primarily from fuel combustion emissions from high altitude supersonic
aircraft, can lead to stratospheric ozone depletion.
20 Carbonaceous aerosols are aerosols that are comprised mainly of organic substances and forms of black carbon (or soot)
(IPCC 2013).
21 The IPCC (2014) defines high confidence as an indication of strong scientific evidence and agreement in this statement.
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).
Introduction 1-7
-------
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 COa, and therefore GWP-weighted emissions are measured in million metric tons of CCh equivalent (MMT
CO2 Eq.).23 The relationship between kilotons (kt) of a gas and MMT COa Eq. can be expressed as follows:
/ MMT
MMT C02 Eq. = (kt of gas} x (GWP} x
U.,000 ktJ
where,
MMT COa Eq. = Million metric tons of CO2 equivalent
kt = Kilotons (equivalent to a thousand metric tons)
GWP = Global warming potential
MMT = Million metric tons
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.
... the global warming potential values used by Parties included in Annex I to the Convention (Annex I
Parties) to calculate the carbon dioxide equivalence of anthropogenic emissions by sources and removals
by sinks of greenhouse gases shall be those listed in the column entitled "Global warming potential for
given time horizon " in table 2.14 of the errata to the contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change, based on the effects of greenhouse
gases over a 100-year time horizon...24
Greenhouse gases with relatively long atmospheric lifetimes (e.g., €62, CH4, N2O, HFCs, PFCs, SF6, NF3) 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., 862 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
C02
CH4a
N20
HFC-23
HFC-32
HFC-125
HFC-134a
HFC-143a
HFC-152a
HFC-227ea
HFC-236fa
HFC-4310mee
Atmospheric Lifetime
b
12
114
270
4.9
29
14
52
1.4
34.2
240
15.9
GWPC
1
25
298
14,800
675
3,500
1,430
4,470
124
3,220
9,810
1,640
Carbon comprises 12/44ths of carbon dioxide by weight.
Framework Convention on Climate Change; < http://unfccc.int/resource/docs/2013/copl9/eng/10a03.pdf>; 31 January 2014;
Report of the Conference of the Parties at its nineteenth session; held in Warsaw from 11 to 23 November 2013; Addendum; Part
two: Action taken by the Conference of the Parties at its nineteenth session; Decision 24/CP.19; Revision of the UNFCCC
reporting guidelines on annual inventories for Parties included in Annex I to the Convention; p. 2. (UNFCCC 2014)
1-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
CF4
C2F6
C4Fio
C6Fi4
SF6
NF3
50,000
10,000
2,600
3,200
3,200
740
7,390
12,200
8,860
9,300
22,800
17,200
Source: (IPCC 2007)
a 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.
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.
0100-year time horizon.
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 Fourth Assessment Report (AR4) (IPCC
2007). Although the AR4 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. Table 1-3 presents the new GWP values, relative to those presented in the AR4 and using
the 100-year time horizon common to UNFCCC reporting.
Table 1-3: Comparison of 100-Year GWP values
Gas
CO2
CH4a
N20
HFC-23
HFC-32
HFC-125
HFC-134a
HFC-143a
HFC-152a
HFC-227ea
HFC-236fa
HFC-4310mee
CF4
C2F6
C4Fio
C6Fi4
SF6
NF3
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
NA
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
10,800
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
17,200
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
16,100
Comparison to AR4
SAR
NC
(4)
12
(3,100)
(25)
(700)
(130)
(670)
16
(320)
(3,510)
(340)
(890)
(3,000)
(1,860)
(1,900)
1,100
NA
TAR
NC
(2)
(2)
(2,800)
(125)
(100)
(130)
(170)
(4)
280
(410)
(140)
(1,690)
(300)
(260)
(300)
(600)
(6,400)
AR5
NC
3
(33)
(2,400)
2
(330)
(130)
330
14
130
(1,750)
10
(760)
(1,100)
340
(1,390)
700
700
Source: (IPCC 2013, IPCC 2007, IPCC 2001, IPCC 1996)
Introduction 1-9
-------
NC (No Change)
NA (Not Applicable)
Note: Parentheses indicate negative values.
a 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 CCh 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-CCh gases in order to be consistent with the
approach used in calculating the CCh lifetime. Additionally, the AR5 reported separate values
for fossil versus biogenic methane in order to account for the CCh oxidation product.
To comply with international reporting standards under the UNFCCC, official emission estimates are reported by
the United States using AR4 GWP values, as required by the 2013 revision to the UNFCCC reporting guidelines for
national inventories.25 All estimates provided throughout this report are also presented in unweighted units. For
informational purposes, emission estimates that use GWPs from other IPCC Assessment Reports are presented in
detail in Annex 6.1 of this report. It should be noted that this Inventory represents the first time that the official U.S.
greenhouse gas emissions are reported using the AR4 GWP values. The use of IPCC AR4 GWP values for the
current Inventory applies across the entire time series of the Inventory (i.e., from 1990 to 2013).26
1.2 National Inventory Arrangements
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, planning methodological approaches and improvements, 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. EPA's 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 Inventory 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
25 See < http://unfccc.int/resource/docs/2013/copl9/eng/10a03.pdf>.
26 "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: 1990-2013
-------
voluntary outreach efforts with EPA. Finally, the U.S. Department of State officially submits the Inventory to the
UNFCCC each April. Figure 1-1 diagrams the National Inventory Arrangements.
Introduction 1-11
-------
Figure 1-1: National Inventory Arrangements Diagram
Inited States
Inventory Submission
United Nations
Framework Convention on
Climate Change
U.S. Department of State
Inventory Compilation
U.S. Environmental
Protection Agency
Inventory Compiler
Emission Calculations
U.S. Environmental
Protection Agency
Other U.S.
Government
Data Collection
Energy
• Bureau of Oce
\ Energy Management
1 Federal Highway Administration
EPAGHGRP
U.S. Department of Defense
U.S. Department of Energy
Agriculture and LULUCF
• Colorado State University
Industrial Processes and Product Use
• Alliance for Responsible Atmospheric Policy
• American Iron and Steel Institute
1 U.S. Aluminum Associa
• U.S. Bureau of Mines
1-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 2006IPCC Guidelines for National Greenhouse Gas Inventories, 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 consumption of electricity, energy, and fossil fuels.
Introduction 1-13
-------
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 and posted online, available for the public.l
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 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006). To the
extent possible, the present report relies on published activity and emission factor data. Depending on the emission
1 See http://epa.gov/climatechange/ghgemissions/usinventoryreport.html.
1-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 2006IPCC 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. 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 2006 IPCC Guidelines (IPCC 2006) defines 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
greenhouse gases in terms of the absolute level, the trend, or the uncertainty in emissions and removals."2 By
definition, key categories include those categories that have the greatest contribution to the absolute level of national
emissions. In addition, when an entire time series of emission and removal 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
emissions. Finally, a qualitative evaluation of key categories is performed to capture any categories that were not
identified in any of the quantitative analyses.
Approach 1, as defined in the 2006 IPCC Guidelines (IPCC 2006), 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 Approach 1, Approach 2, as defined in the 2006 IPCC Guidelines (IPCC 2006), was then implemented to
identify any additional key categories not already identified in Approach 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 Approach 1 and 2 level and trend assessments, a qualitative assessment of the source
categories, as described in the 2006 IPCC Guidelines (IPCC 2006), 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
See Chapter 4 "Methodological Choice and Identification of Key Categories" in IPCC (2006). See < http://www.ipcc-
nggip.iges.or.jp/public/2006gl/index.html>.
Introduction 1-15
-------
with IPCC guidelines. If these emissions were included in the totals, bunker fuels would qualify as a key category
according to Approach 1. 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 Approach 2. 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 2013. The table also indicates the criteria used in
identifying these categories (i.e., level, trend, Approach 1, Approach 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 (1990-2013)
IPCC Source
Categories
Gas
Approach 1
Level Trend Level Trend
Without Without With With
LULUCF LULUCF LULUCF LULUCF
Approach 2
Level Trend Level Trend
Without Without With With
LULUCF LULUCF LULUCF LULUCF
Quala
2013
Emissions
(MMT
CO2 Eq.)
Energy
CO2 Emissions from
Stationary
Combustion - Coal -
Electricity Generation
CO2 Emissions from
Mobile Combustion:
Road
CO2 Emissions from
Stationary
Combustion - Gas -
Industrial
CO2 Emissions from
Stationary
Combustion - Gas -
Electricity Generation
CO2 Emissions from
Stationary
Combustion - Oil -
Industrial
CO2 Emissions from
Stationary
Combustion - Gas -
Residential
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
C02
CO2
C02
C02
CO2
CO2
C02
C02
C02
CO2
C02
267.1
178.2
148.7
119.5
92.0
75.5
1-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
CO2 Emissions from
Stationary
Combustion - Oil -
Residential
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 -
U.S. Territories
CO2 Emissions from
Stationary
Combustion - Oil -
Electricity Generation
CO2 Emissions from
Petroleum Systems
CO2 Emissions from
Stationary
Combustion - Coal -
Commercial
CO2 Emissions from
Stationary
Combustion - Gas -
U.S. Territories
CO2 Emissions from
Stationary
Combustion - Coal -
Residential
CH4 Emissions from
Natural Gas Systems
Fugitive Emissions
from Coal Mining
CH4 Emissions from
Petroleum Systems
Non-CO2 Emissions
from Stationary
Combustion -
Residential
Non-CO2 Emissions
from Stationary
Combustion -
Electricity Generation
N2O Emissions from
Mobile Combustion:
Road
Non-CO2 Emissions
from Stationary
Combustion -
Industrial
International Bunker
Fuelsb
C02
CO2
C02
CO2
CO2
CO2
C02
C02
C02
CO2
CH4
CH4
CH4
CH4
N20
N2O
N20
Several
62.5
38.6
26.0
22.4
2.4
100.7
Industrial Processes and Product Use
CO2 Emissions from
Iron and Steel
Production &
CO2
52.3
Introduction 1-17
-------
Metallurgical Coke
Production
CO2 Emissions from
Cement Production
CO2 Emissions from
Petrochemical
Production
N2O Emissions from
Adipic Acid
Production
Emissions from
Substitutes for Ozone
Depleting Substances
SFe Emissions from
Electrical
Transmission and
Distribution
HFC-23 Emissions
fromHCFC-22
Production
PFC Emissions from
Aluminum Production
Agriculture
CH4 Emissions from
Enteric Fermentation
CH4 Emissions from
Manure Management
CH4 Emissions from
Rice Cultivation
Direct N2O Emissions
from Agricultural Soil
Management
Indirect N2O
Emissions from
Applied Nitrogen
CH4
CH4
CH4
N2O
N2O
164.5
61.4
8.3
224.7
39.0
Waste
CELi Emissions from
Landfills
CH4
114.6
Land Use, Land Use Change, and Forestry
CO2 Emissions from
Land Converted to
Cropland
CO2 Emissions from
Grassland Remaining
Grassland
CO2 Emissions from
Landfilled Yard
Trimmings and Food
Scraps
CO2 Emissions from
Cropland Remaining
Cropland
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
C02
CO2
CO2
CO2
CH4
N2O
16.1
1-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Subtotal Without LULUCF
Tot;
otal Emissions Without LULUCF
6,455.5
6,649.7
Percent of Total Without LULUCF
97%
Subtotal With LULUCF
Total Emissions With LULUCF
5,625.3
5,791.2
Percent of Total With LULUCF
97%
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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
• 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.
Introduction 1-19
-------
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.
Figure 1-2: U.S. QA/QC Plan Summary
+J
l/>
>,
ra
c
<
Ł•
o
+-•
c
ni
>
_c
+->
I/I
>.
ro
c
<
u
a
§
• Obtain data in electronic
formatfif possible)
• Review spreadsheet
construction
• Avoid hardwiring
Use data validation
Protect cells
• Develop automatic
checkers for:
Outliers, negative
values, or missing
data
• Variable types
match values
• Time series
consistency
• Contact reports for non-
electronic communications
• 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
• Maintain trackingtab for II
status of gathering
efforts IH^ ^^L I
• 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
1
• Reproduce calculations
• Reviewtime series
consistency
• Review changes in
data/consistency with IPCC
methodology
Common starting
versions for each
inventoryyear
Utilize unalterable
summary tab foreach
source spreadsheet for
linkingto a master
summary spreadsheet
Follow strictversion
control procedures
Document QA/QC
procedures
Data Gathering
Data Documentation CalculatingEmissions
Cross-Cutting
Coordination
1-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 €62
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 2006IPCC Guidelines (IPCC 2006) 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.
• 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 total U.S. greenhouse gas emissions was developed using the IPCC Approach 2
uncertainty estimation methodology. Estimates of quantitative uncertainty for the total U.S. greenhouse gas
emissions are shown below, in Table 1-5.
The IPCC provides good practice guidance on two approaches—Approach 1 and Approach 2—to estimating
uncertainty for individual source categories. Approach 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 2006 IPCC Guidelines (IPCC 2006),
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 (MMT COz Eq. and Percent)
2013 Emission Uncertainty Range Relative to Emission
Estimate3 Estimateb
Gas (MMTCChEq.) (MMT CCh Eq.) (%)
C02
CH4e
N20e
PFC,HFC, SF6,andNF3e
Total
5,505
636
355
171
6,667
Lower
Bound"1
5,400
573
320
170
6,584
Upper
Bound"1
5,766
751
445
190
7,008
Lower
Bound
-2%
-10%
-10%
-1%
-1%
Upper
Bound
5%
18%
25%
11%
5%
Standard
Mean0 Deviation0
(MMT CO2 Eq.)
5,584
656
376
180
6,795
95
45
32
5
110
Introduction 1-21
-------
Net Emissions (Sources and
Sinks) 5,785.5 5,613 6,220 -3% 8% 5,916 154
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 5.7 MMT 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.
0 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 2013.
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.
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 2013. 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 revision of the UNFCCC reporting guidelines agreed to at the nineteenth Conference of the
Parties (UNFCCC 2014), this Inventory of U.S. Greenhouse Gas Emissions and Sinks is segregated into five 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/IPCC Sector Activities Included
Energy Emissions of all greenhouse gases resulting from stationary and mobile energy
activities including fuel combustion and fugitive fuel emissions, and non-
energy use of fossil fuels.
Industrial Processes and Emissions resulting from industrial processes and product use of greenhouse
Product Use gases.
Agriculture Anthropogenic emissions from agricultural activities except fuel combustion,
which is addressed under Energy.
1-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Land Use, Land-Use Emissions and removals of CCh, CELi, and N2O from forest management, other
Change, and Forestry land-use activities, and land-use change.
Waste Emissions from waste management activities.
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:
Chapter/IPCC Sector Overview of emission trends for each IPCC defined sector
SOUK6 Category. 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 and Timeseries Consistency: A discussion and quantification of the uncertainty in
emission estimates and a discussion of time-series consistency.
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 COa 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.
Introduction 1-23
-------
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 CLU, N2O, and Indirect Greenhouse Gases from Stationary
Combustion
3.2. Methodology for Estimating Emissions of CLU, 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 CEU Emissions from Coal Mining
3.5. Methodology for Estimating CEU and CCh Emissions from Petroleum Systems
3.6. Methodology for Estimating CtLt Emissions from Natural Gas 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 CtLt 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 CLU 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
1-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
2. Trends in Greenhouse Gas Emissions
2.1 Recent Trends in U.S. Greenhouse Gas
Emissions and Sinks
In 2013, total U.S. greenhouse gas emissions were 6,673.0 MMT or million metric tons CC>2 Eq. Total U.S.
emissions have increased by 5.9 percent from 1990 to 2013, and emissions increased from 2012 to 2013 by 2.0
percent (127.9 MMT CCh Eq.). The increase from 2012 to 2013 was due to an increase in the carbon intensity of
fuels consumed to generate electricity due to an increase in coal consumption, with decreased natural gas
consumption. Additionally, cold winter conditions lead to an increase in fuels for the residential and commercial
sectors for heating. In 2013 there also was an increase in industrial production across multiple sectors resulting in
increases in industrial sector emissions. Lastly, transportation emissions increased as a result of a small increase in
vehicle miles traveled (VMT) and fuel use across on-road transportation modes. Since 1990, U.S. emissions have
increased at an average annual rate of 0.3 percent. Figure 2-1 through Figure 2-3 illustrate the overall trend in total
U.S. emissions by gas, annual changes, and absolute changes since 1990.
Figure 2-1: U.S. Greenhouse Gas Emissions by Gas
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
s
s
u
• MFCs, PFCs, SFand NF,
6 3
• Methane
Nitrous CKide
• Carbon Dioxide
5,673
Trends 2-1
-------
Figure 2-2: Annual Percent Change in U.S. Greenhouse Gas Emissions
4% n
2%
11%
3.3%
2.6%
2.11%
-3.4%
-6.5%
1991 1992 1993 1994 1995 1996 1997 1998 1999 2()(H) 20(11 21)02 2(1(13 24 2005 2006 2(107 2008 2009 2010 2(111 2012 2013
Figure 2-3: Cumulative Change in Annual U.S. Greenhouse Gas Emissions Relative to 1990
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
s
Q
,200 -i
,10(1
,000
900
800
700
600
500
400 -
300
200
100
0
-100
-200
912
810
1,049 1,099
1,0*4 981
351 378 M • _ • 391
617
664 692
298
372
20(1
(M
cn
O*
co
a\
&>
o
Q
o
o
o
(M
-------
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.
A brief discussion of the year to year variability in fuel combustion emissions is provided below, beginning with
2009.
From 2009 to 2010, CCh emissions from fossil fuel combustion increased by 3.3 percent, which represents one of
the largest annual increases in CCh emissions from fossil fuel combustion for the twenty four-year period from 1990
to 2013. This increase is primarily due to an increase in economic output from 2009 to 2010, and increased
industrial production and manufacturing output (FRB 2014). 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 four-year period between 1990 and 2013. 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 0.4 percent and cooling degree days increased by 17.3 percent to their highest levels in the twenty one-
year period from 1990 to 2010. 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 1.2 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, CC>2 emissions from fossil fuel combustion decreased by 3.9 percent, with emissions from fossil
fuel combustion at their lowest level since 1994. 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 increased by 1.7 percent
while heating degree days decreased 12.6 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.
From 2012 to 2013, CC>2 emissions from fossil fuel combustion increased by 2.6 percent, this increase is primarily a
result of the increased energy consumption in the residential and commercial sectors, as heating degree days
increased 18.5 percent in 2013 as compared to 2012. The cooler weather led to an increase of 16.9 and 12.4 percent
direct use of fuels in the residential and commercial sectors, respectively. In addition, there was an increase of 1.2
and 0.9 percent in electricity consumption in the residential and commercial sectors, respectively, due to regions that
heat their homes with electricity. The consumption of natural gas used to generate electricity decreased by 10.2
percent due to an increase in the price of natural gas. Electric power plants shifted some consumption from natural
Trends 2-3
-------
gas to coal, and as a result increased coal consumption to generate electricity by 4.2 percent. Lastly, industrial
production increased 2.9 percent from 2012 to 2013, resulting in an increase in the in CO2 emissions from fossil fuel
combustion from the industrial sector by 4.2 percent.
Table 2-1 summarizes emissions and sinks from all U.S. anthropogenic sources in weighted units of MMT CCh Eq.,
while unweighted gas emissions and sinks in kilotons (kt) are provided in Table 2-2.
Table 2-1: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (MMT COz Eq.)
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
Petrochemical Production
Lime Production
Ammonia Production
Incineration of Waste
Petroleum Systems
Liming of Agricultural Soils
Urea Consumption for Non-
Agricultural Purposes
Other Process Uses of Carbonates
Urea Fertilization
Aluminum Production
Soda Ash Production and
Consumption
Ferroalloy Production
Titanium Dioxide Production
Zinc Production
Phosphoric Acid Production
Glass Production
Carbon Dioxide Consumption
Peatlands Remaining Peatlands
Lead Production
Silicon Carbide Production and
Consumption
Magnesium Production and
Processing
Land Use, Land-Use Change, and
Forestry (Sink)"
Wood Biomass and Ethanol
Consumption11
International Bunker Fuelsc
CH4
Enteric Fermentation
Natural Gas Systems
Landfills
Coal Mining
Manure Management
1990
5,123.7
4,740.7
1,820.8
1,493.8
842.5
338.31
217.4B
27.9B
117.71
99.8
37.61
33.3
21.6|
1-1
13.0
8.0
4.4
4.7
3.8
4.9
2.4
6.8
Z.ZJ
1.2
0.6
1.6
1.5
1.5
1.1
0.5
0.4
(775.8)
219.4U
103. jl
745.51
164.2!
179.ll
186.21
96. sl
37.2|
2005
6,134.0
5,747.7
2,400.9
1,887.8
827.8
357.81
223. sl
49.91
138.91
66.7
30.ol
45.9
28.1
14.6
9.2
12.5
4.9
4.3
3.7
6.3
3.5
4.1
2.9
1.4
1.8
1.0
1.4
1.9
1.4
1.1
o.el
0.2
1
229.8U
113. /I
707.8
168.91
176.31
165. sl
64. ll
56. 3|
2009
5,500.6
5,197.1
2,145.7
1,720.3
727.7
336.4
223.5
43.5
106.0
43.0
32.2
29.4
23.7
11.4
8.5
11.3
4.7
3.7
3.4
7.6
3.6
3.0
2.5
1.5
1.6
0.9
1.0
1.0
1.8
1.0
0.5
0.1
+
(870.9)
250.5
106.4
709.5
172.7
168.0
158.1
79.9
59.7
2010
5,704.5
5,367.1
2,258.4
1,732.0
775.7
334.7
220.2
46.2
114.6
55.7
32.3
31.3
27.4
13.4
9.2
11.0
4.2
4.8
4.7
9.6
3.8
2.7
2.6
1.7
1.8
1.2
1.1
1.5
1.2
1.0
0.5
0.2
+
(871.6)
265.1
117.0
667.2
171.1
159.6
121.8
82.3
60.9
2011
5,568.9
5,231.3
2,157.7
1,711.5
774.1
327.2
221.0
39.8
108.4
60.0
35.6
32.0
26.4
14.0
9.3
10.5
4.5
3.9
4.0
9.3
4.1
3.3
2.6
1.7
1.7
1.3
1.2
1.3
0.8
0.9
0.5
0.2
+
(881.0)
268.1
111.7
660.9
168.7
159.3
121.3
71.2
61.4
2012
5,358.3
5,026.0
2,022.2
1,700.8
784.2
283.1
197.1
38.6
104.9
54.3
34.8
35.1
26.5
13.7
9.4
10.4
5.1
5.8
4.4
8.0
4.2
3.4
2.7
1.9
1.5
1.5
1.1
1.2
0.8
0.8
0.5
0.2
+
(880.4)
267.7
105.8
647.6
166.3
154.4
115.3
66.5
63.7
2013
5,505.2
5,157.7
2,039.8
1,718.4
817.3
329.6
220.7
32.0
119.8
52.3
37.8
36.1
26.5
14.1
10.2
10.1
6.0
5.9
4.7
4.4
4.0
3.3
2.7
1.8
1.6
1.4
1.2
1.2
0.9
0.8
0.5
0.2
+
(881.7)
283.3
99.8
636.3
164.5
157.4
114.6
64.6
61.4
2-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Petroleum Systems 31.5 23.5 21.5 21.3 22.0 23.3 25.2
Wastewater Treatment 15.7 15.9B 15.6 15.5 15.3 15.2 15.0
Rice Cultivation 9.2! 8.9! 9.4 11.1 8.5 9.3 8.3
Stationary Combustion S.sB 7.4U 7.4 7.1 7.1 6.6 8.0
Abandoned Underground Coal
Mines 7.2B 6.6B 6.4 6.6 6.4 6.2 6.2
ForestFires 2.5M S.sl 5.8 4.7 14.6 15.7 5.S
Mobile Combustion 5.el 3.oB 2.3 2.3 2.3 2.2 2.1
Composting QAU 1.9| 1.9 1.8 1.9 1.9 2.0
Iron and Steel Production &
Metallurgical Coke Production l.l| 0.9 0.4 0.6 0.7 0.7 0.7
Field Burning of Agricultural
Residues 0.3B 0.2B 0.3 0.3 0.3 0.3 0.3
Petrochemical Production 0.2 ( O.l| + 0.1 + 0.1 0.1
Ferroalloy Production
Silicon Carbide Production and
Consumption
Peatlands Remaining Peatlands
Incineration of Waste
International Bunker Fuelsc 0
N2O 329
Agricultural Soil Management 224
Stationary Combustion 11
Mobile Combustion 41
Manure Management 13
Nitric Acid Production 12
Wastewater Treatment 3
N2O from Product Uses 4
Adipic Acid Production 15
Forest Fires 1
Settlement Soils 1
Composting 0
Forest Soils 0
Incineration of Waste 0
Semiconductor Manufacture
Field Burning of Agricultural
Residues 0.1 • 0.1 • 0.1 0.1 0.1 0.1 0.1
Peatlands Remaining Peatlands
International Bunker Fuelsc 0
HFCs 46.6 131.4
Substitution of Ozone Depleting
Substances'1 O.sB 111.1
HCFC-22 Production 46.1 20.0
Semiconductor Manufacture 0.2 0.2
Magnesium Production and
Processing 0.0 • 0.0 • + + + + 0.1
PFCs 24.3 6.6M 3.9 4.4 6.9 6.0 5.8
Aluminum Production 21.5 3.4B 1.9 1.9 3.5 2.9 3.0
Semiconductor Manufacture 2.sB 3.2B 2.0 2.6 3.4 3.0 2.9
SF6 31.1 14.0 9.3 9.5 10.0 7.7 6.9
Electrical Transmission and
Distribution 25.4 lO.eB 7.3 7.0 6.8 5.7 5.1
Magnesium Production and
0.1
356.1
264.1
20.4
24.6
17.0
9.6
4.6
4.2
2.7
3.8
2.2
1.7
0.5
0.3
0.1
0.1
360.1
264.3
22.2
23.7
17.1
11.5
4.7
4.2
4.2
3.1
2.4
1.6
0.5
0.3
0.1
0.1
371.9
265.8
21.3
22.5
17.3
10.9
4.8
4.2
10.2
9.6
2.5
1.7
0.5
0.3
0.2
0.1
365.6
266.0
21.4
20.2
17.3
10.5
4.9
4.2
5.5
10.3
2.5
1.7
0.5
0.3
0.2
0.1
355.2
263.7
22.9
18.4
17.3
10.7
4.9
4.2
4.0
3.8
2.4
1.8
0.5
0.3
0.2
0.9
142.9
136.0
6.8
0.2
1.0
152.6
144.4
8.0
0.2
1.0
157.4
148.4
8.8
0.2
0.9
159.2
153.5
5.5
0.2
0.9
163.0
158.6
4.1
0.2
Processing
Semiconductor Manufacture
NF3
Semiconductor Manufacture
Total Emissions
5.2
0.5
+1
+
6,301.1
2.7
0.7
0.5
0.5
7,350.2
1.6
0.3
0.4
1 0.4
6,722.7
2.1
0.4
0.5
0.5
6,898.8
2.8
0.4
0.7
0.7
6,776.6
1.6
0.4
0.6
0.6
6,545.1
1.4
0.4
0.6
0.6
6,673.0
Total Sinks3
(775.8)
(911.9)
(870.9) (871.6) (881.0) (880.4) (881.7)
Trends 2-5
-------
Net Emissions (Sources and Sinks) 5,525.2 6,438.3 5,851.9 6,027.2 5,895.6 5,664.7 5,791.2
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
+ Does not exceed 0.05 MMT CO2 Eq.
a Parentheses indicate negative values or sequestration. Sinks (i.e., CCh removals) are only included in the Net
Emissions total. Refer to Table 2-8 for a breakout of emissions and removals for Land Use, Land-Use Change, and
Forestry by gas and source category.
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.
2-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 2-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (kt)
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
Petrochemical Production
Lime Production
Ammonia Production
Incineration of Waste
Petroleum Systems
Liming of Agricultural Soils
Urea Consumption for Non-
Agricultural Purposes
Other Process Uses of
Carbonates
Urea Fertilization
Aluminum Production
Soda Ash Production and
Consumption
Ferroalloy Production
Titanium Dioxide Production
Zinc Production
Phosphoric Acid Production
Glass Production
Carbon Dioxide Consumption
Peatlands Remaining Peatlands
Lead Production
Silicon Carbide Production and
Consumption
Magnesium Production and
Processing
Land Use, Land-Use Change,
and Forestry (Sink)"
WoodBiomass and Ethanol
Consumption11
International Bunker Fuelsc
CH4
Enteric Fermentation
Natural Gas Systems
Landfills
Coal Mining
Manure Management
Petroleum Systems
Wastewater Treatment
Rice Cultivation
Stationary Combustion
1990
5,123,695
4,740,670
1,820,818
1,493,758
842,473
338,347
217,393
27,882
117,658
99,781
37,645 1
3 3, 278 1
21,633
ll,70ol
13,047|
7,972l
4,445l
4,667l
3,784l
4,907l
2,417l
6,831 1
2,74 1!
2,152l
l,195l
6321
1,586 1
l,535l
l,472l
l,055l
516|
=
(775,835)
219,413
103,463
29,820
6,566 1
7,165l
7,450 1
3,860 1
l,486l
1,261 1
626 1
366
339
2005
6,133,969
5,747,683
2,400,874
1,887,799
827,808
357,827
223,453
49,923
138,877
66,666 1
29,995
45,910
28,124
14,552
9,196 1
12,4541
4,904l
4,349l
3,653 1
6,339l
3,504l
4,142!
2,868 •
l,392l
l,755l
l,03ol
l,395l
l,92sl
1,375
* H
553!
219
1
(911,929)
229,844
113,139
28,314
6,755!
7,053l
6,620 1
2,565 1
2,254l
9391
635
358
296
2009
5,500,602
5,197,058
2,145,658
1,720,314
727,724
336,375
223,492
43,495
106,018
43,029
32,201
29,432
23,706
11,411
8,454
11,295
4,656
3,669
3,427
7,583
3,555
3,009
2,488
1,469
1,648
943
1,016
1,045
1,795
1,024
525
145
1
(870,879)
250,491
106,410
28,380
6,908
6,722
6,324
3,194
2,388
860
623
378
295
2010
5,704,531
5,367,144
2,258,399
1,731,971
775,674
334,734
220,195
46,172
114,554
55,746
32,334
31,256
27,388
13,381
9,188
11,026
4,153
4,784
4,730
9,560
3,778
2,722
2,612
1,663
1,769
1,182
1,130
1,481
1,206
1,022
542
181
1
(871,609)
265,110
116,992
26,687
6,844
6,382
4,873
3,293
2,437
854
619
444
283
2011
5,568,891
5,231,341
2,157,688
1,711,538
774,101
327,211
221,022
39,781
108,359
60,008
35,551
32,010
26,396
13,981
9,292
10,550
4,467
3,871
4,029
9,335
4,099
3,292
2,624
1,735
1,729
1,286
1,198
1,299
802
926
538
170
3
(880,999)
268,064
111,660
26,437
6,750
6,371
4,851
2,849
2,457
878
610
339
283
2012
5,358,276
5,026,000
2,022,181
1,700,782
784,227
283,095
197,097
38,617
104,917
54,327
34,764
35,051
26,477
13,715
9,377
10,363
5,060
5,776
4,449
8,022
4,225
3,439
2,672
1,903
1,528
1,486
1,138
1,248
841
812
527
158
2
(880,394)
267,730
105,805
25,905
6,653
6,176
4,611
2,658
2,548
931
606
372
264
2013
5,505,178
5,157,697
2,039,750
1,718,406
817,252
329,609
220,714
31,965
119,850
52,288
37,808
36,146
26,514
14,072
10,152
10,137
6,001
5,925
4,663
4,424
4,011
3,255
2,712
1,785
1,608
1,429
1,173
1,160
903
770
525
169
2
(881,732)
283,337
99,763
25,453
6,581
6,295
4,585
2,584
2,456
1,009
601
332
318
Trends 2-7
-------
Abandoned Underground Coal
Mines
Forest Fires
Mobile Combustion
Composting
Iron and Steel Production &
Metallurgical Coke
Production
Field Burning of Agricultural
Residues
Petrochemical Production
Ferroalloy Production
Silicon Carbide Production and
Consumption
Peatlands Remaining Peatlands
Incineration of Waste
International Bunker Fuelsc
N20
Agricultural Soil Management
Stationary Combustion
Mobile Combustion
Manure Management
Nitric Acid Production
Wastewater Treatment
N2O from Product Uses
Adipic Acid Production
Forest Fires
Settlement Soils
Composting
Forest Soils
Incineration of Waste
Semiconductor Manufacture
Field Burning of Agricultural
Residues
Peatlands Remaining Peatlands
International Bunker Fuelsc
HFCs
Substitution of Ozone
Depleting Substances4
HCFC-22 Production
Semiconductor Manufacture
Magnesium Production and
Processing
PFCs
Aluminum Production
Semiconductor Manufacture
SF6
Electrical Transmission and
Distribution
Magnesium Production and
Processing
Semiconductor Manufacture
NF3
Semiconductor Manufacture
/
1,107
752
40
138
46
41
111
14
51
hi
5!
1
+1
2!
+
+
+
3
M
M
3
+
"1
M
M
M
1
1
+1
+
+
+
264
332
121
75
34
9
6
+
+
+
+
5
1,194
817
68
128
55
38
15
14
24
18
8
6
2
1
+
+
+
3
M
M
1
+
0
M
M
M
+
+
+
+
+
+
254
233
93
75
17
12
2
+
+
+
+
5
1,195
886
69
82
57
32
16
14
9
13
8
6
2
1
+
+
+
3
M
M
+
+
+
M
M
M
+
+
+
+
+
+
263
190
92
73
25
11
2
+
+
+
+
6
1,208
887
74
80
57
39
16
14
14
11
8
5
2
1
+
+
+
3
M
M
1
+
+
M
M
M
+
+
+
+
+
+
257
584
91
75
28
12
2
+
+
+
+
5
1,248
892
71
76
58
37
16
14
34
32
8
6
2
1
1
+
+
3
M
M
1
+
+
M
M
M
+
+
+
+
+
+
249
626
88
77
29
12
3
1
+
+
+
4
1,227
892
72
68
58
35
16
14
19
35
8
6
2
1
1
+
+
3
M
M
+
+
+
M
M
M
+
+
+
+
+
+
249
233
86
79
28
12
3
+
+
+
+
3
1,192
885
77
62
58
36
17
14
13
13
8
6
2
1
1
+
+
3
M
M
+
+
+
M
M
M
+
+
+
+
+
+
+ Does not exceed 0.5 kt.
M Mixture of multiple gases
a Refer to Table 2-8 for a breakout of emissions and removals for Land Use, Land-Use Change, and Forestry by gas and source
category.
2-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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. Parentheses indicate negative values or sequestration.
Emissions of all gases can be summed from each source category into a set of five sectors defined by the
Intergovernmental Panel on Climate Change (IPCC). Over the twenty four-year period of 1990 to 2013, total
emissions in the Energy, Industrial Processes and Product Use, and Agriculture sectors grew by 346.2 MMT CO2
Eq. (6.5 percent), 17.0 MMT CO2 Eq. (5.0 percent), and 67.0 MMT CO2 Eq. (14.9 percent), respectively. Emissions
from the Waste sector decreased by 67.7 MMT CO2 Eq. (32.9 percent). Over the same period, estimates of net C
sequestration for the Land Use, Land-Use Change, and Forestry sector increased by 96.4 MMT CO2 Eq. (12.7
percent).
Figure 2-4: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
o
Q
Industrial Processes and
Product Use
Waste
LULUCF (sources)
X
Land-Use Chanqe and Forestry (
(1,500)
Table 2-3: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC
Sector (MMT COz Eq.)
Chapter/IPCC Sector
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 and Product Use
Substitution of Ozone Depleting
Substances
Iron and Steel Production &
Metallurgical Coke Production
1990
5,290.5
4,740.7
216.8
117.7
96.5
36.0
20.4
46.9
8.4
7.2
342.1
03
100.9
2005
6,273.6
5,747.7
206.3
138.9
64.1
28.4
27.6
41.1
12.8
6.6
367.4
I.I 1,1 1
67.5 1
2009
5,682.1
5,197.1
200.2
106.0
79.9
26.2
27.8
26.9
11.6
6.4
314.9
136.0
43.5
2010
5,854.6
5,367.1
191.9
114.6
82.3
25.5
29.3
26.0
11.4
6.6
353.6
144.4
56.4
2011
5,702.6
5,231.3
194.8
108.4
71.2
26.4
28.4
24.8
10.9
6.4
371.0
148.4
60.7
2012
5,482.2
5,026.0
189.2
104.9
66.5
28.3
28.0
22.4
10.7
6.2
361.2
153.5
55.1
2013
5,636.6
5,157.7
195.2
119.8
64.6
31.2
30.8
20.6
10.4
6.2
359.1
158.6
53.0
Trends 2-9
-------
Cement Production 33.3 45.9
Petrochemical Production 21.9 28.3
Lime Production 11.lU 14.6
Nitric Acid Production 12.1
Ammonia Production 13.0
Aluminum Production 28.3 7.6
Electrical Transmission and
Distribution 25.4 10.6
Urea Consumption for Non-
Agricultural Purpo ses 3.8 M 3.7
Other Process Uses of Carbonates 4.91 6.3
N2O from Product Uses 4.21 4.2
Semiconductor Manufacture 3.6H 4.7
HCFC-22 Production 46.1 20.0
Adipic Acid Production 15.2 7.1
Soda Ash Production and
Consumption 2.7H 2.9
Ferroalloy Production 2.2H 1.4
Titanium Dioxide Production 1.21 1.8
Magnesium Production and
Processing ^-2H 2.7
Zinc Production 0.6H 1.0
Phosphoric Acid Production 1.61 1.4
Glass Production 1.51 1.9
Carbon Dioxide Consumption 1-^1 ^-^
Lead Production O.sB 0.6
Silicon Carbide Production and
Consumption 0.4 0.2
Agriculture 448.7 494.5
Agricultural Soil Management 224.0 243.6
Enteric Fermentation 164.2 168.9
Manure Management 51.0 72.8
Rice Cultivation 9.2U 8.9
Field Burning of Agricultural
Residues 0.4B 0.3
Land Use, Land-Use Change, and
I
29.4
23.8
11.4
9.6
8.5
4.9
7.3
3.4
7.6
4.2
3.1
6.8
2.7
2.5
1.5
1.6
1.6
0.9
1.0
1.0
1.8
0.5
0.2
523.3
264.1
172.7
76.7
9.4
0.4
31.3
27.4
13.4
11.5
9.2
4.6
7.0
4.7
9.6
4.2
3.8
8.0
4.2
2.6
1.7
1.8
2.1
1.2
1.1
1.5
1.2
0.5
0.2
524.8
264.3
171.1
78.0
11.1
0.4
32.0
26.4
14.0
10.9
9.3
6.8
6.8
4.0
9.3
4.2
4.9
8.8
10.2
2.6
1.7
1.7
2.8
1.3
1.2
1.3
0.8
0.5
0.2
522.1
265.8
168.7
78.7
8.5
0.4
35.1
26.5
13.7
10.5
9.4
6.4
5.7
4.4
8.0
4.2
4.5
5.5
5.5
2.7
1.9
1.5
1.7
1.5
1.1
1.2
0.8
0.5
0.2
523.0
266.0
166.3
81.0
9.3
0.4
36.1
26.6
14.1
10.7
10.2
6.2
5.1
4.7
4.4
4.2
4.2
4.1
4.0
2.7
1.8
1.6
1.5
1.4
1.2
1.2
0.9
0.5
0.2
515.7
263.7
164.5
78.7
8.3
0.4
Forestry
Forest Fires
Liming of Agricultural Soils
Urea Fertilization
Settlement Soils
Peatlands Remaining Peatlands
Forest Soils
Waste
Landfills
Wastewater Treatment
Composting
Total Emissions
Total Sinksa
Net Emissions (Sources and Sinks)
13.8
4.2
4.7
2.4
11
0.1
206.0
186.21
19.ol
0.7
6,301.1
(775.8)
5,525.2
125.5
13.8
1
0.5|
189.2
165.5
20.2
3_5_B
7,350.2
(911.9)
6,438.3
20.6
9.7
3.7
3.6
2.2
1.0
0.5
181.8
158.1
20.2
3.6
6,722.7
(870.9)
5,851.9
20.3
7.9
4.8
3.8
2.4
1.0
0.5
145.5
121.8
20.2
3.5
6,898.8
(871.6)
6,027.2
36.1
24.2
3.9
4.1
2.5
0.9
0.5
144.9
121.3
20.1
3.5
6,776.6
(881.0)
5,895.6
39.8
26.0
5.8
4.2
2.5
0.8
0.5
138.9
115.3
20.0
3.7
6,545.1
(880.4)
5,664.7
23.3
9.7
5.9
4.0
2.4
0.8
0.5
138.3
114.6
20.0
3.7
6,673.0
(881.7)
5,791.2
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
a Sinks (i. e., CCh removals) are only included in the Net Emissions total. Refer to Table 2-8 for a breakout of emissions and
removals for Land Use, Land-Use Change, and Forestry by gas and source category.
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values or sequestration.
2-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Energy
Energy-related activities, primarily fossil fuel combustion, accounted for the vast majority of U.S. CC>2 emissions for
the period of 1990 through 2013. In 2013, 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 CC>2 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 (41
percent and 12 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.
Figure 2-5: 2013 Energy Chapter Greenhouse Gas Sources
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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
5,158
Energy as a Portion
of all Emissions
5(1 100 150
MMT CO2 Eq.
200
Trends 2-11
-------
Figure 2-6: 2013 U.S. Fossil Carbon Flows (MMT CO2 Eq.)
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
NEU Emissions 12
Fossil Fuel
Energy Exports
802
Coal Emissions
1*70
NEU Emissions 5
Natural Gas Emissions
U95
NEU Emission: •:-
Natural Gas 157'
Coal2r
Natural Gas Liquids,
Liquefied Refinery Gas,
& Other Liquids
219 ^
Petroleum
1,344 .,
Stock
Changes
(117)
Fossil Fuel
Consumption
NEU Imports u-s-
19 Territories
32
Other Outflows
165
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
Other outflows consists of NEU sequestered carbon emissions and a
fossil fuel combustion residual
Table 2-4: Emissions from Energy (MMT COz Eq.)
Gas/Source
1990
2005
2009
2010
2011
2012
2013
CO2 4,908.4
Fossil Fuel Combustion 4^740.7
Electricity Generation 1,820.8
Transportation 1,493.8
Industrial 842.5
Residential 338.3
Commercial 217.4
U.S. Territories 27.9
Non-Energy Use of Fuels 117.7
Natural Gas Systems 37.6
Incineration of Waste 8.0
Petroleum Systems 4.4
Biomass - Wood" 215.2
International Bunker Fuelsb 103.5
Biomass - Ethanol" 4.2
CH4 328.5
Natural Gas Systems 179.1
Coal Mining 96.5
Petroleum Systems 31.5
Stationary Combustion 8.5
Abandoned Underground Coal
Mines 7.2
Mobile Combustion 5.6
Incineration of Waste +
International Bunker Fuelsb 0.2
N2O 53.6
Stationary Combustion 11.9
Mobile Combustion 41.2
5,933.9
5,747.7
2,400.9
1,887.8
827.8
357.8
223.5
49.9
138.9
30.0
12.5
4.9
206.9
113.1
22.9
280.9
176.3
64.1
23.5
7.4
6.6
3.0
+
0.1
58.7
20.2
38.1
5,351.2
5,197.1
2,145.7
1,720.3
727.7
336.4
223.5
43.5
106.0
32.2
11.3
4.7
188.2
106.4
62.3
285.5
168.0
79.9
21.5
7.4
6.4
2.3
+
0.1
45.3
20.4
24.6
5,529.2
5,367.1
2,258.4
1,732.0
775.7
334.7
220.2
46.2
114.6
32.3
11.0
4.2
192.5
117.0
72.6
279.2
159.6
82.3
21.3
7.1
6.6
2.3
+
0.1
46.2
22.2
23.7
5,390.3
5,231.3
2,157.7
1,711.5
774.1
327.2
221.0
39.8
108.4
35.6
10.5
4.5
195.2
111.7
72.9
268.2
159.3
71.2
22.0
7.1
6.4
2.3
+
0.1
44.1
21.3
22.5
5,181.1
5,026.0
2,022.2
1,700.8
784.2
283.1
197.1
38.6
104.9
34.8
10.4
5.1
194.9
105.8
72.8
259.2
154.4
66.5
23.3
6.6
6.2
2.2
+
0.1
41.9
21.4
20.2
5,331.5
5,157.7
2,039.8
1,718.4
817.3
329.6
220.7
32.0
119.8
37.8
10.1
6.0
208.6
99.8
74.7
263.5
157.4
64.6
25.2
8.0
6.2
2.1
+
0.1
41.6
22.9
18.4
2-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Incineration of Waste
International Bunker Fuelsb
0.5
0.9
0.4
1.0
0.3
0.9
0.3
1.0
0.3
1.0
0.3
0.9
0.3
Total
5,290.5
6,273.6
5,682.1 5,854.6 5,702.6 5,482.2 5,636.6
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.05 MMT 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.
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 CCh 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 CC>2 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 CO2.
Table 2-5: COz Emissions from Fossil Fuel Combustion by End-Use Sector (MMT COz Eq.)
End-Use Sector
Transportation
Combustion
Electricity
Industrial
Combustion
Electricity
Residential
Combustion
Electricity
Commercial
Combustion
Electricity
U.S. Territories3
Total
Electricity Generation
1990
1,496.8
1,493.8
3.0
1,529.2
842.5
686.7
931.4
338.3
593.0
755.4
217.4
538.0
27.9
4,740.7
1,820.8
1
1
1
1
1
5
2005
,892.5
,887.81
4.?|
,564.4
827. 8 1
736.61
,214.1 •
357.81
856.31
,026.7
223.5J
803.31
49.9
,747.7
2,400.9
2009
1,724.8
1,720.3
4.5
1,329.5
727.7
601.8
1,122.6
336.4
786.2
976.7
223.5
753.2
43.5
5,197.1
2,145.7
2010
1,736.5
1,732.0
4.5
1,416.5
775.7
640.8
1,174.8
334.7
840.1
993.2
220.2
773.0
46.2
5,367.1
2,258.4
2011
1,715.8
1,711.5
4.3
1,398.8
774.1
624.7
1,117.9
327.2
790.7
959.1
221.0
738.0
39.8
5,231.3
2,157.7
1
1
1
1
5
2
2012
,704.6
,700.8
3.9
,377.0
784.2
592.8
,008.4
283.1
725.3
897.4
197.1
700.3
38.6
,026.0
,022.2
1
1
1
1
5
2
2013
,722.4
,718.4
4.0
,399.8
817.3
582.5
,070.2
329.6
740.6
933.3
220.7
712.6
32.0
1577
,039.8
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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.
Trends 2-13
-------
Figure 2-7: 2013 COz Emissions from Fossil Fuel Combustion by Sector and Fuel Type
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
$
8
2,50(1
2,000 -
1,500 -
1,000 -
51)11
0
Relative Contribution
by Fuel Type
1,718
Petroleum
• Coal
• Natural Gas
32
Figure 2-8: 2013 End-Use Sector Emissions of COz from Fossil Fuel Combustion
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
3
2,500
2,000 -
1,500 -
1,00(1
500
0
Relative Contribution
by Fuel Type
1,718
2,(MO
The main driver of emissions in the Energy sector is CO2 from fossil fuel combustion. Electricity generation is the
largest emitter of CC>2, and electricity generators consumed 34 percent of U.S. energy from fossil fuels and emitted
40 percent of the CCh from fossil fuel combustion in 2013. 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 1,722.4 MMT CC>2 Eq. in 2013 or approximately 33 percent of total €62 emissions from fossil fuel
combustion. The industrial end-use sector accounted for 27 percent of €62 emissions from fossil fuel combustion.
The residential and commercial end-use sectors accounted for 21 and 18 percent, respectively, of €62 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 69
and 76 percent of emissions from the residential and commercial end-use sectors, respectively. Significant trends in
emissions from energy source categories over the twenty four-year period from 1990 through 2013 included the
following:
• Total CO2 emissions from fossil fuel combustion increased from 4,740.7 MMT CO2 Eq. in 1990 to 5,157.7
MMT CO2 Eq. in 2013 - an 8.8 percent total increase over the twenty four-year period. From 2012 to
2013, these emissions increased by 131.7 MMT CO2 Eq. (2.6 percent).
• CH4 emissions from natural gas systems were the second largest anthropogenic source of CH4 emissions in
the United States with 157.4 MMT €62 Eq. emitted into the atmosphere in 2013; emissions have decreased
by 21.8 MMT CO2 Eq. (12.2 percent) since 1990.
2-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
• CO2 emissions from non-energy use of fossil fuels increased by 2.2 MMT CC>2 Eq. (1.9 percent) from 1990
through 2013. Emissions from non-energy uses of fossil fuels were 119.8 MMT CC>2 Eq. in 2013, which
constituted 2.2 percent of total national CC>2 emissions.
• N2O emissions from stationary combustion increased by 11.0 MMT CO2 Eq. (91.9 percent) from 1990
through 2013. 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 (10.1 MMT CChEq. in 2013) increased by 2.2 MMT CChEq.
(27.2 percent) from 1990 through 2013, as the volume of plastics and other fossil carbon-containing
materials in municipal solid waste grew.
The increase in €62 emissions from fossil fuel combustion in 2013 was a result of multiple factors including: (1) the
increase in the price of natural gas led to an increase of coal-fired generation in the electric power sector; (2) much
colder winter conditions resulted in an increased demand for heating fuel in the residential and commercial sectors;
(3) an increase in industrial production across multiple sectors which resulted in increases in industrial sector
emissions,1 and (4) an increase in transportation emissions resulting from a small increase in vehicle miles traveled
(VMT) and fuel use across on-road transportation modes.
Industrial Processes and Product Use
The Industrial Processes and Product Use (IPPU) chapter includes greenhouse gas emissions occurring from
industrial processes and from the use of greenhouse gases in products. This section includes sources of emissions
formerly represented in the "Industrial Processes" and "Solvent and Other Product Use" sectors in prior versions of
this report.
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 €62,
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, €62 consumption, silicon carbide production and
consumption, aluminum production, petrochemical production, nitric acid production, adipic acid production, lead
production, zinc production, and N2O from product uses (see Figure 2-9). Industrial processes also release HFCs,
PFCs, SF6, and NF3. In addition to their use as ODS substitutes, HFCs, PFCs, SF6, NF3, 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.
Further details on industrial sector combustion emissions are provided by EPA's GHGRP
(http://ghgdata.epa.gov/ghgp/main.do).
Trends 2-15
-------
Figure 2-9: 2013 Industrial Processes and Product Use Chapter Greenhouse Gas Sources
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Substitution of Ozone Depleting Substances j^^^^^^^^^^^^^^^H^^^^^^^^^^^^^K ^H 159
Iron and Steel Prod. & Metallurgical Coke Prod.
Cement Production
Petrochemical Production
Lime Production
Nitric Acid Production
Ammonia Production
Aluminum Production
Urea Consumption for Non-Agricultural Purposes
Ottier Process Uses of Carbonates
N2O from Product Uses
Semiconductor Manufacture
HCFC-22 Production
Adipic Acid Production
Soda Ash Production and Consumption
Ferroalloy Production
Titanium Dioxide Production
Magnesium Production and Processing
Zinc Production
Phosphoric Acid Production
Glass Production
Carbon Dioxide Consumption
Lead Production
Silicon Carbide Production and Consumption
Industrial Processes and Product Use as a Portion
of all Emissions
5,4%
r
< 0.5
10
20
30 40
MMT CO2 Eq.
50
60
70
Table 2-6: Emissions from Industrial Processes and Product Use (MMT COz Eq.)
Gas/Source
1990
2005
2009 2010 2011 2012 2013
CO2 207.2 191.1
Iron and Steel Production & Metallurgical Coke
Production 99.81 66.7
Iron and Steel Production 97.3 64.6
Metallurgical Coke Production 2.5 2.0
Cement Production 33.3 45.9
Petrochemical Production 21.6 28.1
Lime Production ll.?H 14.6
Ammonia Production 13.Ill 9.2
Urea Consumption for Non-Agricultural
Purposes 3.8 3.7
Other Process Uses of Carbonates 4.9 6.3
Aluminum Production 6.8 4.1
Soda Ash Production and Consumption 2.7 2.9
Ferroalloy Production 2.2 1.4
Titanium Dioxide Production 1.2 l.S
Zinc Production 0.6 1.0
Phosphoric Acid Production 1.61 1.4
Glass Production 1.5 1.9
Carbon Dioxide Consumption 1.5 1.4
Lead Production 0.5 0.6
Silicon Carbide Production and Consumption 0.4 0.2
Magnesium Production and Processing +1 +1
CH4 1.4| 1.01
141.1 165.7 169.7 166.4 163.0
43.0
42.1
1.0
29.4
23.7
11.4
8.5
55.7
53.7
2.1
31.3
27.4
13.4
9.2
60.0
58.6
1.4
32.0
26.4
14.0
9.3
54.3
53.8
0.5
35.1
26.5
13.7
9.4
52.3
50.5
1.8
36.1
26.5
14.1
10.2
3.4
7.6
3.0
2.5
1.5
1.6
0.9
1.0
1.0
1.8
0.5
0.1
+
0.5
4.7
9.6
2.7
2.6
1.7
1.8
1.2
1.1
1.5
1.2
0.5
0.2
+
0.7
4.0
9.3
3.3
2.6
1.7
1.7
1.3
1.2
1.3
0.8
0.5
0.2
+
0.8
4.4
8.0
3.4
2.7
1.9
1.5
1.5
1.1
1.2
0.8
0.5
0.2
+
0.8
4.7
4.4
3.3
2.7
1.8
1.6
1.4
1.2
1.2
0.9
0.5
0.2
+
0.8
2-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Iron and Steel Production & Metallurgical Coke
Production
Iron and Steel Production
Metallurgical Coke Production
Petrochemical Production
Ferroalloy Production
Silicon Carbide Production and Consumption
N2O
Nitric Acid Production
N2O from Product Uses
Adipic Acid Production
Semiconductor Manufacturing
HFCs
Substitution of Ozone Depleting Substances*
HCFC-22 Production
Semiconductor Manufacturing
Magnesium Production and Processing
PFCs
Aluminum Production
Semiconductor Manufacturing
SF6
Electrical Transmission and Distribution
Magnesium Production and Processing
Semiconductor Manufacturing
NF3
Semiconductor Manufacturing
Total
342.1
367.4
314.9 353.6 371.0 361.2 359.1
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
+ Does not exceed 0.05 MMT CO2 Eq.
a Small amounts of PFC emissions also result from this source.
Note: Totals may not sum due to independent rounding.
Overall, emissions from the IPPU sector increased by 5.0 percent from 1990 to 2013. Significant trends in emissions
from IPPU source categories over the twenty four-year period from 1990 through 2013 included the following:
• HFC emissions from ODS substitutes have been increasing from small amounts in 1990 to 158.6 MMT
CO2 Eq. in 2013. 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.
• Combined CO2 and CH4 emissions from iron and steel production and metallurgical coke production
decreased by 3.8 percent to 53.0 MMT CO2 Eq. from 2012 to 2013, and have declined overall by 47.9
MMT CO2 Eq. (47.5 percent) from 1990 through 2013, due to restructuring of the industry, technological
improvements, and increased scrap steel utilization.
• CO2 emissions from ammonia production (10.2 MMT CO2 Eq. in 2013) decreased by 2.9 MMT CO2 Eq.
(22.2 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 (4.7 MMT CO2 Eq. in 2013) increased by 0.9 MMT CO2
Eq. (23.2 percent) since 1990.
• In 2013, N2O emissions from product uses constituted 1.2 percent of U.S. N2O emissions. From 1990 to
2013, emissions from this source category decreased by 0.4 percent, though slight increases occurred in
intermediate years.
Trends 2-17
-------
N2O emissions from adipic acid production were 4.0 MMT CC>2 Eq. in 2013, 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 73.8
percent since 1990 and by 76.4 percent since a peak in 1995.
PFC emissions from aluminum production decreased by 86.2 percent (18.5 MMT CO2 Eq.) from 1990 to
2013, due to both industry emission reduction efforts and lower domestic aluminum production.
Agriculture
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 2013, agricultural activities were responsible for emissions of 515.7 MMT €62 Eq., or 7.7 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 25.9 percent and 9.6 percent of total
CH4 emissions from anthropogenic activities, respectively, in 2013. Agricultural soil management activities, such as
fertilizer use and other cropping practices, were the largest source of U.S. N2O emissions in 2013, accounting for
74.2 percent.
Figure 2-10: 2013 Agriculture Chapter Greenhouse Gas Sources
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Agricultural Soil Management
Enteric Fermentation
Manure Management
Rice Cultivation
Field Burning of Agricultural Residues
264
Agriculture as a Portion of all Emissions
7.7%
< 0.5
0 50
Table 2-7: Emissions from Agriculture (MMT COz Eq.)
100
150
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
Note: Emissions values are present
1990
210.8
164.2
37.2
9.2
0.3
237.9
224.0
13.8
0.1
448.7
ed in CO2 ec
2005
234.4
168.9
56.3
8.9
0.2
260.1
243.6
16.4
0.1
494.5
2009
242.1
172.7
59.7
9.4
0.3
281.2
264.1
17.0
0.1
523.3
juivalent mass units using IPCC
2010
243.4
171.1
60.9
11.1
0.3
281.4
264.3
17.1
0.1
524.8
2011
238.9
168.7
61.4
8.5
0.3
283.2
265.8
17.3
0.1
522.1
2012
239.6
166.3
63.7
9.3
0.3
283.4
266.0
17.3
0.1
523.0
2013
234.5
164.5
61.4
8.3
0.3
281.1
263.7
17.3
0.1
515.7
AR4 GWP values.
Note: Totals may not sum due to independent rounding.
2-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Some significant trends in U.S. emissions from Agriculture source categories include the following:
• Agricultural soils produced approximately 74.2 percent of N2O emissions in the United States in 2013.
Estimated emissions from this source in 2013 were 263.7 MMT CO2 Eq. Annual N2O emissions from
agricultural soils fluctuated between 1990 and 2013, although overall emissions were 17.7 percent higher in
2013 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 is the largest anthropogenic source of CH4 emissions in the United States. In 2013,
enteric fermentation CH4 emissions were 164.5 MMT CCh Eq. (25.9 percent of total CH4 emissions),
which represents an increase of 0.4 MMT CC>2 Eq. (0.2 percent) since 1990. This increase in emissions
from 1990 to 2013 in enteric generally follows the increasing trends in cattle populations. From 1990 to
1995 emissions increased and then generally decreased from 1996 to 2004, mainly due to fluctuations in
beef cattle populations and increased digestibility of feed for feedlot cattle. Emissions 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 2013 as beef cattle populations again decreased.
• Overall, emissions from manure management increased 54.4 percent between 1990 and 2013. This
encompassed an increase of 65.2 percent for CH4, from 37.2 MMT CO2 Eq. in 1990 to 61.4 MMT CO2 Eq.
in 2013; and an increase of 25.4 percent for N2O, from 13.8 MMT CO2 Eq. in 1990 to 17.3 MMT CO2 Eq.
in 2013. The majority of the increase observed in CH4 resulted from swine and dairy cow manure, where
emissions increased 48 and 115 percent, respectively, from 1990 to 2013. From 2012 to 2013, there was a
3.6 percent decrease in total CH4 emissions from manure management, 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 a net removal of €62 (sequestration of C) in the United States. Forests (including
vegetation, soils, and harvested wood) accounted for approximately 88 percent of total 2013 €62 removals, urban
trees accounted for 10 percent, mineral and organic soil carbon stock changes accounted for less than 0.5 percent,
and landfilled yard trimmings and food scraps accounted for 1.4 percent of total €62 removals in 2013. 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 2.4 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 2013 resulted in a C sequestration (i.e., total sinks) of 881.7
MMT CO2 Eq. (Table 2-3).2 This represents an offset of approximately 13.2 percent of total (i.e., gross) greenhouse
gas emissions in 2013. Emissions from land use, land-use change and forestry activities in 2013 represent 0.3
percent of total greenhouse gas emissions.3 Between 1990 and 2013, total land use, land-use change, and forestry C
sequestration increased by 13.6 percent, 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.
The total sinks value includes the positive C sequestration reported for Forest Land Remaining Forest Land, Cropland
Remaining Cropland, Land Converted to Grassland, Settlements Remaining Settlements, and Other Land plus the loss in C
sequestration reported for Land Converted to Cropland and Grassland Remaining Grassland.
The emissions value includes the CCh, CH4, andN2O emissions reported for Forest Fires, Forest Soils, Liming of Agricultural
Soils, Urea Fertilization, Settlement Soils, and Peatlands Remaining Peatlands.
Trends 2-19
-------
CO2 removals are presented in Table 2-8 along with CO2, CH4, and N2O emissions for Land Use, Land-Use Change,
and Forestry source categories. Liming of agricultural soils and urea fertilization resulted in CO2 emissions of 9.9
MMT CO2 Eq. in 2013, an increase of about 40.3 percent relative to 1990. Lands undergoing peat extraction (i.e.,
Peatlands Remaining Peatlands) resulted in CO2 emissions of 0.8 MMT CO2Eq. and CH4 and N2O emissions of
less than 0.05 MMT CO2 Eq. each. N2O emissions from the application of synthetic fertilizers to forest soils have
increased from 0.1 MMT CO2 Eq. in 1990 to 0.5 MMT CO2 Eq. in 2013. Settlement soils in 2013 resulted in N2O
emissions of 2.4 MMT CO2Eq., a 76.7 percent increase relative to 1990. Emissions from forest fires in 2013
resulted in CH4 emissions of 5.8 MMT CO2 and inN2O emissions of 3.8 MMT CO2 (see Table 2-8).
Table 2-8: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry
(MMT COz Eq.)
Gas/Land-Use Category
C02
Forest Land Remaining Forest Land:
Changes in Forest Carbon Stocka
1990
(767.7)
(639.4)1
2005
(903.0)
(807.1)
2009
(862.6)
(764.9)
2010
(862.0)
(765.4)
2011
(872.1)
(773.8)
2012
(869.6)
(773.1)
2013
(871.0)
(775.7)
Cropland Remaining Cropland:
Changes in Agricultural Soil Carbon
Stock (65.2)
Cropland Remaining Cropland:
Liming of Agricultural Soils 4.7
Cropland Remaining Cropland:
Urea Fertilization 2.4
Land Converted to Cropland 24.5
Grassland Remaining Grassland (1.9)
Land Converted to Grassland (7.4)
Settlements Remaining Settlements:
Changes in Urban Lree Carbon Stockb (60.4)
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands 1.0
Other:
Landfilled Yard Lrimmings and Food
Scraps (24.2)
CH4 2.5
Forest Land Remaining Forest Land:
Forest Fires 2.5
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands
N2O 3.1
Forest Land Remaining Forest Land:
Forest Fires 1.7
Forest Land Remaining Forest Land:
Forest Soils0 0.1
Settlements Remaining Settlements:
Settlement Soilsd 1.4
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands
(28.0)
4.31
3.5l
19.8J
4.2l
(9.0)1
(80.5)
"
(12.0)
8.3
(27.5)
3.7
3.6
16.2
11.7
(8.9)
(85.0)
1.0
(25.9)
4.8
3.8
16.2
11.7
(8.9)
(86.1)
1.0
(25.8)
3.9
4.1
16.2
11.7
(8.9)
(87.3)
0.9
(25.0)
5.8
4.2
16.1
11.5
(8.8)
(88.4)
0.8
(23.4)
5.9
4.0
16.1
12.1
(8.8)
(89.5)
0.8
2.3
I
(12.9)
5.8
5.8
6.5
3.8
0.5
2.2
(13.6)
4.8
4.7
6.0
3.1
0.5
2.4
(13.5)
14.6
14.6
12.6
9.6
0.5
2.5
(13.0)
15.7
15.7
13.3
10.3
0.5
2.5
(12.8)
5.8
5.8
6.7
3.8
0.5
2.4
Total Flux e
(762.1)
(886.4)
(850.2) (851.3) (844.9) (840.6) (858.5)
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
+ Less than 0.05 MMT CO2 Eq.
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.
0 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.
d Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements, and Land Converted
to Settlements, but not from land-use conversion.
2-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
e "Total Flux" is defined as the sum of positive emissions (i.e., sources) of greenhouse gases to the atmosphere plus
removals of CCh (i.e., sinks or negative emissions) from the atmosphere.
Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
Other significant trends from 1990 to 2013 in emissions from land use, land-use change, and forestry source
categories include:
• Annual C sequestration by forest land (i.e., annual carbon stock accumulation in the five carbon pools) has
increased by approximately 21 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 bio mass 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 four-years, although only at an average rate of 0.1 percent per year.
• Annual C sequestration by urban trees has increased by 48.1 percent over the period from 1990 to 2013.
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 51.6 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 2013, 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 2013,
landfills were the third largest source of U.S. anthropogenic CH4 emissions, accounting for 18.0 percent of total U.S.
CH4 emissions.4 Additionally, wastewater treatment accounts for 14.4 percent of Waste emissions, 2.4 percent of
U.S. CH4 emissions, and 1.4 percent of N2O emissions. Emissions of CEU and N2O from composting grew from
1990 to 2013, and resulted in emissions of 3.7 MMT CO2 Eq. in 2013. A summary of greenhouse gas emissions
from the Waste chapter is presented in Table 2-9.
Figure 2-11: 2013 Waste Chapter Greenhouse Gas Sources
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Landfills
Wastewater Treatment
Composting
Waste as a Portion of all Emissions
2.1%
40 60 80 100 120
MMT CO, Eq.
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 2-21
-------
Overall, in 2013, waste activities generated emissions of 138.3 MMT CO2 Eq., or 2.1 percent of total U.S.
greenhouse gas emissions.
Table 2-9: Emissions from Waste (MMT COz Eq.)
Gas/Source
CH4
Landfills
Wastewater Treatment
Composting
N20
Wastewater Treatment
Composting
Total
1990
202.3
186.2B
15.7M
0.4
3.7
3.4
0.3
206.0
2005
183.2
165.5
15.9
1.9l
6.ol
4.3|
1.7
189.2
2009
175.5
158.1
15.6
1.9
6.3
4.6
1.7
181.8
2010
139.1
121.8
15.5
1.8
6.4
4.7
1.6
145.5
2011
138.4
121.3
15.3
1.9
6.5
4.8
1.7
144.9
2012
132.4
115.3
15.2
1.9
6.6
4.9
1.7
138.9
2013
131.6
114.6
15.0
2.0
6.7
4.9
1.8
138.3
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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 2013, net CH4 emissions from landfills decreased by 71.6 MMT CO2 Eq. (38.4 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,5 which has more than offset the additional CH4 emissions resulting from an
increase in the amount of municipal solid waste landfilled.
• Combined CEU and N2O emissions from composting have generally increased since 1990, from 0.7 MMT
CO2 Eq. to 3.7 MMT CO2 Eq. in 2013, which represents slightly more 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 2013, CEU and N2O emissions from wastewater treatment decreased by 0.6 MMT CO2 Eq.
(4.0 percent) and increased by 1.6 MMT CO2 Eq. (46.5 percent), respectively. Methane emissions from
domestic wastewater treatment have decreased since 1999 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.
2.1 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 (31 percent) of
U.S. greenhouse gas emissions in 2013. Transportation activities, in aggregate, accounted for the second largest
portion (27 percent). Emissions from industry accounted for about 21 percent of U.S. greenhouse gas emissions in
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.
2-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
2013. 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 6 percent, and
primarily consisted of CC>2 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 6 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 2-10 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 2013.
Figure 2-12: Emissions Allocated to Economic Sectors
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
2,500
2,000
=f 1,500 -
8
1,000
500
Electric
Power Industry
Transportation
Industry
Agriculture
i Commercial (Red)
Residential (Blue!
••
$ $ 8
S §
8
Table 2-10: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (MMT COz Eq. and
Percent of Total in 2013)
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
1990
1,864.8
1,820.8
7.7
8.4
25.4
2.5
1,551.3
1,493.8
45.7
11.8
1,587.7
811.4
216.8
100.1
2005
2,443.9
2,400.9
16.5
12.8
10.6
3.2
2,017.7
1,887.8
80.4
39.4
16.2 1
1,462.8
781.0
206.3
120.6
2009
2,185.7
2,145.7
17.2
11.6
7.3
3.8
1,835.3
1,720.3
81.4
25.1
8.5
1,272.5
681.2
200.2
93.5
2010
2,300.5
2,258.4
18.9
11.4
7.0
4.8
1,843.5
1,732.0
77.9
24.1
9.5
1,353.3
728.2
191.9
100.9
2011
2,198.1
2,157.7
18.0
10.9
6.8
4.7
1,815.4
1,711.5
72.0
22.9
9.0
1,353.0
724.9
194.8
95.8
2012
2,060.8
2,022.2
18.2
10.7
5.7
4.0
1,795.9
1,700.8
66.3
20.5
8.3
1,338.9
733.5
189.2
93.3
2013
2,077.0
2,039.8
19.5
10.4
5.1
2.2
1,806.2
1,718.4
60.5
18.6
8.8
1,392.1
767.6
195.2
108.4
Percent3
31.1%
30.6%
0.3%
0.2%
0.1%
+
27.1%
25.8%
0.9%
0.3%
0.1%
20.9%
11.5%
2.9%
1.6%
Trends 2-23
-------
Coal Mining 96.5
Iron and Steel Production 100.9
Cement Production 33.3
Petroleum Systems 36.0
Petrochemical Production 21.9
Substitution of Ozone Depleting
Substances +1
Lime Production 11.7
Nitric Acid Production 12.1
Ammonia Production 13.0
Abandoned Underground Coal Mines 7.2
Aluminum Production 28.3
Urea Consumption for Non-Agricultural
Purposes 3.8
N2O from Product Uses 4.2
Semiconductor Manufacture 3.6
HCFC-22 Production 46.1
Adipic Acid Production 15.2
Stationary Combustion 4.9
Soda Ash Production and Consumption 2.7
Other Process Uses of Carbonates 2.5
Ferroalloy Production 2.2
Titanium Dioxide Production 1.2
Magnesium Production and Processing 5.2
Mobile Combustion 0.9
Zinc Production 0.6
Phosphoric Acid Production 1.6
Glass Production 1.5
Carbon Dioxide Consumption 1.5
Lead Production 0.5
Silicon Carbide Production and
Consumption 0.4
Agriculture 492.5
N2O from Agricultural Soil Management 224.0
Enteric Fermentation 164.2
Manure Management 51.0
CO2 from Fossil Fuel Combustion 31.0
CELi and N2O from Forest Fires 4.2
Rice Cultivation 9.2
Liming of Agricultural Soils 4.7
Urea Fertilization 2.4
CO2, CH4 and N2O from Managed
Peatlands 1.1
Mobile Combustion 0.3
Field Burning of Agricultural Residues 0.4
N2O from Forest Soils 0.1
Stationary Combustion +1
Commercial 424.8
CO2 from Fossil Fuel Combustion 217.4
Landfills 186.2
Substitution of Ozone Depleting
Substances +1
Wastewater Treatment 15.7
Human Sewage 3.4
Composting 0.7
Stationary Combustion 1.4
Residential 346.3
CO2 from Fossil Fuel Combustion 338.3
|64.l|
67.5
4,5'9
64.1
67.5
45.9
28.4
28.3
73
14.6
11.3
9.2
6.6
'
47
20.0l
71
4.6
2.9
32
1.4
1.8
27
13
1.0
1.4
1.9
1.4
0.6
I
AM
/z.o™
»46.8
11 vm
0.2
565.0
243.6
168.9
72.8
46.8
13.8
8.9J
4.3
35
'
2:
-
429.8
223.5
165.5
15.7
15.9
4.3|
3.5
1.4
372.8
357.
79.9
43.5
29.4
26.2
23.8
82.3
56.4
31.3
25.5
27.4
71.2
60.7
32.0
26.4
26.4
66.5
55.1
35.1
28.3
26.5
64.6
53.0
36.1
31.2
26.6
1.0%
0.8%
0.5%
0.5%
0.4%
12.4
11.4
9.6
8.5
6.4
4.9
15.3
13.4
11.5
9.2
6.6
4.6
17.0
14.0
10.9
9.3
6.4
6.8
18.7
13.7
10.5
9.4
6.2
6.4
20.4
14.1
10.7
10.2
6.2
6.2
0.3%
0.2%
0.2%
0.2%
0.1%
0.1%
3.4
4.2
3.1
6.8
2.7
3.6
2.5
3.8
1.5
1.6
1.6
1.3
0.9
1.0
1.0
1.8
0.5
4.7
4.2
3.8
8.0
4.2
3.9
2.6
4.8
1.7
1.8
2.1
1.4
1.2
1.1
1.5
1.2
0.5
4.0
4.2
4.9
8.8
10.2
3.9
2.6
4.7
1.7
1.7
2.8
1.4
1.3
1.2
1.3
0.8
0.5
4.4
4.2
4.5
5.5
5.5
3.9
2.7
4.0
1.9
1.5
1.7
1.4
1.5
1.1
1.2
0.8
0.5
4.7
4.2
4.2
4.1
4.0
3.9
2.7
2.2
1.8
1.6
1.5
1.5
1.4
1.2
1.2
0.9
0.5
1.0
0.5
0.4
0.5
1.0
0.5
0.4
0.5
0.9
0.5
0.4
0.5
0.8
0.6
0.4
0.5
0.8
0.5
0.4
0.5
431.9
223.5
158.1
396.4
220.2
121.8
400.7
221.0
121.3
374.3
197.1
115.3
401.1
220.7
114.6
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.2
588.8
264.1
172.7
76.7
46.5
9.7
9.4
3.7
3.6
0.2
590.8
264.3
171.1
78.0
47.5
7.9
11.1
4.8
3.8
0.2
605.5
265.8
168.7
78.7
49.2
24.2
8.5
3.9
4.1
0.2
611.6
266.0
166.3
81.0
50.7
26.0
9.3
5.8
4.2
0.2
586.8
263.7
164.5
78.7
49.7
9.7
8.3
5.9
4.0
+
8.8%
4.0%
2.5%
1.2%
0.7%
0.1%
0.1%
0.1%
0.1%
6.0%
3.3%
1.7%
25.2
15.6
4.6
3.6
1.4
360.9
336.4
29.3
15.5
4.7
3.5
1.4
363.7
334.7
33.4
15.3
4.8
3.5
1.4
360.5
327.2
37.1
15.2
4.9
3.7
1.2
321.5
283.1
40.8
15.0
4.9
3.7
1.3
375.0
329.6
0.6%
0.2%
0.1%
0.1%
+
5.6%
4.9%
2-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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
Sinks
CO2 Flux from Forestsb
Urban Trees
Landfilled Yard Trimmings and Food
Scraps
CO2 Flux from Agricultural Soil Carbon
Stocks
Net Emissions
I AM
33.7
27.9 •
5.7
0.1
6,301.1
(775.8)
(639.4)
(60.4)
(26.0)|
(50.0)
5,525.2
7.7|
4.9
2.3
58.2
49.9
8.1
0.2
7,350.2
(911.9)
(807.1)
(80.5)(
(11.4)1
(13.0)
6,438.3
17.0
5.3
2.2
47.6
43.5
3.9
0.2
6,722.7
(870.9)
(764.9)
(85.0)
(12.5)
(8.5)
5,851.9
21.8
4.8
2.4
50.6
46.2
4.2
0.2
6,898.8
(871.6)
(765.4)
(86.1)
(13.2)
(6.9)
6,027.2
25.9
4.9
2.5
43.5
39.8
3.6
0.2
6,776.6
(881.0)
(773.8)
(87.3)
(13.2)
(6.7)
5,895.6
31.4
4.5
2.5
42.1
38.6
3.3
0.2
6,545.1
(880.4)
(773.1)
(88.4)
(12.8)
(6.1)
5,664.7
37.0
5.9
2.4
34.8
32.0
2.7
0.1
6,673.0
(881.7)
(775.7)
(89.5)
(12.6)
(4.0)
5,791.2
0.6%
0.1%
+
0.5%
0.5%
+
+
100.0%
-13.2%
-11.6%
-1.3%
-0.2%
-0.1%
86.8%
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Includes all emissions of CCh, CELi, N2O, HFCs, PFCs, SFe, and NFs. Parentheses indicate negative values or sequestration. Totals
may not sum due to independent rounding.
+ Does not exceed 0.05 MMT CO2 Eq. or 0.05 percent.
a Percent of total emissions for year 2013.
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 31 percent of total U.S. greenhouse gas
emissions in 2013. 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 increased from 2012
to 2013 by 0.8 percent, primarily due to increased €62 emissions from fossil fuel combustion. Electricity sales to
the residential and commercial end-use sectors in 2013 increased approximately 1.2 percent and 0.9 percent,
respectively. The trend in the residential and commercial sectors can largely be attributed to colder more energy-
intensive winter conditions compared to 2012. Electricity sales to the industrial sector in 2013 decreased by
approximately 3.1 percent. Overall, in 2013, the amount of electricity generated (in kWh) decreased by 0.1 percent
from the previous year. Despite the decrease in generation, €62 emissions from the electric power sector increased
by 0.8 percent as the consumption of €62 intensive coal and petroleum for electricity generation increased by 4.2
percent and 18.8 percent, respectively, in 2013 and the consumption of natural gas for electricity generation,
decreased by 10.2 percent. Table 2-11 provides a detailed summary of emissions from electricity generation-related
activities.
Table 2-11: Electricity Generation-Related Greenhouse Gas Emissions (MMT COz Eq.)
Gas/Fuel Type or Source 1990 2005 2009 2010 2011 2012 2013
C02 1,831.2 2,416.5
Fossil Fuel Combustion 1,820.8 2,400.9
Coal 1,547.6 1,983.8
Natural Gas 175.3 318.8
Petroleum 97.5 97.9
Geothermal 0.4M 0.4
Incineration of Waste 8.oB 12.5
Other Process Uses of
Carbonates 2.sB 3.2
CH4 0.3 0.5
2,160.7
2,145.7
1,740.9
372.2
32.2
0.4
11.3
3.8
0.4
2,274.2 2,172.9 2,036.6
2,258.4 2,157.7 2,022.2
1,827.6 1,722.7 1,511.2
399.0 408.8 492.2
31.4 25.8 18.3
0.4 0.4 0.4
11.0 10.5 10.4
4.8
0.5
4.7
0.4
4.0
0.4
2,052.1
2,039.8
1,575.0
441.9
22.4
0.4
10.1
2.2
0.4
Trends 2-25
-------
Stationary Combustion*
Incineration of Waste
N2O
Stationary Combustion*
Incineration of Waste
SF6
Electrical Transmission and
Distribution
Total
0.4
+
17.1
16.8
0.3
7.3
7.3
0.5
+
18.8
18.5
0.3
7.0
7.0
0.4
+
17.9
17.6
0.3
6.8
0.4
+
18.1
17.8
0.3
5.7
5.7
0.4
+
19.4
19.1
0.3
5.1
5.1
1,864.8
2,443.9
2,185.7 2,300.5 2,198.1 2,060.8 2,077.0
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding.
a Includes only stationary combustion emissions related to the generation of electricity.
+ Does not exceed 0.05 MMT 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
(EIA 2015, Duffield 2006). These source categories include CCh 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.6
When emissions from electricity are distributed among these sectors, industrial activities account for the largest
share of total U.S. greenhouse gas emissions (28.8 percent), followed closely by emissions from transportation (27.1
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 2-12 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
2013.
Figure 2-13: Emissions with Electricity Distributed to Economic Sectors
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
2,500
Industry (Green)
Transportation
(Purple)
Residential (Red)
Commercial (Blue)
Agriculture
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.
2-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 2-12: U.S. Greenhouse Gas Emissions by Economic Sector and Gas with Electricity-
Related Emissions Distributed (MMT COz Eq.) and Percent of Total in 2013
Sector/Gas
Industry
Direct Emissions
C02
CH4
N20
FIFCs, PFCs, SF6,
andNFs
Electricity-Related
C02
CH4
N20
SF6
Transportation
Direct Emissions
C02
CH4
N20
FIFCsb
Electricity-Related
CO2
CH4
N2O
SF6
Commercial
Direct Emissions
CO2
CH4
N2O
FIFCs
Electricity-Related
C02
CH4
N20
SF6
Residential
Direct Emissions
C02
CH4
N20
FIFCs
Electricity-Related
CO2
CH4
N2O
SF6
Agriculture
Direct Emissions
C02
CH4
N20
Electricity-Related
C02
CH4
N20
SF6
1990
2,229.7
1,587.7
1,158.3
317.7
35.3
76.3
642.0
630.41
0.1
2.7
8.7
1,554.4
1,551.3
1,505.6
5.4l
40.261
3.1
:
975.8
424.8
217.4!
203.3!
4.1
1
551.0
541. ll
Oil
2.3
7.5
953.6
346.3
338.3!
5.2
2.4
0.3M
607.3
596.4!
O.l|
2.5
8.3
553.9
492.5
39.21
213.4!
239.9!
61.31
60.2
.
0.8
2005
2,148.5
1,462.8
1,124.5
273.4
26.6
38.3
685.71
678.ol
0.1
4.6
3.0
2,022.5
2,017.7
1,898.0
2.7l
36.701
80.4
4.8
4.8
1,247.5
429.8
223.5!
184.31
6.3
15.7
817.71
808.51
0.2
5.5
3.5
1,244.4
372.8
357.8|
1 ll
3.2
7.7B
871.61
861.91
0.2|
5.8
3.8
629.1
565.0
55.71
242.91
266AM
64. ll
63.4
+1
0.4
0.3
2009
1,817.7
1,272.5
948.9
277.8
19.9
25.8
545.2
539.0
0.1
4.3
1.8
1,839.9
1,835.3
1,728.9
2.0
23.12
81.4
4.6
4.5
1,199.2
431.9
223.5
176.6
6.6
25.2
767.2
758.5
0.2
6.0
2.6
1,161.8
360.9
336.4
4.4
3.2
17.0
800.9
791.7
0.2
6.3
2.7
656.6
588.8
54.8
248.2
285.9
67.8
67.0
+
0.5
0.2
2010
1,937.7
1,353.3
1,026.5
272.2
23.6
31.1
584.4
577.7
0.1
4.8
1.8
1,848.1
1,843.5
1,741.5
1.9
22.20
77.9
4.6
4.5
1,183.8
396.4
220.2
140.2
6.7
29.3
787.4
778.4
0.2
6.4
2.4
1,219.5
363.7
334.7
4.0
3.2
21.8
855.8
846.0
0.2
7.0
2.6
659.2
590.8
57.1
248.4
285.4
68.4
67.6
+
0.6
0.2
2011
1,923.9
1,353.0
1,025.7
261.3
28.9
37.0
571.0
564.4
0.1
4.7
1.8
1,819.7
1,815.4
1,720.5
1.9
20.98
72.0
4.3
4.3
1,152.6
400.7
221.0
139.4
6.8
33.4
751.9
743.3
0.1
6.1
2.3
1,166.0
360.5
327.2
4.0
3.3
25.9
805.5
796.3
0.2
6.6
2.5
670.9
605.5
58.1
253.7
293.6
65.4
64.7
+
0.5
0.2
2012
1,880.9
1,338.9
1,029.0
252.9
23.8
33.3
542.0
535.6
0.1
4.8
1.5
1,799.8
1,795.9
1,709.1
1.8
18.68
66.3
3.9
3.9
1,088.0
374.3
197.1
133.3
6.8
37.1
713.6
705.2
0.1
6.3
2.0
1,060.6
321.5
283.1
3.7
3.3
31.4
739.1
730.4
0.2
6.5
2.0
673.7
611.6
61.5
255.5
294.6
62.1
61.4
+
0.5
0.2
2013
1,922.6
1,392.1
1,080.6
255.9
22.5
33.1
530.5
524.2
0.1
5.0
1.3
1,810.3
1,806.2
1,727.2
1.7
16.84
60.5
4.1
4.0
1,126.7
401.1
220.7
132.7
7.0
40.8
725.6
716.9
0.1
6.8
1.8
1,129.1
375.0
329.6
5.0
3.4
37.0
754.2
745.1
0.2
7.1
1.9
649.4
586.8
60.4
240.6
285.8
62.6
61.9
+
0.6
0.2
Percent3
28.8%
20.9%
16.2%
3.8%
0.3%
0.5%
8.0%
7.9%
+
0.1%
+
27.1%
27.1%
25.9%
+
0.3%
0.9%
0.1%
0.1%
16.9%
6.0%
3.3%
2.0%
0.1%
0.6%
10.9%
10.7%
0.1%
+
16.9%
5.6%
4.9%
0.1%
0.1%
0.6%
11.3%
11.2%
+
0.1%
+
9.7%
8.8%
0.9%
3.6%
4.3%
0.9%
0.9%
+
+
+
Trends 2-27
-------
U.S. Territories 33.7 58.2 47.6 50.6 43.5 42.1 34.8 0.5%
Total 6,301.1 7,350.2 6,722.7 6,898.8 6,776.6 6,545.1 6,673.0 100.0%
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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 MMT CO2 Eq. or 0.05 percent.
a Percent of total emissions for year 2013.
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 byproduct 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, SF6, and NF3 byproduct 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. 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 27 percent of U.S. greenhouse gas emissions in 2013. The largest sources of transportation greenhouse gases in
2013 were passenger cars (42.2 percent), freight trucks (22.5 percent), light duty trucks, which include sport utility
vehicles, pickup trucks, and minivans (17.9 percent), commercial aircraft (6.4 percent), rail (2.6 percent), pipelines
(2.6 percent), and ships and boats (2.2 percent). These figures include direct CC>2, CH4, and N2O 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 2013, total transportation emissions rose by 16.5 percent due, in large
part, to increased demand for travel as fleetwide light-duty vehicle fuel economy was relatively stable (average new
vehicle fuel economy declined slowly from 1990 through 2004 and then increased more rapidly from 2005 through
2013). The number of vehicle miles traveled by light-duty motor vehicles (passenger cars and light-duty trucks)
increased 35 percent from 1990 to 2013, 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. The decline in new light-
duty vehicle fuel economy between 1990 and 2004 reflected the increasing market share of light-duty trucks, which
grew from about 30 percent of new vehicle sales in 1990 to 48 percent in 2004. Starting in 2005, the rate of VMT
growth slowed considerably (and declined rapidly in 2008) while average new vehicle fuel economy began to
increase. Average new vehicle fuel economy has improved almost every year since 2005, and the truck share has
decreased to about 3 7 percent of new vehicles in MY 2013 (EPA 2014). Between 2012 and 2013, VMT increased
by only 0.6 percent. Table 2-13 provides a detailed summary of greenhouse gas emissions from transportation-
related activities with electricity-related emissions included in the totals.
From 2008 to 2009, CC>2 emissions from the transportation end-use sector declined 4.2 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 2013, CC>2 emissions from the transportation end-use sector stabilized
even as economic activity rebounded slightly.
2-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 CC>2 from fossil fuel combustion, which increased by 15 percent from 1990 to
2013. This rise in CCh emissions, combined with an increase in HFCs from close to zero emissions in 1990 to 60.5
MMT CO2 Eq. in 2013, led to an increase in overall emissions from transportation activities of 16 percent.
Table 2-13: Transportation-Related Greenhouse Gas Emissions (MMT COz Eq.)
Gas/Vehicle
Passenger Cars
CO2
CH4
N20
HFCs
Light-Duty Trucks
CO2
CH4
N20
HFCs
Medium- and Heavy-Duty
Trucks
CO2
CH4
N2O
HFCs
Buses
CO2
CH4
N20
HFCs
Motorcycles
CO2
CH4
N20
Commercial Aircraft3
CO2
CH4
N20
Other Aircraftb
CO2
CH4
N20
Ships and Boats0
CO2
CH4
N20
HFCs
Rail
CO2
CH4
N20
HFCs
Other Emissions from
Electricity Generation"1
Pipelines"
CO2
Lubricants
1990
656.7
629.3 1
3.2
24.1
335. 6 B
321. ll
l.?l
12.8
+
231. ll
230.1 •
1
0.7
+
8.4
84
i
1.7
+
+1
110.9
109.91
,;
78.3
77.5
0.1
,,.^_
44.3
+1
0.6
39.0
38.5
0.1
0.3
*
0.1
36.0
36.0
11.8
2005
711.2
660.1 1
1.4
18.0
31.7B
553.31
504.31
0.9l
14.8
33.3
409.8
395.9!
1.6
+
+1
133.9!
132.7!
,;
59.6
59.1
Oil
0.5
45.2
44.5
+
0.6
53.3
50.3
0.1
0.4
2.5
^
32.2
10.2
2009
792.9
748.0
1.2
13.8
29.9
351.6
310.2
0.4
5.8
35.2
389.6
375.1
0.1
1.2
13.2
16.2
15.6
+
0.1
0.4
4.2
4.1
+
+
120.6
119.5
+
1.1
36.8
36.4
+
0.3
38.9
38.4
+
0.5
+
43.7
40.7
0.1
0.3
2.6
+
36.7
36.7
8.5
2010
783.6
742.0
1.2
12.9
27.5
349.0
308.9
0.4
5.5
34.2
403.0
388.4
0.1
1.2
13.2
15.9
15.4
+
0.1
0.4
3.7
3.6
+
+
114.3
113.3
+
1.0
40.4
40.1
+
0.4
45.0
44.2
+
0.8
+
46.5
43.4
0.1
0.3
2.6
+
37.1
37.1
9.5
2011
774.3
736.9
1.2
12.3
23.9
332.1
295.0
0.4
5.1
31.7
401.3
386.8
0.1
1.1
13.3
16.9
16.4
+
0.1
0.4
3.6
3.6
+
+
115.6
114.6
+
1.1
34.2
33.9
+
0.3
46.7
45.8
+
0.8
+
48.1
45.0
0.1
0.3
2.6
+
37.8
37.8
9.0
2012
768.0
735.6
1.1
10.7
20.6
326.2
292.0
0.4
4.4
29.3
401.4
386.8
0.1
1.1
13.3
18.0
17.4
+
0.1
0.4
4.2
4.1
+
+
114.3
113.3
+
1.0
32.1
31.8
+
0.3
40.4
39.6
+
0.7
+
46.8
43.7
0.1
0.3
2.6
+
40.3
40.3
8.3
2013
763.3
735.5
1.1
9.4
17.3
323.4
292.4
0.3
3.9
26.7
407.7
393.2
0.1
1.1
13.3
18.3
17.7
+
0.1
0.4
4.0
3.9
+
+
115.4
114.3
+
1.1
34.7
34.4
+
0.3
39.6
38.9
+
0.7
+
47.5
44.4
0.1
0.3
2.6
+
47.7
47.7
8.8
Trends 2-29
-------
CCh 11.8 10.2 8.5 9.5 9.0 8.3 8.8
Total Transportation 1,554.4 2,022.5 1,839.9 1,848.1 1,819.7 1,799.8 1,810.3
International Bunker Fuel/ 104.5 114.2 107.5 118.1 112.8 106.8 100.7
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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 MMT CCh 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
issues with data sources.
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 CELi and N2O, these emissions are not directly attributed to pipelines in the U.S. 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 decreasing 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
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 2013, agricultural soil management was the largest
source of N2O emissions, and enteric fermentation was the largest source of CH4 emissions in the United States.
This sector also includes small amounts of CCh emissions from fossil fuel combustion by motorized farm equipment
like tractors. The agriculture sector is less reliant on electricity than the other sectors.
2-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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
Substances 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 Substances 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
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 Substances are apportioned based on their specific end-uses
within the source category, with emissions from residential air-conditioning systems to this economic sector. N2O
Trends 2-31
-------
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 CC>2 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 Substances 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 2013; (4) emissions per unit of total gross domestic product as a measure of national economic activity;
or (5) emissions per capita.
Table 2-14 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 0.3 percent
since 1990. 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
Table 2-14).
Table 2-14: Recent Trends in Various U.S. Data (Index 1990 = 100)
Chapter/IPCC Sector
1990
2005
2009
2010
2011
2012
2013 Growth3
Greenhouse Gas Emissions b 100
Energy Consumption ° 100
Fossil Fuel Consumption ° 100
Electricity Consumption ° 100
GDPd 100
Population6 100
117
I i
^^_^_is
107
112
108
131
161
123
109
116
112
137
165
124
108
115
110
137
168
125
104
112
107
135
172
125
106
115
110
135
175
126
0.3%
0.6%
0.4%
1.3%
2.5%
1.0%
a Average annual growth rate
b GWP-weighted values
0 Energy-content-weighted values (EIA 2015)
d Gross Domestic Product in chained 2009 dollars (BEA 2014)
e U.S. Census Bureau (2014)
2-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 2-14: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic
Product
I
175
165
155
145
135
125
115
105
95
85
75
65
55
Real GDP
Population
Emissions
per capita
Emissions
per $GDP
$
—*
Source: BEA (2014), U.S. Census Bureau (2014), and emission estimates in this report.
2.2 Indirect Greenhouse Gas Emissions (CO,
NOX, NMVOCs, and SO2)
The reporting requirements of the UNFCCC7 request that information be provided on indirect greenhouse gases,
which include CO, NOX, NMVOCs, and SCh. 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 SCh, 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.
7 See < http://unfccc.int/resource/docs/2013/copl9/eng/!Oa03.pdf#page=2>.
Trends 2-33
-------
Since 1970, the United States has published estimates of emissions of CO, NOX, NMVOCs, and SO2 (EPA 2015),8
which are regulated under the Clean Air Act. Table 2-15 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 2-15: Emissions of NOX, CO, NMVOCs, and SOz (kt)
Gas/Activity
NOx
Mobile Fossil Fuel Combustion
Stationary Fossil Fuel Combustion
Oil and Gas Activities
Industrial Processes and Product Use
Forest Fires
Waste Combustion
Agricultural Burning
Waste
CO
Mobile Fossil Fuel Combustion
Forest Fires
Stationary Fossil Fuel Combustion
Industrial Processes and Product Use
Waste Combustion
Oil and Gas Activities
Agricultural Burning
Waste
NMVOCs
Mobile Fossil Fuel Combustion
Industrial Processes and Product Use
Oil and Gas Activities
Stationary Fossil Fuel Combustion
Waste Combustion
Waste
Agricultural Burning
S02
Stationary Fossil Fuel Combustion
Industrial Processes and Product Use
Oil and Gas Activities
Mobile Fossil Fuel Combustion
Waste Combustion
Waste
Agricultural Burning
1990
21,771
10,862
10,023
139
592
64
82
8
+
132,337
119,360
2,300
5,000
4,129
978
302
268
1
20,930
10,932
7,638
554
912
222
673
NA
20,935
18,407
1,307
390
793
38
+
NA
2005
1 17,394
10
5
74
58
7
4
1
,295
,858
321
572
212
128
6
2
,283
,615
,550
,648
,557
1,403
13
5
5
13
11
318
184
7
,154
,724
,849
510
716
241
114
NA
,196
,541
831
180
619
25
1
NA
2009
13,450
7,797
4,452
468
493
149
81
8
1
51,716
39,256
5,313
4,036
1,331
1,164
363
247
5
11,586
4,650
4,337
1,894
553
103
49
NA
8,246
7,228
654
126
220
17
1
NA
2010
12,607
7,290
4,092
545
472
121
77
8
1
50,996
39,475
4,323
4,103
1,280
1,084
487
241
5
11,641
4,591
4,133
2,205
576
92
44
NA
7,015
6,120
618
117
144
16
+
NA
2011
12,630
7,294
3,807
622
452
373
73
8
1
58,868
38,305
13,291
4,170
1,229
1,003
610
255
5
11,726
4,562
3,929
2,517
599
81
38
NA
5,877
5,008
605
108
142
15
+
NA
2012
11,912
6,788
3,567
622
452
400
73
8
1
58,022
36,491
14,262
4,170
1,229
1,003
610
253
5
11,416
4,252
3,929
2,517
599
81
38
NA
4,711
3,859
605
108
125
15
+
NA
2013
11
6
3
47
34
5
4
1
1
11
3
3
2
4
3
,167
,283
,579
622
452
149
73
8
1
,265
,676
,310
,170
,229
,003
610
262
5
,107
,942
,929
,517
599
81
38
NA
,625
,790
605
108
108
15
+
NA
Source: (EPA 2015) 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 kt.
Box 2-3: Sources and Effects of Sulfur Dioxide
Sulfur dioxide (802) 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)
NOX and CO emission estimates from Field Burning of Agricultural Residues were estimated separately, and therefore not
taken from EPA (2015).
2-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 SC>2 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 64.4
percent in 2013. 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 2-35
-------
3. Energy
Energy-related activities were the primary sources of U.S. anthropogenic greenhouse gas emissions, accounting for
84.5 percent of total greenhouse gas emissions on a carbon dioxide (CCh) equivalent basis in 2013.l This included
97, 41, and 12 percent of the nation's CCh, methane (CH4), and nitrous oxide (N2O) emissions, respectively.
Energy-related CC>2 emissions alone constituted 79.9 percent of national emissions from all sources on a CC>2
equivalent basis, while the non-CCh emissions from energy-related activities represented a much smaller portion of
total national emissions (4.6 percent collectively).
Emissions from fossil fuel combustion comprise the vast majority of energy-related emissions, with CCh being the
primary gas emitted (see Figure 3-1). Globally, approximately 32,310 MMT of CCh were added to the atmosphere
through the combustion of fossil fuels in 2012, of which the United States accounted for approximately 16 percent.2
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 third largest source.
Figure 3-1: 2013 Energy Chapter Greenhouse Gas Sources
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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
5,158
200
1 Estimates are presented in units of million metric tons of carbon dioxide equivalent (MMT CCh Eq.), which weight each gas by
its global warming potential, or GWP, value. See section on global warming potentials in the Executive Summary.
Global CO2 emissions from fossil fuel combustion were taken from Energy Information Administration International Energy
Statistics 2013 EIA (2013).
Energy 3-1
-------
Figure 3-2: 2013 U.S. Fossil Carbon Flows (MMT COz Eq.)
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
NEU Emissions 12
Coal Emissions
1,670
Natural Gas Emissions
U95
NEU Emissions 56
Stock
Changes
(117)
Other 224——
Fossil Fuel
Consumption
NEU Imports U.S.
19 Territories
32
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
Other outflows consists of NEU sequestered carbon emissions and a
fossil fuel combustion residual
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
million metric tons of CCh equivalents (MMT CCh Eq.), while unweighted gas emissions in kilotons (kt) are
provided in Table 3-2. Overall, emissions due to energy-related activities were 5,636.6 MMT CCh Eq. in 2013,3 an
increase of 6.5 percent since 1990.
Table 3-1: COz, CH4, and N2O Emissions from Energy (MMT 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
1990
4,908.4
4,740.7
1,820.8
1,493.8
842.5
338.3
217.4
27.9
117.7
37.6
8.0
4.4
215.2
103.5
4.2
328.5
179.1
96.5
2005
5,933.9
5,747.7
2,400.9 1
1,887.8
827.8 1
357.8
223.5
49.9
138.9
30.0
12.5
4.9
206.9
113.1
22.9
280.9
176.3
64.1
2009
5,351.2
5,197.1
2,145.7
1,720.3
727.7
336.4
223.5
43.5
106.0
32.2
11.3
4.7
188.2
106.4
62.3
285.5
168.0
79.9
2010
5,529.2
5,367.1
2,258.4
1,732.0
775.7
334.7
220.2
46.2
114.6
32.3
11.0
4.2
192.5
117.0
72.6
279.2
159.6
82.3
2011
5,390.3
5,231.3
2,157.7
1,711.5
774.1
327.2
221.0
39.8
108.4
35.6
10.5
4.5
195.2
111.7
72.9
268.2
159.3
71.2
2012
5,181.1
5,026.0
2,022.2
1,700.8
784.2
283.1
197.1
38.6
104.9
34.8
10.4
5.1
194.9
105.8
72.8
259.2
154.4
66.5
2013
5,331.5
5,157.7
2,039.8
1,718.4
817.3
329.6
220.7
32.0
119.8
37.8
10.1
6.0
208.6
99.8
74.7
263.5
157.4
64.6
3 Following the revised reporting requirements under the UNFCCC, this Inventory report presents CCh equivalent values based
on the IPCC Fourth Assessment Report (AR4) GWP values. See the Introduction chapter for more information.
3-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Petroleum Systems
Stationary Combustion
Abandoned Underground Coal
Mines
Mobile Combustion
Incineration of Waste
International Bunker Fuels"
N20
Stationary Combustion
Mobile Combustion
Incineration of Waste
International Bunker Fuels"
Total
U.3 |
0.9
5,290.5
23.5
7.4
6.6
3.0
+
0.1
58.7
20.2
38.1
0.4
1.0
6,273.6
21.5
7.4
6.4
2.3
+
0.1
45.3
20.4
24.6
0.3
0.9
5,682.1
21
7,
6,
2,
.3
.1
.6
.3
+
0.1
46.2
22,
23,
0,
7.
5,854.
.2
.7
.3
0
6
22.0
7.1
6.4
2.3
+
0.1
44.1
21.3
22.5
0.3
1.0
5,702.6
23.3
6.6
6.2
2.2
+
0.1
41.9
21.4
20.2
0.3
0.9
5,482.2
25.2
8.0
6.2
2.1
+
0.1
41.6
22.9
18.4
0.3
0.9
5,636.6
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
+ Does not exceed 0.05 MMT 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 N2O Emissions from Energy (kt)
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
4,908,390
4,740,670
117,658 1
37,645
7,972
4,445
215,186 \
103,463 \
4,227
13,139
7,165
3,860
1,261
339 1
288 1
225 1
+ 1
180 1
40 1
138 1
2
3
2005
5,933,912
5,747,683
138,877
29,995
12,454
4,904
206,901
113,139
22,943
11,237
7,053
2,565
939
296
264
121
+
197
68
128
1
3
2009
5,351,228
5,197,058
106,018
32,201
11,295
4,656
188,220
106,410
62,272
11,419
6,722
3,194
860
295
254
93
+
5
152
69
82
1
3
2010
5,529,210
5,367,144
114,554
32,334
11,026
4,153
192,462
116,992
72,647
11,168
6,382
3,293
854
283
263
92
+
6
155
74
80
1
3
2011
5,390,268
5,231,341
108,359
35,551
10,550
4,467
195,182
111,660
72,881
10,729
6,371
2,849
878
283
257
91
+
5
148
71
76
1
3
2012
5,181,104
5,026,000
104,917
34,764
10,363
5,060
194,903
105,805
72,827
10,366
6,176
2,658
931
264
249
88
+
4
141
72
68
1
3
2013
5,331,493
5,157,697
119,850
37,808
10,137
6,001
208,594
99,763
74,743
10,541
6,295
2,584
1,009
318
249
86
+
3
140
77
62
1
3
+ Does not exceed 0.5 kt
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.
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
Energy 3-3
-------
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.
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.
3-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 3-3: COz, CH4, and NzO Emissions from Fossil Fuel Combustion (MMT COz Eq.)
Gas
CO2
CH4
N2O
Total
1990
4,740.7 1
14.1
53.1 I
4,807.9
2005
5,747.7 1
10.4
58.4
5,816.5
2009
5,197.1
9.7
45.0
5,251.8
2010
5,367.1
9.4
45.9
5,422.5
2011
5,231.3
9.3
43.8
5,284.5
2012
5,026.0
8.8
41.6
5,076.4
2013
5,157.7
10.1
41.3
5,209.1
Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP values.
Table 3-4: COz, CH4, and NzO Emissions from Fossil Fuel Combustion (kt)
Gas
C02
CH4
N20
1990
4,740,670
565
178
2005
5,747,683
416
196 |
2009
5,197,058
389
151
2010
5,367,144
375
154
2011
5,231,341
374
147
2012
5,026,000
352
140
2013
5,157,697
404
139
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 2013, CO2 emissions from
fossil fuel combustion increased by 2.6 percent relative to the previous year. The increase in €62 emissions from
fossil fuel combustion was a result of multiple factors, including: (1) an increase in the price of natural gas leading
to increased coal-fired generation in the electric power sector; (2) much colder winter conditions resulting in an
increased demand for heating fuel in the residential and commercial sectors; (3) an increase in industrial production
across multiple sectors resulting in increases in industrial sector emissions;4 and (4) an increase in transportation
emissions resulting from an increase in vehicle miles traveled (VMT) and fuel use across on-road transportation
modes. In 2013, CO2 emissions from fossil fuel combustion were 5,157.7 MMT CChEq., or 8.8 percent above
emissions in 1990 (see Table 3-5).5
Table 3-5: COz Emissions from Fossil Fuel Combustion by Fuel Type and Sector (MMT COz
Eq.)
Fuel/Sector
Coal
Residential
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
Natural Gas
Residential
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
Petroleum
1990
1,718.4
12.0 1
155.3
NE 1
1,547.6
0.6
1,000.3
238.0
142.1
408.9
36.0 1
175.3
NO 1
2,021.5
2005
2,112.3 1
0.8
9.3 1
115.3
NE 1
1,983.8
3.0
1,166.7
262.2
162.9
388.5
33.1
318.8
1.3 1
2,468.4
I 2009
1,834.2
0.0
6.9
83.0
NE
1,740.9
3.4
1,216.9
258.8
168.9
377.6
37.9
372.2
1.5
2,145.5
2010
1,927.7
0.0
6.6
90.1
NE
1,827.6
3.4
1,272.1
258.6
167.7
407.2
38.1
399.0
1.5
2,167.0
2011
1,813.9
0.0
5.8
82.0
NE
1,722.7
3.4
1,291.5
254.7
170.5
417.3
38.9
408.8
1.4
2,125.5
2012
1,592.8
0.0
4.1
74.1
NE
1,511.2
3.4
1,352.6
224.8
156.9
434.8
41.3
492.2
2.6
2,080.2
2013
1,658.1
0.0
3.9
75.8
NE
1,575.0
3.4
1,389.5
267.1
178.2
450.8
48.8
441.9
2.6
2,109.6
4 Further details on industrial sector combustion emissions are provided by EPA's GHGRP
(http://ghgdata.epa.gov/ghgp/main.do).
5 An additional discussion of fossil fuel emission trends is presented in the Trends in U.S. Greenhouse Gas Emissions Chapter.
Energy 3-5
-------
Residential
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
Geothermal3
Total
97.4 1
63.3
278.3
1,457.7
97.5
27.2
0.4
4,740.7
94.9
51.3
324.0 1
1,854.7
97.9
45.6
0.4
5,747.7
177.6
47.7
267.0
1,682.4
32.2
38.6
1 0.4
5,197.1
76.2
45.9
278.4
1,693.9
31.4
41.3
0.4
5,367.1
72.6
44.7
274.8
1,672.7
25.8
34.9
0.4
5,231.3
58.3
36.1
275.4
1,659.5
18.3
32.6
0.4
5,026.0
62.5
38.6
290.6
1,669.6
22.4
26.0
0.4
5,157.7
+ Does not exceed 0.05 MMT CO2 Eq.
NE (Not estimated)
NO (Not occurring)
a Although not technically a fossil fuel, geothermal energy-related CCh 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.6 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 2013 Emissions from Fossil Fuel
Combustion for Selected Fuels and Sectors (MMT 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"
2009 to 2010
86.7 5.0%
26.8 7.2%
-0.8 -2.4%
11.4 0.7%
-0.3 -0.1%
-1.2 -0.7%
7.0 8.5%
29.6 7.8%
170.1 3.3%
2010 to 2011
-104.9 -5.7%
9.8 2.5%
-5.6 -17.8%
-21.2 -1.3%
-3.9 -1.5%
2.7 1.6%
-8.1 -9.0%
10.1 2.5%
-135.8 -2.5%
2011 to 2012
-211.5 -12.3%
83.5 20.4%
-7.5 -29.0%
-13.2 -0.8%
-29.8 -11.7%
-13.6 -8.0%
-7.9 -9.7%
17.5 4.2%
-205.3 -3.9%
2012 to 2013
63.8 4.2%
-50.3 -10.2%
4.1 22.2%
10.2 0.6%
42.3 18.8%
21.4 13.6%
1.8 2.4%
16.0 3.7%
131.7 2.6%
Total 2013
1,575.0
441.9
22.4
1,669.6
267.1
178.2
75.8
450.8
5,157.7
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
a Excludes emissions from International Bunker Fuels.
b Includes fuels and sectors not shown in table.
' 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: 1990-2013
-------
In the United States, 82 percent of the energy consumed in 2013 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 (9 percent) and by a variety of renewable energy sources (10 percent), primarily
hydroelectric power, wind energy and biofuels (EIA 2015).7 Specifically, petroleum supplied the largest share of
domestic energy demands, accounting for 36 percent of total U.S. energy consumption in 2013. Natural gas and
coal followed in order of energy demand importance, accounting for approximately 28 percent and 19 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 2015).
Figure 3-3: 2013 U.S. Energy Consumption by Energy Source (percent)
Renewable
Energy
9.6%
Nuclear Electric
Power
8.5%
Figure 3-4: U.S. Energy Consumption (Quadrillion Btu)
120 -I
100
a
S 80
o
Q- 60
8 40
i
iS 20
Total Energy
Renewable & Nuclear
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 3-5: 2013 COz Emissions from Fossil Fuel Combustion by Sector and Fuel Type (MMT
COz Eq.)
2,500 -i
2,000 -
iff 1,500
Q
° 1,000
500
0
Petroleum
I Coal
I Natural Gas
2,040
1,718
32
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 CCh and smaller amounts of other gases,
including CH4, CO, and NMVOCs.8 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 COz from Fossil Fuel Combustion Trends
In 2013, weather conditions, and a very cold first quarter of the year in particular, caused a significant increase in
energy demand for heating fuels and is reflected in the increased residential emissions during the early part of the
year (EIA 2015). The United States in 2013 also experienced a cooler winter overall compared to 2012, as heating
degree days increased (18.5 percent). Cooling degree days decreased by 12.8 percent and despite this decrease in
cooling degree days, electricity demand to cool homes still increased slightly. While colder winter conditions
compared to 2012 resulted in a significant increase in the amount of energy required for heating, heating degree days
in the United States were 1.2 percent below normal (see Figure 3-6). Summer conditions were slightly cooler in
2013 compared to 2012, and summer temperatures were warmer than normal, with cooling degree days 7.1 percent
above normal (see Figure 3-7) (EIA 2015).9
8 See the sections entitled Stationary Combustion and Mobile Combustion in this chapter for information on non-CCh gas
emissions from fossil fuel combustion.
9 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).
3-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 3-6: Annual Deviations from Normal Heating Degree Days for the United States
(1950-2013)
!i
20
Normal
(4,524 Heating Degree Days)
Figure 3-7: Annual Deviations from Normal Cooling Degree Days for the United States
(1950-2013)
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)10 of existing plants in 2013 remained high at 91 percent. Electricity output by hydroelectric power plants
decreased in 2013 by approximately 3 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 2013 provided nearly 3 times as much of the energy generated in the United States from
hydroelectric plants (EIA 2015). Nuclear, hydroelectric, and wind power capacity factors since 1990 are shown in
Figure 3-8.
10 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 (2015).
Energy 3-9
-------
Figure 3-8: Nuclear, Hydroelectric, and Wind Power Plant Capacity Factors in the United
States (1990-2013)
100
90
80
3" 70 -
3*
I 60
•2 50
Ł•
•jj 40 -
O_
0 30
20
10
0
a
Nuclear
Wind
O'-irsimfl-LftkOf^CQQiO^rsi
S 8
Fossil Fuel Combustion Emissions by Sector
In addition to the CC>2 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 (MMT COz
Eq.)
End-Use Sector
Total
1990
2005
2009
2010
2011
2012
4,807.9
5,816.5
2013
Electricity Generation 1,828.5 2,417.4 2,162.9 2,277.4 2,175.8 2,040.4 2,059.3
C02 1,820.8 2,400.9 I 2,145.7 2,258.4 2,157.7 2,022.2 2,039.8
CH4 0.3 0.5 0.4 0.5 0.4 0.4 0.4
N2O 7.4 16.0 16.8 18.5 17.6 17.8 19.1
Transportation 1,540.6 1,928.9 1,747.2 1,758.0 1,736.3 1,723.2 1,739.0
CO2 1,493.8 1,887.8 I 1,720.3 1,732.0 1,711.5 1,700.8 1,718.4
CH4 5.6 3.0 2.3 2.3 2.3 2.2 2.1
N2O 41.2 38.1 24.6 23.7 22.5 20.2 18.4
Industrial 847.4 I 832.4 731.4 779.6 778.0 788.2 821.2
CO2 842.5 I 827.8 727.7 775.7 774.1 784.2 817.3
N2O 3.1 2.9 2.3 2.5 2.4 2.4 2.4
Residential 344.6 362.8 341.7 339.6 332.1 287.6 335.5
CO2 338.3 I 357.8 336.4 334.7 327.2 283.1 329.6
CH4 5.2 4.1 4.4 4.0 4.0 3.7 5.0
N2O 1.0 I 0.9 0.9 0.8 0.8 0.7 1.0
Commercial 218.8 224.9 224.9 221.6 222.4 198.3 222.1
CO2 217.4 I 223.5 223.5 220.2 221.0 197.1 220.7
CH4 1.0 I 1.1 1.1 1.1 1.0 0.9 1.0
N2O 0.4 0.3 0.3 0.3 0.3 0.3 0.3
U.S. Territories3 28.0 50.1 43.7 46.4 39.9 38.8 32.1
5,251.8 5,422.5 5,284.5 5,076.4 5,209.1
Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP values.
3-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.
a U.S. Territories are not apportioned by sector, and emissions are total greenhouse gas emissions
from all fuel combustion sources.
Other than CO2, gases emitted from stationary combustion include the greenhouse gases CH4 and N2O and the
indirect greenhouse gases NOX, CO, and NMVOCs.11 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.12 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
(MMT COz Eq.)
End-Use Sector
1990
2005
2009
2010
2011
2012
2013
Transportation
CO2
CH4
N2O
Industrial
CO2
CH4
N2O
Residential
CO2
CH4
N2O
Commercial
1,543.7
1,496.8
5.6
41.2
1,537.0
1,529.2
2.0
5.9
940.2
931.4
5.4
3.4
759.1
1,933.7
1,892.5
3.0
38.1
1,574.1
1,564.4
1.9
7.8
1,224.9
1,214.1
4.2
6.6
1,033.7
1,751.7
1,724.8
2.3
24.6
1,338.0
1,329.5
1.5
7.0
1,134.3
1,122.6
4.6
7.1
984.2
1,762.5
1,736.5
2.3
23.8
1,425.8
1,416.5
1.6
7.7
1,186.7
1,174.8
4.2
7.7
1,001.0
1,740.6
1,715.8
2.3
22.5
1,407.9
1,398.8
1.6
7.6
1,129.4
1,117.9
4.2
7.3
966.6
1,727.1
1,704.6
2.2
20.3
1,386.3
1,377.0
1.6
7.7
1,019.4
1,008.4
3.9
7.1
904.9
1,743.0
1,722.4
2.1
18.5
1,409.3
1,399.8
1.6
7.9
1,083.3
1,070.2
5.1
7.9
941.5
11 Sulfur dioxide (SO2) emissions from stationary combustion are addressed in Annex 6.3.
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
-------
C02
CH4
N2O
U.S. Territories3
Total
755.4
1.1
2.5
28.0
4,807.9
11,026.7
1.2
5.7
50.1
5,816.5
1976.7
1.2
6.2
1 43.7
5,251.8
993.2
1.2
6.6
46.4
5,422.5
959.1
1.2
6.3
39.9
5,284.5
897.4
1.1
6.4
38.8
5,076.4
933.3
1.2
7.0
32.1
5,209.1
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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.
a 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 of 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.13 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 (MMT 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
1990
1,820.8
1,547.6
175.3
97.5 1
0.4 1
842.5
155.3
408.9
278.3
217.4
12.0
142.1
63.3 1
338.3
3.0
238.0
97.4 1
27.9
0.6 |
2005
2,400.9
1,983.8
318.8
97.9 1
0.4
827.8
115.3
388.5
324.0
223.5
9.3
162.9
51.3
357.8
0.8
262.2
94.9 1
49.9 1
3.0 |
2009
2,145.7
1,740.9
372.2
32.2
0.4
727.7
83.0
377.6
267.0
223.5
6.9
168.9
47.7
336.4
+
258.8
77.6
43.5
3.4
2010
2,258.4
1,827.6
399.0
31.4
0.4
775.7
90.1
407.2
278.4
220.2
6.6
167.7
45.9
334.7
+
258.6
76.2
46.2
3.4
2011
2,157.7
1,722.7
408.8
25.8
0.4
774.1
82.0
417.3
274.8
221.0
5.8
170.5
44.7
327.2
+
254.7
72.6
39.8
3.4
2012
2,022.2
1,511.2
492.2
18.3
0.4
784.2
74.1
434.8
275.4
197.1
4.1
156.9
36.1
283.1
+
224.8
58.3
38.6
3.4
2013
2,039.8
1,575.0
441.9
22.4
0.4
817.3
75.8
450.8
290.6
220.7
3.9
178.2
38.6
329.6
+
267.1
62.5
32.0
3.4
Since emission estimates for U.S. territories cannot be disaggregated by gas in Table 3-10 and Table 3-11, the values for CH4
andN2O exclude U.S. territory emissions.
3-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Natural Gas
Fuel Oil
Total
NO 1
27.2
3,246.9
Ł
3,859.9
1'5
38.6
3,476.7
1.5
41.3
3,635.2
1.4
34.9
3,519.8
2.6
32.6
3,325.2
2.6
26.0
3,439.3
+ Does not exceed 0.05 MMT CO2 Eq.
NO: Not occurring
Table 3-10: CH4 Emissions from Stationary Combustion (MMT COz Eq.)
Sector/Fuel Type
1990
2005
2009 2010 2011 2012 2013
Electric Power
Coal
Fuel Oil
Natural gas
Wood
Industrial
Coal
Fuel Oil
Natural gas
Wood
Commercial/Institutional
Coal
Fuel Oil
Natural gas
Wood
Residential
Coal
Fuel Oil
Natural Gas
Wood
U.S. Territories
Coal
Fuel Oil
Natural Gas
Wood
0.3 0.5 0.4
0.3 0.3 0.3
+
0.1
+
1.8
0.4
0.2
0.2
1.0
1.0
+
0.2
0.3
0.5
5.2
0.2
0.3
0.5
4.1
+
+
+
+
+
0.1
+
1.7
0.3
0.2
0.2
1.0
1.1
+
0.2
0.4
0.5
4.1
0.1
0.3
0.6
3.1
0.1
+
0.1
+
+
0.1
+
1.4
0.2
0.1
0.2
0.8
1.1
+
0.2
0.4
0.5
4.4
+
0.3
0.6
3.6
0.1
+
0.1
+
+ + +
0.5
0.3
+
0.2
+
1.5
0.2
0.2
0.2
0.9
1.1
+
0.2
0.4
0.5
4.0
+
0.3
0.6
3.1
0.1
+
0.1
+
+
0.4
0.3
+
0.2
+
1.5
0.2
0.1
0.2
0.9
1.0
+
0.2
0.4
0.5
4.0
+
0.3
0.6
3.2
0.1
+
0.1
+
+
0.4
0.2
+
0.2
+
1.5
0.2
0.1
0.2
1.0
0.9
+
0.1
0.4
0.4
3.7
+
0.2
0.5
3.0
0.1
+
0.1
+
+
0.4
0.2
+
0.2
+
1.5
0.2
0.2
0.2
0.9
1.0
+
0.1
0.4
0.5
5.0
+
0.2
0.6
4.1
+
+
+
+
+
Total
8.5
7.4
7.4 7.1
7.1
6.6
8.0
Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP values.
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 3-11: NzO Emissions from Stationary Combustion (MMT COz Eq.)
Sector/Fuel Type
Electricity Generation
Coal
Fuel Oil
Natural Gas
Wood
Industrial
Coal
Fuel Oil
Natural Gas
Wood
Commercial/Institutional
Coal
Fuel Oil
Natural Gas
1990
7.4
6.3 |
2005
16.0
11.6
0.1
4.3
1
2.9
0.5
0.5
0.2
1.6
1
2009
16.8
11.2
+
5.6
+
2.3
0.4
0.4
0.2
1.3
0.3
+
0.1
0.1
2010
18.5
12.5
+
5.9
+
2.5
0.4
0.4
0.2
1.4
0.3
+
0.1
0.1
2011
17.6
11.5
+
6.1
+
2.4
0.4
0.4
0.2
1.5
0.3
+
0.1
0.1
2012
17.8
10.2
+
7.5
+
2.4
0.4
0.3
0.2
1.5
0.3
+
0.1
0.1
2013
19.1
12.1
+
7.0
+
2.4
0.4
0.4
0.2
1.4
0.3
+
0.1
0.1
Energy 3-13
-------
Wood
Residential
Coal
Fuel Oil
Natural Gas
Wood
U.S. Territories
Coal
Fuel Oil
Natural Gas
Wood
0.1
1.0
+
0.2
0.1
0.7
0.1
+
0.1
0.1
0.9
+
0.2
0.1
0.5
0.1
+
0.1
0.1
0.9
+
0.2
0.1
0.6
0.1
+
0.1
0.1
0.8
+
0.2
0.1
0.5
0.1
+
0.1
0.1
0.8
+
0.2
0.1
0.5
0.1
+
0.1
0.1
0.7
+
0.2
0.1
0.5
0.1
+
0.1
0.1
1.0
+
0.2
0.1
0.7
0.1
+
0.1
Total
11.9
20.2
20.4
22.2
21.3
21.4
22.9
Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP values.
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
Electricity Generation
The process of generating electricity is the single largest source of CCh emissions in the United States, representing
37 percent of total CCh emissions from all CCh 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 €62 emissions from fossil fuel
combustion, approximately 39.5 percent in 2013. Methane and N2O from electricity generation represented 4.2 and
46.3 percent of total methane and N2O emissions from fossil fuel combustion in 2013, 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-CCh emitting technologies, relied on coal for approximately 39 percent of their total energy requirements in
2013. Recently an increase in the carbon intensity of fuels consumed to generate electricity has occurred due to an
increase in coal consumption, and decreased natural gas consumption and other generation sources. Total U.S.
electricity generators used natural gas for approximately 27 percent of their total energy requirements in 2013 (El A
2014a).
Figure 3-9: Electricity Generation Retail Sales by End-Use Sector
1,500 -i
1,400
1,300 -
| 1,200
| 1,100 -
a
1,000
900
800
Residential
Commercial
Industrial
S
The electric power industry includes all power producers, consisting of both regulated utilities and nonutilities (e.g.
independent power producers, qualifying cogenerators, and other small power producers). For the underlying energy
data used in this chapter, the Energy Information Administration (EIA) places electric power generation into three
functional categories: the electric power sector, the commercial sector, and the industrial sector. The electric power
sector consists of electric utilities and independent power producers whose primary business is the production of
3-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
electricity, while the other sectors consist of those producers that indicate their primary business is something other
than the production of electricity.14
The industrial, residential, and commercial end-use sectors, as presented in Table 3-8, were reliant on electricity for
meeting energy needs. The residential and commercial end-use sectors were especially reliant on electricity
consumption for lighting, heating, air conditioning, and operating appliances. Electricity sales to the residential and
commercial end-use sectors in 2013 increased approximately 1.2 percent and 0.9 percent, respectively. The trend in
the residential and commercial sectors can largely be attributed to colder, more energy-intensive winter conditions
compared to 2012. Electricity sales to the industrial sector in 2013 decreased approximately 3.1 percent. Overall, in
2013, the amount of electricity generated (inkWh) decreased approximately 0.1 percent relative to the previous
year, while CCh emissions from the electric power sector increased by 0.9 percent. The increase in €62 emissions,
despite the decrease in sales and electricity generation was a result of an increase in the consumption of coal and
petroleum for electricity generation by 4.2 percent and 18.8 percent, respectively, in 2013, and a decrease in the
consumption of natural gas for electricity generation by 10.2 percent.
Industrial Sector
Industrial sector CCh, CH4, and N2O, emissions accounted for 16, 15, and 6 percent of CCh, CH4, and N2O,
emissions from fossil fuel combustion, respectively. COa, 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 2015 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.15 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 2012 to 2013, total industrial production and manufacturing output increased by 2.9 percent (FRB 2014).
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). Through EPA's
Greenhouse Gas Reporting Program (GHGRP), industrial trends can be discerned from the overall EIA industrial
fuel consumption data used for these calculations. For example, from 2012 to 2013 the underlying EIA data showed
increased consumption of natural gas and petroleum fuels in the industrial sector. EPA's GHGRP data highlights
that petroleum refineries, chemical manufacturing, and non-metallic mineral products were contributors to these
trends.16
Utilities primarily generate power for the U.S. electric grid for sale to retail customers. Nonutilities produce electricity for
their own use, to sell to large consumers, or to sell on the wholesale electricity market (e.g., to utilities for distribution and resale
to customers).
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.
Further details on industrial sector combustion emissions are provided by EPA's GHGRP
().
Energy 3-15
-------
Figure 3-10: Industrial Production Indices (Index 2007=100)
110 n
100
90
30
70
50
50
120
110
100
90
80
70
110
100
90
80
70
60
110 i
100
90
80
70
60
Total Industrial
Index..
Total excluding Computers, Communications
Equipment and Semiconductors
Paper
Primary
Metals
Petroleum
Refineries
Despite the growth in industrial output (61 percent) and the overall U.S. economy (75 percent) from 1990 to 2013,
CO2 emissions from fossil fuel combustion in the industrial sector decreased by 3.0 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 2013, €62, CH4, and
N2O emissions from fossil fuel combustion and electricity use within the industrial end-use sector totaled 1,409.3
MMT CO2 Eq., or approximately 1.7 percent above 2012 emissions.
Residential and Commercial Sectors
Residential and commercial sector €62 emissions accounted for 6 and 4 percent of €62 emissions from fossil fuel
combustion, CH4 emissions accounted for 49 and 10 percent of CH4 emissions from fossil fuel combustion, and N2O
emissions accounted for 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 2013, €62, CH4, and N2O emissions from fossil fuel combustion and electricity use within the residential
and commercial end-use sectors were 1,083.3 MMT €62 Eq. and 941.5 MMT €62 Eq., respectively. Total €62,
CH4, and N2O emissions from fossil fuel combustion and electricity use within the residential and commercial end-
use sectors increased by 6.3 and 4.0 percent from 2012 to 2013, 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: 1990-2013
-------
In 2013, combustion emissions from natural gas consumption represent 81 percent of the direct fossil fuel CO2
emissions from both the residential and commercial sectors. Natural gas combustion CC>2 emissions from the
residential and commercial sectors in 2013 increased by 18.8 percent and 13.6 percent from 2012 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 CCh 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, CCh, 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 1,743.0 MMT CCh Eq. in 2013, which represented 33 percent of
CO2 emissions, 21 percent of CH4 emissions, and 45 percent of N2O emissions from fossil fuel combustion,
respectively.17 Fuel purchased in the United States for international aircraft and marine travel accounted for an
additional 100.7 MMT CCh Eq. in 2013; these emissions are recorded as international bunkers and are not included
in U.S. totals according to UNFCCC reporting protocols.
From 1990 to 2013, transportation emissions from fossil fuel combustion rose by 13 percent due, in large part, to
increased demand for travel with limited gains in fuel efficiency for much of this time period. The number of vehicle
miles traveled (VMT) by light-duty motor vehicles (passenger cars and light-duty trucks) increased 35 percent from
1990 to 2013, 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 2012 to 2013, CC>2 emissions from the transportation end-use sector increased by 1.0 percent.18 The increase
in emissions can largely be attributed to small increases in VMT and fuel use across on-road transportation modes,
as well as increases in other non-road sectors such as pipelines. Commercial aircraft emissions increased slightly
between 2012 and 2013, but have decreased 18 percent since 2007. Decreases in jet fuel emissions (excluding
bunkers) since 2007 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 €62 from fossil fuel combustion, which increased by 15 percent from 1990 to
2013. Annex 3.2 presents the total emissions from all transportation and mobile sources, including COa, N2O, CH4,
and HFCs.
17 Note that these totals include CH4 and N2O emissions from some sources in the U.S. Territories (ships and boats, recreational
boats, non-transportation mobile sources) and CH4 and N2O emissions from transportation rail electricity.
18 Note that this value does not include lubricants.
Energy 3-17
-------
Transportation Fossil Fuel Combustion CO2 Emissions
Domestic transportation CC>2 emissions increased by 15 percent (225.6 MMT CCh) between 1990 and 2013, an
annualized increase of 0.6 percent. Among domestic transportation sources, light duty vehicles (including passenger
cars and light-duty trucks) represented 60 percent of CC>2 emissions from fossil fuel combustion, medium- and
heavy-duty trucks 23 percent, commercial aircraft 7 percent, and other sources 11 percent. See Table 3-12 for a
detailed breakdown of transportation CCh emissions by mode and fuel type.
Almost all of the energy consumed by the transportation sector is petroleum-based, including motor gasoline, diesel
fuel, jet fuel, and residual oil. €62 emissions from the combustion of ethanol and biodiesel for transportation
purposes, along with the emissions associated with the agricultural and industrial processes involved in the
production of biofuel, are captured in other Inventory sectors.19 Ethanol consumption from the transportation sector
has increased from 0.7 billion gallons in 1990 to 12.6 billion gallons in 2013, while biodiesel consumption has
increased from 0.01 billion gallons in 2001 to 1.4 billion gallons in 2013. For further information, see the section on
biofuel consumption at the end of this chapter and Table A-93 in Annex 3.2.
CO2 emissions from passenger cars and light-duty trucks totaled 1,028.0 MMT €62 in 2013, an increase of 8
percent (77.6 MMT CCh) from 1990 due, in large part, to increased demand for travel as fleetwide light-duty vehicle
fuel economy was relatively stable (average new vehicle fuel economy declined slowly from 1990 through 2004 and
then increased more rapidly from 2005 through 2013). CCh emissions from passenger cars and light-duty trucks
peaked at 1,181.2 MMT €62 in 2004, and since then have declined about 13 percent. The decline in new light-duty
vehicle fuel economy between 1990 and 2004 (Figure 3-11) reflected the increasing market share of light-duty
trucks, which grew from about 30 percent of new vehicle sales in 1990 to 48 percent in 2004 (Figure 3-12). Starting
in 2005, the rate of VMT growth slowed considerably (and declined rapidly in 2008) while average new vehicle fuel
economy began to increase. Average new vehicle fuel economy has improved almost every year since 2005, and the
truck share has decreased to about 37 percent of new vehicles in model year 2013 (EPA 2014d).
Medium- and heavy-duty truck €62 emissions increased by 71 percent from 1990 to 2013. This increase was
largely due to a substantial growth in medium- and heavy-duty truck VMT, which increased by 92 percent between
1990 and 2013.20 Carbon dioxide from the domestic operation of commercial aircraft increased by 4 percent (4.4
MMT €62) from 1990 to 2013. Across all categories of aviation, excluding international bunkers, €62 emissions
decreased by 21 percent (38.7 MMT CO2) between 1990 and 2013.21 This includes a 69 percent (24.0 MMT CO2)
decrease in €62 emissions from domestic military operations.
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 CO2, CH4, N2O, and MFCs.
19 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 6). More information and additional
analyses on bio fuels are available at EPA's "Renewable Fuels: Regulations & Standards;" See
.
While FHWA data shows consistent growth in medium- and heavy-duty truck VMT over the 1990 to 2013 time period, part of
the growth reflects a method change for estimating VMT starting in 2007. 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 VMT and emissions among on-road vehicle classes
in the 2007 to 2013 time period. During the time period prior to the method change (1990-2006), VMT for medium- and heavy-
duty trucks increased by 51 percent.
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: 1990-2013
-------
Figure 3-11: Sales-Weighted Fuel Economy of New Passenger Cars and Light-Duty Trucks,
1990-2013 (miles/gallon)
24,50 -|
24,00 -
23.50 -
23,00 -
§ 22.50 -
~n 22,00 -
CD
<- 21.50 -
ai
°- 21,00 -
in
2 20,50 -
5 20.00 -
19.50 -
19.00 -
18.50 -
18,00
Model Year
Source: EPA (20 14)
Figure 3-12: Sales of New Passenger Cars and Light-Duty Trucks, 1990-2013 (percent)
100% -,
aj
~n 75%
1/1
Ł 50%
o
+j
c
01
g 25%
Q-
0%
Light-Duty Trucks
Source: EPA (2014)
Table 3-12: COz Emissions from Fossil Fuel Combustion in Transportation End-Use Sector
(MMT COz Eq.)
Fuel/Vehicle Type
Gasoline0
Passenger Cars
Light-Duty Trucks
Medium- and Heavy -Duty Trucksb
Buses
Motorcycles
Recreational Boats
Distillate Fuel Oil (Diesel)0
Passenger Cars
Light-Duty Trucks
1990
983.5
621.4
309.1
38.7
L7
12.2
262.9
7.9
11.5
2005
1,183.9
655.9
477.2
34.8
0.4
14.1
458.1
25.8
2009a
1,101.7
744.3
296.9
42.6
0.7
4.1
13.0
409.0
3.6
12.1
2010
1,092.7
738.2
295.0
42.3
0.7
3.6
12.7
425.5
3.8
12.6
2011
1,069.0
732.8
280.4
38.9
0.7
3.6
12.6
433.7
4.1
13.1
2012
1,064.9
731.5
277.4
38.7
0.8
4.1
12.5
431.3
4.1
13.0
2013
1,065.8
731.5
277.7
39.5
0.8
3.9
12.4
437.6
4.1
13.0
Energy 3-19
-------
Medium- and Heavy-Duty Trucksb 190.5
Buses 8.0
Rail 35.5
Recreational Boats 2.0
Ships and Other Boats 7.5
International Bunker Fuels'1 11.7
Jet Fuel 184.2
Commercial Aircraft6 109.9
Military Aircraft 35.0
General Aviation Aircraft 39.4
International Bunker Fuels'1 38.0
International Bunker Fuels From
Commercial Aviation 30.0
Aviation Gasoline 3.1
General Aviation Aircraft 3.1
Residual Fuel Oil 22.6
Ships and Other Boats 22.6
International Bunker Fuels'1 53.7
Natural Gas 36.0
Passenger Cars +
Light-Duty Trucks +
360.6
10.6
45.6
3.1
8.1
9.4
189.3
132.7
19.4
37.3
60.1
I
55.6
2.4
2.4
19.3
19.3
43.6
33.1
332.0
13.7
36.2
3.5
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
345.6
13.6
39.0
3.5
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
347.3
14.6
40.8
3.6
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
347.5
15.5
39.8
3.7
7.6
6.8
143.4
113.3
12.1
18.0
64.5
61.4
1.7
1.7
15.8
15.8
34.5
41.3
353.0
15.8
40.4
3.7
7.7
5.6
147.1
114.3
11.0
21.8
65.7
62.8
1.5
1.5
15.0
15.0
28.5
48.8
Buses
Pipelinef
LPG
Light-Duty Trucks
Medium- and Heavy -Duty Trucksb
Buses
Electricity
Rail
EthanoW
Total
Total (Including Bunkers)"1
+
36.0 1
1.4 1
0.6 1
0.8
3.0 1
3.0
4.1
1,496.8
1,600.3
0.8 1
32.2 1
1.7 1
0.4 1
4.7 1
4.7
22.4
1,892.5
2,005.7
1.2
36.7
1.7
1.2
0.5
4.5
4.5
61.2
1,724.8
1,831.2
1.1
37.1
1.8
1.3
0.6
4.5
4.5
77.J
1,736.5
1,853.4
1.1
37.8
2.1
1.5
0.6
4.3
4.3
77.5
1,715.8
1,827.5
1.0
40.3
2.3
1.6
0.7
3.9
3.9
77.5
1,704.6
1,810.4
1.0
47.7
2.5
1.8
0.7
4.0
4.0
73.4
1,722.4
1,822.2
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. In addition, this table does not include CO2 emissions from U.S. Territories, since these are
covered in a separate chapter of the Inventory.
a 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 2007-13 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.
b Includes medium- and heavy-duty trucks over 8,500 Ibs.
c Gasoline and diesel highway vehicle fuel consumption estimates are based on data from FHWA Highway Statistics Table VM-1
and MF-27.
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 6), 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 MMT CO2 Eq.
3-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Mobile Fossil Fuel Combustion CH4 andN2O Emissions
Mobile combustion includes emissions of CH4 and N2O from all transportation sources identified in the U.S.
Inventory with the exception of pipelines;22 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.). 23 Annex 3.2 includes a summary of all emissions from both transportation and
mobile sources. Table 3-13 and Table 3-14 provide mobile fossil fuel CH4 and N2O emission estimates in MMT
C02 Eq.24
Mobile combustion was responsible for a small portion of national CH4 emissions (0.3 percent) but was the third
largest source of U.S. N2O emissions (5.2 percent). From 1990 to 2013, mobile source CH4 emissions declined by
62 percent, to 2.1 MMT CO2 Eq. (86 kt CH4), 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 55 percent, to 18.4 MMT CO2 Eq. (62 kt N2O). Earlier generation control technologies initially resulted in
higher N2O emissions, causing a 28 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 65 percent
decrease in mobile source N2O emissions from 1997 to 2013 (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 N2O Emissions (MMT COz Eq.)
60
50 -
. 40
CJ
LJJ
o .,n
u 30 -
20
10 -
0
N,O
CH4
o -I-H rM m ^- in o
o^ o^ o^ o^ o^ o^ o^
o^ o^ o^ o^ o^ o^ o^
C^C^OOOOOOOOOOi—ii—ii—ii—I
c^c^oooooooooooooo
1—I 1—I (^sl (^sl Psl Psl Psl Psl Psl Psl Psl Psl Psl Psl Psl Psl
Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP values.
22 Emissions of CH4 from natural gas systems are reported separately. More information on the methodology used to calculate
these emissions are included in this chapter and Annex 3.4.
23 See the methodology sub-sections of the CCh from Fossil Fuel Combustion and CH4 and N2O from Mobile Combustion
sections of this chapter. Note that N2O and CH4 emissions are reported using different categories than CCh. CCh emissions are
reported by end-use sector (Transportation, Industrial, Commercial, Residential, U.S Territories), and generally adhere to a top-
down approach to estimating emissions. CCh emissions from non-transportation sources (e.g., lawn and garden equipment, farm
equipment, construction equipment) are allocated to their respective end-use sector (i.e., construction equipment CCh emissions
are included in the Commercial end-use sector instead of the Transportation end-use sector). CH4 and N2O emissions are
reported using the "Mobile Combustion" category, which includes non-transportation mobile sources. CH4 and N2O emissions
estimates are bottom-up estimates, based on total activity (fuel use, VMT) and emissions factors by source and technology type.
These reporting schemes are in accordance with IPCC guidance. For informational purposes only, CC>2 emissions from non-
transportation mobile sources are presented separately from their overall end-use sector in Annex 3.2.
24 See Annex 3.2 for a complete time series of emission estimates for 1990 through 2013.
Energy 3-21
-------
Table 3-13: CH4 Emissions from Mobile Combustion (MMT COz Eq.)
Fuel Type/Vehicle Type3
1990
Gasoline On-Roadb
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty
Trucks and Buses
Motorcycles
Diesel On-Roadb
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty
Trucks and Buses
Alternative Fuel On-Road
Non-Road
Ships and Boats
Railf
Aircraft
Agricultural Equipment0
Construction/Mining
Equipment4
Other6
2009
2010
2011
2012
2013
1.7
1.2
0.4
0.1
1.7
1.2
0.4
0.1
1.6
1.2
0.4
0.1
1.5
1.1
0.3
0.1
1.5
1.0
0.3
0.1
0.1
0.5
+
0.1
+
0.2
0.1
0.1
0.1
0.5
+
0.1
+
0.2
0.1
0.1
0.1
0.5
+
0.1
+
0.2
0.1
0.1
0.1
0.5
+
0.1
+
0.2
0.1
0.1
0.1
0.6
+
0.1
+
0.2
0.1
0.1
Total
5.6
3.0
2.3
2.3
2.3
2.2
2.1
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values. Totals may not sum due to
independent rounding.
Note: 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 1990-2010 Inventory and apply to the 2007 through 2013 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.
a See Annex 3.2 for definitions of on-road vehicle types.
b Gasoline and diesel highway vehicle mileage are based on data from FHWA Highway Statistics Table VM-1.
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 "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.
f Rail emissions do not include emissions from electric powered locomotives.
+ Less than 0.05 MMT CO2 Eq.
Table 3-14: NzO Emissions from Mobile Combustion (MMT COz Eq.)
Fuel Type/Vehicle Type3
Gasoline On-Roadb
Passenger Cars
Light-Duty Trucks
1990
37.4
24.1
12.7
2005
33.6
18.0
14.8
2009
20.4
13.8
5.7
2010
19.2
12.9
5.5
2011
18.0
12.3
5.0
2012
15.8
10.7
4.4
2013
13.9
9.3
3.9
Medium- and Heavy-Duty
Trucks and Buses
Motorcycles
Diesel On-Roadb
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty
Trucks and Buses
0.5
0.2
0.2
0.8
0.3
J
0.8
+
0.4
+
+
0.4
O.S
0.4
0.4
0.7
0.4
0.4
0.7
0.4
0.4
0.7
+
0.4
+
+
0.4
3-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Alternative Fuel On-Road O.lH O.ll 0.1 0.2 0.2 0.2 0.2
Non-Road 3.5 4.lB 3.7 4.0 4.0 3.9 3.9
Ships and Boats O.eB O.eB 0.5 0.8 0.8 0.7 0.7
Railf O.sB O.sB 0.3 0.3 0.3 0.3 0.3
Aircraft l.?B l.sB 1.4 1.4 1.4 1.3 1.4
Agricultural Equipment0 0.2U O.sB 0.3 0.4 0.4 0.4 0.4
Construction/Mining
Equipment11 O.sB O.sB 0.5 0.5 0.6 0.6 0.6
Other6 0.4 0.6 0.6 0.6 0.6 0.6 0_6_
Total 41.2 38.1 24.6 23.7 22.5 20.2 18.4
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values. Totals may not sum due to
independent rounding.
Note: 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 1990-2010 Inventory and apply to the 2007 through 2013 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.
a See Annex 3.2 for definitions of on-road vehicle types.
b Gasoline and diesel highway vehicle mileage are based on data from FHWA Highway Statistics Table VM-1.
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 "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.
f Rail emissions do not include emissions from electric powered locomotives.
+ Less than 0.05 MMT CO2 Eq.
CO2 from Fossil Fuel Combustion
Methodology
The methodology used by the United States for estimating CCh 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).25 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 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 2015). 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 2013) and Jacobs (2010).26
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
25 The IPCC Tier 3B methodology is used for estimating emissions from commercial aircraft.
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 32.1 MMT CO2 Eq. in 2013.
Energy 3-23
-------
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 El A 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 four years). These consumption data sets help inform the annual surveys to arrive at
the national total and sectoral breakdowns for that total.27
Also, 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).28
2. Subtract uses accounted for in the Industrial Processes and Product Use 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 and Product
Use 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 (2014), U.S.
Census Bureau (2011), EIA (2014c), 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 201 Ob), USGS (2012a) and USGS (2012b).29
3. Adjust for conversion of fuels and exports of CO 2. Fossil fuel consumption estimates are adjusted
downward to exclude fuels created from other fossil fuels and exports of CCh.30 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.31 Since October 2000,
the Dakota Gasification Plant has been exporting CCh to Canada by pipeline. Since this CCh is not emitted
to the atmosphere in the United States, energy used to produce this CO2 is subtracted from energy
consumption statistics. To make these adjustments, additional data for ethanol were collected from EIA
(2015), data for synthetic natural gas were collected from EIA (2014), and data for CCh 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 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 proportionately. The data sources used
in the bottom-up analysis of transportation fuel consumption include AAR (2008 through 2013), Benson
27 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.
28 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.
29 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 and Product Use chapter.
3" Energy statistics from EIA (2015) are already adjusted downward to account for ethanol added to motor gasoline, and biogas
in natural gas.
3! These adjustments are explained in greater detail in Annex 2.1.
3-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
(2002 through 2004), DOE (1993 through 2014), EIA (2007), EIA (1991 through 2014), EPA (2013b), and
FHWA (1996 through 2014).32
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 (2015).
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).33 The Office of the Under Secretary of Defense (Installations and Environment) and the Defense
Logistics Agency Energy (DLA Energy) of the U.S. Department of Defense (DoD) (DLA Energy 2014)
supplied data on military jet fuel and marine fuel use. Commercial jet fuel use was obtained from FAA
(2015); residual and distillate fuel use for civilian marine bunkers was obtained from DOC (1991 through
2013) for 1990 through 2001 and 2007 through 2013, 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.
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 CO2. 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 GHGRP
(EPA 2010). 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 CO2 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
32 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 1990-2010 Inventory and
apply to the 2007 to 2013 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.
33 See International Bunker Fuels section in this chapter for a more detailed discussion.
Energy 3-25
-------
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. Heat contents and densities
were obtained from EIA (2015) and USAF (1998).34
• 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 2013), Benson (2002 through 2004), DOE (1993
through 2014), DLA Energy (2014), DOC (1991 through 2013), DOT (1991 through 2013), EIA
(2009a), EIA (2015), EIA (2002), EIA (1991 through 2014), EPA (2014c), 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 2006IPCC
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) nets all other
jet fuel use as determined by FAA and DoD. For more information, see Annex 3.2.
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, 2012, and 2013 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.35
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.36 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, 2011, 2012 and 2013. 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
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.
35 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 .
36 See < http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html>.
3-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.37 EIA will work with EPA to back cast these values to 1990.
Box 3-5: Carbon Intensity of U.S. Energy Consumption
Fossil fuels are the dominant source of energy in the United States, and CO2 is the dominant greenhouse gas emitted
as a product from their combustion. Energy-related CO2 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
MMT CO2 Eq./QBtu for natural gas to upwards of 95 MMT CO2 Eq./QBtu for coal and petroleum coke.38 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
37 See .
38 One exajoule (EJ) is equal to 1018 joules or 0.9478 QBtu.
Energy 3-27
-------
sector was closely related to the C content of petroleum products (e.g., motor gasoline and jet fuel, both around 70
MMT 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 (MMT COz
Eq./QBtu)
Sector
Residential3
Commercial*
Industrial*
Transportation*
Electricity Generation15
U.S. Territories0
All Sectors0
1990
57.4
59.1
64.3 1
71.1
87.3 1
73.0
73.0
2005
56.6
57.5
64.3
71.4
85.8
73.4
73.5
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
73.0
72.4
2011
55.8
56.6
62.4
71.5
82.9
73.1
72.0
2012
55.6
56.1
62.0
71.5
79.9
72.3
70.9
2013
55.3
55.9
61.8
71.4
81.3
72.2
70.9
* Does not include electricity or renewable energy consumption.
b Does not include electricity produced using nuclear or renewable energy.
0 Does not include nuclear or renewable energy consumption.
Note: Excludes non-energy fuel use emissions and consumption.
Over the twenty-four-year period of 1990 through 2013, 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 2013 was approximately 9.0 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 2014).
Figure 3-14: U.S. Energy Consumption and Energy-Related COz Emissions Per Capita and Per
Dollar GDP
105 -,
100
, 95
8
85 -
Ł, 80
70
65
60
C02/capita
C02/Energy
Consumption
Energy
Consumption/SGDP
C intensity estimates were developed using nuclear and renewable energy data from EIA (2015), 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
3-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
production of fossil fuel-based products with long-term carbon storage should yield an accurate estimate of CO2
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 CCh 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 €62
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 CCh 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 CO2 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
Approach 2 uncertainty estimation methodology, Monte Carlo Stochastic Simulation technique, with @RISK
software. For this uncertainty estimation, the inventory estimation model for CO2 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 CO2 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.39 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.40
39 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.
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.
Energy 3-29
-------
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).41 For purposes of this uncertainty analysis, each input variable was simulated 10,000 times through Monte
Carlo sampling.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 3-16. Fossil fuel
combustion CC>2 emissions in 2013 were estimated to be between 5,051.0 and 5,403.7 MMT CCh Eq. at a 95 percent
confidence level. This indicates a range of 2 percent below to 5 percent above the 2013 emission estimate of
5,157.7MMTCO2Eq.
Table 3-16: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from Energy-
related Fossil Fuel Combustion by Fuel Type and Sector (MMT COz Eq. and Percent)
Fuel/Sector
2013 Emission Estimate
(MMT CQ2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CCh Eq.) (%)
Coal"
Residential
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
Natural Gasb
Residential
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
Petroleum1"
Residential
Commercial
Industrial
Transportation
Electric Utilities
U.S. Territories
Total (excluding Geothermal)b
Geothermal
Total (including Geothermal)b'c
1,658.1
NE
3.9
75.8
NE
1,575.0
3.4
1,389.5
267.2
178.2
450.8
48.8
441.9
2.6
2,109.6
62.5
38.6
290.6
1,669.6
22.4
26.0
5,157.3
0.4
5,157.7
Lower
Bound
1,600.7
NE
3.7
72.2
NE
1,513.3
3.0
1,374.4
259.6
173.2
437.3
47.4
429.1
2.3
1,982.0
59.0
36.7
236.7
1,560.6
21.2
24.0
5,050.5
NE
5,051.0
Upper
Bound
1,814.7
NE
4.5
87.7
NE
1,726.4
4.1
1,453.5
285.9
190.8
483.2
52.2
464.4
3.1
2,232.6
65.7
40.3
340.7
1,779.5
24.4
28.8
5,403.3
NE
5,403.7
Lower
Bound
-3%
NE
-5%
-5%
NE
-4%
-12%
-1%
-3%
-3%
-3%
-3%
-3%
-12%
-6%
-6%
-5%
-19%
-7%
-5%
-8%
-2%
NE
-2%
Upper
Bound
9%
NE
15%
16%
NE
10%
19%
5%
7%
7%
7%
7%
5%
17%
6%
5%
4%
17%
7%
9%
11%
5%
NE
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.
41 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.
3-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2013. 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 CCh 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 COa 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 2015) updated energy consumption statistics across the time series
relative to the previous Inventory. One such revision is the historical petroleum consumption in the residential sector
in 2011 and 2012. These revisions primarily impacted the previous emission estimates from 2010 to 2012; 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. In addition, EIA revised the heat contents of motor
gasoline, distillate fuel, and petroleum coke.
For motor gasoline, heating values were previously based on the relative volumes of conventional and reformulated
gasoline in the total motor gasoline product supplied to the United States. The revised heating values (first occurring
in the January 2015 publication of the Monthly Energy Review) incorporated inputs of ethanol, methyl tert-buty 1
ether (MTBE) through April 2006, other oxygenates through 2006, and a single national hydrocarbon gasoline
blend-stock from 1993 through 2013. Under the previous MER approach, the heating values of conventional and
reformulated gasoline were not adjusted for annual variation in the volumes of oxygenates, such as ethanol and
MTBE, which have lower heating values than the hydrocarbon components used to produce gasoline. The
calculation from the previous EIA Monthly Energy Review publication resulted in overestimated energy values of
historic gasoline consumption since 2003, when ethanol use began to grow rapidly. The heating value revision
resulted in an historical motor gasoline consumption decrease of approximately 1 percent per year between 1994
through 2012.
Changes to the heat content of distillate fuel resulted in an annual average decrease of approximately 0.1 percent
between 1994 through 2012. This decrease was a result of EIA's heat content revision from a constant sulfur content
across the time series, to a weighted sulfur content. Additionally, in 2009, EIA began subtracting inputs of
renewable diesel fuel from petroleum consumption before converting to energy units. Also, new data from Oak
Ridge National Laboratory's Transportation Energy Book (Edition 33) regarding the use of biodiesel in transit buses
was incorporated and impacted the distribution of fuel consumption and emissions for on-road buses for the time
series starting in 2006.
Petroleum coke consumption decreased by an annual average of approximately 0.1 percent from 2004 to 2012. This
decrease was a result of a similar heat content revision in which the EIA recalculated the historically constant
petroleum coke heat content to include weighted petroleum coke heat contents (by the two categories of petroleum
coke, catalyst and marketable) starting in 2004.
Overall, these changes resulted in an average annual decrease of 9.6 MMT CO2 Eq. (less than 0.2 percent) in CO2
emissions from fossil fuel combustion for the period 1990 through 2012, 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.
Energy 3-31
-------
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
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.42 In line with UNFCCC reporting guidelines, fuel
combustion emissions are included in this chapter, while process emissions are included in the Industrial Processes
and Product Use chapter of this report. In examining data from EPA's GHGRP that would be useful to improve the
emission estimates for the CCh 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 El A. 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.43
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 2015). 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 2015). 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 2013) and Jacobs (2010).44 Fuel consumption for the industrial sector was adjusted to subtract out
construction and agricultural use, which is reported under mobile sources.45 Construction and agricultural fuel use
42 See .
43 See.
44 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.
45 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.
3-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
was obtained from EPA (2014). 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
factors for these three end-use sectors were provided by the 2006IPCC Guidelines for National Greenhouse Gas
Inventories (IPCC 2006). 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 2014a).
This combustion technology- and fuel-use data was available by facility from 1996 to 2013 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 (2014a) and EIA (2015) total energy consumption estimates, the
remaining energy consumption from EIA (2015) was apportioned to each combustion technology type and fuel
combination using a ratio of energy consumption by technology type from 1996 to 2013.
Energy consumption estimates were not available from 1990 to 1995 in the EPA (2014a) dataset, and as a result,
consumption was calculated using total electric power consumption from EIA (2015) and the ratio of combustion
technology and fuel types from EPA (2015). 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 (2014a) and EIA (2015) datasets. The higher wood biomass consumption from EIA (2015) 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 (2015).
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
Approach 2 uncertainty estimation methodology, Monte Carlo Stochastic Simulation technique, with @PJSK
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 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 N2O emission factors, based on the SAIC/EIA (2001) report.46 For these variables, the uncertainty
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
Energy 3-33
-------
ranges were assigned to the input variables based on the data reported in SAIC/EIA (2001).47 However, the CH4
emission factors differ from those used by EIA. These factors and uncertainty ranges are based on IPCC default
uncertainty estimates (IPCC 2006).
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 3-17. Stationary
combustion CH4 emissions in 2013 (including biomass) were estimated to be between 4.6 and 20.4 MMT CCh Eq. at
a 95 percent confidence level. This indicates a range of 42 percent below to 157 percent above the 2013 emission
estimate of 8.0 MMT COa Eq.48 Stationary combustion N2O emissions in 2013 (including biomass) were estimated
to be between 16.8 and 32.0 MMT CCh Eq. at a 95 percent confidence level. This indicates a range of 27 percent
below to 40 percent above the 2013 emissions estimate of 22.9 MMT CO2 Eq.
Table 3-17: Approach 2 Quantitative Uncertainty Estimates for ChU and NzO Emissions from
Energy-Related Stationary Combustion, Including Biomass (MMT COz Eq. and Percent)
2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
°UrCe aS (MMTCChEq.) (MMT CCh Eq.) (%)
Stationary Combustion
Stationary Combustion
CH4
N2O
8.0
22.9
Lower
Bound
4.6
16.8
Upper
Bound
20.4
32.0
Lower
Bound
-42%
-27%
Upper
Bound
+157%
+40%
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 CCh 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 2013. 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
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous Inventories) which results in time-series recalculations
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.
47 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.
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.
3-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in emissions from CH4, HFCs, and PFCs. The GWPs of N2O and SF6 have decreased, leading to a decrease
in emissions. The AR4 GWPs have been applied across the entire time series for consistency. For more information
please see the Recalculations and Improvements Chapter.
Methane and N2O emissions from stationary sources (excluding CO2) across the entire time series were revised due
revised data from EIA (2015) and EPA (2014a) relative to the previous Inventory. In addition, with the adoption of
new GWPs, the entire time series from 1990 through 2012 decreased. The historical data changes resulted in an
average annual decrease of 0.3 MMT CO2 Eq. (4 percent) in CH4 emissions from stationary combustion and an
average annual increase of less than 0.2 MMT CO2 Eq. (1 percent) in N2O emissions from stationary combustion for
the period 1990 through 2012.
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.49
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.50
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
49 See.
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.
Energy 3-35
-------
developed by ICF (2004). These factors were derived from EPA, California Air Resources Board (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 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
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.51
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 2013 were obtained from the Federal Highway Administration's (FHWA)
Highway Performance Monitoring System database as reported in Highway Statistics (FHWA 1996 through
2014).52 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
2014) and information on total motor vehicle fuel consumption by fuel type from FHWA (1996 through 2014).
VMT for AFVs were estimated based on Browning (2003) and Browning (2015). 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 (1994a, 1994b, 1998, 1999a) and IPCC (2006).
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).53 Activity
data were obtained from AAR (2008 through 2013), APTA (2007 through 2013), APTA (2006), BEA (1991 through
2013), Benson (2002 through 2004), DHS (2008), DLA Energy (2014), DOC (1991 through 2013), DOE (1993
through 2013), DOT (1991 through 2013), EIA (2002, 2008, 2007, 2014), EIA (2007 through 2015), EIA (1991
through 2014), EPA (2014d), Esser (2003 through 2004), FAA (2015), FHWA (1996 through 2014), Gaffney
(2007), and Whorton (2006 through 2013). Emission factors for non-road modes were taken from IPCC (2006) and
Browning (2009).
5! Additional information regarding the model can be found online at .
52 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.
The consumption of international bunker fuels is not included in these activity data, but is estimated separately under the
International Bunker Fuels source category.
3-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis was conducted for the mobile source sector using the IPCC-recommended
Approach 2 uncertainty estimation methodology, Monte Carlo Stochastic Simulation technique, using @RISK
software. The uncertainty analysis was performed on 2013 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) 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 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 2013 were estimated to be between 1.9 and 2.6 MMT
CO2 Eq. at a 95 percent confidence level. This indicates a range of 13 percent below to 21 percent above the
corresponding 2013 emission estimate of 2.1 MMT CO2Eq. Also at a 95 percent confidence level, mobile
combustion N2O emissions from mobile sources in 2013 were estimated to be between 16.6 and 22.1 MMT CO2
Eq., indicating a range of 10 percent below to 20 percent above the corresponding 2013 emission estimate of 18.4
MMT CO2 Eq.
Table 3-18: Approach 2 Quantitative Uncertainty Estimates for ChU and NzO Emissions from
Mobile Sources (MMT COz Eq. and Percent)
2013 Emission Estimate3 Uncertainty Range Relative to Emission Estimate3
S°UrCe GaS (MMTCChEq.) (MMT CCh Eq.) (%)
Mobile Sources
Mobile Sources
CH4
N20
2.1
18.4
Lower
Bound
1.9
16.6
Upper
Bound
2.6
22.1
Lower
Bound
-13%
-10%
Upper
Bound
+21%
+20%
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 Approach 2 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 2013. 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.
Energy 3-37
-------
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh-equivalent emissions from CH4, HFCs, and PFCs. The GWPs of N2O and SF6 have decreased,
leading to a decrease in CCh-equivalent emissions for these greenhouse gases. The AR4 GWPs have been applied
across the entire time series for consistency. For more information please see the Recalculations and Improvements
Chapter.
Increases to CH4 and N2O emissions from mobile combustion are largely due to updates made to the Motor Vehicle
Emissions Simulator (MOVES 2014) model that is used to estimate on-road gasoline vehicle distribution and
mileage across the time series. Estimates of alternative fuel vehicle mileage were also revised to reflect updates
made to Energy Information Administration (EIA) data on alternative fuel use and vehicle counts. In addition, the
alternative fuel vehicle emissions estimates now assume a B100 biodiesel mixture (as opposed to B20, which was
used for the previous Inventory report). Overall, these changes resulted in an average annual increase of 0.8 MMT
CO2 Eq. (26 percent) in CH4 emissions and an average annual decrease of 0.4 MMT CO2 Eq. (1 percent) in N2O
emissions from mobile combustion for the period 1990 through 2012, relative to the previous report.
Planned Improvements
While the data used for this report represent the most accurate information available, several 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
domestic marine activity data to improve the estimates is currently being investigated. Additionally, the
feasibility of including data from a broader range of domestic and international sources for domestic bunker
fuels, including data from studies such as the Third IMO GHG Study 2014, is being considered.
• 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
3-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
occur during the product's lifetime, such as during solvent use. Overall, throughout the time series and across all
uses, about 60 percent of the total C consumed for non-energy purposes was stored in products, and not released to
the atmosphere; the remaining 40 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 CCh 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 and Product Use 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 2013 from the non-energy uses of fossil fuels were 119.8 MMT
CO2 Eq., which constituted approximately 2 percent of overall fossil fuel emissions. In 2013, the consumption of
fuels for non-energy uses (after the adjustments described above) was 4,790.7 TBtu, an increase of 7.0 percent since
1990 (see Table 3-20). About 56.2 MMT (205.9 MMT CO2 Eq.) of the C in these fuels was stored, while the
remaining 32.7 MMT C (119.8 MMT CO2 Eq.) was emitted.
Table 3-19: COz Emissions from Non-Energy Use Fossil Fuel Consumption (MMT COz Eq. and
percent)
Year
Potential Emissions
C Stored
Emissions as a % of Potential
Emissions
1990
312.1
194. sl
38%H
117.7
2005
377.5
238.6B
37%
138.9
2009
307.5
201.5
34%
106.0
2010
325.6
211.1
35%
114.6
2011
316.4
208.0
34%
108.4
2012
315.5
206.4
34%
104.9
2013
325.8
205.9
37%
119.8
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
supplied by the EIA (2013, 2015) (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 and Product Use chapter.54'55 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
54 These source categories include Iron and Steel Production, Lead Production, Zinc Production, Ammonia Manufacture, Carbon
Black Manufacture (included in Petrochemical Production), Litanium Dioxide Production, Ferroalloy Production, Silicon
Carbide Production, and Aluminum Production.
55 Some degree of double counting may occur between these estimates of non-energy use of fuels and process emissions from
petrochemical production presented in the Industrial Processes and Produce Use sector. Data integration is not feasible at this
time as feedstock data from EIA used to estimate non-energy uses of fuels are aggregated by fuel type, rather than disaggregated
by both fuel type and particular industries (e.g. petrochemical production) as currently collected through EPA's GHGRP and
used for the petrochemical production category.
Energy 3-39
-------
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 (2006), 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
+8
8.2!
281. fM
1,170.2
1,120.5
186.31
H7.6B
326.3 B
662. ll
36.78
27.2
100.9B
Ł
137.8B
176.oB
176.oB
86.7
0.7
86.0
4,478.5
2005
5,110.9
80.4
11.9B
260.9B
1,323.2
1,610.1
160.28
95.58
679.68
499.58
67.78
105.28
60.98
11.78
31.48
112.88
151.38
151.38
121.98
4.6B
117.3
5,384.1
2009
4,283.0
6.4
11.9
220.3
873.1
1,663.9
134.5
61.0
451.0
392.8
133.9
108.4
44.3
17.5
12.2
151.8
127.1
127.1
59.6
1.0
58.5
4,469.6
2010
4,572.9
64.8
10.3
298.7
877.8
1,834.1
149.5
75.3
474.6
433.2
147.8
+
25.3
5.8
17.1
158.7
141.2
141.2
63.7
1.0
62.7
4,777.8
2011
4,470.5
60.8
10.3
297.1
859.5
1,865.8
141.8
26.4
469.4
368.2
163.6
+
21.8
5.8
15.1
164.7
133.9
133.9
54.1
1.0
53.1
4,658.5
2012
4,376.7
132.5
10.3
292.6
826.7
1,886.9
130.5
40.2
432.2
267.4
160.6
+
14.1
5.8
15.3
161.6
123.2
123.2
50.6
1.0
49.5
4,550.5
2013
4,619.9
119.6
10.3
296.9
783.3
2,062.0
138.1
45.4
498.5
209.0
166.7
+
96.5
5.8
16.5
171.2
130.4
130.4
40.5
1.0
39.4
4,790.7
+ Does not exceed 0.05 TBtu
NA (Not applicable)
Table 3-21: 2013 Adjusted Non-Energy Use Fossil Fuel Consumption, Storage, and Emissions
Adjusted
Non-Energy
Sector/Fuel Type
Industry
Industrial Coking Coal
Industrial Other Coal
Natural Gas to
Chemical Plants
Asphalt & Road Oil
Use3
(TBtu)
4,619.9
119.6
10.3
296.9
783.3
Carbon
Content
Coefficient
(MMT
C/QBtu)
NA
31.00
25.82
14.47
20.55
Potential
Carbon
(MMTC)
85.4
3.7
0.3
4.3
16.1
Storage
Factor
NA
0.10
0.66
0.66
1.00
Carbon
Stored
(MMTC)
55.8
0.4
0.2
2.8
16.0
Carbon
Emissions
(MMTC)
29.6
3.3
0.1
1.5
0.1
Carbon
Emissions
(MMT C02
Eq.)
108.4
12.2
0.3
5.3
0.3
3-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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
2,062.0
138.1
45.4
498.6
209.0
166.7
0.0
96.5
5.8
16.5
171.2
130.4
130.4
40.5
1.0
39.4
4,790.7
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
35.2
2.8
0.9
9.2
4.2
2.9
0.0
1.9
0.1
0.3
3.5
2.6
2.6
0.8
0.0
0.8
88.9
0.66
0.09
0.66
0.66
0.66
0.66
0.30
0.66
0.50
0.58
0.00
NA
0.09
NA
0.09
0.10
23.3
0.3
0.6
6.1
2.8
1.9
0.0
1.3
0.1
0.2
0.0
0.2
0.2
0.1
0.0
0.1
56.2
11.9
2.5
0.3
3.1
1.4
1.0
0.0
0.6
0.1
0.1
3.5
2.4
2.4
0.7
0.0
0.7
32.7
43.7
9.3
1.1
11.5
5.2
3.6
0.0
2.4
0.2
0.5
12.7
8.8
8.8
2.7
0.1
2.6
119.8
+ 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. Emissions values are presented in CO2 equivalent mass units using IPCC
AR4 GWP values.
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 (EPA2015a), Toxics Release
Inventory, 1998 (2000b), Biennial Reporting System (EPA 2004, 2009), Resource Conservation and Recovery Act
Information System (EPA 2013b, 2015b), pesticide sales and use estimates (EPA 1998, 1999, 2002, 2004, 2011),
and the Chemical Data Access Tool (EPA 2012); 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, 2014); Financial Planning Association
(2006); INEGI (2006); the United States International Trade Commission (1990-2014); Gosselin, Smith, and Hodge
(1984); EPA's Municipal Solid Waste (MSW) Facts and Figures (EPA 2013a; 2014a); the Rubber Manufacturers'
Association (RMA 2009, 2011, 2014); the International Institute of Synthetic Rubber Products (IISRP 2000, 2003);
the Fiber Economics Bureau (FEE 2001-2013); the EPA Chemical Data Access Tool (CDAT) (EPA 2014b); and the
American Chemistry Council (ACC 2003-2011, 2012, 2013, 2014a, 2014b). 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
Approach 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.
Energy 3-41
-------
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 2006IPCC 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 Approach 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 2013 was estimated to be between
89.0 and 164.9 MMT CCh Eq. at a 95 percent confidence level. This indicates a range of 26 percent below to 38
percent above the 2013 emission estimate of 119.8 MMT CCh 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: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from Non-
Energy Uses of Fossil Fuels (MMT COz Eq. and Percent)
Source
Gas
2013 Emission Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Feedstocks
Asphalt
Lubricants
Waxes
Other
Total
CO2
CO2
C02
C02
CO2
C02
73.2
0.3
18.1
0.5
27.8
119.8
Lower
Bound
48.8
0.1
14.9
0.4
16.0
89.0
Upper
Bound
122.0
0.6
21.0
0.8
30.1
164.9
Lower
Bound
-33%
-58%
-18%
-27%
-42%
-26%
Upper
Bound
67%
120%
16%
59%
8%
38%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence
interval.
Table 3-23: Approach 2 Quantitative Uncertainty Estimates for Storage Factors of Non-
Energy Uses of Fossil Fuels (Percent)
Source
Gas
2013 Storage Factor
Uncertainty Range Relative to Emission Estimate3
(%) (%, Relative)
Feedstocks
Asphalt
Lubricants
Waxes
Other
C02
C02
C02
CO2
CO2
66%
100%
9%
58%
6%
Lower
Bound
53%
99%
4%
49%
5%
Upper
Bound
72%
100%
17%
71%
44%
Lower
Bound
-20%
0%
-57%
-16%
-15%
Upper
Bound
9%
0%
90%
22%
607%
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
3-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 2013. 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 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).
Recalculations Discussion
Relative to the previous Inventory, emissions from non-energy uses of fossil fuels decreased by an average of 0.61
MMT CO2 Eq. (0.2 percent) across the entire time series. The greatest change was an increase of 7 MMT CO2 Eq. in
2011. The 2014 Guide to the Business of Chemistry contained several new data points for 2008 through 2013, and
those values were updated in this Inventory. Additionally, the Rubber Manufacturers Association released a new
report with scrap tire characteristics and statistics for 2011 and 2013. Preliminary data for the 2012 Economic
Census (U.S. Bureau of the Census 2014) were released which contains data on cleanser shipments in 2012. The
hazardous waste data from the Biennial Report (EPA 2015b) provided updated data for 2011, which changed the
hazardous waste emissions slightly. EPA's Chemical Data Access Tool (CDAT) (EPA 2014b) was the source of the
production data for food additives, antifreeze, and silicones in 2012. Data were interpolated from the latest data
point to 2012, to account for this new data source. Import and export data (U.S. International Trade Commission
2014) for synthetic rubber was included in the synthetic rubber stocks in the current inventory.
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.
Energy 3-43
-------
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 C 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.
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.
Reviewing the storage of carbon black across various sectors in the Inventory; in particular, the carbon
black abraded and stored in tires.
Box 3-6: Reporting of Lubricants, Waxes, and Asphalt and Road Oil Product Use in Energy Sector
The 2006 IPCC Guidelines provides methodological guidance to estimate emissions from the first use of fossil fuels
as a product for primary purposes other than combustion for energy purposes (including lubricants, paraffin waxes,
3-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
bitumen/asphalt, and solvents) under the Industrial Processes and Product Use (IPPU) sector.56 In this Inventory, C
storage and C emissions from product use of lubricants, waxes, and asphalt and road oil are reported under the
Energy sector in the Carbon Emitted from Non-Energy Uses of Fossil Fuels source category (IPCC Source Category
1A).57
The emissions are reported in the Energy sector, as opposed to the IPPU sector, to reflect national circumstances in
its choice of methodology and to increase transparency of this source category's unique country-specific data
sources and methodology. The country-specific methodology used for the Carbon Emitted from Non-Energy Uses of
Fossil Fuels source category is based on a carbon balance (i.e., C inputs-outputs) calculation of the aggregate
amount of fossil fuels used for non-energy uses, including inputs of lubricants, waxes, asphalt and road oil (see
section 3.2, Table 3-21). For those inputs, U.S. country-specific data on C stocks and flows are used to develop
carbon storage factors, which are calculated as the ratio of the C stored by the fossil fuel non-energy products to the
total C content of the fuel consumed, taking into account losses in the production process and during product use.58
The country-specific methodology to reflect national circumstances starts with the aggregate amount of fossil fuels
used for non-energy uses and applies a C balance calculation, breaking out the C emissions from non-energy use of
lubricants, waxes, and asphalt and road oil. Due to U.S. national circumstances, reporting these C emissions
separately under IPPU would involve making artificial adjustments to both the C inputs and C outputs of the non-
energy use C balance. These artificial adjustments would also result in the C emissions for lubricants, waxes, and
asphalt and road oil being reported under IPPU, while the C storage for lubricants, waxes, and asphalt and road oil
would be reported under Energy. To avoid presenting an incomplete C balance and a less transparent approach for
the Carbon Emitted from Non-Energy Uses of Fossil Fuels source category calculation, the entire calculation of C
storage and C emissions is therefore conducted in the Non-Energy Uses of Fossil Fuels category calculation
methodology, and both the C storage and C emissions for lubricants, waxes, and asphalt and road oil are reported
under the Energy sector.
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 et al. 2004, Simmons et al. 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 CCh.
According to IPCC guidelines, when the CC>2 emitted is of fossil origin, it is counted as a net anthropogenic
emission of CCh 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
56 See Volume 3: Industrial Processes and Product Use, Chapter 5: Non-Energy Products from Fuels and Solvent Use of the
2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006).
57 Non-methane volatile organic compound (NMVOC) emissions from solvent use are reported separately in the IPPU sector,
following Chapter 5 of the 2006 IPCC Guidelines.
Data and calculations for lubricants and waxes and asphalt and road oil are in Annex 2.3: Methodology and Data for
Estimating CCh Emissions from Fossil Fuel Combustion.
Energy 3-45
-------
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 were incinerated in the United States in 2013 (EPA 2014). CO2
emissions from incineration of waste rose 42 percent since 1990, to an estimated 10.1 MMT CC^Eq. (10,137 kt) in
2013, 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 CH4 and N2O emissions (De Soete 1993, IPCC 2006). CH4 emissions
from the incineration of waste were estimated to be less than 0.05 MMT CC>2 Eq. (less than 0.5 kt CH4) in 2013, and
have not changed significantly since 1990. N2O emissions from the incineration of waste were estimated to be 0.3
MMT CO2 Eq. (1 kt N2O) in 2013, and have not changed significantly since 1990.
Table 3-24: COz, CH4, and NzO Emissions from the Incineration of Waste (MMT COz Eq.)
Gas/Waste Product
C02
Plastics
Synthetic Rubber in Tires
Carbon Black in Tires
Synthetic Rubber in
MSW
Synthetic Fibers
CH4
N20
Total
1990
8.0
5.6 1
0.3 1
0.4 1
0.9 1
0.8 1
+
0.5
8.4
2005
12.5
6.9 1
1.6 1
2.0 1
0.8 1
1.2 1
+ 1
0.4
12.8
2009
11.3
5.9
1.6
1.9
0.7
1.2
+
0.3
11.6
2010
11.0
6.0
1.5
1.8
0.7
1.1
+
0.3
11.4
2011
10.5
5.8
1.4
1.7
0.7
1.1
+
0.3
10.9
2012
10.4
5.7
1.3
1.5
0.7
1.1
+
0.3
10.7
2013
10.1
5.7
1.2
1.4
0.7
1.1
+
0.3
10.4
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
+ Does not exceed 0.05 MMT.
Table 3-25: COz, CH4, and NzO Emissions from the Incineration of Waste (kt)
Gas/Waste Product
CCh
Plastics
Synthetic Rubber in Tires
Carbon Black in Tires
Synthetic Rubber in MSW
Synthetic Fibers
CH4
N2O
1990
7,972
5,588
308 1
385
854 1
838
+ 1
2
2005
12,454
6,919
1,599
1,958
765 1
1,212
+
1
2009
11,295
5,946
1,560
1,903
731
1,155
+
1
2010
11,026
5,969
1,461
1,783
701
1,112
+
1
2011
10,550
5,757
1,363
1,663
712
1,056
+
1
2012
10,363
5,709
1,262
1,537
705
1,149
+
1
2013
10,137
5,709
1,161
1,412
705
1,149
+
1
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.05 MMT.
Methodology
Emissions of CCh from the incineration of waste include CCh 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
3-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 CO2 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). For 2013, this data was assumed to be equal to that in 2012, due to the lack
of available data. The proportion of total waste discarded that is incinerated was derived from data inBioCycle'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 2013 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 2013 data (RMA 2014). 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).
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 for 2011 were derived from information
forthcoming in Themelis and Shin (in press) and Shin (2014). Data on total waste incinerated was not available for
2012 or 2013, so these values were assumed to equal to the 2011 value.
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 (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%
2009
2010
2011
2012
2013
270,067,786
271,592,991
273,116,704
273,116,704a
273,116,704a
23,674,017
22,714,122
21,741,734
20,756,870
20,756,870
8.4%
8.0%
7.6%
7.6%
7.6%
a Assumed equal to 2011 value.
Source: van Haaren et al. (2010), Themelis and Shin (in press) and Shin (2014).
Energy 3-47
-------
Uncertainty and Time-Series Consistency
An Approach 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 Approach 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 synthetk^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 Approach 2 quantitative uncertainty analysis are summarized in Table 3-27. Waste incineration
CO2 emissions in 2013 were estimated to be between 9.1 and 11.5 MMT CCh Eq. at a 95 percent confidence level.
This indicates a range of 10 percent below to 13 percent above the 2013 emission estimate of 10.1 MMT €62 Eq.
Also at a 95 percent confidence level, waste incineration N2O emissions in 2013 were estimated to be between 0.2
and 1.3 MMT €62 Eq. This indicates a range of 50 percent below to 325 percent above the 2013 emission estimate
of0.3MMTCO2Eq.
Table 3-27: Approach 2 Quantitative Uncertainty Estimates for COz and NzO from the
Incineration of Waste (MMT COz Eq. and Percent)
2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
Source Gas (MMT CCh Eq.) (MMT CCh Eq.) (%)
Incineration of Waste
Incineration of Waste
CO2
N20
10.1
0.3
Lower
Bound
9.1
0.2
Upper
Bound
11.5
1.3
Lower
Bound
-10%
-50%
Upper
Bound
+13%
+325%
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 2013. 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
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
3-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Assessment Report (SAR) (IPCC 1996) (used in the previous Inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh-equivalent emissions from CH4, HFCs, and PFCs. The GWPs of N2O and SF6 have decreased,
leading to a decrease in CCh-equivalent emissions for these greenhouse gases. The AR4 GWPs have been applied
across the entire time series for consistency. For more information please see the Recalculations and Improvements
chapter.
In addition, the data for synthetic rubber and carbon black in scrap tires were updated for 2010 through 2013, based
on data obtained from RMA 2013 Scrap Tire Markets Report, which was released in November 2014. This update
resulted in an average of a 3 percent decrease of emissions for 2010 through 2012.
The data which calculates the percent incineration was updated in the current inventory. Biocycle has not released a
new State of Garbage in America Report since 2010 (with 2008 data), which used to be a semi-annual publication
which publishes the results of the nation-wide MSW survey. The results of the survey have been submitted for
publishing in Themelis and Shin (in press). This provided updated MSW figures for 2011, so the generation and
incineration data for 2009 through 2013 are proxied to the 2011 values.
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,59 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 and Product Use 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 CO2 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 use of facility-level data in national inventories will be relied upon.60 GHGRP data is available for MSW
combustors, which contains information on the CC>2, CH4, and N2O emissions from MSW combustion, plus the
fraction of the emissions that are biogenic. To calculate biogenic versus total CO2 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 conducted 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 Non-Energy Uses of Fossil Fuels category. Waste incineration activities that do not include
energy recovery will also be examined.
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
59 See .
60 See.
Energy 3-49
-------
production (see Table 3-30), underground coal mines contribute the largest share of CH4 emissions (see Table 3-28
and Table 3-29) due to the higher CH4 content of coal in the deeper underground coal seams. In 2013, 395
underground coal mines and 637 surface mines were operating in the U.S. Also in 2013, the U.S. was the second
largest coal producer in the world (891 MMT), after China (3,561 MMT) and followed by India (613 MMT) (IEA
2014).
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 2013 were estimated to be 64.6 MMT CCh Eq. (2,584 kt CH4), a decline of 33 percent since
1990 (see Table 3-28 and Table 3-29). Of this amount, underground mines accounted for approximately 71.6
percent, surface mines accounted for 15.0 percent, and post-mining emissions accounted for 13.4 percent.
Table 3-28: CH4 Emissions from Coal Mining (MMT COz Eq.)
Activity
UG Mining
Liberated
Recovered & Used
Surface Mining
Post-Mining (Under Ground)
Post-Mining (Surface)
Total
1990
74.2
80.8
(6.6)
10.8
9.2
2.3
96.5
2005
42,
59,
.0
7
(17.7)
11.9
7.
2.
64.
,6
,6
1
2009
59.2
78.7
(19.5)
11.5
6.7
2.5
79.9
2010
61.6
85.2
(23.6)
11.5
6.8
2.5
82.3
2011
50.2
71
(20.!
11
6
2
71,
0
*)
.6
.9
.5
.2
2012
47.3
65.8
(18.5)
10.3
6.7
2.2
66.5
2013
46.2
65.8
(19.6)
9.7
6.6
2.1
64.6
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table 3-29: CH4 Emissions from Coal Mining (kt)
Activity
UG Mining
Liberated
Recovered & Used
Surface Mining
Post-Mining (UG)
Post-Mining (Surface)
Total
1990
2,968
3,234
(266)
430
368 1
93
3,860
2005
1,682
2,390
(708)
475
306 1
103
2,565
2009
2,367
3,149
1(782)
461
267
1 100
3,194
2010
2,463
3,406
(943)
461
270
100
3,293
2011
2,008
2,839
(831)
465
276
101
2,849
2012
1,891
2,631
(740)
410
268
89
2,658
2013
1,849
2,633
(784)
388
263
84
2,584
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.
3-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Step 1: Estimate CH4 Liberated and CH4 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 CH4 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 concentrations61 to ensure miner safety, these mine-by-mine measurements are
used to estimate CH4 emissions from ventilation systems. 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 CO2 Eq.) through its GHGRP (EPA 2014).62 Many of the underground coal mines
reporting to EPA's GHGRP use the quarterly CH4 emission data collected by MSHA. However, some mines use
their own measurements and samples, which are taken on a quarterly basis. The 2013 ventilation emissions were
calculated using the GHGRP data from the mines that take their own measurements and the MSHA data for all other
mines.
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
2013. For Alabama 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. The well data was also used to estimate
CH4 collected from mined-through pre-drainage wells. For most other mines that either sold CH4 to a pipeline, used
CH4 on site, or vented CH4 from degasification systems, data on degasification emissions reported to the EPA's
GHGRP (EPA 2014) were used.
Step 1.3: Estimate CH4 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 2013, fifteen active coal mines had CH4 recovery and use projects, of which thirteen 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 two other mines used recovered CH4 to heat mine
ventilation air or dry coal. Emissions avoided as a result of pipeline sales projects at Alabama and West Virginia
mines were estimated using gas sales data reported by the state agencies. For all other mines with pipeline sales or
used methane for electric power or heating, either the coal mine operators or project developers supplied information
regarding methane recovery or GHGRP data were used.
Step 2: Estimate CH4 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 2014) 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
61 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.
62 Underground coal mines report to EPA under Subpart FF of the program.
Energy 3-51
-------
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). Beginning in 2006, revised data on in
situ CH4 content and emission factors have been taken from EPA (1996) and EPA (2005).
Table 3-30: Coal Production (kt)
Year Underground Surface
Total
546,808 931,052
^^H
691,448 1,025,846
2009
2010
2011
2012
2013
301,241
305,862
313,529
310,608
309,546
671,475
676,177
684,807
610,307
581,270
972,716
982,039
998,337
920,915
890,815
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis was conducted for the coal mining source category using the IPCC-
recommended Approach 2 uncertainty estimation methodology. Because emission estimates from underground
ventilation systems were based on actual measurement data from MSHA or EPA's GHGRP, 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).
GHGRP data was used for a number of the mines beginning in 2013, however, the equipment uncertainty is applied
to both MSHA and GHGRP data.
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.
Continuous CH4 monitoring is required of mines that report utilized methane on or off-site to EPA's GHGRP.
Beginning in 2013, use of GHGRP data for mines without publicly-available gas usage or sales records has reduced
the uncertainty from previous estimations. In addition, since 2012, GHGRP data has been used to estimate CH4
emissions from vented degasification wells, thus reducing the uncertainty associated with 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
Approach 2 quantitative uncertainty analysis are summarized in Table 3-31. Coal mining CH4 emissions in 2013
were estimated to be between 56.6 and 74.7 MMT CC>2 Eq. at a 95 percent confidence level. This indicates a range
of 12.4 percent below to 15.6 percent above the 2013 emission estimate of 64.6 MMT CChEq.
Table 3-31: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Coal
Mining (MMT COz Eq. and Percent)
Source
Gas
2013 Emission Estimate
(MMT CCh Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CCh Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Coal Mining
CH4
64.6
56.6
74.7
-12.4%
+15.6%
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
3-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section.
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh-equivalent emissions from CH4. The GWPs of N2O and SF6 have decreased, leading to a decrease in
CCh-equivalent emissions for these greenhouse gases. The AR4 GWPs have been applied across the entire time
series for consistency. For more information please see the Recalculations and Improvements Chapter.
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 and 2013 were used to estimate vented
degasification volumes for those mines. GHGRP data was also used in 2013 for degas-used volumes at mines using
methane on-site or without available gas sales records. In addition, for forty-nine mines, the 2013 VAM emission
estimates included VAM data measured at least quarterly and reported to the GHGRP. Emissions avoided at mines
with VAM mitigation projects (2) were estimated based on emission reductions registered at the Climate Action
Reserve GHG Registry (CAR 2014).
Planned Improvements
Future improvements to the Coal Mining category will include continued analysis and integration into the national
inventory of the degasification quantities and ventilation emissions data reported by underground coal mines to
EPA's GHGRP. A higher reliance on the GHGRP will provide greater consistency and accuracy in future
inventories. MSHA data will serve as a quality assurance tool for validating GHGRP data. Reconciliation of the
GHGRP and Inventory data sets are still in progress. In implementing improvements and integrating 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 (IPCC 2011).
3.5 Abandoned Underground Coal Mines (IPCC
Source Category IBla)
Underground coal mines contribute the largest share of coal mine methane (CMM) 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:
Energy 3-53
-------
• 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.
Annual gross abandoned mine CH4 emissions ranged from 7.2 to 10.8 MMT CO2 Eq. from 1990 through 2013,
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 (10.8 MMT CCh Eq.) due to the large number
of mine closures from 1994 to 1996 (72 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 2002, there have been fewer than
twelve gassy mine closures each year. There were eight gassy mine closures in 2013. In 2013, gross abandoned
mine emissions decreased slightly to 8.8 MMT CCh Eq. (see Table 3-32 and Table 3-33). Gross emissions are
reduced by CH4 recovered and used at 37 mines, resulting in net emissions in 2013 of 6.2 MMT €62 Eq.
Table 3-32: CH4 Emissions from Abandoned Coal Mines (MMT COz Eq.)
Activity
Abandoned Underground Mines
Recovered & Used
Total
1990
Tl
7.2
2005
8.4
1.8
6.6
2009
9.9
1 3.6
6.4
2010
9.7
3.2
6.6
2011
9.3
2.9
6.4
2012
8.9
2.7
6.2
2013
8.8
2.6
6.2
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 3-33: ChU Emissions from Abandoned Coal Mines (kt)
Activity 1990 2005 2009 2010 2011 2012 2013
Abandoned Underground Mines 288 334 398 389 373 358 353
Recovered & Used + B 70 H 143 126 116 109 104
Total 288 264 254 263 257 249 249
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.5 kt
Methodology
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
3-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
reservoir pressure (Pr) declines as described by the isotherm's characteristics. The emission rate declines because
the mine pressure (Pw) is essentially constant at atmospheric pressure for a vented mine, and the productivity index
(PI), which is expressed as the flow rate per unit of pressure change, is essentially constant at the pressures of
interest (atmospheric to 30 psia). The CH4 flow rate is determined by the laws of gas flow through porous media,
such as Darcy's Law. 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:
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 2004).
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 2004).
where,
q = Gas flow rate at time t in mmcfd
qi = 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
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 2004).
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 the 492
abandoned mines closed after 1972 produced emissions greater than 100 mcfd when active. Further, the status of
283 of the 492 mines (or 58 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 42
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 2004).
Energy 3-55
-------
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
33
32
42
0
27
134
25
3
22
0
3
53
48
14
16
16
2
96
106
49
80
16
32
283
137
27
36
0
9
209
243
76
116
16
41
492
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, is helpful but does not provide
enough data to directly employ the methodology used to calculate emissions from mines abandoned before 1972. 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 2004).
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 2013. 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 2013, 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.
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.
3-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 3-35. Annual abandoned
coal mine CH4 emissions in 2013 were estimated to be between 5.0 and 7.7 MMT CO2 Eq. at a 95 percent
confidence level. This indicates a range of 20 percent below to 24 percent above the 2013 emission estimate of 6.2
MMT CO2 Eq. One of the reasons for the relatively narrow range is that mine-specific data is available for use in
the methodology for mines closed after 1972. Emissions from mines closed prior to 1972 have the largest degree of
uncertainty because no mine-specific CH4 liberation rates exist.
Table 3-35: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from
Abandoned Underground Coal Mines (MMT COz Eq. and Percent)
^ „ 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
6 (MMT CCh Eq.) (MMT CCh Eq.) (%)
Abandoned Underground ~ +24%
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 2013. 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 onshore and offshore 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. Total CH4 and CO2 emissions from petroleum
systems in 2013 were 25.2 MMT CO2 Eq. (1,009 kt CH4)63 and 6.0 MMT CO2 Eq. (6,001 kt), respectively. Since
1990, CH4 emissions have decreased by 20 percent. The net decrease is due mainly to increasing voluntary
reductions through Natural Gas STAR in the production segment. From 2012 to 2013, CH4 emissions increased 8
percent, due mainly to increases in tank venting and pneumatic controller vents. CO2 emissions have increased by
35 percent since 1990, and 19 percent from 2012 to 2013, due to increased domestic production, with the largest
increases occurring in crude refining CO2 emissions.
Production Field Operations. Production field operations account for 96 percent of total CH4 emissions from
petroleum systems. Vented CH4 from field operations account for approximately 79 percent of the emissions from
the production sector, uncombusted CH4 emissions (i.e. unburned fuel) account for 11 percent, fugitive emissions
are 9 percent, and process upset emissions are approximately 0.3 percent. The most dominant sources of emissions,
in order of magnitude, are high bleed pneumatic controllers, oil tanks, shallow water offshore oil platforms, low
bleed pneumatic controllers, gas engines, oil wellheads (light crude services), and chemical injection pumps,. These
seven sources alone emit 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
63 The CH4 emission estimate for 2013 for petroleum systems decreased by approximately 15 MMT CCh Eq. from the value
presented in the public review draft. This change is largely due to a decrease in the number of pneumatic controllers calculated
for the petroleum production segment and an increase in the Natural Gas STAR emissions reductions allocated to petroleum
systems (correction of a spreadsheet error noted in the public review draft). For more information, please see Recalculations
Discussion.
Energy 3-57
-------
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 21 percent. This net decrease is due
mainly to increasing voluntary reductions through Natural Gas STAR in the production segment.
Vented CCh associated with production field operations account for approximately 99 percent of the total €62
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 CC>2 emissions are oil tanks, high bleed pneumatic
controllers, shallow water offshore oil platforms, low bleed pneumatic controllers, and oil wellheads (light crude
services). These five sources together account for slightly over 98 percent of the non-combustion CCh emissions
from production field operations, while the remaining 1 percent of the emissions is distributed among 24 additional
activities within the three categories: vented, fugitive, and process upsets. Note that CCh from associated gas flaring
is accounted in natural gas systems production emissions. CCh emissions from flaring for both natural gas and oil
were 16 MMT CO2 Eq. in 2013.
Crude Oil Transportation. Crude oil transportation activities account for approximately 0.7 percent of total CH4
emissions from the oil industry. Venting from tanks, truck loading, rail loading, and marine vessel loading
operations account for 82 percent of CH4 emissions from crude oil transportation. Fugitive emissions, almost
entirely from floating roof tanks, account for 14 percent of CH4 emissions from crude oil transportation. The
remaining 4 percent is distributed among two additional sources within the vented emissions category (i.e., pump
station maintenance and pipeline pigging). Emissions from pump engine drivers and heaters were not estimated due
to lack of data.
Since 1990, CH4 emissions from transportation have increased by almost 4 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. Methane emissions have increased by approximately 11 percent
from 2012 levels.
Crude Oil Refining. Crude oil refining processes and systems account for slightly above 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,
combustion emissions account for about 60 percent of the CH4 emissions, while vented and fugitive emissions
account for approximately 26 and 13 percent, respectively. Flare emissions are the primary combustion emissions
contributor. Refinery system blowdowns for maintenance and process vents are the primary venting contributors.
Most of the fugitive CH4 emissions from refineries are from equipment leaks and storage tanks.
CH4 emissions from refining of crude oil have increased by approximately 24 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 27 and 34 kt.
Flare emissions from crude oil refining accounts for 95 percent of the total CC>2 emissions in petroleum systems.
Refinery CC>2 emissions increased by 36 percent from 1990 to 2013.
Table 3-36: CH4 Emissions from Petroleum Systems (MMT COz Eq.)
Activity
Production Field Operations
(Potential)
Pneumatic controller venting*
Tank venting
Combustion & process upsets
Misc. venting & fugitives
Wellhead fugitives
Production Voluntary Reductions
1990
30.8 |
12,
6.
2.
7.
1.
(0.1
.2
,3
,9 1
2005
25.1
10
4,
2,
.1
.7
.3
,9 6.9
,5 1.2
) 1 (2.6)
2009
26.3
10.6
5.0
2.4
7.0
1.3
(5.7)
2010
26.9
10.8
5.3
2.5
7.1
1.3
(6.4)
2011
27.6
11.1
5.5
2.5
7.1
1.4
(6.6)
2012
29.6
11.6
6.7
2.7
7.2
1.5
(7.3)
2013
31
11
7
2
7
1
(7.
.3
.9
.9
.8
.2
.5
1)
3-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Production Field Operations
(Net)
Crude Oil Transportation
Refining
Total
30.8
0.2
0.7
31.5
0.8
23.5
20.6
0.1
0.7
21.5
20.6
0.1
0.7
21.3
21.1
0.1
0.8
22.0
22.3
0.2
0.8
23.3
24.
0.
0.
25.
,2
,2
8
,2
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding.
a Values presented in this table for pneumatic controllers are potential emissions. Net emissions from pneumatic
controllers are presented in the Recalculations Discussion.
Table 3-37: CH4 Emissions from Petroleum Systems (kt)
Activity
Production Field Operations
(Potential)
Pneumatic controller venting*
Tank venting
Combustion & process upsets
Misc. venting & fugitives
Wellhead fugitives
Production Voluntary Reductions
Production Field Operations
(Net)
Crude Oil Transportation
Refining
Total
1990
1,230
489 1
250 1
115 1
317 1
58 1
(3) 1
1,227
7 1
27
1,261
2005
1,006
405
187
91
275
47
(103)
903
5
31
939
2009
1,053
425
202
95
279
52
(227)
826
5
29
860
2010
1,077
433
210
98
282
54
(255)
822
5
27
854
2011
1,106
443
222
101
284
56
(263)
843
5
30
878
2012
1,184
463
267
107
287
59
(290)
893
6
32
931
2013
1,253
474
317
113
289
60
(285)
969
7
34
1,009
Note: Totals may not sum due to independent rounding.
a Values presented in this table for pneumatic controllers are potential emissions. Net emissions from
pneumatic controllers are presented in the Recalculations Discussion.
Table 3-38: COz Emissions from Petroleum Systems (MMT COz Eq.)
Activity
Production Field Operations
Pneumatic controller venting
Tank venting
Misc. venting & fugitives
Wellhead fugitives
Process upsets
Crude Refining
Total
1990 2005
0.4 1 0.3
+ +
0.3 1 0.2
: :
4.1 4.6
4.4 4.9
2009 2010 2011 2012 2013
0.3 0.3 0.3 0.4 0.5
+ + + + +
0.3 0.3 0.3 0.4 0.4
4.4 3.8 4.1 4.7 5.5
4.7 4.2 4.5 5.1 6.0
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not sum due to
independent rounding.
Energy 3-59
-------
Table 3-39: COz Emissions from Petroleum Systems (kt)
Activity
Production Field Operations
Pneumatic controller venting
Tank venting
Misc. venting & fugitives
Wellhead fugitives
Process upsets
Crude Refining
Total
1990
4
4
375
27
328
16
3
0.2
,070
,445
2005
4
4
285
23
246
13
3
0.1
,620
,904
2009
305
24
265
14
3
0.1
4,351
4,656
2010
317
24
276
14
3
0.2
3,836
4,153
2011
333
25
291
14
3
0.2
4,134
4,467
2012
394
26
351
14
3
0.2
4,666
5,060
2013
461
26
417
14
3
0.2
5,540
6,001
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 (GRI/EPA 1996, EPA 1999) and EPA's GHGRP data. The 1996 and 1999
studies calculated emission estimates for 57 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 13 for refining operations. Annex 3.5 provides greater detail on the emission estimates for these 57
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 Drillinglnfo (2014), the Energy Information Administration
annual and monthly reports (EIA 1990 through 2014), (EIA 1995 through 2014a, 2014b, 2014c), "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 2004, BOEM 2011), analysis of BOEMRE data (EPA 2005, BOEMRE
2004), the Oil & Gas Journal (OGJ 2014a, 2013b), the Interstate Oil and Gas Compact Commission (IOGCC 2011),
the United States Army Corps of Engineers, (1995-2012), and the GHGRP (2010-2013).
The methodology for estimating CH4 emissions from the 46 oil industry activities (excluding refining activities)
employs emission factors initially developed by EPA (1999). Activity data for the years 1990 through 2013 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 for all federal offshore platforms (EPA 2015, BOEM 2014). 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 2013. 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
201 la,b,c). 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, HPDI 2011).
For some years, complete activity data were not available. In such cases, one of three approaches was employed.
Where appropriate, the activity data were 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
were held constant from 1990 through 2013 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.
For petroleum refining activities, 2010 to 2013 emissions were directly obtained from EPA's GHGRP. All refineries
are required to report their CH4 and CO2 emissions for all major activities since 2010. The national totals of these
3-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
emissions for each activity were used for the 2010 to 2013 emissions. The national emission totals for each activity
were also divided by refinery feed rates for those four Inventory years to develop average activity-specific emission
factors. These emission factors were used to estimate national emissions for each refinery activity from 1990 to
2009 based on national refinery feed rates for the respective Inventory year. (EPA 2015c).
The Inventory estimate for Petroleum Systems takes into account Natural Gas STAR reductions. Voluntary
reductions included in the Petroleum Sector calculations were those reported to Natural Gas STAR for the following
activities: artificial lift: gas lift; artificial lift: use compression; artificial lift: use pumping unit; consolidate crude oil
production 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 vapor recovery unit or compressor. In addition, a portion of the
total Gas STAR reductions from pneumatic controllers in the production sector are applied to potential emissions in
the petroleum sector.
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 three activities from
petroleum refining. For the production field operations, emissions are estimated for each activity by multiplying
emission factors by their corresponding activity data. The emission factors for €62 are generally estimated by
multiplying the CH4 emission factors by a conversion factor, which is the ratio of €62 content and CH4 content in
produced associated gas. One exception 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). Other exceptions to this methodology are the three
petroleum refining activities (i.e., flares, asphalt blowing, and process vents); the CO2 emissions data for 2010 to
2013 were directly obtained from the GHGRP. The 2010 to 2013 CO2 emissions GHGRP data along with the
refinery feed data for 2010 to 2013 were used to derive CO2 emission factors (i.e., sum of activity emissions/sum of
refinery feed). The emission factors were then applied to the annual refinery feed to estimate CO2 emissions for
1990 to 2009.
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis was conducted in 2010 to determine the level of uncertainty surrounding
estimates of emissions from petroleum systems using the IPCC-recommended Approach 2 methodology (Monte
Carlo Simulation technique). The @RISK software model was used to quantify the uncertainty associated with the
emission estimates using the 7 highest-emitting sources ("top 7 sources") for the year 1995. The @RISK 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 emission
estimates for this category evolves and improves as the underlying methodologies and datasets improve.
The uncertainty analysis conducted in 2010 has not yet been updated for the 1990 through 2013 Inventory years;
instead, the uncertainty percentage ranges calculated previously were applied to 2013 emission 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 explained in the Methodology
section above and the Recalculations Discussion below, several emission sources have undergone recent
methodology revisions, and the 2009 uncertainty ranges will not reflect the uncertainty associated with the recently
revised emission factors and activity data sources. Please see discussion on Planned Improvements.
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 2013, based on the previously conducted uncertainty assessment using the
recommended IPCC methodology. The heterogeneous nature of the petroleum 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 Approach 2 quantitative uncertainty analysis are summarized in Table 3-40. Petroleum systems CH4 emissions
in 2013 were estimated to be between 19.2 and 62.8 MMT CO2 Eq., while CO2 emissions were estimated to be
between 4.6 and 14.9 MMT CO2 Eq. at a 95 percent confidence level. This indicates a range of 24 percent below to
149 percent above the 2013 emission estimates of 25.2 and 6.0 MMT CO2 Eq. for CH4 and CO2, respectively.
Energy 3-61
-------
Table 3-40: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from
Petroleum Systems (MMT COz Eq. and Percent)
Source
Gas
2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.)b
(MMT CO2 Eq.)
Petroleum Systems
Petroleum Systems
CH4
C02
25.2
6.0
Lower
Boundb
19.2
4.6
Upper
Boundb
62.8
14.9
Lower
Boundb
-24%
-24%
Upper
Boundb
149%
149%
a Range of 2013 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 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification Discussion
The petroleum system 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. EPA performs a thorough review of information associated with
new studies, GHGRP data, regulations, public webcasts, and the Natural Gas STAR Program to assess whether the
assumptions in the Inventory are consistent with current industry practices. In addition, EPA receives feedback
through annual expert and public review period. Feedback received is noted in the Recalculations and Planned
Improvement sections.
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time -series recalculations
for most Inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in calculated CCh-equivalent emissions from CH4, HFCs, and PFCs. The GWPs of N2O and SF6 have
decreased, leading to a decrease in calculated CCh equivalent emissions for these greenhouse gases. The AR4 GWPs
have been applied across the entire time series for consistency. For more information please see the Recalculations
and Improvements Chapter.
EPA received information and data related to the emission estimates through the Inventory preparation process,
previous Inventories' formal public notice periods, GHGRP data, and new studies. EPA carefully evaluated relevant
information available, and made several updates, such as updates to offshore platforms, pneumatic controllers,
refineries, and well count data. In addition, revisions to use the latest activity data resulted in changes to emissions
for several sources.
Methodological changes made in the current (2015) Inventory are described below.
The net impacts of the changes (comparing 2012 estimate from the previous (2014) Inventory and current (2015)
Inventory) are a decrease in CH4 emissions of around 14.5 MMT CC>2 Eq., or 38 percent, and an increase in
3-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
emissions of around 6 MMT CC>2, or 1,400 percent.64 Recalculations in the offshore petroleum platforms estimates
resulted in a large decrease in 2012 the CH4 emission estimate from this source in the production segment, from 15.2
MMT CO2 Eq. in the 2014 Inventory, to 4.7 MMT CO2 Eq. in the current (2015) Inventory. Recalculations to the
onshore petroleum production emissions estimates resulted in a small decrease in the 2012 CH4 emission estimate
for onshore sources, from 22.0 MMT CO2 Eq. in the previous (2014) Inventory, to 19.5 MMT CO2 Eq. in the
current (2015) Inventory. Methane emission estimates for other segments (i.e., refining and transport) changed by
around 0.5 percent. The increase in the CCh emissions estimates is due to the update to the petroleum refineries
calculations.
Across the 1990-2012 time series, compared to the previous (2014) Inventory, in the current (2015) Inventory, the
CH4 emission estimate decreased by 11.8 MMT CCh Eq. on average (or 32 percent), and the CCh emission estimate
increased by 4.4 MMT CC>2 on average (or around 1,300 percent).
Offshore Platforms
The U.S. Department of the Interior (DOI) began inventorying offshore platform greenhouse gas emissions in the
Bureau of Ocean Energy Management's (BOEM) Gulf Offshore Activity Data System (GOADS) in 2000 with
subsequent revisions in 2005, 2008, and 2011. The original year 2000 GOADS data were used to develop the
emission factors used in the previous GHG Inventory calculations. There have been significant improvements in
GOADS data collection and processing since 2000. For the final version of the 1990-2013 Inventory, the 2011
GOADS data were used to revise the emission factors used to calculate offshore oil and gas emissions in the
Inventory. The platforms in GOADS were separated into the four categories used in the Inventory methodology: oil
versus gas platforms and deep water versus shallow water platforms. Then, the reported emissions for each platform
group were used to develop average platform emission factors for Natural Gas Systems and Petroleum Systems.
EPA is in the process of calculating emission factors based on the 2005 and 2008 GOADS data that will be applied
to years in the time series on either side of the GOADS inventory year that provides the emission factors. Updated
activity data were also sought for oil and gas offshore platforms, as the current Inventory activity data is based on
DOI 2010 data. At this time no new references were identified that provide current year (2013) and historic platform
counts, on a consistent basis. For the year 2012, this revision results in a decrease in CH4 of 9 MMT COa Eq., or 69
percent and a decrease in CO2 of >0.1 MMT CO2, or 24 percent.65 Commenters on the public review draft
supported this update, and recommended that EPA improve its activity data for the number of platforms by using
Lexco/OWL, and that EPA improve data on flaring of offshore gas, for example, by reviewing platform data to
determine which platforms have a flare.
Well Counts and Completion and Workover Counts
In previous Inventories, data on well counts for petroleum systems were from EIA, while well count data for natural
gas systems came from Drillinglnfo. In the current GHG Inventory, the time series has been updated to use data
from Drillinglnfo (HPDI) for producing oil well counts.
The update resulted in an increase in the number of producing oil well counts, which increased calculated potential
emissions from sources relying on this activity data. The activity data for many emission sources such as
pneumatics, pumps, compressors, separators, and heater treaters are scaled from the 1992 base year, in part based on
the ratio of oil well count in a given year to the count of oil wells in 1992. While oil well counts increased by nearly
50 percent on average across all Inventory years, the differential between 1992 and 2012 also slightly increased,
leading to an increase in activity data of approximately 6 percent for these sources. For example, the increase in the
number of oil wells resulted in an increase in the number of pneumatic controllers estimated in petroleum systems,
from around 415,000 for 2012 in the previous Inventory, to around 440,000 in the final current (2015) Inventory.
64 Additional information on recent changes to the Inventory can be found at
65 For additional information, please see memo "Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Revision to
Offshore Platform Emissions." EPA (2015b) available at
Energy 3-63
-------
66,67 Activity data for other emission sources such fugitives from wellheads and headers are calculated by applying
activity factors to the count of oil wells; due to the increase in oil well counts for all Inventory years, the activity
data for these sources increased by approximately 50 percent up to 150 percent.
Pneumatic Controllers
In previous Inventories, all production segment reductions related to pneumatic controllers that were reported to
Natural Gas STAR were assigned to the natural gas systems category. Since some portion of these reductions would
be more appropriately assigned to the petroleum systems category, in the final version of the current Inventory, the
production segment reductions related to pneumatic controllers have been allocated to the natural gas and petroleum
systems categories based upon the calculated potential emissions for pneumatic controllers in each source category.
EPA calculated the fraction of potential emissions from pneumatic controllers in petroleum systems out of the total
potential pneumatic controller emissions from both natural gas and petroleum systems. On average across all
Inventory years, potential pneumatic controller emissions from petroleum systems make up 35 percent of total
potential pneumatic controller emissions from both source categories. EPA then applied the year-specific potential
emissions fraction to the reported Natural Gas STAR pneumatic controller reductions and allocated that portion of
the reductions to the natural gas systems source category. This update decreases net CH4 emissions by as little as
<0.1 MMT CChEq. in 1990 (1 percent of potential emissions), but reported reductions increase overtime such that
in 2013 the decrease is 6.3 MMT CC>2 Eq. (over 50 percent of potential emissions). Reviewers supported
apportioning the Gas STAR reductions to both oil and gas.
Table 3-41: Pneumatic Controllers Activity Data and Emissions
Data Element
# of Pneumatic Controllers
Calculated Potential Methane (kt)
Natural Gas STAR Reductions (kt)
Net Emissions (kt)
1990
466,603
489
3
487
2000
395,557
415
42
373
2005
386,058
405
67
338
2010
412,712 1
433
195 1
238
• 2012
441,311
463
1 245
218
2013
452,170
474
254
221
Petroleum Refineries
The calculations for the refineries portion of petroleum systems were revised to use data available from GHGRP
subpart Y. All refineries have been reporting to the GHGRP since 2010. For petroleum refining activities, 2010
through 2013 emissions were directly obtained from EPA's GHGRP and used for these years in the Inventory time
series. Since GHGRP data only cover recent years of the Inventory time series, an extrapolation approach was
employed to develop consistent emissions estimates back to 1990. Publicly available throughput data from
DOE/EIA (i.e., refinery feed data from DOE/EIA's Petroleum Supply Annual) were used to scale GHGRP
emissions to reflect activity in earlier years. The national emission totals for each activity over the period 2010
through 2013 were divided by total refinery feed rate during 2010 through 2013 to develop average activity-specific
emission factors. These emission factors were used to estimate national emissions for each refinery activity from
1990 to 2009 based on national refinery feed rates for the respective Inventory year. The impact of this improvement
resulted in an increase in emissions across the time series. For the year 2012, this revision results in an increase in
66 For additional information, please see memo "Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Revision to
Data Source for Well Counts." EPA (2015a) available at
67 The estimate for the number in pneumatic controllers in the petroleum production segment for 2013 decreased 38 percent from
the public review draft of the 2015 Inventory to the final 2015 Inventory. This was due to correcting the count of oil wells in the
base year 1992, data which drives the pneumatic controller count in later years. The total count of oil wells in 1992 increased,
which in turn decreased the difference between 1992 and 2013 well counts from the public review draft and therefore the count
of pneumatic controllers from 1992 to 2013 was scaled by a lower factor.
3-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
CH4 of 0.4 MMT CO2 Eq., or 100 percent, and an increase in CO2 of 4.7 MMT CO2, or by a factor of 467.68
Commenters on the public review draft supported this update.
Planned Improvements
Oil Well Completions and Workovers
The Inventory does not currently distinguish between oil well completions and workovers with hydraulic fracturing
and oil well completions and workovers without hydraulic fracturing. In addition, current Inventory emission factors
for all oil well completions and workovers were developed using an assumption that all oil well workovers and
completions are flared. EPA is seeking data on completions and workovers of oil wells using hydraulic fracturing to
better reflect emissions from this rapidly growing and changing sector.
On April 15, 2014, EPA released for external peer review five technical white papers on potentially significant
sources of emissions in the oil and gas sector.69 The white papers focus on technical issues covering emissions and
mitigation techniques that target methane and volatile organic compounds (VOCs). The white paper "Emissions
from completions and ongoing production of hydraulically fractured oil wells" discussed available data on this
source. In addition, EPA's proposed revisions to the GHGRP to add reporting requirements for oil well completions
and workovers with hydraulic fracturing would provide EPA with data that could inform updates to the Inventory of
U.S. Greenhouse Gas Emissions and Sinks.
Commenters suggested use of data from Allen (2013) to calculate emission factors. A commenter calculated that
using an estimate of 7.7 tons CH4 per completion event and an assumption of 75 percent of new oil wells completed
with hydraulic fracturing would increase the current emission estimate for this source by a factor of 400.
Commenters suggested that some oil producers voluntarily report completions data to GHGRP and suggested that
EPA use this data to develop emission estimates, and then reevaluate these estimates as more data become available.
Pneumatic Controllers
EPA is considering options for updating its estimates for pneumatic controllers in the Inventory. Data sources
reviewed include GHGRP (EPA 2014), Allen et al. (2014) and others.70 Commenters supported the use of direct
measurement data to update this emissions source. Commenters supported the use of technology-specific emission
factors and categories (e.g., high bleed, intermittent bleed, low bleed, zero bleed) to track emissions and changes in
technology. Commenters suggested using GHGRP data on the split between high bleed, intermittent bleed and low
bleed to develop data for this approach. A commenter suggested adding a category to address malfunction
emissions, which were observed to be substantial in Allen et al. 2014. Commenters supported updating activity data
from this source and suggested use of GHGRP data on number of controllers when it becomes available,
68 For additional information, please see memo "Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Proposed
Revision to Refinery Emissions Estimate." EPA (2015c) available at
.
69 Available online at .
70 For more information, please see memo "Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Potential
Revisions to Pneumatic Controller Estimates." (EPA 2015d) available at
.
Energy 3-65
-------
extrapolated to national numbers, or use of data sources such as Allen et al. (2014) or OIPA 2014. EPA is
considering these updates for the 2016 GHG Inventory.
Offshore Platforms
EPA is in the process of calculating emission factors based on the 2005 and 2008 GOADS data that will be applied
to years in the time series on either side of the GOADS inventory year that provides the emission factors in future
versions of the Inventory.71 Commenters supported this update.
GHGRP
Beginning March 2015, petroleum and natural gas systems reporters to EPA's GHGRP will begin reporting
additional data to EPA. The additional data will include, in some cases, information on equipment counts and other
additional information that could allow for further improvements to the Inventory.
Commenters on the public review draft recommended that EPA analyze and screen GHGRP data and exclude or
correct outliers. Commenters also recommended use of only measured GHGRP data in some cases.
EPA plans to review data reported to GHGRP for potential updates to data and methodology across all segments of
petroleum systems.
Other Updates
EPA is evaluating several other sources for potential updates to future inventories.
Abandoned wells are not currently accounted for in the Inventory. EPA is seeking appropriate emission factors and
national activity data available to calculate these emissions. Commenters supported including this source category.
EPA received comments process suggesting that bradenhead/casinghead gas emissions may be underestimated in the
Inventory. In the Inventory, casinghead gas emissions are calculated using the population of stripper wells and an
assumption that 80 percent of stripper wells vent casinghead gas. An emission factor of 2,345 scf CH4/year per
stripper well is applied. Comments on the Inventory noted that casinghead gas emissions are occurring in relatively
new and high-production areas. EPA plans to review the method, emission factor, and assumptions used (such as
that casinghead emissions occur only at stripper wells) to calculate emissions from casinghead gas in the Inventory.
EPA also received comments that associated gas may be underestimated and suggesting use of GHGRP data to
calculate associated gas for the Inventory. A commenter suggested that EPA use associated gas venting and flaring
data from GHGRP and apply it to the population of associated gas wells in the Inventory to address the concern that
casinghead gas emissions occur at a wider set of associated gas wells, not only at stripper wells.
Methane Measurement Studies
Large amounts of data and information are becoming available through EPA's GHGRP and external studies,
allowing EPA to re-evaluate and make updates to inventory data. There are a variety of potential uses of data from
new studies, including replacing a previous estimate or factor, verifying or QA of an existing estimate or factor, and
identifying areas for updates.
In general, there are two major types of studies related to oil and gas greenhouse gas data: studies that focus on
measurement or quantification of emissions from specific activities, processes and equipment (e.g., EPA's GHGRP,
EOF series), and studies that focus on verification of estimates through inverse modeling (e.g., NOAA verification
studies). The first type of study can lead to direct improvements to or verification of Inventory estimates. The
second type of study can provide general indications on potential over- and under-estimates. EPA reviews both
types of studies for data that can inform inventory updates.
71 For more information, please see memo "Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to
Offshore Oil and Gas Platform Emission Estimates." (EPA 2015b) available at
.
3-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
EPA considers several factors in review of new data for use in the Inventory, including representativeness (national,
regional, production-level, emissions-level), availability of data on controls, practices, and other relevant
information, availability of relevant activity data, ability to develop emission factors and activity data for the time
series, and whether the study includes a robust and transparent sampling approach, measurement method, and key
background data.
EPA will continue to review new data from measurement studies, including upcoming data from the EDF series of
CH4 studies, to assess and potentially update Inventory estimates. EPA seeks stakeholder information on studies
with data relevant to the Inventory.
Uncertainty
As described in the above section on Uncertainty, EPA calculates uncertainty for the Petroleum Systems source
category based on analysis of uncertainty for the seven highest-emitting sources in the Inventory. Since conducting
the 2010 uncertainty analysis there have been methodological improvements in two of the seven sources analyzed in
2010. The seven highest-emitting sources (both in the current and in previous inventories) are offshore oil platforms
(shallow water), high bleed pneumatics, oil tanks, low bleed pneumatics, gas engines, offshore oil platforms (deep
water) and chemical injection pumps. To update the uncertainty analysis to reflect changes that have occurred since
2010, EPA intends to collect updated information on the uncertainties associated with emission and activity factors
for the current top 7 emission sources, and reanalyze the uncertainty of the petroleum industry inventory. This
analysis will be conducted using the same @RISK model and IPCC methodology applied in the 2010 uncertainty
analysis. EPA seeks comment on updated information on uncertainty for the top seven sources and on the approach
to calculate uncertainty. For more information, see
http://www.epa.gov/climatechange/ghgemissions/usinventoryreport/natural-gas-systems.html.
Box 3-7: 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 CCh is produced from both naturally-occurring CCh
reservoirs and from industrial sources such as natural gas processing plants and ammonia plants. In the Inventory,
emissions from naturally-produced CCh are estimated based on the application.
In the Inventory, CC>2 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
underground, including facilities conducting EOR, 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 MMT CO2
Eq. (46,198 kt) (see Table 3-41 and 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
2011 to 2013 were proxied from 2010 data.
Energy 3-67
-------
Table 3-41: Potential Emissions from COz Capture and Transport (MMT COz Eq.)
Stage 1990 2005 2009 2010 2011 2012 20lF
Acid Gas Removal Plants 4.8 5.8 7.0 11.6 11.6 11.6 11.6
Naturally Occurring CO2 20.8 28.3 39.7 34.0 34.0 34.0 34.0
Ammonia Production Plants + I 0.7 0.6 0.7 0.7 0.7 0.7
Pipelines Transporting CCh + + + + + + +_
Total 25^6347 4X3 462462462462~
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 3-42: Potential Emissions from COz Capture and Transport (kt)
Stage
Acid Gas Removal Plants
Naturally Occurring CO2
Ammonia Production Plants
Pipelines Transporting CO2
Total
1990
4,832
20,811
+
8
25,643
2005
5,798
28,267
676
7
34,742
2009
7,035
39,725
580
8
47,340
2010
11,554
33,967
677
8
46,198
2011
11,554
33,967
677
8
46,198
2012
11,554
33,967
677
8
46,198
2013
11,554
33,967
677
8
46,198
+ Does not exceed 0.5 kt.
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 157.4 MMT
CO2 Eq. (6,295 kt) of CH4 in 2013, a 12 percent decrease compared to 1990 emissions, and a 2 percent increase
compared to 2012 emissions (see Table 3-43, Table 3-44, and Table 3-45) and 37.8 MMT CO2 Eq. (37,808 kt) of
non-combustion CC>2 in 2013, a less than 1 percent increase compared to 1990 emissions, and a 9 percent increase
from 2012 emissions (see Table 3-46 and Table 3-47). The 1990 to 2013 decrease in CH4 emissions is due primarily
to the decrease in emissions from distribution and production. The 1990 to 2013 decrease in distribution emissions is
due to a decrease in unprotected steel and cast iron pipelines and their replacement with plastic pipelines. The
decrease in production emissions is due to increased use of plunger lifts for liquids unloading, regulatory reductions
such as reductions from hydraulically fractured gas well completions and workovers resulting from the 2012 New
Source Performance Standards (NSPS) for oil and gas, and from a variety of voluntary reduction activities. The
2012 to 2013 increase in CC>2 is due to increased flaring.
CH4 and non-combustion CC>2 emissions from natural gas systems include those resulting from normal operations,
routine maintenance, and system upsets. Emissions from normal operations include: natural gas engine and turbine
uncombusted exhaust, bleed and discharge emissions from pneumatic controllers, 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 controllers, kimray pumps, venting for liquids unloading, condensate tanks,
pipeline leaks, and offshore platforms account for the majority of CH4 emissions in 2013. Flaring emissions account
for the majority of the non-combustion CCh emissions. Emissions from production account for 30 percent of CH4
3-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
emissions and 42 percent of non-combustion CO2 emissions from natural gas systems in 2013. CH4 emissions from
field production decreased by 21 percent from 1990 to 2013; however, the trend was not stable over the time
series—emissions from production generally increased through 2006 due primarily to increases in emissions from
pneumatic controllers and hydraulically fractured gas well completions and workovers, and then declined from 2007
to 2013. Reasons for the 2007 to 2013 trend include an increase in plunger lift use for liquids unloading, increased
voluntary reductions over that time period (including those associated with pneumatic controllers), and increased
reduced emissions completions (RECs) use for well completions and workovers with hydraulic fracturing. CO2
emissions from production increased 63 percent from 1990 to 2013 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 CO2
emissions come from acid gas removal units, which are designed to remove CO2 from natural gas. Processing plants
account for 14 percent of CH4 emissions and 58 percent of non-combustion CO2 emissions from natural gas
systems. CH4 emissions from processing increased by 6 percent from 1990 to 2013 as emissions from compressors
increased as the quantity of gas produced increased. CO2 emissions from processing decreased by 22 percent from
1990 to 2013, 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 U.S. transmission system. Fugitive CH4 emissions from these
compressor stations, and from pneumatic controllers account for the majority of the emissions from this stage.
Uncombusted engine exhaust and pipeline venting are also sources 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 34 percent of
emissions from natural gas systems, while CO2 emissions from transmission and storage account for less than 1
percent of the non-combustion CO2 emissions from natural gas systems. CH4 emissions from this source decreased
by 7 percent from 1990 to 2013 due to increased voluntary reductions (e.g., replacement of high bleed pneumatics
with low bleed pneumatics). CO2 emissions from transmission and storage have increased by 5 percent from 1990 to
2013 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 1,252,866 miles of distribution mains in 2013, an increase of nearly 310,000 miles since 1990
(PHMSA 2014). Distribution system emissions, which account for 21 percent of CH4 emissions from natural gas
systems and less than 1 percent of non-combustion CO2 emissions, result mainly from fugitive emissions from
pipelines and stations. An increased use of plastic piping, which has lower emissions than other pipe materials, has
reduced both CH4 and CO2 emissions from this stage. Distribution system CH4 emissions in 2013 were 16 percent
lower than 1990 levels (changed from 39.8 MMT CO2 Eq. to 33.3 MMT CO2 Eq.), while distribution CO2 emissions
in 2013 were 14 percent lower than 1990 levels (CO2 emission from this segment are less than 0.1 MMTCO2 Eq.
across the time series).
Total CH4 emissions for the four major stages of natural gas systems are shown in MMT CO2 Eq. (Table 3-43) and
kt (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 Annex 3.6. See Methodology for Estimating CH4 and CO2 Emissions from
Natural Gas Systems.
Energy 3-69
-------
Table 3-43: CH4 Emissions from Natural Gas Systems (MMT COz Eq.)a
Stage
Field Production
Processing
Transmission and Storage
Distribution
Total
1990
59.5
21.3
58.6 1
39.8
179.1
2005
75.5
16.4
49.1
35.4 I
176.3
2009
62.0
19.2
52.7
34.1
168.0
2010
56.5
17.9
51.6
33.5
159.6
2011
51.3
21.3
53.9
32.9
159.3
2012
49.7
22.3
51.8
30.7
154.4
2013
47.0
22.7
54.4
33.3
157.4
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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 3-44: CH4 Emissions from Natural Gas Systems (kt)a
Stage
1990 2005 2009 2010 2011 2012 2013
Field Production 2,380 1 3,018 1 2,482 2,262 2,052 1,989 1,879
Processing 852 655 768 717 851 891 906
Transmission and Storage 2,343 1 1,963 2,107 2,065 2,154 2,070 2,176
Distribution 1,591 H 1,417 1,365 1,338 1,315 1,226 1,333
Total
7,165 7,053 6,722 6,382 6,371 6,176 6,295
1 These values represent CELi emitted to the atmosphere. CEU 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 (MMT COz Eq.)
1990 H
Calculated Potential3
Field Production
Processing
Transmission and
Distribution
Storage
Captured/Combusted
Field Production
Processing
Transmission and
Distribution
Net Emissions
Field Production
Processing
Transmission and
Distribution
Storage
Storage
179.3 •
59,
21.
58,
39,
0.
0.
179.
59,
21,
58,
39,
7
.3
.6
.8
,2
?
+
+
1
.5
.3
.6
.8
2005
208.8
89.8
20.6 1
61.7 1
36.6 1
32.4
14.4
4.2 1
12.7 1
1.2 1
176.3
75.5
16.4 1
49.1
35.4
2009
210.7
89.5
23.0
62.5
35.7
42.6
27.5
3.8
9.8
1 1.6
168.0
62.0
19.2
52.7
34.1
2010
211.
3
90.0
23
62
34
51
33
5
11
1
.6
.8
.8
.7
5
.7
.2
.4
159.6
56
17
51
33
.5
.9
.6
.5
2011
210.6
88.4
25.2
62.7
34.3
51.3
37.1
3.9
8.9
1.5
159.3
51.3
21.3
53.9
32.9
2012
206.7
87.2
26.2
61.5
31.8
52.3
37.5
3.9
9.8
1.1
154.4
49.7
22.3
51.8
30.7
2013
209.6
85.6
26.6
63.1
34.3
52.2
38.6
3.9
8.7
1.0
157.4
47.0
22.7
54.4
33.3
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding.
+ Emissions are less than 0.1 MMT 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 (MMT COz Eq.)
Stage
1990
2005
2009 2010 2011 2012 2013
Field Production
Processing
9.8
27.8
8.1
21.7
10.9
21.2
10.9
21.3
14.0
21.5
13.2
21.5
15.9
21.8
3-70 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Transmission and Storage 0.1 0.1 0.1 0.1 0.1 0.1 0.1
Distribution + + B + + + + +
Total 37.6 30.0 32.2 32.3 35.6 34.8 37.8
Note: Totals may not sum due to independent rounding.
+ Emissions are less than 0.1 MMT CCh Eq.
Table 3-47: Non-combustion COz Emissions from Natural Gas Systems (kt)
Stage
Field Production
Processing
Transmission and Storage
Distribution
Total
1990
9,775
27,763
62
46
37,645
2005
8
21
29
2009
,142 1 10
,746 1 21
64
42
,995
32
,906
,188
65
41
,201
2010
10,883
21,346
65
40
32,334
2011
13,980
21,466
65
40
35,551
2012
13,196
21,469
63
37
34,764
2013
15,947
21,757
65
40
37,808
Note: Totals may not sum due to independent rounding.
Methodology
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 Natural Gas
STAR, 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 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
Energy 3-71
-------
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/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. 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 the Gas Technology Institute (GTI, formerly GRI) Unconventional Natural Gas and Gas
Composition Databases (GTI 2001) 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
CH4 were also used to calculate non-combustion CCh emissions. Data from GTI 2001 were used to adapt the CH4
emission factors into non-combustion related CCh emission factors. Additional information about COa content in
transmission quality natural gas was obtained from numerous U.S. transmission companies to help further develop
the non-combustion CCh emission factors.
Although the Inventory primarily uses EPA/GRI emission factors, updates were made to the emissions estimates for
several sources in recent Inventories. For 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). In this methodology, the emission factors used for liquids
unloading are not potential factors, but are factors for actual emissions. For gas well completions and workovers
(refracturing) with hydraulic fracturing, in this Inventory, EPA used the 2011, 2012, and 2013 GHGRP Subpart W
data to stratify the emission sources into four different categories and developed CH4 emission factors for each
category. See the Recalculations Discussion below, and EPA memos "Inventory of U.S. Greenhouse Gas Emissions
and Sinks 1990-2013: Revision to Hydraulically Fractured Gas Well Completions and Workovers Estimate" and
"Updating GHG Inventory Estimate for Hydraulically Fractured Gas Well Completions and Workovers" for more
information on the methodology for this emission source (EPA 2013d and EPA 2015c).
In addition, in 2015, an update was made to the emission factors applied to offshore platforms. Previously, the
Inventory relied on the Bureau of Ocean Energy Management's (BOEM's) Gulf Offshore Activity Data System
(GOADS) year 2000 inventory to develop emission factors for offshore platforms; the methodology has been
updated to use more recent GOADS inventory data to develop emission factors. See the Recalculations Discussion
below, and EPA memo "Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Revision to Offshore
Platforms Emissions Estimate" (EPA 2015b).
See Annex 3.6 for more detailed information on the methodology and data used to calculate CH4 and non-
combustion CO2 emissions from natural gas systems.
Updates to emission factors using GHGRP data for natural gas systems 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, 201 Ib, 201 Ic, 201 Id); Natural Gas Liquids Reserves Report
(EIA 2005); Natural Gas Monthly (EIA 2014a, 2014b, 2014c); the Natural Gas STAR Program annual emissions
savings (EPA 2013c); Oil and Gas Journal (OGJ 1997-2014); Pipeline and Hazardous Materials Safety
Administration (PHMSA 2014); Federal Energy Regulatory Commission (FERC 2014); Greenhouse Gas Reporting
Program (EPA 2014); 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 2014)
and the Alabama State Oil and Gas Board (Alabama 2014).
For a few sources, recent direct activity data are not available. For these sources, either 2012 data was used as proxy
for 2013 data, or a set of industry activity data drivers was developed and used to update activity data. Drivers
3-72 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 3.6.
Step 2. Compile Reductions Data—Calculate the amount of the CH4 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 Natural 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.
The Inventory includes 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 hydraulic
fracturing into four categories and developing control technology-specific CH4 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 CH4 that is not emitted from the total CH4 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 CH4 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 in 2010 to determine the level of uncertainty surrounding
estimates of emissions from natural gas systems using the IPCC-recommended Approach 2 methodology (Monte
Carlo Simulation technique). The @RISK software model was used to quantify the uncertainty associated with the
emissions estimates using the 12 highest-emitting sources ("top 12 sources") for the year 2009. The @RISK 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 evolves and improves as the underlying methodologies and datasets improve.
The uncertainty analysis conducted in 2010 has not yet been updated for the 1990 through 2013 Inventory years;
instead, the uncertainty percentage ranges calculated previously were applied to 2013 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 explained in the Methodology
section above and the recalculations discussion below, several emission sources have undergone recent methodology
Energy 3-73
-------
revisions, and the 2009 uncertainty ranges will not reflect the uncertainty associated with the recently revised
emission factors and activity data sources. Please see discussion on Planned Improvements.
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 2013, 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 Approach 2 quantitative uncertainty analysis are summarized in Table 3-48. Natural gas systems CH4 emissions
in 2013 were estimated to be between 127.5 and 187.3 MMT CCh Eq. at a 95 percent confidence level. Natural gas
systems non-energy CO2 emissions in 2013 were estimated to be between 30.6 and 49.1 MMT CO2 Eq. at 95
percent confidence level.
Table 3-48: Approach 2 Quantitative Uncertainty Estimates for CH4 and Non-energy COz
Emissions from Natural Gas Systems (MMT COz Eq. and Percent)
_ 2013 Emission Estimate Uncertainty Ranee Relative to Emission Estimate3
Source Gas
(MMT C02 Eq.)b (MMT CCh Eq.) (%)
Natural Gas Systems
Natural Gas Systems0
CH4
CO2
157.4
37.8
Lower
Bound"
127.5
30.6
Upper
Bound"
187.3
49.1
Lower
Bound"
-19%
-19%
Upper
Bound"
+30%
+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 2013. 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. EPA performs a thorough review of information associated with
new studies, GHGRP data, regulations, public webcasts, and the Natural Gas STAR Program to assess whether the
assumptions in the Inventory are consistent with current industry practices. In addition, EPA receives feedback
through annual expert and public review periods. Feedback received is noted in the Recalculations and Planned
Improvement sections.
Several recent studies have measured emissions at the source level and at the national or regional level (e.g., EDF
series of studies) with results that often differ from EPA's estimate of emissions. Commenters to the Inventory noted
discrepancies between bottom-up inventory estimates and emissions estimated with satellite and aircraft data.
Please see note on Methane Measurement Studies in the Planned Improvements section.
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
3-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in calculated CO2-equivalent emissions from CH4, HFCs, and PFCs. The GWPs of N2O and SF6 have
decreased, leading to a decrease in calculated CO2-equivalent emissions from N2O. The AR4 GWPs have been
applied across the entire time series for consistency. For more information please see the Recalculations and
Improvements Chapter.
EPA received information and data related to the emission estimates through the Inventory preparation process,
previous Inventories' formal public notice periods, GHGRP data, and new studies. EPA carefully evaluated relevant
information available, and made several updates, including revisions to offshore platforms, pneumatic controllers,
well counts data, and hydraulically fractured gas well completions and workovers.
In addition, revisions to activity data resulted in changes to emission estimates for several sources. For example, the
previous (2014) Inventory used 2011 data as a proxy for condensate production for 2012. The current (2015)
Inventory was updated to use the most recent data on condensate production. Large increases in production in the
Rocky Mountain and Gulf Coast regions resulted in an increase in calculated 2012 CH4 emissions from condensate
tanks of 0.6 MMT CO2 Eq., or 15 percent.
The combined impact of all revisions on 2012 natural gas production segment emissions described below, compared
to the 2014 Inventory, is a decrease in CH4 emissions of approximately 0.2 MMT CO2 Eq., and a decrease in CO2
emissions of 0.5 MMT, or around 1 percent.72 Recalculations in the offshore gas platforms estimates resulted in a
large decrease in the 2012 CH4 emission estimate from this source in the production segment, from 7.2 MMT CO2
Eq. in the previous (2014) Inventory, to 3.8 MMT CO2 Eq. in the current (2015) Inventory. Recalculations to the
onshore gas production emissions estimates resulted in an increase in the 2012 CH4 emission estimate for onshore
sources, from 42.6 MMT CO2 Eq. in the previous (2014) Inventory, to 46.0 MMT CO2 Eq. in the current (2015)
Inventory. Methane emission estimates for other segments (i.e., processing, transmission and storage, and
distribution) changed by less than 0.5 percent.
Across the 1990-2012 time series, compared to the previous (2014) Inventory, in the current (2015) Inventory, the
total CH4 emissions estimate decreased by 5.2 MMT CO2 Eq. on average (or 3 percent), with the largest decreases in
the estimate occurring in early years of the time series; and the CO2 emissions estimate decreased <0.1 MMT CO2
on average (<1 percent).
Offshore Platforms
The U.S. Department of the Interior (DOI) began Inventorying offshore platform greenhouse gas emissions in the
Bureau of Ocean Energy Management's (BOEM) Gulf Offshore Activity Data System (GOADS) in 2000 with
subsequent revisions in 2005, 2008, and 2011. The original year 2000 GOADS data were used to develop the
emission factors used in the previous Inventory calculations. There have been significant improvements in GOADS
data collection and processing since 2000. For the final version of the 1990-2013 Inventory, the 2011 GOADS data
were used to revise the emission factors used to calculate offshore oil and gas emissions in the Inventory. The
platforms in GOADS were separated into the four categories used in the GHG Inventory methodology: oil versus
gas platforms and deep water versus shallow water platforms. Then, the reported emissions for each platform group
were used to develop average platform emission factors for Natural Gas Systems and Petroleum Systems. EPA is in
the process of calculating emission factors based on the 2005 and 2008 GOADS data that will be applied to years in
the time series on either side of the GOADS inventory year that provides the emission factors in future versions of
the Inventory. Updated activity data were also sought for oil and gas offshore platforms, as the current Inventory
activity data is based on DOI 2010 data. At this time no new references were identified that provide current year
(2013) and historic platform counts, on a consistent basis. The impact of this improvement is a decrease in emissions
across the time series. For the year 2012, the CH4 emissions decrease due to use of revised emission factors is
approximately 3.5 MMT CO2Eq.73 Commenters on the public review draft supported this update, and
recommended that EPA improve its activity data for the number of platforms by using Lexco/OWL, and that EPA
Additional information on recent changes to the Inventory can be found at
73 For additional information, please see memo "Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Revision to
Offshore Platform Emissions." EPA (2015b) available at
Energy 3-75
-------
improve data on flaring of offshore gas, for example, by reviewing platform data to determine which platforms have
a flare.
Gas Well Completions with Hydraulic Fracturing and Workovers with Hydraulic
Fracturing (Refracturing)
In the previous Inventory, completions and workovers from the 2011 and 2012 GHGRP data sets 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.
In the current Inventory, the latest GHGRP data available for 2011, 2012, and 2013 were used to develop updated
emission factors for the four categories. The emission factors were applied throughout the time series.
Using the same method as was used in the previous Inventory, a time series of activity data was developed for each
category for 1990 through 2013. For RECs, 0 percent was assumed for RECs use from 1990 to 2000; GHGRP RECs
percentage was used for 2011, 2012, and 2013; and then linear interpolation was used between the 2000 and 2011
percentages for RECs use. For flaring, 10 percent (the average of the percentage of completions and workovers that
were flared in 2011 and 2012 GHGRP data) flaring was assumed from 1990 to 2010 to recognize that some flaring
occurred over that time period. For 2011, 2012, and 2013, the GHGRP data on flaring was used. The remaining
completions and workovers are assigned to the venting category.
This methodology allows the Inventory to reflect changes in RECs counts and flaring, including those resulting from
NSPS Subpart OOOO.
Changes made to the emission factors for gas well completions and workovers with hydraulic fracturing resulted in a
decrease in the estimate of CH4 emissions for all years in the time series. This overall decrease due to the change in
the data source is accompanied by declining emissions over time, reflecting impacts of the 2012 NSPS for oil and
gas (in effect as of October 2012) which requires control of gas from hydraulically fractured gas well completions
and workovers.
This update resulted in a decrease in the emission estimate for 2012 of approximately 2 MMT CO2 Eq.74
Commenters on the Inventory generally supported this update. However, commenters also suggested use of only
measured data from GHGRP, removal of outliers from GHGRP, and the consolidation of the emission factors into
two categories (controlled versus uncontrolled) instead of four. A commenter suggested removing 2011 data from
the GHGRP data used to develop the emission factors for hydraulically fractured gas well completions and
workovers. Commenters suggested further subcategorization between completions and workovers.
EPA will consider these comments as it reviews data for this and other GHGRP categories for potential updates in
next year's GHG Inventory.
Natural Gas STAR Reductions
In general, the Inventory continues to use aggregated Natural Gas STAR reductions by natural gas system segment
(i.e., production, processing, transmission and storage, and distribution). For some sources, specific emissions
reductions activities reported to Natural Gas STAR are matched to potential emissions calculated in the Inventory to
calculate net emissions for those sources.
For additional information on the revisions, please see memo "Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-
2013: Proposed Revision to Hydraulically Fractured Gas Well Completions and Workovers Estimate." (EPA 2015c) available at
3-76 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Natural Gas STAR Reductions—Pneumatic Controllers
In previous Inventories, all production segment reductions related to pneumatic controllers that are reported to
Natural Gas STAR were assigned to the natural gas systems category. In the final version of the current Inventory,
the production segment reductions related to pneumatic controllers have been allocated to the natural gas and
petroleum systems categories based upon the calculated potential emissions for pneumatic controllers in each source
category. EPA calculated the fraction of potential emissions from pneumatic controllers from natural gas systems
out of the total potential pneumatic controller emissions from both natural gas and petroleum systems. On average
across all Inventory years, potential pneumatic controller emissions from natural gas systems make up 65 percent of
total potential pneumatic controller emissions from both source categories. EPA then applied the year-specific
potential emissions fraction to the reported Natural Gas STAR pneumatic controller reductions and allocated that
portion of the reductions to the natural gas systems source category. This update resulted in an increase in natural
gas CH4 emissions (increase of approximately 8 MMT CC>2 Eq. from the previous Inventory estimate for 2012) and
a decrease in petroleum systems CH4 emissions.
Table 3-50: Pneumatic Controllers Activity Data and Emissions
Data Element
# of Pneumatic Controllers
Calculated Potential Methane (kt)
Natural Gas STAR Reductions (kt)
Net Emissions (kt)
1990
233,792
539
3 I
537
2000
300,408
759
76
683
2005
384,433
967
160
807
2010
466,536
1,178
1 530
647
2012
468,466
1,185
628
557
2013
459,304
1,159
620
539
Well Counts and Completion and Workover Counts
For the public review draft, the time series has been updated with revised well counts and completion and workover
counts based on Drillinglnfo (HPDI) data. Due to revisions to EPA's processing of Drillinglnfo (HPDI) data, well
counts across the time series have changed from previous years. For additional information, please see memo
"Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Revision to Data Source for Well Counts."
(EPA 2015a). Commenters to the public review draft suggested that EPA may be overestimating the number of
associated gas wells, and that EPA's approach may be inconsistent with El A and state approaches. See Planned
Improvements section on associated gas wells.
For completions and workovers, the Inventory uses GHGRP Subpart W event counts for available years (2011 to
2013) as the activity data basis. Due to the reporting threshold in EPA's GHGRP, using data from EPA's GHGRP
alone will not provide a complete national estimate of activity data and emissions. However, the completion and
workover counts in GHGRP exceed those calculated using the Drillinglnfo data and therefore provide a more
complete data set than the Drillinglnfo approach. If EPA identifies an opportunity to use Drillinglnfo (HPDI) data
for improved completion and workover counts for recent years, then activity data and therefore emissions from
completions and workovers are expected to increase in years 2011 forward.75
Planned Improvements
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
EPA's GHGRP, research studies by various organizations, government and academic researchers, and industry.
Relevant ongoing studies are collecting new information related to natural gas system emissions (e.g. Environmental
Defense Fund (EOF) study series data on natural gas systems, including new measurements on gathering and
boosting, processing, transmission and storage, and distribution). EPA looks forward to reviewing information and
data from these studies as they become available for potential incorporation in the Inventory.
75 For additional information, please see memo "Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to
Data Source for Well Counts." EPA (2015a) available at
Energy 3-77
-------
Gas Well Liquids Unloading
EPA is considering updates to its estimates for liquids unloading. Data from a 2012 report published by the
American Petroleum Institute (API) and America's Natural Gas Alliance (ANGA) were used beginning with the
1990-2011 Inventory (published 2013) to develop regional activity data and regional emission factors for gas well
liquids unloading activities for Natural Gas Systems. EPA is considering how data from GHGRP and Allen et al.
(2014a) can be used to update the Inventory estimates forthis source.76 Commenters supported the use of direct
measurement data to update this emission source.
Offshore Platforms
EPA is in the process of calculating emission factors based on the 2005 and 2008 GOADS data that will be applied
to years in the time series on either side of the GOADS inventory year that provides the emission factors for future
versions of the Inventory.77
Pneumatic Controllers
EPA is considering options for updating its estimates for pneumatic controllers in the Inventory. Data sources
reviewed include EPA's GHGRP (2014), Allen et al. (2014) and others.78 Commenters supported the use of direct
measurement data to update this emissions source. Commenters supported the use of technology-specific emission
factors and categories (e.g. high bleed, intermittent bleed, low bleed, zero bleed) to track emissions and changes in
technology. Commenters suggested using GHGRP data on the split between high bleed, intermittent bleed and low
bleed to develop data for this approach. A commenter suggested adding a category to address malfunction
emissions, which were observed to be substantial in Allen et al. 2014b. Commenters supported updating activity
data from this source and suggested use of GHGRP data on number of controllers when it becomes available,
extrapolated to national numbers, or use of data sources such as Allen et al. (2014b) or OIPA 2014. EPA is
considering these updates for the 2016 Inventory.
GHGRP
Beginning March 2015, petroleum and natural gas systems reporters to EPA's GHGRP will begin reporting
additional data to EPA. The additional data will include, in some cases, information on equipment counts and other
additional information that could allow for further improvements to the Inventory.
Commenters on the public review draft recommended that EPA analyze and screen GHGRP data and exclude or
correct outliers. Commenters also recommended use of only measured GHGRP data in some cases.
EPA plans to review data reported to its GHGRP for potential updates to data and methodology across all segments
of natural gas systems.
Transmission and Storage
Commenters noted opportunities to update estimates for transmission and storage using data from EPA's GHGRP,
noting the use of direct measurements for many sources in transmission and storage. Commenters noted additional
76 Please see the memo "Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Potential Revisions to Liquids
Unloading Estimates" (EPA 2015e) available at
77 Please see the memo "Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Offshore Oil and Gas
Platforms Emissions Estimate" (EPA 2015b) available at
78 For more information, please see the memo "Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Potential
Revisions to Pneumatic Controller Emission Estimate" (EPA 2015d) available at
3-78 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
sources of data that could potentially be used for Inventory updates include the EDF series (e.g., Colorado State
University paper), and a Pipeline Research Council International project.
Commenters suggested reconsidering the approaches used to calculate activity data in transmission and storage. For
example, the estimate for national storage facilities is based on residential gas consumption.
EPA will review data from its GHGRP and other sources for potential updates to the data and methods used to
calculate emissions from transmission and storage.
Distribution
Commenters recommended revisions to distribution segment emissions estimates. EPA looks forward to reviewing
new data on distribution systems (such as data from the EDF series of studies) as they become available.
Commenters suggested updating the approach to estimating M&R station activity data, which is currently based on
annual throughput value, which can cause volatility in the annual activity data. EPA plans to review available data
for potential updates to this source.
Associated Gas Wells
Commenters to the public review draft of the Inventory suggested that EPA's approach to estimating the number of
associated gas wells may overestimate this population. EPA's approach was to include any well with a gas-to-oil
ratio (GOR) greater than 0 Mcf/bbl in the associated gas well category. Commenters noted that it is very common
for wells that produce mainly oil to also produce a small amount of gas. EPA will investigate alternative thresholds
such as a GOR greater than 6 Mcf/bbl for the 2016 Inventory. In addition, EPA will consider whether the emissions
source calculations that include associated gas wells should be expanded.
Other Updates
EPA is evaluating several other sources for potential updates to future Inventories.
Abandoned wells are not currently accounted for in the Inventory. EPA is seeking appropriate emission factors and
national activity data available to calculate these emissions. Commenters supported including this source category.
Commenters recommended that EPA separate out emissions from gathering and boosting facilities from those from
field production sites and noted that upcoming studies and GHGRP data may inform emissions estimates from this
source.
Commenters recommended updating production segment fugitive emissions estimates.
Commenters recommended development of emission factors and activity data on a regional as opposed to a national
basis.
Methane Measurement Studies
Large amounts of data and information are becoming available through EPA's GHGRP and external studies,
allowing EPA to re-evaluate and make updates to Inventory data. There are a variety of potential uses of data from
new studies, including replacing a previous estimate or factor, verifying or QA of an existing estimate or factor, and
identifying areas for updates.
In general, there are two major types of studies related to oil and gas greenhouse gas data: studies that focus on
measurement or quantification of emissions from specific activities, processes and equipment (e.g., EPA's GHGRP,
EDF series), and studies that focus on verification of estimates through inverse modeling (e.g., NOAA verification
studies). The first type of study can lead to direct improvements to or verification of Inventory estimates. The
second type of study can provide general indications on potential over- and under-estimates. EPA reviews both
types of studies for data that can inform inventory updates.
EPA considers several factors in review of new data for use in the Inventory, including representativeness (national,
regional, production-level, emissions-level), availability of data on controls, practices, and other relevant
information, availability of relevant activity data, ability to develop emission factors and activity data for the time
series, and whether the study includes a robust and transparent sampling approach, measurement method, and key
background data
Energy 3-79
-------
EPA will continue to review new data from measurement studies, including upcoming data from the EDF series of
methane studies, to assess and potentially update Inventory estimates. EPA seeks stakeholder information on studies
with data relevant to the Inventory.
Uncertainty
As described in the above section on Uncertainty, EPA calculates uncertainty for the Natural Gas Systems source
category based on analysis of uncertainty for the twelve highest-emitting sources in the Inventory. Since conducting
the 2010 uncertainty analysis there have been methodology improvements in seven of the twelve top sources
analyzed in 2010, which have resulted in a shift in which sources make up the top twelve sources list. Sources
included in the top twelve methane emissions sources for 2009 were reciprocating compressor fugitives
(processing), reciprocating compressor fugitives (transmission), Northeast liquids unloading, Midcentral pneumatic
device vents, centrifugal compressors (wet seals, transmission), Midcentral liquids unloading, Rocky Mountain
pneumatic device vents, Rocky Mountain gas well workovers with hydraulic fracturing, Rocky Mountain liquids
unloading, South West gas well completions with hydraulic fracturing, Gulf Coast liquids unloading, and shallow
water gas platforms. Sources in the top twelve methane emissions sources in the current Inventory for year 2013
emissions (without separating sources by region and taking into account Gas STAR reductions, which were not
accounted for in the previous assessment) are reciprocating compressor fugitives (transmission), pneumatic device
vents (production), reciprocating compressor fugitives (processing), kimray pumps (production), liquids unloading
(production), centrifugal compressors (wet seals, processing), condensate tanks (production), pneumatic controllers
(transmission), gas engines (processing), reciprocating compressors (storage), fugitives from cast iron steel
(distribution), gas engines (production).
In response to the change in the composition of the top twelve sources, EPA intends to collect updated information
on the uncertainties associated with emission and activity factors for the current top emission sources, and reanalyze
the uncertainty of the natural gas systems inventory. This analysis will be conducted using the same @RISK model
and IPCC methodology applied in the 2010 uncertainty analysis. EPA seeks comment on updated information on
uncertainty for the top 12 sources and on the approach to calculate uncertainty. For more information, see
http://www.epa.gov/climatechange/ghgemissions/usinventoryreport/natural-gas-systems.html.
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 2013 are reported in Table 3-49.
Table 3-49: NOX, CO, and NMVOC Emissions from Energy-Related Activities (kt)
Gas/Source
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
1990
21,106
10,862
10,023
139
82
1,956
125,640
119,360
5,000
978
302
103
12,620
2005
16
10
5
1
64
58
4
,602
,295 1
,858 1
321
128 1
,704 1
,985 1
,615
,648 1
1,403
7
318 1
133 1
,191 |
I 2009
12,798
7,797
4,452
468
81
1, 692
44,819
39,256
4,036
1,164
363
121
7,200
2010
12,004
7,290
4,092
545
77
1,790
45,148
39,475
4,103
1,084
487
136
7,464
2011
11,796
7,294
3,807
622
73
1,553
44,088
38,305
4,170
1,003
610
137
7,759
2012
11
6
3
1
42
,051
,788
,567
622
73
,398
,273
36,491
4
1
7
,170
,003
610
133
,449
2013
10,557
6,283
3,579
622
73
1,139
40,459
34,676
4,170
1,003
610
129
7,139
3-80 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Mobile Combustion 10,932 I 5,724 I 4,650 4,591 4,562 4,252 3,942
Oil and Gas Activities 554 510 1,894 2,205 2,517 2,517 2,517
Stationary Combustion 912 716 553 576 599 599 599
Waste Combustion 222 241 103 92 81 81 81
International Bunker Fuels" 57 54 53 56 51 46 41
" These values are presented for informational purposes only and are not included in totals.
Note: Totals may not sum due to independent rounding.
Methodology
Emission estimates for 1990 through 2013 were obtained from data published on the National Emission Inventory
(NEI) Air Pollutant Emission Trends web site (EPA 2015), and disaggregated based on EPA (2003). Emission
estimates for 2012 and 2013 for non-EGU and non-mobile sources are held constant from 2011 in EPA (2015).
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.
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 2013. 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.79 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).80
79 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).
Note that the definition of international bunker fuels used by the UNFCCC differs from that used by the International Civil
Aviation Organization.
Energy 3-81
-------
Two transport modes are addressed under the IPCC definition of international bunker fuels: aviation and marine.81
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.82
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
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 2013 from the combustion of international bunker fuels from both
aviation and marine activities were 100.7 MMT CO2 Eq., or 3.6 percent below 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 72.6 percent and decreased by 47.9 percent, respectively, since 1990. The majority of these
emissions were in the form of CO2; 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 (MMT COz Eq.)
Gas/Mode
C02
Aviation
Commercial
Military
Marine
CH4
Aviation a
Marine
N2O
Aviation
Marine
Total
1990
103.5
38.0
30.0
8.1
65.4
0.2
0.0
0.2
0.9
0.4
0.5
104.5
2005
113.1
60.1 1
55.6 1
4.5 1
53.0 1
0.1 1
0.0 1
0.1 1
1.0 1
0.6 1
0.4 •
114.2
2009
106.4
52.8
49.2
3.6
53.6
0.1
0.0
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
0.1
1.0
0.6
0.4
118.1
2011
111.7
64.8
61.7
3.1
46.9
0.1
0.0
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
0.1
0.9
0.6
0.3
106.8
2013
99.8
65.7
62.8
2.9
34.1
0.1
0.0
0.1
0.9
0.6
0.2
100.7
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding. Includes aircraft cruise altitude emissions.
aCH4 emissions from aviation are estimated to be zero.
81 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).
82 Naphtha-type jet fuel was used in the past by the military in turbojet and turboprop aircraft engines.
3-82 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 3-51: COz, ChU and NzO Emissions from International Bunker Fuels (kt)
Gas/Mode
1990
2005
2009
2010
2011
2012
2013
C02
Aviation
Marine
CH4
Aviationa
Marine
N20
Aviation
Marine
103,463
38,034
65,429
7
0
7
3
1
2
113,139
60,125
53,014
Note: Totals may not sum due to independent rounding. Includes aircraft cruise altitude emissions.
aCH4 emissions from aviation are estimated to be zero.
Table 3-52: Aviation COz and NzO Emissions for International Transport (MMT COz Eq.)
Aviation Mode
Commercial Aircraft
Military Aircraft
Total
1990
30.0
8.1
38.0
2005
55.6
4.5
60.1
2009
49.2
1 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
2013
62.8
2.9
65.7
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding. Includes aircraft cruise altitude emissions.
Methodology
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 El A 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 (2015) and USAF (1998), and heat content for jet fuel was taken from EIA (2015). 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 2006 IPCC 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
Energy 3-83
-------
(EDB). This bottom-up approach is in accordance with the Tier 3B method from the 2006IPCC Guidelines (IPCC
2006).
International aviation CC>2 estimates for 1990 and 2000 through 2013 are obtained from FAA's AEDT model (FAA
2015). The radar-informed method that was used to estimate CC>2 emissions for commercial aircraft for 1990, and
2000 through 2013 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 for years 1991 through 1999 are
unavailable, consumption estimates for these years were calculated using fuel consumption estimates from the
Bureau of Transportation Statistics (DOT 1991 through 2013), 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 from DoD's Defense Logistics Agency Energy (DLA Energy 2014). 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 (2014). The total amount
of fuel provided to naval vessels was reduced by 21 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 1990 2005 2009 2010 2011 2012 2013
U.S. and Foreign Carriers 3,222 5,983 I 5,293 6,173 6,634 6,604 6,748
U.S. Military 862 462 367 367 319 321 294
Total 4,084 6,445 5,660 6,540 6,953 6,925 7,042
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 1
522
5,920
2005
3,881
444
471
4,796
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
2013
2,537
235
308
3,081
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
3-84 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
from domestic transport activities.83 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 CC>2 in the 2006IPCC Guidelines (IPCC 2006) 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 CCh.84
There is also concern regarding the reliability of the existing DOC (2013) 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 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
83 See uncertainty discussions under Carbon Dioxide Emissions from Fossil Fuel Combustion.
84 U.S. aviation emission estimates for CO, NOX, and NMVOCs are reported by EPA's National Emission Inventory (NET) 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 3-85
-------
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
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous Inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh-equivalent emissions from CH4, HFCs, and PFCs. The GWPs of N2O and SF6 have decreased,
leading to a decrease in CCh-equivalent emissions for these greenhouse gases. The AR4 GWPs have been applied
across the entire time series for consistency. For more information please see the Recalculations and Improvements
Chapter.
In addition, changes to emission estimates are due to revisions made to historical activity data for military aircraft
consumption from DLA Energy 2014. These historical data changes resulted in changes to the emission estimates
for the most recent inventory year compared to the previous Inventory. This equaled a decrease in emissions from
international bunker fuels of less than 0.1 MMT CO2 Eq. (less than 0.01 percent) in total emissions in 2012.
Planned Improvements
The feasibility of including data from a broader range of domestic and international sources for bunker fuels,
including data from studies such as the Third IMO GHG Study 2014, is being considered.
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 CCh 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 6), which
accounts for the contribution of any resulting CC>2 emissions to U. S. totals within the Land Use, Land-Use Change
and Forestry sector's approach.
In 2013, total CC>2 emissions from the burning of woody biomass in the industrial, residential, commercial, and
electricity generation sectors were approximately 208.6 MMT CCh Eq. (208,594 kt) (see Table 3-55 and Table
3-56). As the largest consumer of woody biomass, the industrial sector was responsible for 57.6 percent of the CCh
emissions from this source. The residential sector was the second largest emitter, constituting 28.7 percent of the
total, while the commercial and electricity generation sectors accounted for the remainder.
3-86 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 3-55: COz Emissions from Wood Consumption by End-Use Sector (MMT COz Eq.)
End-Use Sector
Industrial
Residential
Commercial
Electricity Generation
Total
1990
135.3
59.8
6.8
13.3
215.2
2005
136.3
44.3
7.2
19.1
206.9
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
125.7
43.3
6.3
19.6
194.9
2013
120.2
59.8
7.2
21.3
208.6
Note: Totals may not sum due to independent rounding.
Table 3-56: COz Emissions from Wood Consumption by End-Use Sector (kt)
End-Use Sector
Industrial
Residential
Commercial
Electricity Generation
Total
1990
135,348
59,808
6,779
13,252 I
215,186
2005
136,269
44,340 1
7,218 1
19,074
206,901
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
125,724
43,309
6,257
19,612
194,903
2013
120,202
59,808
7,241
21,344
208,594
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 2013, the United States consumed an estimated 1,091.8 trillion Btu of ethanol, and as a result, produced
approximately 74.7 MMT CO2 Eq. (74,743 kt) (see Table 3-57 and Table 3-58) of CO2 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.
Table 3-57: COz Emissions from Ethanol Consumption (MMT COz Eq.)
End-Use Sector
Transportation
Industrial
Commercial
Total
1990
4.1
0.1
+
4.2
2005
22.4
0.5 1
0.1
22.9
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
2013
73.4
1.2
0.2
74.7
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 3-58: COz Emissions from Ethanol Consumption (kt)
End-Use Sector
Transportation*
Industrial
Commercial
Total
1990
4,136
56
34
4,227
2005
22,414 1
468 1
60
22,943
2009
61,193
885
193
62,272
2010
71,287
1,134
226
72,647
2011
71,537
1,146
198
72,881
2012
71,510
1,142
175
72,827
2013
73,354
1,206
183
74,743
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.
Energy 3-87
-------
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 CC>2 with 100 percent efficiency. The emissions from ethanol consumption were calculated by
applying an emission factor of 18.67 MMT C/QBtu (EPA 2010) to U.S. ethanol consumption estimates that were
provided in energy units (EIA 2015) (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
0 1
0 1
,0
,7
2009
1,178.4
500.
72,
180.
1,931.
,0
.6
,0
0
2010
1,273
.5
440.0
71
195,
.6
.6
1,980.7
2011
1,308.9
450.0
69.2
182.2
2,010.2
2012
1,339.4
420.0
60.7
190.2
2,010.3
2013
1,280.6
580.0
70.2
207.0
2,137.8
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 1
0.5
61.7
1 2005
327.4
6.8
1 0.9
335.1
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
1,044.6
16.7
2.6
1,063.8
2013
1,071.5
17.6
2.7
1,091.8
Note: Totals may not sum due to independent rounding.
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 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Wood consumption values were revised relative to the previous Inventory for 2012 based on updated information
fmmEI A's Monthly Energy Review (EIA 2015). These revisions of historical data for wood biomass consumption
resulted in an average annual increase in emissions from wood biomass consumption of less than 0.1 MMT CCh Eq.
(less than 0.1 percent) from 1990 through 2012. Total overall ethanol consumption values remained constant relative
to the previous inventory for 2012, although there were small differences in historical consumption among the
industrial, transportation, and commercial sectors. Consumption increased within the industrial sector and decreased
3-88 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
in the transportation and commercial sectors (EIA 2015), resulting in changes less than 0.1 MMT CO2 Eq. (less than
0.1 percent) from 1990 through 2012.
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.85 In line with UNFCCC reporting guidelines, fuel
combustion emissions are included in this chapter, while process emissions are included in the Industrial Processes
and Product Use 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 CC>2 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.86
85 See .
86 See.
Energy 3-89
-------
4. Industrial Processes and Product Use
The Industrial Processes and Product Use (IPPU) chapter includes greenhouse gas emissions occurring from
industrial processes and from the use of greenhouse gases in products. This chapter includes sources of emissions
formerly represented in the 'Industrial Processes' and 'Solvent and Other Product Use' chapters in prior versions of
this report. The industrial processes and product use categories included in this chapter are presented in Figure 4-1.
Greenhouse gas emissions are produced as the by-products of various non-energy-related industrial activities. That
is, these emissions are produced either from an industrial process itself, and are not directly a result of energy
consumed during the process. For example, raw materials can be chemically or physically 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 included 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.
In addition, greenhouse gases are often used in products or by end-consumers. These gases include industrial
sources of man-made compounds such as hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride
(SF6), nitrogen trifluoride (NF3), and nitrous oxide (N2O). The present contribution of HFCs, PFCs, SF6, and NF3
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. Use 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. HFCs, PFCs, SF6, and NF3 are employed and
emitted by a number of other industrial sources in the United States such as aluminum production, HCFC-22
production, semiconductor manufacture, electric power transmission and distribution, and magnesium metal
production and processing. N2O is emitted by the production of adipic acid and nitric acid, semiconductor
manufacturing, end-consumers in product uses through the administration of anesthetics, and by industry as a
propellant in aerosol products.
In 2013, IPPU generated emissions of 359.1 million metric tons of CO2 equivalent (MMT CO2 Eq.),147 or 5.4
percent of total U.S. greenhouse gas emissions. Carbon dioxide emissions from all industrial processes were 163.0
MMT CO2 Eq. (162,979 kt) in 2013, or 3.0 percent of total U.S. CO2 emissions. Methane emissions from industrial
processes resulted in emissions of approximately 0.8 MMT CO2Eq. (32 kt) in 2013, which was less than 1 percent
of U.S. CH4 emissions. N2O emissions from IPPU were 19.1 MMT CO2 Eq. (64 kt) in 2013, or 5.4 percent of total
U.S. N2O emissions. In 2013 combined emissions of HFCs, PFCs, SF6, and NF3 totaled 176.3 MMT CO2 Eq. Total
emissions from IPPU in 2013 were 5.0 percent more than 1990 emissions. Indirect greenhouse gas emissions also
result from IPPU, and are presented in Table 4-106 in kilotons (kt).
Following the revised reporting requirements under the UNFCCC, this Inventory report presents CCh equivalent values based
on the IPCC Fourth Assessment Report (AR4) GWP values. See the Introduction chapter for more information.
Industrial Processes and Product Use 4-1
-------
Figure 4-1: 2013 Industrial Processes and Product Use Chapter Greenhouse Gas Sources
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
158
Industrial Processes and Product Use
as a Portion of all Emissions
Substitution of Ozone Depleting Substances
Iron dnd Steel Prod, & Metallurgical Coke Prod.
Cement Production
Petrochemical Production
Lime Production
Nitric Acid Production
Ammonia Production
Aluminum Production
Urea Consumption For Non-Agricultural Purposes
Otfier Process Uses of Carbonates
N2O from Product Uses
Semiconductor Manufacture
HCFC-22 Production
Adipic Acid Production
Soda Ash Production and Consumption
Ferroalloy Production
Titanium Dioxide Production
Magnesium Production and Processiig
Zinc Production
Phosphoric Acid Production
Glass Production
Carbon Dioxide Consumption
Lead Production
Silicon Carbide Production and Consumption
0 10 20 30 40 SO 60 70
MMT 002 Eq.
The increase in overall IPPU emissions since 1990 reflects a range of emission trends among the 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 CCh 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, SF6, and NF3 from other sources are mixed. N2O emissions from the production of
adipic and nitric acid have decreased, while N2O emissions from product uses has remained nearly constant over
time. Trends are explained further within each emission source category throughout the chapter.
Table 4-1 summarizes emissions for the IPPU chapter in MMT CC>2 Eq. using IPCC Fourth Assessment Report
(AR4) GWP values, following the requirements of the revised UNFCCC reporting guidelines for national
inventories (IPCC 2007).148 Unweighted native gas emissions in kt are also 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
148 See < http://unfccc.int/resource/docs/2013/copl9/eng/10a03.pdf>.
4-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
reporting format tables, corresponding generally to: mineral products, chemical production, metal production, and
emissions from the uses of HFCs, PFCs, SF6, and NF3.
Table 4-1: Emissions from Industrial Processes and Product Use (MMT COz Eq.)
Gas/Source
1990
2005
2009
2010
2011
2012
2013
CO2 207.2
Iron and Steel Production &
Metallurgical Coke Production 99.8
Iron and Steel Production 97.3
Metallurgical Coke Production 2.5
Cement Production 33.3
Petrochemical Production 21.6
Lime Production 11.7
Ammonia Production 13.0
Urea Consumption for Non-
Agricultural Purpo ses 3.8
Other Process Uses of Carbonates 4.9
Aluminum Production 6.8
Soda Ash Production and
Consumption 2.7
Ferroalloy Production 2.2
Titanium Dioxide Production 1.2
Zinc Production 0.6
Phosphoric Acid Production 1.6
Glass Production 1.5
Carbon Dioxide Consumption 1.5
Lead Production 0.5
Silicon Carbide Production and
Consumption 0.4
Magnesium Production and
Processing +
CH4 1.4
Iron and Steel Production &
Metallurgical Coke Production 1.1
Iron and Steel Production 1.1
Metallurgical Coke Production +
Petrochemical Production 0.2
Ferroalloy Production +
Silicon Carbide Production and
Consumption +
N2O 31.6
Nitric Acid Production 12.1
N2O from Product Uses 4.2
Adipic Acid Production 15.2
Semiconductor Manufacturing +
HFCs 46.6
Substitution of Ozone Depleting
Substances* 0.3
HCFC-22 Production 46.1
Semiconductor Manufacture 0.2
Magnesium Production and
Processing 0.0
PFCs 24.3
Aluminum Production 21.5
Semiconductor Manufacture 2.8
SF6 31.1
191.1
66.7
64.6
2.0
45.9
28.1
14.6
9.2
3.7
6.3
4.1
2.9
1.4
1.8
1.0
1.4
1.9
1.4
0.6
0.2
1.0
0.9
0.9
+
0.1
141.1
165.7
169.7
166.4
163.0
22.8
11.3
4.2
7.1
0.1
131.4
111.1
20.0
0.2
0.0
6.6
3.4
3.2
14.0
43.0
42.1
1.0
29.4
23.7
11.4
8.5
3.4
7.6
3.0
2.5
1.5
1.6
0.9
1.0
1.0
1.8
0.5
55.7
53.7
2.1
31.3
27.4
13.4
9.2
4.7
9.6
2.7
2.6
1.7
1.8
1.2
1.1
1.5
1.2
0.5
60.0
58.6
1.4
32.0
26.4
14.0
9.3
4.0
9.3
3.3
2.6
1.7
1.7
1.3
1.2
1.3
0.8
0.5
54.3
53.8
0.5
35.1
26.5
13.7
9.4
4.4
8.0
3.4
2.7
1.9
1.5
1.5
1.1
1.2
0.8
0.5
52.3
50.5
1.8
36.1
26.5
14.1
10.2
4.7
4.4
3.3
2.7
1.8
1.6
1.4
1.2
1.2
0.9
0.5
0.1
0.5
0.4
0.4
0.2
0.7
0.6
0.6
+
0.1
+
0.2
0.8
0.7
0.7
0.2
0.8
0.7
0.7
+
0.1
+
0.2
0.8
0.7
0.7
+
0.1
+
16.7
9.6
4.2
2.7
0.1
142.9
136.0
6.8
0.2
+
3.9
1.9
2.0
9.3
20.1
11.5
4.2
4.2
0.1
152.6
144.4
8.0
0.2
+
4.4
1.9
2.6
9.5
25.5
10.9
4.2
10.2
0.2
157.4
148.4
8.8
0.2
+
6.9
3.5
3.4
10.0
20.4
10.5
4.2
5.5
0.2
159.2
153.5
5.5
0.2
+
6.0
2.9
3.0
7.7
19.1
10.7
4.2
4.0
0.2
163.0
158.6
4.1
0.2
0.1
5.8
3.0
2.9
6.9
Industrial Processes and Product Use 4-3
-------
Electrical Transmission and
Distribution
Magnesium Production and
Processing
Semiconductor Manufacture
NF3
Semiconductor Manufacture
25.4
10.6
2.7
0.7
0.5
0.5
7.3
1.6
0.3
0.4
0.4
7.0
2.1
0.4
0.5
0.5
0.7
0.7
5.7
1.6
0.4
0.6
0.6
5.1
1.4
0.4
0.6
0.6
Total
342.1
367.4
314.9
353.6
371.0
361.2
359.1
Notes: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.05 MMT CO2 Eq.
a Small amounts of PFC emissions also result from this source.
Table 4-2: Emissions from Industrial Processes and Product Use (kt)
Gas/Source
1990
2005
2009
2010
2011
2012
2013
C02 207,166 191,101
Iron and Steel Production &
Metallurgical Coke Production 99,781 I 66,666
Iron and Steel Production 97,311 64,623
Metallurgical Coke Production 2,470 I 2,043
Cement Production 33,278 I 45,910
Petrochemical Production 21,633 I 28,124
Lime Production 11,700 I 14,552
Ammonia Production 13,047 9,196
Urea Consumption for Non-
Agricultural Purposes 3,784 I 3,653
Other Process Uses of Carbonates 4,907 I 6,339
Aluminum Production 6,831 4,142
Soda Ash Production and
Consumption 2,741 I 2,868
Ferroalloy Production 2,152 I 1,392
Titanium Dioxide Production 1,195 1,755
Zinc Production 632 1,030
Phosphoric Acid Production 1,586 1,395
Glass Production 1,535 1,928
Carbon Dioxide Consumption 1,472 1,375
Lead Production 516 553
Silicon Carbide Production and
Consumption 375 219
Magnesium Production and
Processing I I 3
CH4 56 40
Iron and Steel Production &
Metallurgical Coke Production 46 34
Iron and Steel Production 46 34
Metallurgical Coke Production + I +
Petrochemical Production 9 I 6
Ferroalloy Production I I +
Silicon Carbide Production and
Consumption I I +
N20 106 76
Nitric Acid Production 41 38
N2O from Product Uses 14 14
Adipic Acid Production 51 24
Semiconductor Manufacture + I +
HFCs M M
141,126 165,737 169,727 166,359 162,979
43,029
42,073
956
29,432
23,706
11,411
8,454
3,427
7,583
3,009
55,746
53,662
2,084
31,256
27,388
13,381
9,188
4,730
9,560
2,722
60,008
58,583
1,425
32,010
26,396
13,981
9,292
4,029
9,335
3,292
54,327
53,786
542
35,051
26,477
13,715
9,377
4,449
8,022
3,439
52,288
50,466
1,822
36,146
26,514
14,072
10,152
4,663
4,424
3,255
2,488
1,469
1,648
943
1,016
1,045
1,795
525
145
1
20
17
17
56
32
14
9
+
M
2,612
1,663
1,769
1,182
1,130
1,481
1,206
542
181
1
27
25
25
68
39
14
14
+
M
2,624
1,735
1,729
1,286
1,198
1,299
802
538
170
3
30
28
28
86
37
14
34
1
M
2,672
1,903
1,528
1,486
1,138
1,248
841
527
158
2
33
29
29
+
3
1
69
35
14
19
1
M
2,712
1,785
1,608
1,429
1,173
1,160
903
525
169
2
32
28
28
64
36
14
13
1
M
4-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Substitution of Ozone Depleting
Substances*
HCFC-22 Production
Semiconductor Manufacture
Magnesium Production and
Processing
PFCs
Aluminum Production
Semiconductor Manufacture
SF6
Electrical Transmission and
Distribution
Magnesium Production and
Processing
Semiconductor Manufacture
NF3
Semiconductor Manufacture
M
3
0
M
M
M
2
M
1
0
M
M
M
1
M
M
M
M
M
1
M
M
M
M
1
M
M
M
1
M
M
M
M
M
M
M
M
+ Does not exceed 0.5 kt
M (Mixture of gases)
Note: Totals may not sum due to independent rounding.
a Small amounts of PFC emissions also result from this source.
The UNFCCC incorporated the 2006IPCC Guidelines for National Greenhouse Gas Inventories (2006IPCC
Guidelines) as the standard for Annex I countries at the Nineteenth Conference of the Parties (Warsaw, November
11-23, 2013). This chapter presents emission estimates calculated in accordance with the methodological guidance
provided in these guidelines.
QA/QC and Verification Procedures
For industrial processes and product use sources, a detailed QA/QC plan was developed and implemented. This plan
was based on the overall U.S. QA/QC plan, 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 checks of
the emission factors, activity data, and methodologies used for estimating emissions from the relevant industrial
process and product use 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 and product use sources. Tier 2 procedures were performed for more
significant emission categories, consistent with the IPCC Good Practice Gudelines.
For most industrial process and product use 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 reported 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 2013 emission estimates from
industrial processes and product use 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
Industrial Processes and Product Use 4-5
-------
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 connections among sources, a more comprehensive approach that accounts for all linkages will be
identified as the uncertainty analysis moves forward.
Box 4-1: Industrial Processes Data from EPA's Greenhouse Gas Reporting Program
On October 30, 2009, the U.S. EPA published a rule requiring annual of greenhouse gas data from large GHG
emissions sources in the United States. Implementation of the rule, codified at 40 CFR part 98, is referred to as
EPA's Greenhouse Gas Reporting Program (GHGRP). The rule 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 sources or suppliers in 41 industrial categories. Annual 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, but reporting is required for all facilities in some industries. Calendar year
2010 was the first year for which data were reported for facilities subject to 40 CFR part 98, though some source
categories first reported data for calendar year 2011.
EPA's GHGRP dataset and the data presented in this Inventory report are complementary. EPA presents the data
collected by EPA's GHGRP through a data publication tool (ghgdata.epa.gov) that allows data to be viewed in
several formats, including maps, tables, charts, and graphs for individual facilities or groups of facilities. 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 from
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 (IPCC 2011). 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.
For certain source categories in this Inventory (e.g., nitric acid production and petrochemical production), EPA has
also integrated data values that have been calculated by aggregating GHGRP data that is considered confidential
business information (CBI) at the facility level. EPA, with industry engagement, has put forth criteria to confirm
that a given data aggregation shields underlying CBI from public disclosure. EPA is publishing only data values that
meet these aggregation criteria.149 Specific uses of aggregated facility-level data are described in the respective
methodological sections. For other source categories in this chapter, as indicated in the respective planned
improvements sections, EPA is continuing to analyze how facility-level GHGRP data may be used to improve the
national estimates presented in this Inventory, giving particular consideration to ensuring time series consistency and
completeness.
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.
149 U.S. EPA Greenhouse Gas Reporting Program, September 16, 2014 Developments on Publication of Aggregated Greenhouse
Gas Data, see http://www.epa.gov/climate/ghgreporting/reporters/cbi/index.html
4-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
During the cement production process, calcium carbonate (CaCO3) 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 €62 in a process known as calcination or
calcining. The quantity of CC>2 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 CCh:
CaC03 + heat -> CaO + C02
Next, the lime is combined with silica-containing materials to produce clinker (an intermediate product), with the
earlier byproduct CCh 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. 15°
CO2 emitted from the chemical process of cement production is the second largest source of industrial €62
emissions in the United States. Cement is produced in 35 states and Puerto Rico. Texas, Missouri, California,
Pennsylvania, and Florida were the five leading cement-producing States in 2013 and accounted for approximately
48 percent of total U.S. production (USGS 2014). Clinker production in 2013 increased approximately 3 percent
from 2012 levels. This increase can be attributed to an increase in spending in new residential construction and
nonresidential buildings. In 2013, U.S. clinker production totaled 69,901 kilotons (USGS 2014). The resulting CO2
emissions were estimated to be 36.1 MMT CO2 Eq. (36,146 kt) (see Table 4-3).
Table 4-3: COz Emissions from Cement Production (MMT COz Eq. and kt)
Year MMT CCh Eq. kt
2009
2010
2011
2012
2013
29.4
31.3
32.0
35.1
36.1
29,432
31,256
32,010
35,051
36,146
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 28 percent lower 2008 emissions
and 12 percent lower than 1990). Since 2010, emissions have increased slightly. In 2013, emissions from cement
production increased by 3 percent from the 2012 levels.
Emissions since 1990 have increased by 9 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, 2012, and in 2013 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.
Methodology
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
150 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 and Product Use 4-7
-------
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 CCh released per unit of lime.
The USGS mineral commodity expert for cement has confirmed that this is a reasonable assumption for the United
States (Van Oss 2013a). This calculation yields an emission factor of 0.51 tons of €62 per ton of clinker produced,
which was determined as follows:
EFciinker = 0.6460 CaO x [(44.01 g/mole C02) H- (56.08 g/mole CaO)] = 0.5070 tons C02/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 €62
emissions should be estimated as two percent of the €62 emissions calculated from clinker production (when data
on CKD generation are not available). 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 (1 to 2 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). Clinker production data for 2013 were also obtained from USGS (USGS 2014).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 (kt)
Year Clinker
1990 64,355
8,783
2009 56,918
2010 60,444
2011 61,903
2012 67,784
2013 69,901
Note: Clinker production from 1990-2013 includes Puerto Rico. Data were obtained from USGS (Van Oss 2013a; USGS
2014), whose original data source was USGS and U.S. Bureau of Mines Minerals Yearbooks (2013 data obtained from
mineral industry surveys for cement in June 2014).
Uncertainty and Time-Series Consistency
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.
4-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-5. Based on the
uncertainties associated with total U.S. clinker production, the €62 emission factor for clinker production, and the
emission factor for additional CO2 emissions from CKD, 2013 CO2 emissions from cement production were
estimated to be between 34.0 and 38.3 MMT €62 Eq. at the 95 percent confidence level. This confidence level
indicates a range of approximately 6 percent below and 6 percent above the emission estimate of 36.1 MMT €62
Eq.
Table 4-5: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from Cement
Production (MMT COz Eq. and Percent)
^ „ 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
°UrCe ^ (MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Cement Production CCh 36.1 34.0 38.3 -6% +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 consistency in emissions from 1990
through 2013. 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.151
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
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.152 Emissions from fuels consumed for energy
purposes during the production of lime are accounted for in the Energy chapter.
151 See
PCC is 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.
Industrial Processes and Product Use 4-9
-------
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 19,210 kilotons in 2013
(Corathers 2014). Principal lime producing states are Alabama, Kentucky, Missouri, Nevada, Ohio, Pennsylvania,
and Texas.
U.S. lime production resulted in estimated net CO2 emissions of 14.1 MMT CO2 Eq. (14,072 kt) (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 (MMT COz Eq. and kt)
Year MMT CCh Eq.
kt
1990
11.7
11,700
2009
2010
2011
2012
2013
11.4
13.4
14.0
13.7
14.1
11,411
13,381
13,981
13,715
14,072
Table 4-7: Potential, Recovered, and Net COz Emissions from Lime Production (kt)
Year
Potential
Recovered3 Net Emissions
1990
11,959
259
11,700
2009
2010
2011
2012
2013
11,872
13,776
14,389
14,188
14,539
461
395
407
473
467
11,411
13,381
13,981
13,715
14,072
a For sugar refining and PCC production.
Note: Totals may not sum due to independent rounding.
In 2013, lime production was nearly the same as 2011 levels (increase of 1 percent) at 19,210 kilotons. In 2013, lime
production increased from 2012 levels by approximately 3 percent. Lime production in 2010 rebounded from a 21
percent decline in 2009 to 18,219 kilotons, 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.
4-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Methodology
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 C02) H- (56.08 g/mole CaO)] x (0.9500 CaO/lime) = 0.7455 g C02/g lime
For dolomitic lime:
[(88.02 g/mole C02) H- (96.39 g/mole CaO)] x (0.9500 CaO/lime) = 0.8675 gC02/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 2006). 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.
The 2006 IPCC Guidelines (Tier 2 method) also recommends accounting for emissions from lime kiln dust (LKD).
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 emissions from LKD (IPCC 2006).
Lime emission estimates were further adjusted to account for the amount of CO2 captured for use in on-site
processes. All the domestic lime facilities are required to report these data to EPA under its GHGRP. The total
national-level annual amount of CO2 captured for on-site process use was obtained from EPA's GHGRP (EPA
2014) based on reported facility level data. The amount of CO2 captured/recovered for on-site process use is
deducted from the total potential emissions (i.e., from lime production and LKD). The net lime emissions are
presented in Table 4-6 and Table 4-7. GHGRP data on CO2 removals (i.e., CO2 captured/recovered) was available
only for 2010 through 2013. Since GHGRP data are not available for 1990 through 2009, IPCC "splicing"
techniques were used as per the 2006 IPCC Guidelines on time series consistency (2006 IPCC Guidelines, Volume
1, Chapter 5). The prior estimates for CO2 removal for 1990 through 2009 were adjusted based on the "overlap"
technique recommended by IPCC. Refer to the Recalculations Discussion section, below, for more details.
Lime production data (by type, high-calcium- and dolomitic-quicklime, high-calcium- and dolomitic-hydrated, and
dead-burned dolomite) for 1990 through 2013 (see Table 4-8) were obtained from USGS (1992 through 2013,
Corathers 2014) and are compiled by USGS to the nearest ton. Natural hydraulic lime, which is produced from CaO
and hydraulic calcium silicates, is not manufactured 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 2006). 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) were not provided prior to 1997, 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 (kt)
Year
High-Calcium
Quicklime
Dolomitic
Quicklime
High-Calcium
Hydrated
Dolomitic
Hydrated
Dead-Burned
Dolomite
2009
2010
11,800
13,300
200
200
Industrial Processes and Product Use 4-11
-------
Year
2011
2012
2013
High-Calcium
Quicklime
13,900
13,600
13,800
Dolomitic
Quicklime
2,690
2,710
2,870
High-Calcium
Hydrated
2,010
2,020
2,050
Dolomitic
Hydrated
230
237
260
Dead-Burned
Dolomite
200
200
230
Table 4-9: Adjusted Lime Production (kt)
Year High-Calcium Dolomitic
1990 12,466 2,800
2009
2010
2011
2012
2013
13,034
14,694
15,367
15,075
15,297
2,213
2,937
3,051
3,076
3,282
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 CCh recovery rates for on-site process use over the time series. 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, €62 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 CO 2;
whereas most of the lime used in steel making reacts with impurities such as silica, sulfur, and aluminum
compounds. Quantifying the amount of €62 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 €62
are "reused" are required to quantify the amount of €62 that is reabsorbed. Research conducted thus far has not
yielded the necessary information to quantify €62 reabsorption rates.153 However, some additional information on
the amount of €62 consumed on site at lime facilities has been obtained fromEPA's GHGRP.
In some cases, lime is generated from calcium carbonate byproducts at pulp mills and water treatment plants.154
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
I53 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).
154 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.
4-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 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.
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 United States could be closer to 6
percent. They also note that additional emissions (~2 percent) may also be generated through production of other
byproducts/wastes (off-spec lime that is not recycled, scrubber sludge) 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 and data is needed to improve understanding of additional calcination emissions to
consider revising the current assumptions that are based on the IPCC Guidelines.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-10. Lime CCh emissions
for 2013 were estimated to be between 13.7 and 14.4 MMT CCh Eq. at the 95 percent confidence level. This
confidence level indicates a range of approximately 3 percent below and 3 percent above the emission estimate of
14.1 MMT CO2Eq.
Table 4-10: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from Lime
Production (MMT COz Eq. and Percent)
„ „ 2013 Emission Estimate Uncertainty Ranee Relative to Emission Estimate3
Source Gas J &
(MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Lime Production CCh 141 13.7 14.4 -3% +3%
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 consistency in emissions from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Updated CCh recovery data was used for this category, aggregating reported facility level data from the GHGRP
data on amount of CCh captured for on-site process use 2010 through 2013 (EPA 2014). Since these data were not
available for the entire time series, IPCC-recommended "splicing" techniques were followed to estimate CCh
removals for 1990 through 2009. In cases where the same method and data source is not available for the entire
time series, IPCC recommends the use of "splicing" techniques to maintain time series consistency.
Of these, overlap is the only suitable method that could be applied to revise the 1990 through 2009 CO2 removal
estimates. The surrogate data method is not applicable due to absence of appropriate surrogate data for CCh removal.
Interpolation and trend extrapolation methods are not suitable for longer time-periods (1990 through 2009).
Therefore, the overlap method was selected to revise the prior 1990 through 2009 removal estimates.
According to the IPCC overlap method (IPCC 2006), the prior CO2 removal estimates for 1990 through 2009 were
multiplied by an adjustment factor. The adjustment factor is the average ratio of the removal estimates prepared
using the new and the method previously used during the period of overlap (2010 through 2013).
Industrial Processes and Product Use 4-13
-------
(n - m + 1)
where:
= the recalculated emission or removal estimate computed using overlap method
= the estimate developed using the previously used method
= estimate(s) prepared using the new method during the period of overlap (2010-2013)
Xi = estimate(s) prepared using the previously-used method during the period of overlap (2010-2013)
m = starting year for the period of overlap (2010)
n = ending year for the period of overlap (2013)
Using the above equation, the adjustment factor was calculated to be 0.4815. The prior removal estimates for 1990
through 2009 were multiplied by this adjustment factor to obtain the revised removal estimates. This change resulted
in a decrease of the annual CCh removal estimates by approximately half. As a result of the decreased removal
estimates, the net CCh emissions from lime production increase for the entire time series. In the previous Inventory
reports, the CCh removal estimates (i.e., CCh captured/recovered) were calculated using lime consumption data for
PCC production and sugar refining. PCC producers and sugar refineries recover CC>2 emitted by lime production
facilities for use as an input into production or refining processes. For CC>2 recovery by sugar refineries, lime
consumption estimates (Corathers 2014) were multiplied by a CC>2 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 CCh for 100 percent of their CC>2 input (Lutter 2009, Miller 2013). Carbon dioxide recovery
by PCC producers was determined by multiplying lime consumption for PCC production (USGS 1992 through
2013, Corathers 2014) with the percentage CC>2 of production weight for PCC production at lime plants (i.e.,
CCVCaCOs = 44/100) and a CCh recovery factor based on the amount of purchased CC>2 by 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).
Planned Improvements
Future improvements involve continuing 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 per comments from the National Lime Association. Pending resources and data availability, historical CCh
recovery rates at U. S. facilities producing lime will be investigated to further evaluate results from use of overlap
method to improve time series consistency.
4.3 Glass Production (IPCC Source Category
2A3)
Glass production is an energy and raw-material intensive process that results in the generation of CC>2 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 CCh emissions
during the glass melting process are limestone, dolomite, and soda ash. The main former in all types of glass is silica
(SiCh). 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
4-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
limestone (CaCO3), dolomite (CaCO3MgCO3), alumina (A12O3), magnesia (MgO), barium carbonate (BaCO3),
strontium carbonate (SrCO3), 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 CCh 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 CC>2 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.155
In 2013, 335 kilotons of limestone and 2,440 kilotons of soda ash were consumed for glass production in 2013
(USGS 2014b, Willett 2014). Dolomite consumption data for glass manufacturing was not publicly available for
2013. Use of limestone and soda ash in glass production resulted in aggregate CC>2 emissions of 1.2 MMT CCh Eq.
(1,160 kt) (see Table 4-11). Overall, emissions have decreased 24 percent from 1990 through 2013.
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 4-11: COz Emissions from Glass Production (MMT COz Eq. and kt)
Year _ MMT CCh Eq.
1990 1.5 1,535
2009
2010
2011
2012
2013
1.0
1.5
1.3
1.2
1.2
1,045
1,481
1,299
1,248
1,160
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 2013 of limestone, dolomite, and soda ash used for glass manufacturing were
obtained from the USGS Minerals Yearbook: Crushed Stone Annual Report (1995 through 2014), 2013 preliminary
data from the USGS Crushed Stone Commodity Expert (Willett 2014), the USGSMinerals Yearbook: SodaAsh
Annual Report (1995 through 2013), USGS Mineral Industry Surveys for Soda Ash in August 2014 (USGS 2014)
Excerpt from Glass & Glass Product Manufacturing Industry Profile, First Research. Available online at
.
Industrial Processes and Product Use 4-15
-------
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.156
Based on the 2013 reported data, the estimated distribution of soda ash consumption for glass production compared
to total domestic soda ash consumption is 48 percent (USGS 2014b).
Table 4-12: Limestone, Dolomite, and Soda Ash Consumption Used in Glass Production (kt)
Activity
Limestone
Dolomite
Soda Ash
Total
1990
430
39|
3,177
3,666
2005
920
541 1
3,050
4,511
2009
139
0
2,370
2,509
2010
999
0
2,510
3,509
2011
614
0
2,480
3,094
2012
555
0
2,420
2,975
2013
335
0
2,440
2,775
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,
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 2013, 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 Approach 2 quantitative uncertainty analysis are summarized in Table 4-13. In 2013, glass
production CC>2 emissions were estimated to be between 1.1 and 1.2 MMT CCh Eq. at the 95 percent confidence
156 This approach was recommended by USGS.
4-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
level. This indicates a range of approximately 5 percent below and 5 percent above the emission estimate of 1.2
MMT CO2 Eq.
Table 4-13: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from Glass
Production (MMT COz Eq. and Percent)
„ „ 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
source lias (MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Glass Production CCh \_2 LI \_2 -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 consistency in emissions from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Limestone consumption data for 2012 were revised to reflect updated USGS data. This change resulted in an
insignificant increase of CCh emissions (less than 1 kt of CCh). The preliminary data for 2012 was obtained directly
from the USGS Crushed Stone Commodity Expert (Willett 2013). In June 2014, USGS published the 2012 Minerals
Yearbook for Crushed Stone and the preliminary data was revised to reflect the latest USGS published data. 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
Currently, only limestone and soda ash consumption data for glass manufacturing is publicly available. While
limestone and soda ash are the predominant carbonates used in glass manufacturing, there are other carbonates that
are also consumed for glass manufacturing, although in smaller quantities (e.g. dolomite). Pending resources, future
improvements will include research into other sources of data for carbonate consumption by the glass industry.
Additionally, future improvements will also 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.157
4.4 Other Process Uses of Carbonates (IPCC
Source Category 2A4)
Limestone (CaCOs), dolomite (CaCOsMgCOs)158, and other carbonates such as magnesium carbonate and iron
carbonate are basic materials used by a wide variety of industries, including construction, agriculture, chemical,
157 See.
Limestone and dolomite are collectively referred to as limestone by the industry, and intermediate varieties are seldom
distinguished.
Industrial Processes and Product Use 4-17
-------
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 2014). 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 2013, 10,010 kt of limestone and 1,212 kt of dolomite were consumed for these emissive applications, excluding
glass manufacturing (Willett 2014). Usage of limestone and dolomite resulted in aggregate CO2 emissions of 4.4
MMT CO2 Eq. (4,424 kt) (see Table 4-14 and Table 4-15). Overall, emissions have decreased 10 percent from 1990
through 2013.
Table 4-14: COz Emissions from Other Process Uses of Carbonates (MMT COz Eq.)
Year Flux Stone
FGD
Magnesium
Production
Other
Miscellaneous
Uses
Total
1990
2.6
1.4
0.1
2009
2010
2011
2012
2013
1.8
1.6
1.5
1.1
0.9
5.4 +
7.1 +
5.4 +
5.8 +
3.0 +
0.4
0.9
2.4
1.1
0.5
7.6
9.6
9.3
8.0
4.4
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 0.05 MMT CO2 Eq.
Table 4-15: COz Emissions from Other Process Uses of Carbonates (kt)
Year Flux Stone
FGD
Magnesium
Production
Other
Miscellaneous
Uses
Total
1990
2,592
1,432
+ Emissions are less than 0.5 kt
64
4,907
2009
2010
2011
2012
2013
1,784
1,560
1,467
1,077
947
5,403 +
7,064 +
5,420 +
5,797 +
3,002 +
396
937
2,449
1,148
474
7,583
9,560
9,335
8,022
4,424
4-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Methodology
CO2 emissions were calculated based on the 2006IPCC 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 CCVtonne carbonate.159 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 €62 emissions ceased its
operations (USGS 1995 through 2012b; USGS 2013a).
Consumption data for 1990 through 2013 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
4-16) were obtained from the USGS M/'wera/s Yearbook: Crushed Stone Annual Report (1995 through 2014),
preliminary data for 2013 from USGS Crushed Stone Commodity Expert (Willett, 2014), and the U.S. Bureau of
Mines (1991 and 1993a), which are reported to the nearest ton. The production capacity data for 1990 through 2013
of dolomitic magnesium metal also came from the USGS (1995 through 2012, USGS 2013a) 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. 16°
Table 4-16: Limestone and Dolomite Consumption (kt)
Activity
Flux Stone
Limestone
Dolomite
FGD
Other Miscellaneous Uses
Total
1990
6,737
5, 8041
933M
3,258 •
1,835
11,830
2005
7,022
3,165
3,857
6,761
1,632
15,415
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
3,666
3,108
559
13,185
2,610
19,461
2013
3,317
2,119
1,199
6,827
1,078
11,222
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
159 IPCC 2006 Guidelines, Volume 3: Chapter 2
160 This approach was recommended by USGS, the data collection agency.
Industrial Processes and Product Use 4-19
-------
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 Approach 2 quantitative uncertainty analysis are summarized in Table 4-17. Other Process Uses of
Carbonates CC>2 emissions in 2013 were estimated to be between 4.1 and 4.8 MMT CCh Eq. at the 95 percent
confidence level. This indicates a range of approximately 8 percent below and 8 percent above the emission
estimate of 4.4 MMT CO2 Eq.
Table 4-17: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from Other
Process Uses of Carbonates (MMT COz Eq. and Percent)
2 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 CCh 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 57 percent of domestic ammonia production capacity is concentrated in the States of Louisiana (30
percent), Oklahoma (21 percent), and Texas (6 percent) (USGS 2014). 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
4-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 + 3//2 -> 2NH3
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 this chapter.
Total emissions of CO2 from ammonia production in 2013 were 10.2 MMT CO2 Eq. (10,152 kt), and are
summarized in Table 4-18 and Table 4-19. 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. Since 1990,
emissions from ammonia production have decreased by 22 percent. Emissions in 2013 have increased by
approximately 8 percent from the 2012 levels.
Table 4-18: COz Emissions from Ammonia Production (MMT COz Eq.)
Source 1990 2005 2009 2010 2011 2012 2013
Ammonia Production 13.0 9.2 8.5 9.2 9.3 9.4 10.2
Total 13.0 9.2 8.5 9.2 9.3 9.4 10.2
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values
Table 4-19: COz Emissions from Ammonia Production (kt)
Source 1990 2005 2009 2010 2011 2012 2013
Ammonia Production 13,047 9,196 8,454 9,188 9,292 9,377 10,152
Total 13,047 9,196 8,454 9,188 9,292 9,377 10,152
Industrial Processes and Product Use 4-21
-------
Methodology
CO2 emissions from production of synthetic ammonia from natural gas feedstock is based on the 2006IPCC
Guidelines (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 CC>2 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 CCh emission factor of 1.2 metric tons
CCVmetric ton NH3 (EFMA 2000a) is applied to the percent of total annual domestic ammonia production from
natural gas feedstock.
Emissions of CCh from ammonia production are then adjusted to account for the use of some of the CCh 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 stoichiometric CCh/urea factor of 44/60, assuming complete conversion of NH3 and CCh 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
4-20. The CCh 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 CCh (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 CCh emission factor of 3.57 metric tons CCh/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 CCh/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 CO2/metric ton NH3, with 1.2 metric ton
CCVmetric 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 CCVmetric 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 2011, 2012, and 2013 were obtained from American
Chemistry Council (2014). For years before 2011, ammonia production data (See Table 4-20) was obtained from
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 and 2014) for 2012 and 2013. 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 obtained
from the Minerals Yearbook: Nitrogen (USGS 2014). The urea production data for 2013 are not yet published and
so 2012 data has been used as proxy for 2013.
4-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 4-20: Ammonia Production and Urea Production (kt)
Year
Ammonia
Production
Urea
Production
1990
15,425
7,450
2009
2010
2011
2012
2013
9,372
10,084
10,325
10,305
10,930
5,084
5,122
5,430
5,220
5,220
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 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 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 €62 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.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-21. Ammonia Production
CO2 emissions were estimated to be between 9.4 and 10.9 MMT €62 Eq. at the 95 percent confidence level. This
indicates a range of approximately 8 percent below and 8 percent above the emission estimate of 10.2 MMT €62
Eq.
Table 4-21: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from
Ammonia Production (MMT COz Eq. and Percent)
Source
Gas
2013 Emission Estimate
(MMT CCh Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CCh Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Ammonia Production
CO2
10.2
9.4
10.9
-8%
+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 consistency in emissions from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Industrial Processes and Product Use 4-23
-------
Recalculations Discussion
Production estimates for urea production for the years 2011 and 2012 were updated using information obtained from
the Minerals yearbook: Nitrogen (USGS 2014). This update resulted in an increase of emissions by approximately
3.5 percent in 2011 and 0.3 percent in 2012 emissions relative to the previous report.
Planned Improvements
Future improvements involve continuing to evaluate and analyze data reported under EPA's GHGRP 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.161 Specifically, the planned improvements include assessing data to update the emission factors to
include both fuel and feedstock CCh emissions and incorporate CCh 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.
4.6 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 United States.
The chemical reaction that produces urea is:
2NH3+ C02 -^NH2COONH4 -> CO(NH2)2 + H20
This section accounts for CC>2 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 N2O emissions from coal-fired power plants and diesel
transportation motors.
Emissions of CC>2 from urea consumed for non-agricultural purposes in 2013 were estimated to be 4.7 MMT CC>2
Eq. (4,663 kt), and are summarized in Table 4-22 and Table 4-23. Net CCh emissions from urea consumption for
non-agricultural purposes in 2013 have increased by approximately 23 percent from 1990.
161 See
4-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 4-22: COz Emissions from Urea Consumption for Non-Agricultural Purposes (MMT COz
Eq.)
Source 1990 2005 2009 2010 2011 2012 2013
Urea Consumption 3.8 3/7 3.4 4.7 4.0 4.4 4.7
Total 3.8 3.7 3.4 4.7 4.0 4.4 4.7
Table 4-23: COz Emissions from Urea Consumption for Non-Agricultural Purposes (kt)
Source 1990 2005 2009 2010 2011 2012 2013
Urea Consumption 3,784 3,653 3,427 4,730 4,029 4,449 4,663
Total 3,784 3,653 3,427 4,730 4,029 4,449 4,663
Methodology
Emissions of CC>2 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 CO2 during use, and consistent with the 2006IPCC Guidelines (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 6-26) and is reported in Table 4-24, 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 CC>2 per ton of urea consumed is then applied to the resulting supply of urea for
non-agricultural purposes to estimate CC>2 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 CC>2. This corresponds to a stoichiometric CCh/urea 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 2011 and 2012 were obtained from the Minerals Yearbook: Nitrogen (USGS 2014). Urea
production data for 2013 are not yet publicly available and so 2012 data has been used as proxy. 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 (TFI 2002) 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 4-24). Urea export data for 1990 through 2012 were taken from U.S. Fertilizer
Import/Exports from USD A Economic Research Service Data Sets (U.S. Department of Agriculture 2012). Urea
exports and imports data for 2013 is not yet available and so 2012 data has been used as proxy.
Industrial Processes and Product Use 4-25
-------
Table 4-24: Urea Production, Urea Applied as Fertilizer, Urea Imports, and Urea Exports (kt)
Year
Urea
Production
Urea Applied
as Fertilizer
Urea
Imports
Urea
Exports
2009
2010
2011
2012
2013
5,084
5,122
5,430
5,220
5,220
4,848
5,152
5,589
5,762
5,469
4,727
6,631
5,860
6,944
6,944
289
152
207
336
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 CC>2 during use.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-25. CCh emissions
associated with urea consumption for non-agricultural purposes were estimated to be between 4.2 and 5.1 MMT
CO2 Eq. at the 95 percent confidence level. This indicates a range of approximately 10 percent below and 10
percent above the emission estimate of 4.7 MMT CCh Eq.
Table 4-25: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from Urea
Consumption for Non-Agricultural Purposes (MMT COz Eq. and Percent)
Source
Gas
2013 Emission Estimate
(MMT CCh Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CCh Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Urea Consumption for
Non-Agricultural
Purposes
CO2
4.7
4.2
5.1
-10%
+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 consistency in emissions from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Production estimates for urea production for the years 2011 and 2012 were updated using information obtained from
the Minerals Yearbook: Nitrogen (USGS 2014). Also, the amount of urea consumed for agricultural purposes in the
United States for 2012 was revised based on the most recent data obtained from the Land Use, Land-Use Change,
and Forestry chapter (see Table 6-26). These updates resulted in an increase of emissions by approximately 1
percent in 2011 and a decrease of approximately 15 percent in 2012 emissions.
4-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
4.7 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). There are two different nitric acid production methods: weak nitric acid
and high-strength nitric acid. The first method utilizes oxidation, condensation, and absorption to produce nitric acid
at concentrations between 30 and 70 percent nitric acid. High-strength acid (90 percent or greater nitric acid) can be
produced from dehydrating, bleaching, condensing, and absorption of the weak nitric acid. The basic process
technology for producing nitric acid has not changed significantly over time. Most U.S. plants were built between
1960 and 2000. As of 2013, there are 35 active weak nitric acid production plants and one high-strength nitric acid
production plant in U.S. (EPA 2010b; EPA 2014).
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:
4-NH3 +802 -> 4-HN03 + 4-H20
Currently, the nitric acid industry controls 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 installed in nitric plants built between 1971 and 1977, approximately one-third
of the weak acid production plants have NSCRs. U.S. facilities are using both tertiary (e.g., NSCR) and secondary
controls (alternate catalysts).
N2O emissions from this source were estimated to be 10.7 MMT CO2 Eq. (36 kt of N2O) in 2013 (see Table 4-26).
Emissions from nitric acid production have decreased by 12 percent since 1990, with the trend in the time series
closely tracking the changes in production. Emissions have decreased by 26 percent since 1997, the highest year of
production in the time series.
Table 4-26: NzO Emissions from Nitric Acid Production (MMT COz Eq. and kt NzO)
Year MMT CCh Eq. kt N26
1990 12.1
2009
2010
2011
2012
2013
9.6
11.5
10.9
10.5
10.7
32
39
37
35
36
Note: Emissions values are presented in
CO2 equivalent mass units using IPCC
AR4 GWP values.
Methodology
Emissions of N2O were calculated using the estimation methods provided by the 2006 IPCC Guidelines (IPCC
2006) and country specific methods from N2O EPA's Greenhouse Gas Reporting Program. The 2006 IPCC
Industrial Processes and Product Use 4-27
-------
Guidelines Tier 2 method was used to estimate emissions from nitric acid production for 1990 through 2009, and a
country specific approach similar to the IPCC Tier 3 method was used to estimate N2O emissions for 2010 through
2013.
2010 through 2013
Process N2O emissions and nitric acid production data were obtained directly from EPA's GHGRP for 2010 through
2013 by aggregating reported facility-level data (EPA 2014). In the United States, all nitric acid facilities producing
weak nitric acid (30-70 percent in strength) are required to report annual GHG emissions data to EPA as per the
requirements of its Greenhouse Gas Reporting Program (GHGRP). As of 2013, there are 35 facilities that report to
EPA, including the known single high-strength nitric acid production facility in the United States (EPA 2014). All
nitric acid (weak acid) facilities are required to calculate process emissions using a site-specific emission factor
developed through annual performance testing under typical operating conditions or by directly measuring N2O
emissions using monitoring equipment. The high-strength nitric acid facility also reports N2O emissions associated
with weak acid production and this may capture all relevant emissions, pending additional further EPA research.
More details on the calculation and monitoring methods applicable to Nitric Acid facilities can be found under
Subpart V: Nitric Acid Production of the regulation, Part 98.162
1990 through 2009
Using the GHGRP data for 2010,163 country-specific N2O emission factors were calculated for nitric acid
production with abatement and without abatement (i.e., controlled and uncontrolled emission factors). These
emission factors were used to estimate N2O emissions from nitric acid production for years prior to the GHGRP
data (i.e., 1990 through 2009): 3.3 kg N2O/metric ton HNOs produced at plants using abatement technologies (e.g.,
tertiary systems such as NSCR systems) and 5.98 kg N2O/metric ton HNOs produced at plants not equipped with
abatement technology. Based on the available data, it was assumed that emission factors for 2010 would be more
representative of abatement application in 1990 through 2009. Initial review of historical data indicates that percent
production with and without abatement change over time and also year over year due to changes in application of
facility-level abatement technologies, maintenance of abatement technologies, and also due to plant closures and
start-ups (EPA 2010a, 2012, 2013b; Desai 2012; CAR 2013). The installation dates of N2O abatement technologies
are not known at most facilities, but it is assumed that facilities reporting abatement technology use have had this
technology installed and operational for the duration of the time series considered in this report (especially NSCRs).
The country-specific N2O emission factors were used in conjunction with annual production and national share of
production with and without abatement technologies to estimate N2O emissions for 1990 through 2009, using the
following equation:
EI = [(Pi x %PW x EFC) + (PL x %PunC:i x EFunc}\
where,
Ej = Annual N2O Emissions for year i (kg/yr)
Pi = Annual nitric acid production for year i (metric tons HNOs)
%Pc,i = Percent national production of HNOs with N2O abatement technology (%)
EFC = N2O emission factor, with abatement technology (kg N2O/metric ton HNOs)
%Punc,i = Percent national production of HNOs without N2O abatement technology (%)
EFmc = N2O emission factor, without abatement technology (kg N2O/metric ton HNOs)
i = year from 1990 through 2009
162 Located at .
163 National N2O process emissions, national production, and national share of nitric acid production with abatement and without
abatement technology was aggregated from the GHGRP facility-level data for 2010-2013 (i.e., percent production with and
without abatement).
4-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Nitric acid production data for the United States for 1990 through 2009 were obtained from the U.S. Census Bureau
(U.S. Census Bureau 2008 through 2010) (see Table 4-27). Publicly-available information on plant-level abatement
technologies was used to estimate the shares of nitric acid production with and without abatement for 2008 and 2009
(EPA 2010a, 2012, 2013b; Desai 2012; CAR 2013). Publicly-available data on use of abatement technologies were
not available for 1990-2007. Therefore, the share of national production with and without abatement for 2008 was
assumed to be constant for 1990 through 2007.
Table 4-27: Nitric Acid Production (kt)
Year kt
1990 7,195
2009 5,924
2010 7,444
2011 7,606
2012 7,453
2013 7,572
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
(especially prior to 2010), and the associated 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 (especially prior to 2010) 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 Approach 2 quantitative uncertainty analysis are summarized in Table 4-28. N2O emissions from
nitric acid production were estimated were estimated to be between 10.1 and 11.3 MMT CChEq. at the 95 percent
confidence level. This indicates a range of approximately 5 percent below to 5 percent above the 2013 emissions
estimate of 10.7 MMT CO2 Eq.
Table 4-28: Approach 2 Quantitative Uncertainty Estimates for NzO Emissions from Nitric
Acid Production (MMT COz Eq. and Percent)
Source Gas 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
(MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Nitric Acid Production N2O 10.7 10.1 11.3 -5% +5%
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
Industrial Processes and Product Use 4-29
-------
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CO2-equivalent emissions from CH4, HFCs, and PFCs. The GWPs of N2O and SF6 have decreased,
leading to a decrease in CO2-equivalent emissions for these greenhouse gases. The AR4 GWPs have been applied
across the entire time series for consistency. For more information please see the Recalculations and Improvements
Chapter.
In addition, GHGRP data from subpart V of regulation 40 CFR Part 98 were used to recalculate emissions from
nitric acid production over the entire time series (EPA 2014), and used directly for emission estimates for 2010
through 2013. Nitric acid production and N2O emissions data were available for 2010 through 2013 from EPA's
GHGRP, given nearly all nitric acid production facilities, with the exception of the strong acid facility, in the United
States are required to report annual data under subpart V. Country-specific N2O emission factors were developed
using the 2010 GHGRP emissions and production data for nitric acid production with abatement and without
abatement. Due to differences in operational efficiencies and recent installation of abatement technology at some
U.S. facilities, 2010 GHGRP production data were used for recalculating time series emissions (1990 through 2009)
instead of average factors developed from 2010 through 2013 GHGRP data. As per the 2010 GHGRP data, 70.7
percent of total domestic nitric acid production was estimated to be produced without any abatement.
Using the 2010 GHGRP data, emission factors for production with abatement and without abatement were
calculated to be 3.3 kg N2O/metric ton nitric acid produced and 5.98 kg N2O/metric ton nitric acid produced,
respectively. These emission factors and historical production data from the U.S. Census Bureau were used to
calculate emissions for 1990 through 2009. The emission factors were used in conjunction with existing estimates on
the share of production with and without N2O abatement technology to estimate N2O emissions for 1990 through
2009.
For 2009, an estimated 19.7 percent of nitric acid production was produced using N2O abatement technology and
80.3 percent production was without abatement technology (EPA 2010a, 2013b, 2012; Desai 2012; CAR 2013).
Similarly for 2008, an estimated 12.3 percent of nitric acid production was without abatement and 87.7 percent
production was with abatement technology (EPA 2012). Since data on the use of abatement technology was not
publicly available for 1990 through 2007, the national shares of production with and without abatement for 2008
were used for all prior years (i.e., 1990 through 2007).
Time series emissions for 1990 through 2009 were recalculated, and the revised emission estimates are
approximately 30 percent lower than the prior estimates.
4.8 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:
(CH2}3CO(cyclohexanone)+ (CH2)5CHOH (cyclohexanol) + wHN03
-^HOOC(CH2)4COOH(adipicacid) + 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 major adipic acid-producing plants had N2O abatement technologies
in place and, as of 1998, 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). In 2013, catalytic reduction, non-selective
4-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
catalytic reduction (NSCR) and thermal reduction abatement technologies were applied as N2O abatement measures
at adipic acid facilities (EPA 2014).
Worldwide, only a few adipic acid plants exist. The United States, Europe, and China are the major producers. In
2013, the United States had two companies with a total of three adipic acid production facilities (two in Texas and
one in Florida), all of which were operational (EPA 2014). 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)
(SEI 2010). 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 4.0 MMT CO2 Eq. (13 kt) in 2013 (see Table
4-29). National adipic acid production has increased by approximately 11 percent over the period of 1990 through
2013, to approximately 840,000 metric tons (ACC 2014). Over the period 1990 to 2013, emissions have been
reduced by 74 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 2013. Very little information on annual trends in the
activity data exist for adipic acid.
Table 4-29: NzO Emissions from Adipic Acid Production (MMT COz Eq. and kt NzO)
Year MMT CCh Eq. kt N2O
1990 15.2
2009
2010
2011
2012
2013
2.7
4.2
10.2
5.5
4.0
9
14
34
19
13
Note: Emissions values are presented in
CO2 equivalent mass units using IPCC
AR4 GWP values.
Methodology
Emissions are estimated using both Tier 2 and Tier 3 methods consistent with consistent the 2006 IPCC Guidelines
(IPCC 2006). Facility level greenhouse gas emissions data were obtained from the GHGRP for the years 2010
through 2013 (EPA 2014) and aggregated to national N2O emissions. Consistent with IPCC Tier 3 methods, all
adipic acid production facilities are required to calculate emissions using a facility -specific emission factor
developed through annual performance testing under typical operating conditions or by directly measuring N2O
emissions using monitoring equipment. More information on the monitoring methods for process N2O emissions
applicable to adipic acid production facilities under Subpart E can be found in the electronic code of federal
regulations.164
Due to confidential business information, plant names are not provided in this section. Therefore, the four adipic
acid-producing facilities will be referred to as Plants 1 through 4. Plant 4 was closed in April 2006. Overall, as noted
164See.
Industrial Processes and Product Use 4-31
-------
above, the three plants that are currently operating facilities use abatement equipment. Plants 1 and 2 employ
catalytic destruction and Plant 3 employs thermal destruction.
2010 through 2013
All emission estimates for 2010 through 2013 were obtained through analysis of the GHGRP data (EPA 2014),
which is consistent with the 2006IPCC Guidelines (IPCC 2006) Tier 3 method.
1990 through 2009
For years prior to EPA's GHGRP reporting, for both Plants 1 and 2, 1990 to 2009 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. These prior estimates are considered confidential business information and hence are not published
(Desai 2010). 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 per reporting to EPA's GHGRP (EPA
2012; Desai 201 Ib).
For the Plant 4, 1990 through 2009 N2O emissions were estimated using the following Tier 2 equation from the
2006 IPCC Guidelines until shutdown of the plant in 2006:
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. No abatement equipment was installed the
Inolex/Allied Signal facility, which last operated in April 2006 (VA DEQ 2009). Plant-specific production data for
this facility were obtained across the time series from 1990 through 2006 from the Virginia Department of
Environmental Quality (VA DEQ 2010). The plant-specific production data were then used for calculating
emissions as described above.
For Plant 3, 2005 through 2009 emissions were obtained directly from the plant (Desai 201 la). For 1990 through
2004, emissions were estimated using plant-specific production data and IPCC factors as described above for Plant
4. Plant-level adipic acid production 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
(ACC 2014; CMR 2001, 1998; CW 1999; C&EN 1995, 1994, 1993, and 1992). 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
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.
National adipic acid production data (see Table 4-30) from 1990 through 2013 were obtained from the American
Chemistry Council (ACC 2014).
4-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 4-30: Adipic Acid Production (kt)
Year kt
1990755~
2009 650
2010 720
2011 810
2012 810
2013 840
Uncertainty and Time-Series Consistency
Uncertainty associated with N2O emission estimates includes the methods used by companies to monitor and
estimate emissions. While some information has been obtained through outreach with facilities, limited information
is available over the time series on these methods, but also abatement technology destruction and removal efficiency
rates and plant specific production levels.
The results of this Approach 2 quantitative uncertainty analysis are summarized in Table 4-31. N2O emissions from
adipic acid production for 2013 were estimated to be between 3.8 and 4.2 MMT CCh Eq. at the 95 percent
confidence level. These values indicate a range of approximately 4 percent below to 4 percent above the 2013
emission estimate of 4.0 MMT CO2 Eq.
Table 4-31: Approach 2 Quantitative Uncertainty Estimates for NzO Emissions from Adipic
Acid Production (MMT COz Eq. and Percent)
„ „ 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
6 (MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Adipic Acid Production N2O 4.0 3.8 4.2 -4% +4%
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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 consistency in emissions from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh-equivalent emissions from CH4, HFCs, and PFCs. The GWPs of N2O and SF6 have decreased,
leading to a decrease in CCh-equivalent emissions for these greenhouse gases. The AR4 GWPs have been applied
across the entire time series for consistency. For more information please see the Recalculations and Improvements
Chapter.
Industrial Processes and Product Use 4-33
-------
4.9 Silicon Carbide Production and
Consumption (IPCC Source Category 2B5)
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 CCh 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 (SiCh) 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 2013 were 0.17 MMT CO2 Eq. (169 kt).
Approximately 54 percent of these emissions resulted from SiC production while the remainder resulted from SiC
consumption. Methane emissions from SiC production in 2013 were 0.01 MMT CO2 Eq. (0.4 kt CH4) (see Table
4-32: and Table 4-33). Emissions have fluctuated in recent years, but 2013 emissions are only about 45 percent of
emissions in 1990.
4-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 4-32: COz and ChU Emissions from Silicon Carbide Production and Consumption (MMT
COz Eq.)
Year 1990 2005 2009 2010 2011 2012 2013
CO2 0.4 0.2 0.1 0.2 0.2 0.2 0.2
CH4 + I j_H + + + + j_
Total 0.4 0.2 0.1 0.2 0.2 0.2 0.2
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
+ Does not exceed 0.05 MMT CO2 Eq.
Table 4-33: COz and CH4 Emissions from Silicon Carbide Production and Consumption (kt)
Year 1990 2005 2009 2010 2011 2012 2013
CO2 375 219 145 181 170 158 169
CH4 1_H + | + + + + j_
+ Does not exceed 0.5 kt.
Methodology
Emissions of CO2 and CH4 from the production of SiC were calculated using the Tier 1 method provided by the
2006 IPCC Guidelines (IPCC 2006). Annual estimates of SiC production were multiplied by the appropriate
emission factor, as shown below:
^sc,C02 = Ł•^sc,C02 X Qsc
/I metric ton
ESC,CH4 = EFscfH4 x Qsc x (^ lQQOkg
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 (IPCC 2006):
• 2.62 metric tons CCVmetric ton SiC
• 11.6 kg CH4/metric ton SiC
Emissions of CO2 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 2012 were obtained from the Minerals Yearbook: Manufactured Abrasives (USGS
1991a through 2013a). Production data for 2013 were obtained from the Minerals Industry Surveys: Abrasives
(Manufactured) (USGS 2014). Silicon carbide consumption by major end use was obtained from the Minerals
Yearbook: Silicon (USGS 199Ib through 201 Ib, 2012c, and 2013b) (see Table 4-34). Net imports for the entire time
series were obtained from the U.S. Census Bureau (2005 through 2014).
Industrial Processes and Product Use 4-35
-------
Table 4-34: Production and Consumption of Silicon Carbide (Metric Tons)
Year Production Consumption
1990 105,000 172,465
2009
2010
2011
2012
2013
35,000
35,000
35,000
35,000
35,000
92,280
154,540
136,222
114,265
134,054
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 Approach 2 quantitative uncertainty analysis are summarized in Table 4-35. Silicon carbide
production and consumption CCh emissions were estimated to be between 9 percent below and 9 percent above the
emission estimate of 0.17 MMT CC>2 Eq. at the 95 percent confidence level. Silicon carbide production CH4
emissions were estimated to be between 9 percent below and 10 percent above the emission estimate of 0.01 MMT
CO2 Eq. at the 95 percent confidence level.
Table 4-35: Approach 2 Quantitative Uncertainty Estimates for CH4 and COz Emissions from
Silicon Carbide Production and Consumption (MMT COz Eq. and Percent)
Source
Gas
2013 Emission Estimate
(MMT CCh Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CCh Eq.) (%)
Silicon Carbide Production
and Consumption
Silicon Carbide Production
CO2 0.17
CH4 +
Lower
Bound
0.15
+
Upper
Bound
0.18
+
Lower
Bound
-9%
-9%
Upper
Bound
+9%
+10%
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
+ Does not exceed 0.05 MMT CO2 Eq.
Methodological recalculations were applied to the entire time series to ensure consistency in emissions from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
4-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh-equivalent emissions from CH4, HFCs, and PFCs. The GWPs of N2O and SF6 have decreased,
leading to a decrease in CCh-equivalent emissions for these greenhouse gases. The AR4 GWPs have been applied
across the entire time series for consistency. For more information please see the Recalculations and Improvements
Chapter. This change caused a slight increase of emissions over the entire time series relative to the previous report.
Planned Improvements
Future improvements involve continuing to evaluate and analyze data reported under EPA's GHGRP to improve the
emission estimates for the Silicon Carbide Production 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 20 10, 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. 165 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.
4.10 Titanium Dioxide Production (IPCC
Source Category 2B6)
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:
2FeTW3 +7C12 + 3C -^2TiCl4 +2FeCl3 + 3C02
2TiCl4 +202 -^2TW2 + 4C/2
The sulfate process does not use petroleum coke or other forms of carbon as a raw material and does not emit CO2.
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 CO2. 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 2013, U.S. TiCh production totaled 1,200,000 metric
tons (USGS 2014b). 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 2013 were 1.6 MMT CO2 Eq. (1,608 kt), which represents an increase of 35 percent since 1990
(see Table 4-36).
165 See.
Industrial Processes and Product Use 4-37
-------
Table 4-36: COz Emissions from Titanium Dioxide (MMT COz Eq. and kt)
Year MMT CCh Eg.
kt
1990
1,195
2009
2010
2011
2012
2013
1.6
1.8
1.7
1.5
1.6
1,648
1,769
1,729
1,528
1,608
Methodology
Emissions of CC>2 from TiCh production were calculated by multiplying annual national TiCh production by
chloride-process-specific emission factors using a Tier 1 approach provided in 2006IPCC Guidelines (IPCC 2006).
The Tier 1 equation is as follows:
Etd = EFtd x Q,
td
where,
EFtd
Qtd
CO2 emissions from TiCh production, metric tons
Emission factor (chloride process), metric ton CCVmetric
Quantity of TiCh produced
Data were obtained for the total amount of TiCh 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
CCVmetric ton TiCh 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 (IPCC 2006).
Titanium dioxide production data and the percentage of total TiO2 production capacity that is chloride process for
1990 through 2012 (see Table 4-37:) were obtained through the Minerals Yearbook: Titanium Annual Report
(USGS 1991 through 2014a). Production data for 2013 was obtained from the Minerals Commodity Summary:
Titanium and Titanium Dioxide (USGS 2014b). Data on the percentage of total TiC>2 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 4-37: Titanium Dioxide Production (kt)
Year
2009
2010
2011
2012
kt
1,230
1,320
1,290
1,140
4-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
2013 1,200
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
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 Approach 2 quantitative uncertainty analysis are summarized in Table 4-38: Titanium dioxide
consumption CO2 emissions were estimated to be between 1.4 and 1.8 MMT CO2 Eq. at the 95 percent confidence
level. This indicates a range of approximately 13 percent below and 13 percent above the emission estimate of 1.6
MMT CO2 Eq.
Table 4-38: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from Titanium
Dioxide Production (MMT COz Eq. and Percent)
^ „ 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
6 (MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Titanium Dioxide Production CO2 1.6 1.4 1.8 -13% +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 consistency in emissions from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Production data for 2012 were updated relative to the previous Inventory based on recently published data in the
USGS Minerals Yearbook: Titanium 2012 (USGS 2014a). This resulted in a 12 percent decrease in 2012 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 annual data reported under EPA's GHGPJ3 for 2010
through 2013 (i.e. aggregated emissions and titanium production). Information on titanium dioxide production is
Industrial Processes and Product Use 4-39
-------
collected by EPA's GHGRP for all facilities for years 2010 through 2013 and would also have to be assessed against
criteria EPA has established to publish aggregated confidential business information (CBI) reported under EPA's
GHGRP. 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 IPCC on the use of facility-level data in
national inventories will be relied upon.166
In addition, the planned improvements include researching the significance of titanium-slag production in electric
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.
4.11 Soda Ash Production and Consumption
(IPCC Source Category 2B7)
Carbon dioxide is generated as a byproduct of calcining trona ore to produce soda ash, and is eventually emitted into
the atmosphere. In addition, CCh 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 CCh during trona-based production is based on the
following reaction:
2Na2C03 • NaHC03 • 2H20 (Trona) -> 3Na2C03(Soda Ash) + 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 2014). 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.167 Based on preliminary 2013 reported data,
the estimated distribution of soda ash by end-use in 2013 (excluding glass production) was chemical production, 54
percent; soap and detergent manufacturing, 14 percent; distributors, 11 percent; flue gas desulfurization, 8 percent;
other uses, 8 percent; pulp and paper production, 3 percent; and water treatment, 2 percent (USGS 2014).
166 See.
167 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
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.
4-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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
2013).
In 2013, CO2 emissions from the production of soda ash from trona were approximately 1.6 MMT CCh Eq. (1,610
kt). Soda ash consumption in the United States generated 1.1 MMT CCh Eq. (1,102 kt) in 2013. Total emissions
from soda ash production and consumption in 2013 were 2.7 MMT CCh Eq. (2,712 kt) (see Table 4-39 and Table
4-40).
Total emissions in 2013 increased by approximately 1.5 percent from emissions in 2012, and have decreased overall
by approximately 1.1 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 2013 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 4-39: COz Emissions from Soda Ash Production and Consumption Not Associated with
Glass Manufacturing (MMT COz Eq.)
Year
Production Consumption
Total
1990
1.4
1.4
2009
2010
2011
2012
2013
1.4
1.5
1.5
1.6
1.6
1.1 2.5
1.1 2.6
1.1 2.6
1.1 2.7
1.1 2.7
Note: Totals may not sum due to independent rounding.
Table 4-40: COz Emissions from Soda Ash Production and Consumption Not Associated with
Glass Manufacturing (kt)
Year
Production Consumption
Total
1990
1,360
1,381
2,741
2009
2010
2011
2012
2013
1,397
1,471
1,526
1,582
1,610
1,091
1,141
1,098
1,090
1,102
2,488
2,612
2,624
2,672
2,712
Note: Totals may not sum due to independent rounding.
Industrial Processes and Product Use 4-41
-------
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 CC>2 per
metric ton trona (IPCC 2006). Thus, the 17.4 million metric tons of trona mined in 2013 for soda ash production
(USGS 2014) resulted in CO2 emissions of approximately 1.6 MMT CO2 Eq. (1,610 kt).
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 (excluding soda ash consumption for glass manufacturing). As soda
ash is consumed for these purposes, additional CCh 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 CCh) are released for every metric ton of soda ash consumed.
The activity data for trona production and soda ash consumption (see Table 4-41) between 1990 and 2013 were
taken from USGS Minerals Yearbook for Soda Ash (1994 through 2013) and USGS Mineral Industry Surveys for
Soda Ash (USGS 2014). Soda ash production and consumption data were collected by the USGS from voluntary
surveys of the U.S. soda ash industry.
Table 4-41: Soda Ash Production and Consumption Not Associated with Glass Manufacturing
(kt)
Year Production3 Consumption1*
1990 14,700 3,351
17,000
2009
2010
2011
2012
2013
15,100
15,900
16,500
17,100
17,400
2,647
2,768
2,663
2,645
2,674
a Soda ash produced from trona ore only.
b 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 2014a). 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
1995).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.
4-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-42. Soda Ash Production
and Consumption CCh emissions were estimated to be between 2.5 and 2.9 MMT CCh Eq. at the 95 percent
confidence level. This indicates a range of approximately 7 percent below and 6 percent above the emission
estimate of 2.7 MMT CO2 Eq.
Table 4-42: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from Soda Ash
Production and Consumption (MMT COz Eq. and Percent)
„ „ 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
xoii t*f*p I-w-nc
(MMTCChEq.) (MMT CCh Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
SodaAshProduction ^ ^ ^ ^ _J% +6%
and Consumption
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 consistency in emissions from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future Inventory reports are anticipated to estimate emissions from other uses of soda ash. To add specificity, future
Inventory reports 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 examining data from EPA's GHGRP 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 fromthe IPCC on the use of facility-
level data in national inventories will be relied upon.168
4.12 Petrochemical Production (IPCC Source
Category 2B8)
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. CCh emissions from the production
of acrylonitrile, carbon black, ethylene, ethylene dichloride, ethylene oxide and methanol; and CH4 emissions from
the production of methanol and acrylonitrile are presented here and reported under IPCC Source Category 2B5. The
petrochemical industry uses primary fossil fuels (i.e., natural gas, coal, petroleum, etc.) for non-fuel purposes in the
production of carbon black and other petrochemicals. Emissions from fuels and feedstocks transferred out of the
system for use in energy purposes e.g. such as indirect or direct process heat or steam production are currently
accounted for in the Energy Sector.
Worldwide more than 90 percent of acrylonitrile (vinyl cyanide, CsH3N) is made by way of direct ammoxidation of
propylene with ammonia (NH3) and oxygen over a catalyst. This process is referred to as the SOHIO process,
168 See.
Industrial Processes and Product Use 4-43
-------
after the Standard Oil Company of Ohio (SOHIO) (IPCC 2006). The primary use of acrylonitrile is as the raw
material for the manufacture of acrylic and modacrylic fibers. Other major uses include the production of plastics
(acrylonitrile-butadiene-styrene (ABS) and styrene-acrylonitrile (SAN)), nitrile rubbers, nitrile barrier resins,
adiponitrile and acrylamide. All U.S. acrylonitrile facilities use the SOHIO process (AN 2014). The SOHIO
process involves a fluidized bed reaction of chemical-grade propylene, ammonia, and oxygen over a catalyst. The
process produces acrylonitrile as its primary product and the process yield depends on the type of catalyst used and
the process configuration. The ammoxidation process also produces by-product CO2, CO, and water from the direct
oxidation of the propylene feedstock, and produces other hydrocarbons from side reactions in the ammoxidation
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 CO2 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).
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..
Ethylene dichloride (C2H4Cl2) 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
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 + |02 + 2HCI -> C2H4C12 + 2H20 (oxychlorination)
C2H4 + 302 -> 2C02 + 2H20 (direct oxidation of ethylene during oxychlorination)
In addition to the by-product CO2 produced from the direction oxidation of the ethylene feedstock, CO2 and CH4
emissions are also generated from combustion units.
Ethylene oxide (C2H4O) is used in the manufacture of glycols, glycol ethers, alcohols, and amines. Worldwide
approximately 70 percent of ethylene oxide produced is used in the manufacture of glycols, including monoethylene
glycol. Ethylene oxide is produced by reacting ethylene with oxygen over a catalyst. The oxygen may be supplied to
the process through either an air (air process) or a pure oxygen stream (oxygen process). The by-product CO2 from
the direct oxidation of the ethylene feedstock is removed from the process vent stream using a recycled carbonate
solution, and the recovered CO2 may be vented to the atmosphere or recovered for further utilization in other
sectors, such as food production (IPCC 2006). The combined ethylene oxide reaction and by-product CO2 reaction is
exothermic and generates heat, which is recovered to produce steam for the process. The ethylene oxide process also
produces other liquid and off-gas by-products (e.g., ethane) that may be burned for energy recovery within the
process. Almost all facilities, except one in Texas, use the oxygen process to manufacture ethylene oxide (EPA
2008).
4-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 United States only two facilities use steam reforming of natural gas. Other syngas production processes in the
United States include partial oxidation of natural gas and coal gasification.
Emissions of CO2 and CH4 from petrochemical production in 2013 were 26.5 MMT CO2 Eq. (26,514 kt CO2) and
0.1 MMT CO2 Eq. (3 kt CH4), respectively (see Table 4-43 and Table 4-44). Since 1990, the total CO2 emissions
from petrochemical production increased by approximately 23 percent. Methane emissions from petrochemical
(methanol and acrylonitrile) production have decreased by approximately 63 percent since 1990, given declining
production.
Table 4-43: COz and CH4 Emissions from Petrochemical Production (MMT COz Eq.)
Year
CO2
CH4
Total
1990
21.6
0.2
21.9
2005
28.1
0.1
28.3
2009
23.7
+
23.8
2010
27.4
0.1
27.4
2011
26.4
+
26.4
2012
26.5
0.1
26.5
2013
26.5
0.1
26.6
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Emission totals may not add up due to rounding
Table 4-44: COz and CH4 Emissions from Petrochemical Production (kt)
Year 1990 2005 2009 2010 2011 2012 20lT
23,706 27,388 26,396 26,477 26,514
22233
Methodology
Emissions of CO2 and CH4 were calculated using the estimation methods provided by the 2006 IPCC Guidelines
(IPCC 2006) and country specific methods from EPA's Greenhouse Gas Reporting Program (GHGRP). The 2006
IPCC Guidelines Tier 1 method was used to estimate CO2 and CH4 emissions from production of acrylonitrile and
methanol, and a country specific approach similar to the IPCC Tier 2 method was used to estimate CO2 emissions
from carbon black, ethylene, ethylene oxide, and ethylene dichloride. The Tier 2 method for petrochemicals is a total
feedstock carbon mass balance method used to estimate total CO2 emissions but is not applicable for estimating CH4
emissions. The Tier 2 mass balance is based on the assumption that all of the carbon input to the process is
converted either into primary and secondary products or into CO2. This method accounts for all the carbon as CO2,
including CH4.
Carbon Black, Ethylene, Ethylene Dichloride and Ethylene Oxide
CO2 emissions and national production were aggregated directly from the GHGRP data set for 2010 through 2013.
In 2013, GHGRP data reported CO2 emissions of 3,190,199 metric tons from carbon black production; 19,545,363
metric tons of CO2from ethylene production; 403,122 metric tons of CO2 from ethylene dichloride production; and
1,395,936 metric tons of CO2 from ethylene oxide production. These emissions reflect application of a country
specific approach similar to the IPCC Tier 2 method and were used to estimate CO2 emissions from the production
of carbon black, ethylene, ethylene dichloride, and ethylene oxide. Since 2010, EPA's GHGRP, under Subpart X,
requires all domestic producers of petrochemicals to report annual emissions and supplemental emissions
information (e.g., production data) to facilitate verification of reported emissions. Under EPA's GHGRP,
petrochemical production facilities are required to use either a mass balance approach or CEMS to measure and
report emissions for each petrochemical process unit to estimate facility-level process CO2 emissions. The mass
Industrial Processes and Product Use 4-45
-------
balance method is used by most facilities169 and assumes that all the carbon input is converted into primary and
secondary products, byproducts, or is emitted to the atmosphere as €62. To apply the mass balance, facilities must
measure the volume or mass of each gaseous and liquid feedstock and product, mass rate of each solid feedstock and
product, and carbon content of each feedstock and product for each process unit and sum for their facility. 17° More
details on the GHG calculation and monitoring methods applicable to Petrochemical facilities can be found under
Subpart X (Petrochemical Production) of the regulation (40 CFR Part 98).171
For prior years, for these petrochemical types, an average national CCh emission factor was calculated based on the
2010 through 2013 GHGRP data and applied to production for earlier years in the time series (1990 through 2009)
to estimate CC>2 emissions from carbon black, ethylene, ethylene dichloride, and ethylene oxide. €62 emission
factors were derived from EPA's GHGRP by dividing annual €62 emissions for petrochemical type "i" with annual
production for petrochemical type "i" and then averaging the derived emission factors obtained for each calendar
year 2010 through 2013 (EPA GHGRP 2014). The average emission factors for each petrochemical type were
applied across all prior years because petrochemical production processes in the United States have not changed
significantly since 1990, though some operational efficiencies have been implemented at facilities over the time
series.
The average country-specific CCh emission factors that were calculated from the 2010-2013 GHGRP data are as
follows:
• 2.59 metric tons CCVmetric ton carbon black produced
• 0.79 metric tons CCVmetric ton ethylene produced
• 0.040 metric tons CCVmetric ton ethylene dichloride produced
• 0.46 metric tons CCVmetric ton ethylene oxide produced
Annual production data for carbon black for 1990 through 2009 were obtained from the International Carbon Black
Association (Johnson 2003 and 2005 through 2010). Annual production data for ethylene and ethylene dichloride for
1990 through 2009 were obtained from the American Chemistry Council's (ACC's) Guide to the Business of
Chemistry (ACC 2002, 2003, 2005 through 2010). Annual production data for ethylene oxide were obtained from
ACC's U.S. Chemical Industry Statistical Handbook for 2003 through 2009 (ACC2014a) and from ACC's Business
of Chemistry for 1990 through 2002 (ACC 2014b). As noted above, annual 2010 through 2013 production data for
carbon black, ethylene, ethylene dichloride, and ethylene oxide, were obtained from EPA's GHGRP (EPA GHGRP
2014).
Acrylonitrile
CO2 and CH4 emissions from acrylonitrile production were estimated using the Tier 1 method in the 2006IPCC
Guidelines (IPCC 2006). Annual acrylonitrile production data were used with IPCC default Tier 1 CO2 and CH4
emission factors to estimate emissions for 1990 through 2013. Emission factors used to estimate acrylonitrile
production emissions are as follows:
• 0.18 kg CH4/metric ton acrylonitrile produced
• 1.00 metric tons CCVmetric ton acrylonitrile produced
Annual acrylonitrile production data for 1990 through 2013 were obtained from ACC's Business of Chemistry
(ACC2014b).
A few facilities producing Ethylene Dichloride used CO2 CEMS, which has been included in the aggregated GHGRP emissions.
1/0 por ethylene processes only, because nearly all process emissions are from the combustion of process off-gas. Under GHGRP, Subpart X,
ethylene facilities can report emissions from burning of process gases using the optional combustion methodology for ethylene production
processes, which is requires estimating emissions based on fuel quantity and carbon contents of the fuel. This is consistent with the 2006 IPCC
Guidelines (p. 3.57) which recommends including combustion emissions from fuels obtained from feedstocks (e.g. off gases) in petrochemical
production under in the IPPU sector.
Available online at: http://www.ecfr.gov/cgi-bin/text-idx?tpl=/ecfrbrowse/Title40/40cfr98_main_02.tpl>
4-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Methanol
CO2 and CEU emissions from methanol production were estimated using Tier 1 method in the 2006IPCC Guidelines
(IPCC 2006). Annual methanol production data were used with IPCC default Tier 1 CCh and CH4 emission factors
to estimate emissions for 1990 through 2013. Emission factors used to estimate methanol production emissions are
as follows:
• 2.3 kg CH/i/metric ton methanol
• 0.67 metric tons CO^metric ton methanol
Annual 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 were obtained through the Methanol Institute (Jordan 2011). Methanol
production data for 2009 through 2013 were obtained from Argus Media Inc. (Argus JJ&A 2014). ACC
discontinued publication of this data due to confidentiality concerns given the small number of facilities producing
methanol in the United States.
Industrial Processes and Product Use 4-47
-------
Table 4-45: Production of Selected Petrochemicals (kt)
Chemical
Carbon Black
Ethylene
Ethylene Bichloride
Ethylene Oxide
Acrylonitrile
Methanol
1990
1,307
16,542
6,283
2,429
1,215
3,785
2005
1,651
23,975
11,260
3,220
1,325
2,336
2009
1,080
22,610
8,120
2,580
925
790
2010
1
24
8
2
1
,309
,355
,149
,925
,270
778
2011
1
25
8
3
1
,338
,143
,621
,014
,135
685
2012
1,283
24,763
11,309
3,106
1,220
1,015
2013
1,228
25,341
11,462
3,148
1,075
1,350
Uncertainty and Time-Series Consistency
The CH4 and CCh emission factors used for acrylonitrile and methanol 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.
The results of the quantitative uncertainty analysis for the CC>2 emissions from carbon black production, ethylene,
ethylene dichloride, and ethylene oxide are based on reported GHGRP data. Refer to the methodology section for
more details on how these emissions were calculated and reported to EPA's GHGRP. There is some uncertainty in
the applicability of the average emission factors for each petrochemical type across all prior years. While
petrochemical production processes in the United States have not changed significantly since 1990, some
operational efficiencies have been implemented at facilities over the time series. The uncertainty estimates for
national methanol production quantity were obtained from Argus (Argus JJ&A 2014).
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-46. Petrochemical
production CC>2 emissions were estimated to be between 25.3 and 27.7 MMT CCh Eq. at the 95 percent confidence
level. This indicates a range of approximately 5 percent below to 5 percent above the emission estimate of 26.5
MMT CO2 Eq. Petrochemical production CEU emissions were estimated to be between 0.03 and 0.10 MMT CO2
Eq. at the 95 percent confidence level. This indicates a range of approximately 54 percent below to 44 percent
above the emission estimate of 0.08 MMT CO2 Eq.
Table 4-46: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from
Petrochemical Production and COz Emissions from Carbon Black Production (MMT COz Eq.
and Percent)
2013 Emission
Source Gas Estimate
(MMT CO2 Eq.)
Petrochemical „_ ,. , .
T-, i •• C(J2 26. 5
Production
Petrochemical
T-. , ,• L-rl4 U.Uo
Production
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower
Bound
25.3
0.03
Upper
Bound
111
0.10
Lower
Bound
-5%
-54%
Upper
Bound
+5%
+44%
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 consistency in emissions from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculation Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
4-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in emissions from CH4, HFCs, PFCs, SF6, and NF3. The GWP of N2O has decreased, leading to a decrease
in emissions. The AR4 GWPs have been applied across the entire time series for consistency. For more information
please see the Recalculations and Improvements Chapter.
In addition, methodological recalculations were applied to the entire time series to ensure time series consistency.
As noted above, emission information from EPA's GHGRP were used to update estimates. Average country-
specific CO2 emission factors were derived from the 2010 through 2013 GHGRP data for carbon black, ethylene,
ethylene dichloride, and ethylene oxide. Annual production and CC>2 emission factor data were obtained from EPA's
GHGRP for 2010 through 2013, and were used to estimate emissions for 2010 through 2013. An average CO2
emission factor was calculated from the 2010 through 2013 GHGRP data and was used to estimate emissions for
1990 through 2009 for carbon black, ethylene, ethylene dichloride, and ethylene oxide using historic production data
compiled for 1990 through 2009 (ACC 2014a; ACC 2014b).
Note, ethylene oxide is included in the IPCC petrochemical production source category but had not been included in
previous versions of this Inventory due to lack of publicly-available data. Similarly, acrylonitrile is included in the
IPCC Petrochemical Production source category but had not been included in the previous Inventory due to lack of
publicly-available data. Annual acrylonitrile production data for 1990 through 2013 was obtained from ACC (ACC
2014b). CO2 and CH4 emissions from acrylonitrile were estimated using the IPCC default Tier 1 emission factors
and annual acrylonitrile production.
For the previous Inventory, only CH4 emissions were estimated for methanol using the IPCC default Tier 1 emission
factor. For the current Inventory, CO2 emissions were also estimated for methanol using the IPCC default Tier 1
CO2 emission factor. In the current version of the Inventory, updated methanol production data were made available
through Argus (Argus JJ&A 2014) for the years 2009 through 2012. This update reflected in a decrease of CH4
emissions from Methanol production.
Planned Improvements
Pending resources, a potential improvement to the inventory estimates for this source category would focus on
analyzing the fuel and feedstock data from EPA's GHGRP to better disaggregate energy related emissions and
allocate them more accurately between the Energy and IPPU sectors of the Inventory. Some degree of double
counting may occur between CO2 estimates of non-energy use of fuels in the energy sector and CO2 process
emissions from petrochemical production in this sector. Data integration is not feasible at this time as feedstock data
from El A used to estimate non-energy uses of fuels are aggregated by fuel type, rather than disaggregated by both
fuel type and particular industries (e.g., petrochemical production). EPA, through GHGRP, currently does not
collect complete data on quantities of fuel consumed as feedstocks by petrochemical producers, only feedstock type.
Updates to reporting requirements may address this issue future reporting years for the GHGRP data allowing for
easier data integration between the non-energy uses of fuels category and the petrochemicals category presented in
this chapter.
4.13 HCFC-22 Production (IPCC Source
Category 2B9a)
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
Industrial Processes and Product Use 4-49
-------
for non-feedstock uses is scheduled to be phased out by 2020 under the U.S. Clean Air Act.m 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.
Two facilities produced HCFC-22 in the U.S. in 2013. Emissions of HFC-23 from this activity in 2013 were
estimated to be 4.1 MMT CCh Eq. (0.3 kt) (see Table 4-47). This quantity represents a 25 percent decrease from
2012 emissions and a 91 percent decline from 1990 emissions. The decrease from 2012 emissions and the decrease
from 1990 emissions were caused by a decrease in HCFC-22 production and a decrease in the HFC-23 emission rate
(kg HFC-23 emitted/kg HCFC-22 produced). The decrease in the emission rate is primarily attributable to six
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, (e) another plant began
destroying HFC-23, and (f) the same plant, whose emission factor was higher than that of the other two plants,
ceased production of HCFC-22 in 2013.
Table 4-47: HFC-23 Emissions from HCFC-22 Production (MMT COz Eq. and kt HFC-23)
Year MMTCChEq. kt HFC-23
1990 46.1
2009
2010
2011
2012
2013
6.8
8.0
8.8
5.5
4.1
0.5
0.5
0.6
0.4
0.3
Note: Emission values are presented in
CO2 equivalent mass units using IPCC
AR4 GWP values
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 2006 IPCC Guidelines (IPCC 2006) were used. Emissions
for 2010 through 2013 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.
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]
4-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 measure (or, for the plants that have since closed, 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 2013 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 (RTI 1997; 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 4-48.
Table 4-48: HCFC-22 Production (kt)
Year kt
1990
2009 91
2010 101
2011 110
2012 96
2013 C
Note: HCFC-22 production in 2013 is
considered Confidential Business Information
(CBI) as there were only two producers of
HCFC-22 in 2013.
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 2013. 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 2013
(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 Approach 2 quantitative uncertainty analysis are summarized in Table 4-49. HFC-23 emissions
from HCFC-22 production were estimated to be between 3.8 and 4.5 MMT CCh Eq. at the 95 percent confidence
Industrial Processes and Product Use 4-51
-------
level. This indicates a range of approximately 7 percent below and 10 percent above the emission estimate of 4.1
MMT CO2 Eq.
Table 4-49: Approach 2 Quantitative Uncertainty Estimates for HFC-23 Emissions from
HCFC-22 Production (MMT COz Eq. and Percent)
s P 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
(MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
HCFC-22 Production HFC-23 41 3.8 45 -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 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories), which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWP of HFC-23 has increased, leading to an overall increase in emissions. For more
information please see the Recalculations and Improvements Chapter.
4.14 Carbon Dioxide Consumption (IPCC
Source Category 2B10)
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, CCh used in non-EOR applications will eventually be
released to the atmosphere, and for the purposes of this analysis CCh 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 CO2 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 CO 2
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
4-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
are therefore accounted for under Ammonia Production, Fossil Fuel Combustion, or other appropriate source
category.173
CO2 is produced as a byproduct of crude oil and natural gas production. This €62 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 (New Mexico
Bureau of Geology and Mineral Resources 2006). 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 2013, the amount of CO2 produced by the Colorado, Mississippi, and New Mexico facilities for commercial
applications and subsequently emitted to the atmosphere was 0.9 MMT CO2Eq. (903 kt) (see Table 4-50). This is
an increase of 7 percent from the previous year and a decrease of 39 percent since 1990.
Table 4-50: COz Emissions from COz Consumption (MMT COz Eq. and kt)
Year MMT CCh Eq. kt
1990 1.5 1,472
2009
2010
2011
2012
2013
1.8
1.2
0.8
0.8
0.9
1,795
1,206
802
841
903
Methodology
CO2 emission estimates for 1990 through 2013 were based on production data for the four facilities currently
producing CO2 from naturally-occurring CO2 reservoirs for use in non-EOR applications. Some of the CO2
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, from the Annual Reports of Denbury Resources (Denbury Resources 2002 through 2010) for 2001 to 2009,
and from EPA's GHGRP data for 2010 through 2013 (EPA 2014) (see Table 4-51). Denbury Resources reported
the average CO2 production in units of MMCF CO2 per day for 2001 through 2009 and reported the percentage of
the total average annual production that was used for EOR. Production from 1990 to 1999 was set equal to 2000
production, due to lack of publicly available production data for 1990-1999. Carbon dioxide production data for the
Bravo Dome, New Mexico facilities were obtained from ARI for 1990 through 2009 (ARI 1990-2010), and from
173 There are currently four known electric power plants operating in the United States that capture CCh for use as food-grade
CO2 or other industrial processes; however, insufficient data prevents estimating emissions from these activities as part of CCh
Consumption.
Industrial Processes and Product Use 4-53
-------
EPA's GHGRP data for 2010 through 2013 (EPA 2014). Data for the West Bravo Dome facility were only available
starting 2009 (i.e., only for 2009 through 2013). The percentage of total production that was used for non-EOR
applications for 2010 through 2013 was obtained from EPA's GHGRP (EPA 2014) data. The percentage of total
production that was used for non-EOR applications for the Bravo Dome facilities for 1990 through 2009 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 (Jackson County), Colorado
facility were obtained from the Colorado Oil and Gas Conservation Commission (COGCC) for 1999 through 2013
(COGCC 2014). Production data for 1990 to 1998 and percentage of production used for EOR were assumed to be
the same as for 1999, due to lack of publicly-available data.
Table 4-51: COz Production (kt COz) and the Percent Used for Non-EOR Applications
Year Jackson Dome, MS
CCh Production
(kt)(% Non-EOR)
Bravo Dome, NM
CCh Production
(kt)(% Non-EOR)
West Bravo Dome,
NMCO2
Production
(kt) (% Non-EOR)
McCallum Dome,
CO
CO2 Production
(kt)(% Non-EOR)
1990
2009
2010
2011
2012
2013
1,705(13%) 46(1%)
1,156(21%) +
770(15%) +
808(16%) +
891174 +
21 (1%) 23 (100%)
+ 50 (100%)
+ 32 (100%)
+ 33 (100%)
+ 12 (100%)
+ Does not exceed 0%.
Uncertainty and Time-Series Consistency
Uncertainty is associated with the number of facilities that are currently producing CO2 from naturally occurring
CO2 reservoirs for commercial uses other than EOR, and for which the CO2 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 CO2 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 Approach 2 quantitative uncertainty analysis are summarized in Table 4-52. Carbon dioxide
consumption CO2 emissions for 2013 were estimated to be between 0.8 and 1.1 MMT CO2 Eq. at the 95 percent
confidence level. This indicates a range of approximately 12 percent below to 13 percent above the emission
estimate of 0.9 MMT CO2 Eq.
Table 4-52: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from COz
Consumption (MMT COz Eq. and Percent)
Source
Gas
2013 Emission Estimate
(MMT CQ2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CQ2 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
CCh Consumption
1.1
-12%
+13%
1 Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
CCh quantity used for EOR applications is not yet available. The indicated quantity (891 kt) for Jackson Dome is for non-
EOR applications only.
4-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Methodological recalculations were applied to the entire time series to ensure consistency in emissions from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Relative to the previous Inventory, 1990 through 2009 CO2 consumption data for the McCallum Dome facility in
Colorado was corrected after a unit conversion error was identified. The revised time-series data were double
checked against data reported by the Colorado Oil and Gas Conservation Commissions (COGCC 1990-2013). This
revision caused an increase in CO2 emissions for McCallum Dome for 1990 through 2009.
Planned Improvements
CO2 production data for 1990 through 1998 for McCallum dome needs to be compiled and improved. Currently,
only 1999 through 2013 data is available online (COGCC 2014). Similarly, 1990 through 1999 production data for
the Jackson Dome facility is not publicly available and needs to be compiled. For example, the information could be
in hard copy records at the Oil and Gas Conservation Commission and a request or site visit is required to gather the
data.
4.15 Phosphoric Acid Production (IPCC
Source Category 2B10)
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 CO2 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 CO2 is due to the associated rimestone-sulfuric acid reaction, as shown below:
CaC03 + H2S04 + H20 -> CaS04 »2H20 + C02
Total U.S. phosphate rock production sold or used in 2013 was 29.0 million metric tons (USGS 2014).
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 2013 were 2.6 million metric tons (USGS 2014). Most of the imported phosphate rock (70
percent) is from Morocco, with the remaining 30 percent being from Peru (USGS 2014). 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 2013 period, domestic production has decreased by nearly 42 percent. Total CO2 emissions from
phosphoric acid production were 1.2 MMT CO2 Eq. (1,173 kt) in 2013 (see Table 4-53). Domestic consumption of
Industrial Processes and Product Use 4-55
-------
phosphate rock in 2013 was estimated to have increased by approximately 4 percent over 2012 levels, owing to
increased production of phosphoric acid (USGS 2014).
Table 4-53: COz Emissions from Phosphoric Acid Production (MMT COz Eq. and kt)
Year MMT CCh Eq. kt
1990 1.6 1,586
2009
2010
2011
2012
2013
1.0
1.1
1.2
1.1
1.2
1,016
1,130
1,198
1,138
1,173
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
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:
Epa = ^pr X Qpr
where,
Epa = CO2 emissions from phosphoric acid production, metric tons
Cpr = Average amount of carbon (expressed as CCh) in natural phosphate rock, metric ton CCV
metric ton phosphate rock
Qpr = 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 €62 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 €62 and that all of the organic carbon content remains in the phosphoric acid product.
From 1993 to 2004, the USGS Mineral 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 4-54). For the years 1990 through
1992, and 2005 through 2013, 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 2013, 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 2013 were obtained from USGS Minerals Yearbook:
Phosphate Rock (USGS 1994 through 2013), and from USGS Minerals Commodity Summaries: Phosphate Rock in
2013 (USGS 2014). From 2004 through 2013, the USGS reported no exports of phosphate rock from U.S.
producers (USGS 2005 through 2014).
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 4-55).
4-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 4-54: Phosphate Rock Domestic Consumption, Exports, and Imports (kt)
Location/Year
U.S. Domestic
Consumption*
FLandNC
ID and UT
Exports — FL and NC
Imports
Total U.S.
Consumption
1990
49,800
42,494
7,306
6,240
451
44,011
2005
35,200 1
28,160 1
7,040 1
+ 1
2,630
37,830
2009
25,500
20,400
5,100
+
2,000
27,500
2010
28,100
22,480
5,620
+
2,400
30,500
2011
28,600
22,880
5,720
+
3,350
31,950
2012
27,300
21,840
5,460
+
3,080
30,380
2013
29,000
23,200
5,800
+
2,600
31,600
Table 4-55: 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 2013. 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 2013 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 2013 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 CCh 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 C 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 CCh 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
Industrial Processes and Product Use 4-57
-------
of some of the organic C in the phosphate rock into CO2. 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 CCh 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 CCh emissions from 100 percent of the inorganic carbon content in phosphate rock, but none
from the organic carbon content.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-56. Phosphoric acid
production CC>2 emissions were estimated to be between 1.0 and 1.4 MMT 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 1.2
MMT CO2 Eq.
Table 4-56: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from
Phosphoric Acid Production (MMT COz Eq. and Percent)
„ „ 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
6 (MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Phosphoric Acid Production CCh L2 LO L4 -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 2013. 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 phosphate rock consumption data (sold or used and imports for consumption)
for 2012 were revised based on updated data publicly available from USGS (2014). This revision caused an increase
in the 2012 emission estimate by approximately 3 percent.
Planned Improvements
Pending resources, a potential improvement to the Inventory estimates for this source category would include direct
integration of GHGRP data for 2010 through 2013 and use of reported GHGRP data to update the inorganic C
content of phosphate rock for prior years. In order to provide estimates for the entire time series (i.e. 1990 through
2009), the applicability of the averaged inorganic C content data (by region) from 2010 through 2013 GHGRP data
to previous years' estimates will need to be evaluated. 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
175 See
4-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
4.16 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, €62 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, €62 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.
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; crude
steel production slightly decreased to 95,766,000 tons in 2013 (AISI 2014a). The United States was the third largest
producer of raw steel in the world, behind China and Japan, accounting for approximately 5.4 percent of world
production in 2013 (AISI 2014a).
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 C from pig iron used to produce steel.
According to the 2006 IPCC Guidelines (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 2006 IPCC Guidelines suggest that emissions from the production of
metallurgical coke should be reported separately in the Energy sector, while emissions from coke consumption in
iron and steel production should be reported in the IPPU sector. 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.
Industrial Processes and Product Use 4-59
-------
Metallurgical Coke Production
Emissions of CO2 and CH4 from metallurgical coke production in 2013 were 1.8 MMT CO2 Eq. (1,822 kt) and less
than 0.05 MMT CO2 Eq. (less than 0.5 kt), respectively (see Table 4-57 and Table 4-58), totaling 1.8 MMT CO2 Eq.
Emissions increased in 2013 from 2012 levels, but have decreased overall since 1990. In 2013, domestic coke
production increased by 1 percent from the previous year, and has decreased overall since 1990. Coke production in
2013 was 26 percent lower than in 2000 and 45 percent below 1990. Overall, emissions from metallurgical coke
production have declined by 26 percent (0.6 MMT CO2 Eq.) from 1990 to 2013.
Table 4-57: COz and CH4 Emissions from Metallurgical Coke Production (MMT COz Eq.)
Gas
CO2
CH4
Total
1990
2.sl
+
2.5
2005
2.0
+
2.0
2009
1.0
+
1.0
2010
2.1
+
2.1
2011
1.4
+
1.4
2012
0.5
+
0.5
2013
1.8
+
1.8
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4
GWP values.
+ Does not exceed 0.05 MMT CO2 Eq.
Table 4-58: COz and CH4 Emissions from Metallurgical Coke Production (kt)
Gas 1990 2005 2009 2010 2011 2012 2013
CO2 2,470
CH4 +
+ Does not exceed 0.5 kt
Iron and Steel Production
Emissions of CO2 and CH4 from iron and steel production in 2013 were 50.5 MMT CO2 Eq. (50,466 kt) and 0.7
MMT CO2 Eq. (27.7 kt), respectively (see Table 4-59 through Table 4-62), totaling approximately 51.2 MMT CO2
Eq. Emissions decreased in 2013 and have decreased overall since 1990 due to restructuring of the industry,
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 2013, domestic production of pig iron decreased by 5 percent from 2012 levels. Overall, domestic pig iron
production has declined since the 1990s. Pig iron production in 2013 was 37 percent lower than in 2000 and 39
percent below 1990. Carbon dioxide emissions from steel production have increased by 8 percent (0.7 MMT CO2
Eq.) since 1990, while overall CO2 emissions from iron and steel production have declined by 48 percent (46.8
MMT CO2 Eq.) from 1990 to 2013.
Table 4-59: COz Emissions from Iron and Steel Production (MMT COz Eq.)
Source/Activity Data
Sinter Production
Iron Production
Steel Production
Other Activities*
Total
1990
2.4
47.6
8.0
39.3
97.3
2005
19.4
9.4
34.2
64.6 1
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
2013
1.1
13.4
8.6
27.3
50.5
1 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.
Note: Totals may not sum due to independent rounding.
Table 4-60: COz Emissions from Iron and Steel Production (kt)
Source/Activity Data 1990 2005 2009 2010 2011 2012 2013
Sinter Production
Iron Production
2,448
47,650 1
1,663
19,414
763
15,941
1,045
19,109
1,188
19,901
1,159
12,557
1,117
13,411
4-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Steel Production 7,958 9,386 7,555 9,248 9,262 9,874 8,629
Other Activitiesa 39,256 34,160 17,815 24,260 28,232 30,195 27,309
Total 97,311 64,623 42,073 53,662 58,583 53,786 50,466
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.
Note: Totals may not sum due to independent rounding.
Table 4-61: CH4 Emissions from Iron and Steel Production (MMT COz Eq.)
Source/Activity Data 1990 2005 2009 2010 2011 2012 2013
Sinter Production + + + + + + +
Iron Production 1_1 0.8 0.4 0.6 0.7 0.7 0.7
Total y 0.9 0.4 0.6 0.7 0.7 0.7
Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP
values.
+ Does not exceed 0.05 MMT CO2 Eq.
Table 4-62: ChU Emissions from Iron and Steel Production (kt)
Source/Activity Data
Sinter Production
Iron Production
Total
1990
0.9
44.7
45.6
2005
6~6>
33.5
34.1
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
2013
0.4
27.3
27.7
Methodology
Emission estimates presented in this chapter are largely based on Tier 2 methodologies provided by the 2006 IPCC
Guidelines (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 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
ECO? -
x
12
La b
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
Es,p =
The Tier 1 methodology equations are as follows:
I
Ed,P = Qdx EFd:p
where,
ES)P = Emissions from sinter production process for pollutant p (CO2 or CH4), metric ton
Industrial Processes and Product Use 4-61
-------
Qs = Quantity of sinter produced, metric tons
EFS)P = Emission factor for pollutant p (CCh or CH4), metric ton/>/metric ton sinter
Ed,P = Emissions from DRI production process for pollutant p (CCh or CH4), metric ton
Qd = Quantity of DRI produced, metric tons
EFd,P = Emission factor for pollutant p (CCh 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 (IPCC 2006) was utilized. The amount of C 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 C contained in these materials is calculated by
multiplying the material-specific carbon content by the amount of material consumed or produced (see Table 4-63).
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 (AISI 2008c; DOE 2000). 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 4-63: Material Carbon Contents for Metallurgical Coke Production
Material kg C/kg
Coal Tar
Coke
Coke Breeze
Coking Coal
0.62
0.83
0.83
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 of coke production)
taken from the 2006 IPCC Guidelines (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 2014d) (see Table 4-64). 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 2014a) and through personal communications with AISI (2008c) (see Table 4-65). The factor
for the quantity of coal tar produced per ton of coking coal consumed was provided by AISI (2008c). 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
4-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
emission estimate. Carbon contents for coking coal, metallurgical coke, coal tar, coke oven gas, and blast furnace
gas were provided by the 2006IPCC Guidelines (IPCC 2006). The carbon content for coke breeze was assumed to
equal the C content of coke.
Table 4-64: 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
25,054(
2,645 !
1,058
2005
21,259
15,167
1,594
638
2009
13,904
10,109
1,043
417
2010
19,135
13,628
1,435
574
2011
19,445
13,989
1,458
583
2012
18,825
13,764
1,412
565
2013
19,481
13,898
1,461
584
Table 4-65: 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
114,2131
2,996
4,460
2009
66,155
2,121
2,435
2010
95,405
3,108
3,181
2011
109,044
3,175
3,853
2012
113,772
3,267
4,351
2013
108,162
3,247
4,255
Iron and Steel Production
Emissions of CC>2 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 CC>2 emission factors (see
Table 4-66). Because estimates of sinter production and direct reduced iron production were not available,
production was assumed to equal consumption.
Table 4-66: COz Emission Factors for Sinter Production and Direct Reduced Iron Production
Metric Ton
Material Produced CCh/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 C contained in the produced pig
iron and blast furnace gas were deducted from the amount of C contained in inputs (i.e., metallurgical coke, sinter,
natural ore, pellets, natural gas, fuel oil, coke oven gas, and direct coal injection). The C contained in the pig iron,
blast furnace gas, and blast furnace inputs was estimated by multiplying the material-specific C content by each
material type (see Table 4-67). 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 C contained in the steel produced from
the C contained in the EAF anode, charge carbon, and scrap steel added to the EAF. Small amounts of C from direct
reduced iron, pig iron, and flux additions to the EAFs were also included in the EAF calculation. For BOFs,
estimates of C contained in EOF steel were deducted from C 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 4-67). 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 2008c]). 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.
Industrial Processes and Product Use 4-63
-------
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 4-67).
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 €62 emissions from iron and steel
production (see Table 4-59 and Table 4-60).
Table 4-67: 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
kgC/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 (IPCC 2006)
for sinter production and the 1995 IPCC Guidelines (IPCC/UNEP/ OECD/IEA 1995) (see Table 4-68) 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 emission estimates are excluded due to data limitations.
Table 4-68: ChU 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 for 1990 through 2013 were obtained from AISFs Annual Statistical Report (AISI2004
through 2014a) and through personal communications with AISI (2008c) (see Table 4-69). In general, direct
reduced iron (DRI) consumption data were obtained from the USGSMinerals Yearbook - Iron and Steel Scrap
(USGS 1991 through 2013) and personal communication with the USGS Iron and Steel Commodity Specialist
(Fenton 2014). 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 CO2 emission factors for sinter production and direct reduced iron production were obtained through the
2006 IPCC Guidelines (IPCC 2006). Time series 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 AISFs Annual Statistical
Report (AISI 2004 through 2014a) and through personal communications with AISI (2008c) (see Table 4-69 and
Table 4-70).
Data for EAF steel production, flux, EAF charge carbon, and natural gas consumption were obtained from AISFs
Annual Statistical Report (AISI 2004 through 2014a) and through personal communications with AISI (2006
4-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
through 2014b and 2008c). The factor for the quantity of EAF anode consumed per ton of EAF steel produced was
provided by AISI (AISI 2008c). Data for EOF steel production, flux, natural gas, natural ore, pellet sinter
consumption as well as EOF steel production were obtained from AISFs Annual Statistical Report (AISI 2004
through 2014a) and through personal communications with AISI (2008c). Data for EAF and EOF scrap steel, pig
iron, and DRI consumption were obtained from the USGS Minerals Yearbook - Iron and Steel Scrap (USGS 1991
through 2013). 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 MSTs Annual Statistical Report (AISI 2004 through 2014a) and
through personal communications with AISI (2008c).
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 (ElA 2012b). Carbon contents for direct reduced iron, EAF carbon electrodes, EAF charge
carbon, limestone, dolomite, pig iron, and steel were provided by the 2006IPCC Guidelines (IPCC 2006). The C
contents for natural gas, fuel oil, and direct injection coal were obtained from EIA (2013c) and EPA (2010). Heat
contents for the same fuels were obtained from EIA (1992, 2013a). 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 4-69: 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
2009
2010
2011
2012
2013
Sinter Production
Sinter Production
Direct Reduced Iron
Production
Direct Reduced Iron
Production
Pig Iron Production
Coke Consumption
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
12,239
516
24,946
49,669
1,485
67
42,691
319
33,511
13,832
37,222
2,573
1,127
46,600
695J
52,194
3,814
1,165
8,572
19,019
1,674
845
43,200
476
36,725
5,225 5,941 5,795
1,441
10,883
26,844
2,279
1,189
47,500
640
49,339
1,582 3,530
11,962
30,228
2,604
1,257
50,500
726
52,108
9,571
32,063
2,802
1,318
50,900
748
52,415
5,583
3,350
9,308
30,309
2,675
1,122
47,327
771
52,641
47,307 34,400
25,900 31,200 31,300 31,500 29,570
14,713
576
43,973
11,400
582
42,705
7,110
318
22,659
9,860
431
31,158
8,800
454
34,291
8,350
476
36,282
7,894
454
34,238
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Table 4-70: 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
2009 2010
2011
2012
2013
Pig Iron Production
Natural Gas
Consumption
Fuel Oil Consumption
(thousand gallons)
Coke Oven Gas
Consumption
Blast Furnace Gas
Production
56,273
163,397
22,033
59,844
16,170
16,557
35,933 47,814 59,132
23,179 27,505 21,378
9,951 14,233 17,772
62,469 48,812
19,240 17,468
18,608 17,710
1,439,380 1,299,980 672,486 911,1801,063,326 1,139,5781,026,973
Industrial Processes and Product Use 4-65
-------
EAF Steel Production
Natural Gas
Consumption
EOF Steel Production
Coke Oven Gas
Consumption
Other Activities
Coke Oven Gas
Consumption
Blast Furnace Gas
Consumption
15,90sB 19,985
3,851
7,848 10,403 6,263 11,145 10,514
I I
373
546
554
568
568
224,883 97,132 55,831 80,626 90,718 94,596 89,884
1,414,778 1,295,520 670,051 907,9991,059,473 1,135,2271,022,718
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 C 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 C 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 C 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 C 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 CC>2 emissions
calculation is not corrected by subtracting the C content of the CH4, which means there may be a slight double
counting of C as both CC>2 and CH4.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-71 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 43.3 and 61.2 MMT CCh Eq. at the 95 percent confidence level. This
indicates a range of approximately 17 percent below and 17 percent above the emission estimate of 52.3 MMT CCh
Eq. Total CH4 emissions from metallurgical coke production and iron and steel production were estimated to be
between 0.5 and 0.8 MMT CC>2 Eq. at the 95 percent confidence level. This indicates a range of approximately 21
percent below and 22 percent above the emission estimate of 0.7 MMT CCh Eq.
4-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 4-71: Approach 2 Quantitative Uncertainty Estimates for COz and CH4 Emissions from
Iron and Steel Production and Metallurgical Coke Production (MMTCOz Eq. and Percent)
„ „ 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
e (MMT CCh Eq.) (MMT CCh Eq.) (%)
Metallurgical Coke & Iron
and Steel Production
Metallurgical Coke & Iron
and Steel Production
C02
CH4
52.3
0.7
Lower
Bound
43.3
0.5
Upper
Bound
61.2
0.8
Lower
Bound
-17%
-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 consistency in emissions from 1990
through 2013. 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 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.176
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.
4.17 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.
176 See.
Industrial Processes and Product Use 4-67
-------
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 €62. A representative reaction equation for the
production of 50 percent ferrosilicon (FeSi) is given below:
Fe203 + 2Si02 + 7C -> 2FeSi + 7CO
While most of the carbon contained in the process materials is released to the atmosphere as CCh, 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 2013 were 1.8 MMT CO2 Eq. (1,785 kt) (see Table 4-72 and Table
4-73), which is a 17 percent reduction since 1990. Emissions of CH4 from ferroalloy production in 2013 were 0.01
MMT CO2 Eq. (0.5 kt CH4), which is a 26 percent decrease since 1990.
Table 4-72: COz and CH4 Emissions from Ferroalloy Production (MMT COz Eq.)
Gas 1990 2005 2009 2010 2011 2012 2013
C02 2.2 1.4 1.5 1.7 1.7 1.9 1.8
CH4 + + + + + + +
Total 2.2 1.4 1.5 1.7 1.7 1.7 1.8
+ Does not exceed 0.05 MMT CO2 Eq.
Table 4-73: COz and ChU Emissions from Ferroalloy Production (kt)
Gas 1990 2005 2009 2010 2011 2012 2013'
1,469 1,663 1,735 1,903 1,785
+ + + 1 j_
+ Does not exceed 0.5 kt
Methodology
Emissions of €62 and CH4 from ferroalloy production were calculated using a Tier 1 method from the 2006IPCC
Guidelines (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 CCh/metric ton of alloy produced) and an emission factor
for 65 percent silicon was applied for CH4 (i.e., 1 kg CHVmetric 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 CO2 and CH4 (i.e., 4 metric tons CCh/metric ton alloy produced and 1 kg CHVmetric ton of alloy
produced, respectively). The emission factors for silicon metal equaled 5 metric tons CCh/metric ton metal
produced and 1.2 kg CH4/metric 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 2013 (see Table 4-74) were obtained from the USGS through the
Minerals Yearbook: Silicon (USGS 1996 through 2013) and the Mineral Industry Surveys: Silicon in September
2014 (USGS 2014). The following data were available from the USGS publications for the time-series:
• Ferrosilicon, 25%-55% Si: Annual production data were available from 1990-2010.
4-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
• Ferrosilicon, 56%-95% Si: Annual production data were available from 1990-2010.
• Silicon Metal: Annual production data were available from 1990-2005. The production data for 2005 were
used as proxy for 2006-2010.
• Miscellaneous Alloys, 32%-65% Si: Annual production data were available from 1990-1999. Starting
2000, USGS reported miscellaneous alloys and ferrosilicon containing 25 to 55 percent silicon as a single
category.
Starting with the 2011 publication, USGS reported all the ferroalloy production data as a single category (i.e., Total
Silicon Materials Production). This is due to the small number of ferroalloy manufacturers in the United States and
government information disclosure rules. Ferroalloy product shares developed from the 2010 production data (i.e.,
ferroalloy product production/total ferroalloy production) were used with the total silicon materials production
quantity to estimate the production quantity by ferroalloy product type for 2011 through 2013 (USGS 2013, 2014).
The composition data for petroleum coke was obtained from Onder and Bagdoyan (1993).
Table 4-74: Production of Ferroalloys (Metric Tons)
Year
Ferrosilicon
25%-55%
Ferrosilicon
56%-95%
Silicon Metal
Misc. Alloys
32-65%
1990
2005
321,385
123,000
109,566
86,100
145,744
148,000
72,442
NA
2009
2010
2011
2012
2013
123,932
153,000
159,667
175,108
164,229
104,855
135,000
140,883
154,507
144,908
148,000
148,000
154,450
169,385
158,862
NA
NA
NA
NA
NA
NA (Not Available for product type, aggregated along with ferrosilicon, 25%-
55% Si)
Uncertainty and Time-Series Consistency
Annual ferroalloy production was reported by the USGS in three broad categories till the 2010 publication:
ferroalloys containing 25 to 55 percent silicon (including miscellaneous alloys), ferroalloys containing 56 to 95
percent silicon, and silicon metal (through 2005 only, 2005 value used as proxy for 2005 through 2010). Starting
with the 2011 minerals yearbook, USGS started reporting all the ferroalloy production under a single category: Total
silicon materials production. The total silicon materials quantity was allocated across the three categories based on
the 2010 production shares for the three categories. Refer to the Methodology section for further details.
Additionally, 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.177 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 €62 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
177 Emissions and sinks of biogenic carbon are accounted for in the Land Use, Land-Use Change, and Forestry chapter.
Industrial Processes and Product Use 4-69
-------
reduce CH4 emissions; however, specific furnace information was not available or included in the CH4 emission
estimates.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-75. Ferroalloy
production CC>2 emissions were estimated to be between 1.6 and 2.0 MMT 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 1.8
MMT CO2 Eq. Ferroalloy production CH4 emissions were estimated to be between a range of approximately 12
percent below and 12 percent above the emission estimate of 0.01 MMT CC>2 Eq.
Table 4-75: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from
Ferroalloy Production (MMT COz Eq. and Percent)
„ „ 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
e (MMT CCh Eq.) (MMT CCh Eq.) (%)
Ferroalloy Production
Ferroalloy Production
CO2 1.8
CH4 +
Lower Upper Lower
Bound Bound Bound
1.6 2.0 -12%
+ + -12%
Upper
Bound
+12%
+12%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
+ Does not exceed 0.05 MMT CO2 Eq.
Methodological recalculations were applied to the entire time series to ensure consistency in emissions from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Starting in 2011, USGS ceased publication of ferrosilicon production data disaggregated by product type. Instead,
total silicon materials production was reported for 2011 through 2013. The previous versions of the Inventory used
2010 production data (by product type) as proxy for 2011 and 2012. In this version of the Inventory, production
shares by product type were developed using the 2010 production data (by product type). These ferrosilicon product
shares were applied to the total ferrosilicon production quantity to estimate annual production by product type for
2011 through 2013.
Planned Improvements
According to the 2006IPCC Guidelines (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
from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in national inventories will
be relied upon.178
178 See
4-70 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
4.18 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 recent reporting, the United States was the
fourth largest producer of primary aluminum, with approximately 4 percent of the world total production (USGS
2014). 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 CCh and two
perfluorocarbons (PFCs): Perfluoromethane (CF4) and perfluoroethane (CJe).
CO2 is emitted during the aluminum smelting process when alumina (aluminum oxide, AhOs) 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 C mass of paste, coke briquettes, or prebaked C
blocks from petroleum coke. During reduction, most of this C is oxidized and released to the atmosphere as CCh.
Process emissions of CCh from aluminum production were estimated to be 3.3 MMT CCh Eq. (3,255 kt) in 2013
(see Table 4-76). The C anodes consumed during aluminum production consist of petroleum coke and, to a minor
extent, coal tar pitch. The petroleum coke portion of the total €62 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 €62 from Fossil
Fuel Combustion source category of the Energy sector. Similarly, the coal tar pitch portion of these €62 process
emissions is accounted for here.
Table 4-76: COz Emissions from Aluminum Production (MMT COz Eq. and kt)
Year MMT CCh Eq. kt
1990 6.8 6,831
2005
2009
2010
2011
2012
2013
3.0
2.7
3.3
3.4
3.3
3,009
2,722
3,292
3,439
3,255
In addition to €62 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 C from the
anode and fluorine from the dissociated molten cryolite bath to combine, thereby producing fugitive emissions of
CF4 and C2p6. 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 C2p6 have declined by 87 percent and 81 percent, respectively, to 2.3 MMT €62
Eq. of CF4 (0.31 kt) and 0.7 MMT CO2 Eq. of C2F6 (0.05 kt) in 2013, as shown in Table 4-77 and Table 4-78. This
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 52 percent, while the combined CF4 and C2p6 emission rate (per metric
ton of aluminum produced) has been reduced by 71 percent. Emissions increased by approximately 1 percent
between 2012 and 2013 due to a slight increase in both CF4 and C2p6 emissions per metric ton of aluminum
produced.
Industrial Processes and Product Use 4-71
-------
Table 4-77: PFC Emissions from Aluminum Production (MMT COz Eq.)
Year CF4 C2F6
Total
1990
2005
17.9 3.5
2.9
0.6
21.5
3.4
2009
2010
2011
2012
2013
1.5
1.4
2.7
2.3
2.3
0.4
0.5
0.8
0.7
0.7
1.9
1.9
3.5
2.9
3.0
Note: Emissions values are presented
in CO2 equivalent mass units using
IPCC AR4 GWP values.
Note: Totals may not sum due to
independent rounding.
Table 4-78: PFC Emissions from Aluminum Production (kt)
Year CF4
C2F6
1990
2009
2010
2011
2012
2013
2.4
+ Does not exceed 0.05 kt.
In 2013, U.S. primary aluminum production totaled approximately 1.9 million metric tons, a 6 percent decrease from
2012 production levels (USAA 2014). In 2013, five companies managed production at ten operational primary
aluminum smelters. Three smelters were closed temporarily for the entire year in 2013 (USGS 2014). During 2013,
monthly U.S. primary aluminum production was lower for every month in 2013, when compared to the
corresponding months in 2012 (USAA 2014).
For 2014, total production was approximately 1.7 million metric tons compared to 1.9 million metric tons in 2013, a
12 percent decrease (USAA 2014). Based on the decrease in production, process CCh and PFC emissions are likely
to be lower in 2014 compared to 2013 if there are no significant changes in process controls at operational facilities.
Methodology
Process CC>2 and perfluorocarbon (PFC)—i.e., perfluoromethane (CF4) and perfluoroethane (C2F6)—emission
estimates from primary aluminum production for 2010 through 2013 are available from EPA's GHGRP—Subpart F
(Aluminum Production) (EPA 2014). Under EPA's GHGRP, facilities began reporting primary aluminum
production process emissions (for 2010) in 2011; as a result, GHGRP data (for 2010 through 2013) 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 C2F6 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 CO2 emissions from onsite anode baking. To estimate the process emissions, EPA's GHGRP uses the process-
4-72 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
specific equations (and certain technology-specific defaults) detailed in subpart F (aluminum production).179 These
equations are based on the Tier 2/Tier 3 IPCC (2006) methods for primary aluminum production, and Tier 1
methods when estimating missing data elements. It should be noted that the same methods (i.e., 2006 IPCC
Guidelines) were used for estimating the emissions prior to the availability of the reported GHGRP data in the
Inventory.
Process COz 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 Voluntary Aluminum Industrial
Partnership (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 C
anode, as described by the following reaction:
2A1203 + 3C -» 4A1 + 3C02
For prebake smelter technologies, CCh 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 CCh 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 C 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.
179 See Code of Federal Regulations, Title 40: Protection of Environment, Part 98: Mandatory Greenhouse Gas Reporting,
Subpart F—Aluminum Production. Available online at:
.
Industrial Processes and Product Use 4-73
-------
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 €62
per metric ton of aluminum produced) from IPCC (2006).
Process RFC Emissions from Anode Effects
Smelter-specific PFC emissions from aluminum production for 2010 through 2013 were reported to EPA under its
GHGRP. To estimate their PFC emissions and report them under EPA's GHGRP, smelters use an approach
identical to the Tier 3 approach in the 2006 IPCC Guidelines (IPCC 2006). 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 GHGRP.
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 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 2013 were obtained via The Aluminum Association (USAA 2014).
For 1990 through 2001, and 2006 (see Table 4-79) 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
2004-2006,2008-2013).
4-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 4-79: Production of Primary Aluminum (kt)
Year
1990
2009
2010
2011
2012
2013
kt
4,048
1,727
1,727
1,986
2,070
1,948
Uncertainty and Time Series Consistency
Uncertainty was assigned to the CCh, 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 CC>2, CF4, and C2p6 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 CCh, CF4, and C2F6 emission estimates
for the U.S. aluminum industry as a whole, and the results are provided below.
The results of this Approach 2 quantitative uncertainty analysis are summarized in Table 4-80. Aluminum
production-related €62 emissions were estimated to be between 3.2 and 3.3 MMT €62 Eq. at the 95 percent
confidence level. This indicates a range of approximately 2 percent below to 2 percent above the emission estimate
of 3.3 MMT CO2 Eq. Also, production-related CF4 emissions were estimated to be between 2.2 and 2.4 MMT €62
Eq. at the 95 percent confidence level. This indicates a range of approximately 6 percent below to 7 percent above
the emission estimate of 2.3 MMT €62 Eq. Finally, aluminum production-related C2p6 emissions were estimated to
be between 0.6 and 0.7 MMT €62 Eq. at the 95 percent confidence level. This indicates a range of approximately
11 percent below to 11 percent above the emission estimate of 0.7 MMT €62 Eq.
Table 4-80: Approach 2 Quantitative Uncertainty Estimates for COz and PFC Emissions from
Aluminum Production (MMT COz Eq. and Percent)
Source
Gas
2013 Emission Estimate
(MMT CCh Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CCh Eq.) (%)
Aluminum Production
Aluminum Production
Aluminum Production
C02
CF4
C2F6
3.3
2.3
0.7
Lower
Bound
3.2
2.2
0.6
Upper
Bound
3.3
2.4
0.7
Lower
Bound
-2%
-6%
-11%
Upper
Bound
+2%
+7%
+11%
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 2013. 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 Aluminum Production 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.
Industrial Processes and Product Use 4-75
-------
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous Inventory reports) which results in time-series
recalculations for most Inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries
are required to report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of
each greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an
overall increase in CO2-equivalent emissions from PFCs. The AR4 GWPs have been applied across the entire time
series for consistency. For more information please see the Recalculations and Improvements Chapter.
As a result, emission estimates for each year from 1990 to 2012 increased by 14 percent for CF4, and increased by
33 percent for C2p6, relative to the emission estimates in the previous Inventory report.
Planned Improvements
Future improvements involve plans to replace proxy (e.g., interpolated) data with additional historical VAIP data
that recently became available in order to calculate more accurate PFC emission estimates for the historical time
series.
4.19 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 thirty years. A dilute gaseous mixture of SF6 with dry air and/or CO2 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. , Alternative cover gases, such as AM-cover™ (containing HFC-
134a), Novec™ 612 (FK-5-1-12) and dilute SO2 systems can, and are being used by some facilities in the United
States. However, many facilities in the United States are still using traditional SF6 cover gas systems.
The magnesium industry emitted 1.4 MMT CO2 Eq. (0.06 kt) of SF6, 0.08 MMT CO2 Eq. (0.06 kt) of HFC-134a,
and 0.002 MMT CO2Eq. (2.1 kt) of CO2, in 2013. This represents a decrease of approximately 8 percent from total
2012 emissions (see Table 4-81). The decrease can be attributed to reduction in primary, secondary, and die casting
SF6 emissions between 2012 and 2013 as reported through EPA's GHGRP, with the largest absolute reduction being
seen for secondary emissions. The reduction in SF6 emissions is likely due in part to decreased production from
reporting facilities in 2013. The decrease in SF6 emissions can also be attributed by continuing industry efforts to
utilize SF6 alternatives, such as HFC-134a, Novec™612 and SO2, to reduce greenhouse gas emissions. In 2013, total
HFC-134a emissions increased from 0.01 MMT CO2 Eq. to 0.08 MMT CO2 Eq., while the FK 5-1-12 emissions
were constant. The emissions of carrier gas, CO2, also decreased from 2.3 kt in 2012 to 2.1 kt in 2013.
Table 4-81: SFe, HFC-134a, FK 5-1-12 and COz Emissions from Magnesium Production and
Processing (MMT COz Eq.)
Year
SFe
HFC-134a
CO2
FK5-1-12
Total3
1990
5.2
0.0 1
+
0.0 1
5.2
2005
2.7
0.0
+
0.0
2.8
2009
1.6
+
+
+
1 17
2010
2.1
+
+
+
2.1
2011
2.8
+
+
+
2.8
2012
1.6
+
+
+
1.7
2013
1.4
0.1
+
+
1.5
4-76 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Note: Emission values are presented in CCh equivalent mass units using IPCC AR4
GWP values.
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.05 MMT CO2 Eq.
a Total does not include FK 5-1-12. Values shown for informational purposes only.
Table 4-82: SFe, HFC-134a, FK 5-1-12 and COz Emissions from Magnesium Production and
Processing (kt)
Year
SFe
HFC-134a
C02
FK 5-1-1 2
1990
0.2
0.0 1
1.4 1
0.0
2005
0.1
0.0
2.9
0.0 |
2009
0.1
+
1.2
| +
2010
0.1
+
1.3
+
2011
0.1
+
3.1
+
2012
0.1
+
2.3
+
2013
0.1
0.1
2.1
+
+ Does not exceed 0.5 kt
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). SF6 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. Along with SF6, some Partners also reported their HFC-134a and FK 5-1-12 usage, which is assumed
to be equal to emissions. 2010 was the last reporting year under the Partnership. Emissions data for 2011 through
2013 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 CC>2. Consequently, cover and carrier gas 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 2013. The methodologies described below also make use of
magnesium production data published by the U.S. Geological Survey (USGS).
1990 through 1998
To estimate emissions for 1990 through 1998, industry SF6 emission factors were multiplied by the corresponding
metal production and consumption (casting) statistics from USGS. For this period, it was assumed that there was no
use of HFC-134a or FK 5-1-12 cover gases and hence emissions were not estimated for these alternatives.
SF6 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 SF6 per metric ton for 1990 through 1993, and 1.1 kg SF6 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 4-81.
These emission factors for the other processes (i.e., permanent mold, wrought, and anode casting) were based on
discussions with industry representatives.
The quantities of CCh carrier gas used for each production type have been estimated using the 1999 estimated €62
emissions data and the annual calculated rate of change of SF6 use in the 1990 through 1999 time period. For each
Industrial Processes and Product Use 4-77
-------
year and production type, the rate of change of SF6 use between the current year and the subsequent year was first
estimated. This rate of change is then applied to the CCh emissions of the subsequent year to determine the CCh
emission of the current year. The emissions of carrier gases for permanent mold, wrought and anode processes are
not estimated in this Inventory.
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, including SF6 consumption
and metal processed. 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. For alternative cover
gases, including HFC-134a and FK 5-1-12, mainly reported data was relied upon. That is, unless a Partner reported
using an alternative cover gas, it was not assumed it was used. Emissions of alternate gases were also estimated
through linear interpolation where possible.
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 usage 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 1999-2001, the sand
casting emission factor was held constant at the 2002 Partner-reported level. 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 for the 1999 to 2010 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 4-83.
The emissions of HFC-134a and FK-5-1-12 were included in the estimates for only instances where Partners
reported that information to the Partnership. Emissions of these alternative cover gases were not estimated for
instances where emissions were not reported.
CO2 carrier gas emissions were estimated using the emission factors developed based on GHGRP-reported carrier
gas and cover gas data, by production type. It was assumed that the use of carrier gas, by production type, is
proportional to the use of cover gases. Therefore, an emission factor, in kg CCh per kg cover gas and weighted by
the cover gases used, was developed for each of the production types. GHGPJ3 data on which these emissions
factors are based was available for primary, secondary, die casting and sand casting. The emission factors were
applied to the total quantity of all cover gases used (SF6, HFC-134a, and FK5-1-12) by production type in this time
period. Carrier gas emissions for the 1999 through 2010 time period were only estimated for those Partner
companies that reported using CCh as a carrier gas through the GHGPJ3. Using this approach helped ensure time
series consistency. The emissions of carrier gases for permanent mold, wrought and anode processes are not
estimated in this Inventory.
4-78 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 4-83: SFe Emission Factors (kg SFe per metric ton of magnesium)
Year Die Casting3 Permanent Mold Wrought Anodes
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2.14b
0.72
0.72
0.71
0.81
0.79
0.77
0.88
0.64
0.10
2.30
2.94
2
2
2
2
2
2
2
2
2
2
2
2
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 Weighted average includes all die casters, Partners and non-Partners. For
the majority of the time series (2000-2007), Partners made up 100 percent
of die casters in the U.S.
b Weighted average that includes an estimated emission factor of 5.2 kg SFe
per metric ton of magnesium for die casters that do not participate in the
Partnership.
2011 through 2013
For 2011 through 2013, for the primary and secondary producers, GHGRP-reported cover and carrier gases
emissions data were used. For die and sand casting, some emissions data was obtained through EPA's GHGRP. The
balance of the emissions for these industry segments were estimated based on previous Partner reporting (i.e., for
Partners that did not report emissions through EPA's GHGRP) or were estimated by multiplying emission factors by
the amount of metal produced or consumed. Partners who did not report through EPA's GHGRP were assumed to
have continued to emit SF6 at the last reported level, which was from 2010 in most cases. All Partners were
assumed to have continued to consume magnesium at the last reported level. Where the total metal consumption
estimated for the Partners fell below the U.S. total reported by USGS, the difference was multiplied by the emission
factors discussed in the section above. For the other types of production and processing (i.e., permanent mold,
wrought, and anode casting), emissions were estimated by multiplying the industry emission factors with the metal
production or consumption statistics obtained from USGS. For 2013, pre-published USGS consumption statistics
were obtained via communications with USGS (USGS 2013)
Uncertainty and Time Series Consistency
Uncertainty surrounding the total estimated emissions in 2013 is attributed to the uncertainties around SF6, HFC-
134a and CCh emission estimates. To estimate the uncertainty surrounding the estimated 2013 SF6 emissions from
magnesium production and processing, the uncertainties associated with three variables were estimated: (1)
emissions reported by magnesium producers and processors for 2013 through EPA's GHGRP, (2) emissions
estimated for magnesium producers and processors that reported via the Partnership in prior years but did not report
2013 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 emissions (usage) data reported by each GHGRP reporter for all the cover and carrier gases (per the 2006IPCC
Guidelines). If facilities did not report emissions data during the current reporting year through EPA's GHGRP
reporting program, SF6 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. Alternate cover gas and carrier gases data was set equal to zero if the facilities did not report via the
GHGRP program. 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 SF6 usage for the sand casting Partner was 85 percent. For those
industry processes that are not represented in the Partnership, such as permanent mold and wrought casting, SF6
emissions were estimated using production and consumption statistics reported by USGS and estimated process-
Industrial Processes and Product Use 4-79
-------
specific emission factors (see Table 4-84). 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 SF6 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 SF6 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.
The results of this Approach 2 quantitative uncertainty analysis are summarized in Table 4-84. Total emissions
associated with magnesium production and processing were estimated to be between 1.3 and 1.7 MMT CCh Eq. at
the 95 percent confidence level. This indicates a range of approximately 11 percent below to 12 percent above the
2013 emission estimate of 1.5 MMT CCh Eq. The uncertainty estimates for 2013 are similar relative to the
uncertainty reported for 2012 in the previous Inventory report.
Table 4-84: Approach 2 Quantitative Uncertainty Estimates for SFe, HFC-134a and COz
Emissions from Magnesium Production and Processing (MMT COz Eq. and Percent)
„ „ 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
source lias (MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Magnesium
Production
SF6, HFC-
ma, CO2
1.3 1.7 -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 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
In the current Inventory, emission estimates for alternate cover gases and carrier gas has been incorporated as the
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 began using
these cover gases starting in around 2006, as based on Partnership reported data. The amounts being used by
companies on the whole are low and have a minor effect on the overall emissions from the industry. This is also
attributable to their relatively lower GWPs. SF6 has a GWP of 22,800, whereas HFC-134a has a GWP of 1,430.
Similarly, EPA's GHGRP now provides access to data on carrier gases, allowing for this information to be
integrated in the Inventory. Emissions of CC>2 have also been included in the total emissions from the industry. This
has led to slight increase in overall emissions for each year compared to the previous Inventory. €62 carrier gas
emissions have been included across the entire time series to ensure time series consistency.
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmo spheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh-equivalent emissions from CH4, HFCs, and PFCs. The GWPs of N2O and SF6 have decreased,
leading to a decrease in CCh-equivalent emissions for SF6. The AR4 GWPs have been applied across the entire time
series for consistency. For more information please see the Recalculations and Improvements Chapter.
4-80 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
As a net result, emission estimates for each year from 1990 to 2013 have slightly decreased, relative to the previous
Inventory report.
For one facility, a recalculation for 2011 SF6 emissions was performed to ensure methodological consistency. The
emissions for this facility and year were previously estimated using a company-specific growth rate based on data
reported through the Partnership. This estimate has been revised by interpolating the reported emissions between
2010 and 2012, reported via the Partnership and EPA's GHGRP respectively. This has caused a slight increase in
the SF6 emissions for 2011 compared to the previous Inventory.
Planned Improvements
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. Additional
research may lead to a revision of the 2006 IPCC Guidelines to reflect this phenomenon and until such time,
developments in this sector will be monitored for possible application to the inventory methodology.
Usage and emission details of carrier gases in permanent mold, wrought and anode processes will be researched as
part of a future inventory. Based on this research, it will be determined if CC>2 carrier gas emissions are to be
estimated.
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 CCh
(Sjardin 2003). Emissions from fuels consumed for energy purposes during the production of lead are accounted for
in the Energy chapter.
Primary production of lead through the direct smelting of lead concentrate produces CO2 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 was closed at the end of
2013(USGS2014b).
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 and post-consumer scrap at secondary smelters. Of all the
domestic secondary smelters operational in 2013, 12 smelters had capacities of 30,000 tons or more and were
collectively responsible for more than 95 percent of secondary lead production in 2013 (USGS 2014a). Secondary
lead production has increased in the United States over the past decade while primary lead production has decreased.
In 2013, secondary lead production accounted for nearly 90 percent of total lead production. Similarly, secondary
lead accounted for approximately 68 percent of total domestic lead consumption (USGS 2014a).
In 2013, total secondary lead production in the United States was slightly less than that in 2012. Domestic secondary
lead producers expanded capacity and others closed plants, but total production capacity remained essentially
unchanged. In April 2013, a leading producer closed its 70,000 ton capacity smelter in Reading, PA, and in
September reduced production at its 90,000 ton capacity smelter in Vernon, CA, by 15 percent. Increases in exports
of spent lead-acid batteries in recent years have decreased the amount of scrap available to secondary smelters
(USGS2014a).
U.S. primary lead production increased by approximately 6 percent from 2012 to 2013, and has decreased by 71
percent since 1990. In 2013, U.S. secondary lead production slightly decreased from 2012 levels by approximately
1 percent, but has increased by 19 percent since 1990 (USGS 1995 through 2013, USGS 2014a).
Industrial Processes and Product Use 4-81
-------
In 2013, U.S. primary and secondary lead production totaled 1,218,000 metric tons (USGS 2014a). The resulting
emissions of CC>2 from 2013 lead production were estimated to be 0.5 MMT CCh Eq. (525 kt) (see Table 4-85). The
majority of 2013 lead production is from secondary processes, which accounted for 94 percent of total 2013 CCh
emissions from lead production. At last reporting, the United States was the third largest mine producer of lead in
the world, behind China and Australia, accounting for approximately 6 percent of world production in 2013 (USGS
2014a).
Table 4-85: COz Emissions from Lead Production (MMT COz Eq. and kt)
Year MMT CCh Eq.
kt
2009
2010
2011
2012
2013
0.5
0.5
0.5
0.5
0.5
525
542
538
527
525
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 2013 than in 1990. Although primary production has decreased significantly (71
percent since 1990), secondary production has increased by about 19 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 2013; USGS
2014a).
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 (IPCC 2006). The Tier 1 equation is as
follows:
Where,
DS
S
EFa, b =
C02 Emissions = (DS x EFa) + (S x EFb)
Lead produced by direct smelting, metric ton
Lead produced from secondary materials
Applicable emission factor, metric tons CCh/metric ton product
For primary lead production using direct smelting, Sjardin (2003) and the IPCC (2006) provide an emission factor of
0.25 metric tons COVmetric ton lead. For secondary lead production, Sjardin (2003) and IPCC (2006) provide an
emission factor of 0.25 metric tons CCh/metric ton lead for direct smelting, as well as an emission factor of 0.2
metric tons CCh/metric 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 2013 activity data for primary and secondary lead production (see Table 4-86) were obtained from
the USGS (USGS 1995 through 2013; 2014a).
Table 4-86: Lead Production (Metric Tons)
Year Primary
Secondary
1990
404,000
922,000
4-82 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
2005 143,000 1,150,000
2009
2010
2011
2012
2013
103,000
115,000
118,000
111,000
118,000
1,110,000
1,140,000
1,130,000
1,110,000
1,100,000
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 Approach 2 quantitative uncertainty analysis are summarized in Table 4-87. Lead production CCh
emissions were estimated to be between 0.4 and 0.6 MMT CO2 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 MMT €62
Eq.
Table 4-87: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from Lead
Production (MMT COz Eq. and Percent)
„ ~ 2013 Emission Estimate Uncertainty Ranee Relative to Emission Estimate3
xoii t*f*p I-w-nc
(MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Lead Production CCh 0.5 0.4 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 consistency in emissions from 1990
through 2013. 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 to improve the emission
estimates for the Lead Production source category. Particular attention would 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.180
180
See.
Industrial Processes and Product Use 4-83
-------
4.21 Zinc Production (IPCC Source Category
2C6)
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 CC>2 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 2013, U.S. primary and secondary refined zinc production were estimated to total 250,000 metric tons (USGS
2014b) (see Table 4-88). Domestic zinc mine production increased slightly in 2013 compared to 2012 levels,
primarily owing to increase in zinc production at a zinc-lead mine in Alaska and two zinc-mining complexes in
Tennessee. Zinc metal production decreased by 4 percent owing to a decline in secondary production; a zinc-
recycling company closed its smelter in Pennsylvania towards the end of 2013 as it began production at its new
recycling facility in North Carolina starting 2014 (USGS 2014b). Primary zinc production (primary slab zinc)
increased slightly in 2013, while, secondary zinc production in 2013 decreased relative to 2012.
Emissions of CO2 from zinc production in 2013 were estimated to be 1.4 MMT CO2 Eq. (1,429 kt) (see Table 4-89).
All 2013 CO2 emissions resulted from secondary zinc production processes. Emissions from zinc production in the
4-84 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
U.S. have increased overall since 1990 due to a gradual shift from non-emissive primary production to emissive
secondary production. In 2013, emissions were estimated to be 126 percent higher than they were in 1990.
Table 4-88: Zinc Production (Metric Tons)
Year Primary
Secondary
1990 262,704
95,708
2009
2010
2011
2012
2013
94,000
120,000
110,000
114,000
120,000
109,000
129,000
138,000
147,000
130,000
Table 4-89: COz Emissions from Zinc Production (MMT COz Eq. and kt)
Year MMT CCh Eq.
kt
1990
2009
2010
2011
2012
2013
0.9
1.2
1.3
1.5
1.4
943
1,182
1,286
1,486
1,429
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 (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.
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:
EF,
Waelz Kiln ~
1.19 metric tons coke 0.85 metric tons C 3.67 metric tons C02
X : :—X-
metric tons zinc
metric tons coke
metric tons C
3.70 metric tons C02
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:
Industrial Processes and Product Use 4-85
-------
0.4 metric tons coke 0.85 metric tons C 3.67 metric tons C07 1.24 metric tons C07
P _ _
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 foryears 2006 through 2013 (Horsehead 2007, 2008, 2010a, 2011, 2012, 2013, and 2014). 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 2006).
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 CCh emission estimates for Horsehead' s Waelz kiln facilities.
The amount of EAF dust consumed by Steel Dust Recycling (SDR) and their total production capacity were
obtained from SDR's facility in Alabama for the years 201 1 through 2013 (Rowland 2012 and 2014). SDR's facility
in Alabama underwent expansion in 201 1 to include a second unit (operational since early- to mid-2012). SDR's
facility has been operational since 2008. Annual consumption data for SDR was not publicly available for the years
2008, 2009, and 2010. These data were estimated using data for Horsehead's Waelz kilns for 2008-2010 (Horsehead
2007, 2008, 2010a, 2010b, and 201 1). Annual capacity utilization ratios were calculated using Horsehead's annual
consumption and total capacity for the years 2008-2010. Horsehead's annual capacity utilization ratios were
multiplied with SDR's total capacity to estimate SDR's consumption for each of the years, 2008 through 2010 (Steel
Dust Recycling LLC 2013).
PIZO Technologies Worldwide LLC's facility in Arkansas has been operational since 2009. The amount of EAF
dust consumed by PIZO's facility for 2009 through 2013 was not publicly available. EAF dust consumption for
PIZO's facility for 2009 and 2010 were estimated by calculating annual capacity utilization of Horsehead's Waelz
kilns and multiplying this utilization ratio by PIZO's total capacity (PIZO 2012). EAF dust consumption for PIZO's
facility for 201 1 through 2013 were estimated by applying the average annual capacity utilization rates for
Horsehead and SDR (Grupo PROMAX) to PIZO's annual capacity (Horsehead 2012, 2013, and 2014; Rowland
2012 and 2014; PIZO 2012 and 2014). The 1.24 metric tons CXVmetric 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.
Refined zinc production levels for Horsehead's Monaca, PA facility (utilizing electrothermic technology) were
available from the company foryears 2005 through2013 (Horsehead 2008, 2011, 2012, 2013, and 2014).
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 2005). The 3.70 metric tons CO2/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
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 for 201 1-2013 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 for 201 1-
2013 which were obtained from SDR's recycling facility in Alabama).
4-86 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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. The results of the Approach 2 quantitative uncertainty analysis
are summarized in Table 4-90. Zinc production CC>2 emissions were estimated to be between 1.2 and 1.7 MMT CC>2
Eq. at the 95 percent confidence level. This indicates a range of approximately 16 percent below and 18 percent
above the emission estimate of 1.4 MMT CCh Eq.
Table 4-90: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from Zinc
Production (MMT COz Eq. and Percent)
Source Gas 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
(MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Zinc Production CCh 1.4 L2 1/7 -16% +18%
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 consistency in emissions from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
In the previous version of the Inventory (i.e., 1990-2012), EAF dust consumption data for SDK's Alabama facility
were not available for 2012. Therefore, 2011 data were used as proxy for 2012. During 2013 updates to the
Inventory, these data were obtained from SDR (Rowland 2014). This change caused an increase of approximately
4.5 percent in the 2012 emissions.
Planned Improvements
Future improvements involve evaluating and analyzing data reported under EPA's GHGRP 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.181
4.22 Semiconductor Manufacture (IPCC
Source Category 2E1)
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 (C2p6),
181 See.
Industrial Processes and Product Use 4-87
-------
nitrogen trifluoride (NF3), sulfur hexafluoride (SF6), and nitrous oxide (N2O), although other compounds such as
perfluoropropane (CsFg) and perfluorocyclobutane (c-C^s) 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 600 semiconductor products (devices or chips) may
require 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
polysilicon films and refractory metal films like tungsten.
Nitrous oxide is used in manufacturing semiconductor devices to produce thin films by CVD and nitridation
processes as well as for N-doping of compound semiconductors and reaction chamber conditioning (Doering 2000).
For 2013, total CCh weighted emissions of all fluorinated greenhouse gases and nitrous oxide by the U.S.
semiconductor industry were estimated to be 4.2 MMT CCh Eq. Combined emissions of all greenhouse gases are
presented in Table 4-91 and Table 4-92 below for years 1990, 2005 and the period 2009 to 2013. The rapid growth
of this industry and the increasing complexity (growing number of layers182) of semiconductor products led to an
increase in emissions of 153 percent between 1990 and 1999, when emissions peaked at 9.1 MMT CO2 Eq. The
emissions growth rate began to slow after 1999, and emissions declined by 54 percent between 1999 and 2013.
Together, industrial growth, adoption of emissions reduction technologies, including but not limited to abatement
technologies, and shift in gas usages resulted in a net increase in emissions of 16 percent between 1990 and 2013.
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 25 percent and between 2010 and 2011 by 29 percent; reductions in emissions were observed
between 2011 and 2012, and 2012 and 2013 at 9 percent and 7 percent, respectively.
Table 4-91: RFC, HFC, SFe, NFs, and NzO Emissions from Semiconductor Manufacture (MMT
COz Eq.)
Year
CF4
C2F6
C3F8
C4F8
HFC-23
SFe
NFs
Total F-
GHGs
N2O
1990
0.8
2.0 1
0.2 1
°:
3.6
+
2005
1.1
1.9
0.1 1
0.1
0.2 1
0.7 1
0.5 •
4.6 1
0.1
2009
0.8
1.1
0.1
+
0.2
0.3
0.4
2.9
0.1
2010
1.1
1.4
0.1
+
0.2
0.4
0.5
3.7
0.1
2011
1.4
1.8
0.2
0.1
0.2
0.4
0.7
4.7
0.2
2012
1.3
1.6
0.1
0.1
0.2
0.4
0.6
4.3
0.2
2013
1.2
1.5
0.1
0.1
0.2
0.4
0.6
4.0
0.2
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.
4-88 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Total 3.6 4/7 3.1 3.8 4.9 4.5 4.2
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.05 MMT CO2 Eq.
Table 4-92: RFC, HFC, SFe, NFs, and NzO Emissions from Semiconductor Manufacture (kt)
Year 1990 2005 2009 2010 2011 2012 2013
CF4 0.11 0.14 0.11 0.14 0.19 0.17 0.16
C2F6 0.16 0.16 0.09 0.11 0.14 0.13 0.12
C3F8 + I + I + + + + +
C4F8 + I + I + + + + +
HFC-23 + I + I + + + + +
SFe + I + I + + + + +
NF3 + + + + + + +_
N2Q 0.12 0.41 0.45 0.49 0.79 0.65 0.61
+ Does not exceed 0.05 kt
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 PFC183 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)184, 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 GHGRP differs across the 1990 through
2013 time series. Consequently, F-GHG 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 2011 through 2013. N2O emissions were estimated using three distinct methods, one each for the period
1990 through 1994, 1995 through 2010, and 2011 through 2013.
1990 through 1994
From 1990 through 1994, Partnership data were unavailable and emissions were modeled using the PEVM (Burton
and Beizaie 2001).185 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 fluorinated greenhouse gas 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.
I83 in the context of the EPA Partnership and PEVM, PFC refers to perfluorocompounds, not perfluorocarbons.
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.
185 Various versions of the PEVM exist to reflect changing industrial practices. From 1990 to 1994 emissions estimates are from
PEVM vl.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.
Industrial Processes and Product Use 4-89
-------
PEVM incorporates information on the two attributes of semiconductor devices that affect the number of layers: (1)
linewidth technology (the smallest manufactured feature size),186 and (2) product type (discrete, memory or
logic).187 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
consumption of silicon (i.e., square inches) by linewidth technology from published data on annual wafer starts and
average wafer size (VLSI Research, Inc. 2012).
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).
To estimate N2O emissions, it is assumed the proportion of N2O emissions estimated for 1995 (discussed below)
remained constant for the period of 1990 through!994.
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 2012 and 2013). Gas-specific emissions were estimated using the same method as
for 1990 through 1994.
For this time period, the N2O emissions were estimated using an emission factor that is applied to the annual, total
U.S. TMLA manufactured. The emission factor was developed using a regression-through-the-origin (RTO) model:
GHGRP reported N2O emissions were regressed against the corresponding TMLA of facilities that reported no use
of abatement systems. Details on the GHGRP reported emissions and development of emission factor using the RTO
model are presented in the 2011 through2013 section. ThetotalU.S. TMLA manufactured were estimated using
PEVM.
186 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 65 nm feature sizes 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).
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.
4-90 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
2000 through 2006
Emissions for the years 2000 through 2006—the period during which Partners began the consequential application
of fluorinated greenhouse gas-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, 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 (a new emission
factor determined for the non-Partner 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.188 Gas-specific
emissions from non-Partners were estimated using linear interpolation of gas-specific emission distribution of 1999
(assumed same as total U.S. 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,
and changes in 1C fabrication practices within the semiconductor industry (see ITRS 2008 and Semiconductor
Equipment and Materials Industry 2011).189>190-19l
The N2O emissions were estimated using the same methodology as 1995-1999 methodology.
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
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.
189 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.
190 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.
191 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.
Industrial Processes and Product Use 4-91
-------
therefore greater numbers of layers.192 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 WFF. 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.
The N2O emissions were estimated using the same methodology as 1995 through 1999 methodology.
2011 through 2013
The fifth and final method for estimating emissions from semiconductor manufacturing covers the period 2011
through 2013, the years after EPA's Partnership with the semiconductor industry ended (in 2010) and reporting
under the GHGRP began. Manufacturers whose estimated uncontrolled emissions equal or exceed 25,000 mt CO2
Eq. per year (based on default emission factors and total capacity in terms of substrate area) are required to report
their emissions to the EPA. This population of reporters to EPA's GHGRP included both historical Partners of
EPA's PFC Reduction/Climate Partnership as well as non-Partners. In EPA's GHGRP, the population of non-
Partner facilities also included manufacturers that use GaAs technology in addition to Si technology193. Emissions
from the population of manufacturers that were below the reporting threshold were also estimated for this time
period using EPA-developed emission factors and estimates of facility-specific production obtained from WFF.
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.
The fluorinated GHGs, and N2O were 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, which is below EPA's GHGRP reporting threshold, and for R&D
facilities, which are not covered by EPA's GHGRP, emission estimates are based on EPA-developed emission
factors for the fluorinated GHGs and N2O. The new emission factors (in units of mass of CO2 Eq. / TMLA [MSI])
are based on the emissions reported by facilities under EPA's GHGRP and TMLA estimates for these facilities from
the WFF (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, one for
facilities that manufacture devices on Si wafers and one for facilities that manufacture on GaAs wafers. An analysis
of the emission factors of reporting fabs showed that the characteristics that had the largest impacts on emission
factors were the substrate (i.e., Si or GaAs) used at the fab, whether the fab contained R&D activities, and whether
the fab reported using point-of-use fluorinated greenhouse gas abatement194. For each of these groups, a
subpopulation-specific emission factor was obtained using a regression-through-the-origin (RTO) model: facility-
reported aggregate emissions of seven fluorinated GHGs (CF4, C2F6, CsFg, C4p8, CHF3, SF6 and NF3)195 were
regressed against the corresponding TMLA to estimate an aggregate F-GHG emissions factor (CO2 Eq./MSI TMLA)
192 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.
193 GaAs and Si technologies refer to the wafer on which devices are manufactured, which use the same PFCs but in different
ways.
For the non-reporting segment of the industry using GaAs technology, emissions were estimated only for those fabs that
manufactured the same products as manufactured by reporters. The products manufactured were categorized as discrete
(emissions did not scale up with decreasing feature size).
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.
4-92 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
and facility-reported N2O emissions were regressed against the corresponding TMLA to estimate a N2O emissions
factor (CO2 Eq./MSI 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 1.15 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 F-GHGs and N2O 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 F-GHGs
and N2O per TMLA. However, as was assumed for 2007 through 2010, fabs with only R&D activities were assumed
to utilize only 20 percent of their manufacturing capacity. Other fabs were assumed to utilize 89 percent of their
manufacturing capacity, held constant at 2012 levels which is slightly lower than 2011 levels. Fabs that produce
discrete products are assumed to utilize 84 percent of their manufacturing capacity, held constant at 2011 levels.
These utilizations at 2011 levels are based on the Semiconductor Industry Association report (SICAS, 2011).
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 GWP-weighted 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 9, 10 and 10 percent
of U.S. emissions in 2011, 2012 and 2013, 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 this time period 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., 4 percent in
2013), 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 2006 IPCC Guidelines. Estimates of operating plant capacities
and characteristics for Partners and non-Partners were derived from the Semiconductor Equipment and Materials
Industry (SEMI) WFF (formerly World Fab Watch) database (1996 through 2013) (e.g., Semiconductor Materials
and Equipment Industry, 2013). Actual worldwide capacity utilizations for 2011 were obtained from Semiconductor
International Capacity Statistics (SICAS) (SIA, 2011). Estimates of the number of layers for each linewidth was
obtained from International Technology Roadmap for Semiconductors: 2013 Edition (Burton and Beizaie 2001,
ITRS 2007, ITRS 2008, ITRS 2011, ITRS 2013). PEVM utilized the WFF, SICAS, and ITRS, as well as 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 Approach 2
uncertainty estimation methodology, the Monte Carlo Stochastic Simulation technique. The equation used to
estimate uncertainty is:
Total Emissions (ET) = GHGRP Reported F-GHG Emissions (ER,F.GHG) + Non-Reporters' Estimated F-GHG
Emissions (ENR.F-GHG) + GHGRP Reported N2O Emissions (ER)N2o) + Non-Reporters' Estimated N2O Emissions
Industrial Processes and Product Use 4-93
-------
where ER and ENR denote totals for the indicated subcategories of emissions for F-GHG and N2O, respectively.
The uncertainty in ET presented in Table 4-93 below results from the convolution of four distributions of emissions,
each reflecting separate estimates of possible values of ERJ-GHG, ERjN20, ENR.F-GHG, and ENRJ^O. 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, F-GHG, or GHGRP reported F-GHG emissions, is developed based on gas-specific
uncertainty estimates of emissions for two industry segments, one processing 200 mm wafers and one processing
300 mm wafers. Uncertainties in emissions for each gas and industry segment 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).1% The 2012 analysis did not take into account the use of
abatement. For the industry segment that processed 200 mm wafers, estimates of uncertainties at a 95 percent CI
ranged from ±29 percent for CsF8 to ±10 percent for CF4. For the corresponding 300 mm industry segment,
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 to the total emissions of the 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 industry segments 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. The triangular distributions range from an asymmetric and highly uncertain distribution of 0 percent minimum
to 90 percent maximum with 70 percent most likely value for CF4 to a symmetric and less uncertain distribution of
85 percent minimum to 95 percent maximum with 90 percent most likely value for C4p8, NF3 and SF6. For facilities
reporting partial abatement, the distribution of fraction of the gas fed through the abatement device, for each gas, is
assumed to be triangularly distributed as well. It is assumed that no more than 50 percent of the gases area abated
(i.e., the maximum value) and that 50 percent is the most likely value and the minimum is 0 percent. Consideration
of abatement then resulted in four additional industry segments, two 200 mm wafer-processing segments (one fully
and one partially abating each gas) and two 300 mm wafer-processing segment (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,F-GHG is obtained by allocating the estimates of uncertainties to the total GHGRP-reported
emissions from each of the six industry segments, and then running a Monte Carlo simulation which results in the 95
percent CI for emissions from GHGRP reporting facilities (ER,F-GHG).
The uncertainty in ER)N2o is obtained by assuming that the uncertainty in the emissions reported by each of the
GHGRP reporting facilities results from the uncertainty in quantity of N2O consumed and the N2O emission factor
(or utilization). Similar to analyses completed for subpart I (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), the uncertainty of N2O consumed was assumed to be 20 percent. Consumption
of N2O for GHGRP reporting facilities was estimated by back- calculating from emissions reported and assuming no
abatement. The quantity of N2O utilized (the complement of the emission factor) was assumed to have a triangular
196 OnNovember 13, 2013, EPA published a final rule revising subpartl (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.
4-94 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
distribution with a minimum value of 0 percent, mode of 20 percent and maximum value of 84 percent. The
minimum was selected based on physical limitations, the mode was set equivalent to the subpart I default N2O
utilization rate for chemical vapor deposition, and the maximum was set equal to the maximum utilization rate found
in ISMI Analysis of Nitrous Oxide Survey Data (ISMI, 2009). The inputs were used to simulate emissions for each
of the GHGRP reporting, N2O-emitting facilities. The uncertainty for the total reported N2O emissions was then
estimated by combining the uncertainties of each of the facilities reported emissions using Monte Carlo simulation.
The estimate of uncertainty in ENR.F-GHG and ENRJTO 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 100 percent and 63 percent, respectively. The corresponding values for facilities that
manufacture discrete devices are, 84 percent, 100 percent, and 66 percent, respectively, while the values for
utilization for R&D facilities, are assumed to be 20 percent, 33 percent, and 9 percent, respectively. The most
probable utilizations are unchanged compared to 2012 Inventory year. To address the uncertainty in the capacity
utilization for Inventory year 2013, the lower bound has been decreased by 10 percent, and the upper bound has
been increased by 10 percent (or 100 percent if greater than 100 percent) compared to the bounds used in the 2012
Inventory year. 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 (MMT CO2 Eq. 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 Approach 2 quantitative uncertainty analysis are summarized in Table 4-93, 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. F-GHG and N2O emissions from semiconductor manufacturing were
estimated to be between 4.0 and 4.4 MMT CO2 Eq. at a 95 percent confidence level. This range represents 5 percent
below to 5 percent above the 2013 emission estimate of 4.2 MMT 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.
Industrial Processes and Product Use 4-95
-------
Table 4-93: Approach 2 Quantitative Uncertainty Estimates for HFC, RFC, SF6, NF3 and N2O
Emissions from Semiconductor Manufacture (MMT COz Eq. and Percent)
Source
Semiconductor
Manufacture
2013 Emission
Gas Estimate Uncertainty Range Relative to Emission Estimate3
(MMT C02 Eq.) (MMT CCh Eq.) (%)
HFC,
PFC
' 42
SF6, NF3,
andN2O
Lower
Boundb
4.0
Upper Lower
Boundb Bound
4.4 -5%
Upper
Bound
5%
1 Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence
interval.
b 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 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in emissions from CH4, HFCs, PFCs, SF6, and NF3. The AR4 GWPs have been applied across the entire
time series for consistency. For more information please see the Recalculations and Improvements Chapter.
The decrease in the GWP of SF6 and increase in the GWP of all other gases had several impacts on Inventory
estimates. In the 1990 through 1994 time period, an overall increase in total annual GWP-weighted emissions is
seen. In the 1995 through 2010 time period, the Inventory methodology relies on various gas distributions based on
Partner reported emissions and PEVM estimated emissions. The changes in GWP carry through to changes in the
estimated gas distributions, and hence changes in gas-by-gas emission estimates, in CCh Eq., and total annual
fluorinated greenhouse gas emission estimates, in CC>2 Eq..
For the first time, NF3 and N2O have been included in total annual GWP-weighted emission estimates for the United
States. This, along with an increased weighted GWP from SAR to AR4 led to increase in total emissions for all
years as compared to previous Inventories. The emissions of each gas were impacted by the increase in overall
emissions as well as the percent distribution of each gas as a result of changes in their GWPs.
Emissions in years 2011 and 2012 were updated to reflect updated emissions reporting in EPA's GHGRP. For the
non-reporting population, the methodology to determine the non-reporting population for GaAs using facilities has
been updated. In the updated methodology, revised assumptions were made about the GaAs using facilities that use
fluorinated greenhouse gases (e.g., only the non-reporters that use wafers greater than or equal to four inches have
been assumed to use fluorinated greenhouse gases, facilities that use wafers less than 4 inches are assumed to use
wet etching and hence do not consume or emit any fluorinated greenhouse gases). Further, EPA has drawn an
analogy between GaAs-using GHGRP reporters and non-reporters provided the non-reporters use wafers greater
than 4 inches and manufacture the many versions of high electron mobility transistors (HEMT, PHEMT, MHEMT,
HET, MOFETs), which are discrete devices and may be made to specific order by certain foundries. By virtue of
this analogy, EPA has estimated emissions only from the non-reporters that use GaAs technology and manufacture
HEMT and their variations. While other devices may be made using GaAs technology, EPA has no reporters under
the GHGRP that manufacture them and hence has no basis for estimating an emission factor. EPA has thus assumed
that they do not use or emit F-GHGs. This has decreased the non-reporting facilities subpopulation, and
subsequently total emissions for the years 2011 and 2012.
4-96 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Planned Improvements
This Inventory contains 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 heat transfer fluid emission data, are available. Therefore, a point of consideration for future Inventory
reports is the inclusion of other fluorinated gases, and emissions from heat transfer fluid (RTF) loss to the
atmosphere.
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
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. Emissions
from manufacturing of flat panel displays and photovoltaic cells may be included in future Inventory reports;
however, estimation methodologies would need to be developed.
4.23 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 1990.197 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
4-94 and Table 4-95.
Table 4-94: Emissions of HFCs and PFCs from ODS Substitutes (MMT COz Eq.)
Gas
HFC-23
HFC-32
HFC-125
HFC-134a
HFC-143a
HFC-236fa
CF4
Others*
Total
1990
0.0
0.0 |
u.u I
0.3
0.3
2005
+
0.3 1
11.0
81.9
10.7
'i
5.9
111.1
2009
+
1.8
22.0
87.9
15.5
1.4
7.4
136.0
2010
+
2.6
28.1
86.5
17.9
1.4
7.8
144.4
2011
+
3.3
33.7
81.4
20.3
1.4
8.2
148.4
2012
+
4.3
40.0
76.5
22.8
1.5
8.6
153.5
2013
+
5.2
46.3
71.3
25.3
1.5
9.0
158.6
+ Does not exceed 0.05 MMT CO2 Eq.
197 [42 U.S.C § 7671, CAA Title VI]
Industrial Processes and Product Use 4-97
-------
* 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 4-95: Emissions of MFCs and PFCs from ODS Substitution (MT)
Gas 1990 2005 2009 2010 2011 2012 2013
HFC-23 +1 TT 2 2 2 2 2~~
HFC-32 + 505 2,611 3,849 4,925 6,309 7,733
HFC-125 + I 3,147 6,290 8,038 9,615 11,415 13,236
HFC-134a + I 57,286 I 61,467 60,509 56,929 53,478 49,837
HFC-143a + I 2,401 I 3,460 3,996 4,547 5,091 5,651
HFC-236fa + I 125 144 146 147 148 151
CF4 + 2 33444
Others* M M M M M M M
M (Mixture of Gases)
+ Does not exceed 0.5 MT
* 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.
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 suchasR-404A.198 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 158.6 MMT CO2 Eq. in 2013. 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 4-96 presents emissions of HFCs and PFCs as ODS substitutes by end-use sector for 1990 through 2013. The
end-use sectors that contributed the most toward emissions of HFCs and PFCs as ODS substitutes in 2013 include
refrigeration and air-conditioning (137.6 MMT CO2 Eq., or approximately 87 percent), aerosols (10.5 MMT CO2
Eq., or approximately 7 percent), and foams (7.4 MMT CO2 Eq., or approximately 5 percent). Within the
refrigeration and air-conditioning end-use sector, motor vehicle air-conditioning was the highest emitting end-use
(44.1 MMT CO2 Eq.), followed by refrigerated retail food and refrigerated transport. Each of the end-use sectors is
described in more detail below.
Table 4-96: Emissions of HFCs and PFCs from ODS Substitutes (MMT COz Eq.) by Sector
Sector 1990 2005 2009 2010 2011 2012 2013
Refrigeration/Air
Conditioning + I 99.2 119.7 126.0 129.0 133.3 137.6
Aerosols 0.3 7.6 9.4 9.7 10.1 10.3 10.5
Foams + I 2.1 4.2 5.9 6.4 6.9 7.4
Solvents + I 1.7 1.6 1.7 1.7 1.7 1.8
Fire Protection + 0.7 1.0 1.1 1.2 1.3 1.3
Total 0.3 111.1 136.0 144.4 148.4 153.5 158.6
+ Does not exceed 0.05 MMT CO2 Eq.
198 R.4Q4A contains HFC-125, HFC-143a, andHFC-134a.
4-98 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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-410A,199 R-404A, and R-507A.200 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,
polyolefm, 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 CC>2, 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.
Solvents
CFCs, methyl chloroform (1,1,1-trichloroethane orTCA), and to a lesser extent carbon tetrachloride (CCU) 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
199 R-410A contains HFC-32 and HFC-125.
200 R-507A, also called R-507, contains HFC-125 and HFC-143a.
Industrial Processes and Product Use 4-99
-------
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 3.9.
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 153.3 MMT 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
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 residential unitary AC, as well as the percent of non-MDI aerosol
propellant that is HFC-152a.
4-100 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-97. Substitution of ozone
depleting substances HFC and PFC emissions were estimated to be between 153.0 and 172.3 MMT CCh Eq. at the
95 percent confidence level. This indicates a range of approximately 0.22 percent below to 12.4 percent above the
emission estimate of 158.6 MMT CCh Eq.
Table 4-97: Approach 2 Quantitative Uncertainty Estimates for HFC and PFC Emissions from
ODS Substitutes (MMT COz Eq. and Percent)
Source
Gases
2013 Emission
Estimate
(MMT CO2 Eq.)a
Uncertainty Range Relative to Emission Estimate1"
(MMT C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Substitution of Ozone
Depleting Substances
HFCs and
PFCs
158.6
153.0 172.3 -0.22% +12.4%
a 2013 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 uncertainty 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 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh-equivalent emissions from HFCs and PFCs. The GWPs of N2O and SF6 have decreased, leading to a
decrease in CCh-equivalent emissions for these greenhouse gases. The AR4 GWPs have been applied across the
entire time series for consistency. For more information please see the Recalculations and Improvements Chapter.
The decrease in the GWP of HFC-152a and increase in the GWP of all other gases had several impacts on Inventory
estimates. In the 1990 through 1991 time period, an overall decrease in total annual GWP-weighted emissions is
seen. After 1991, there is an overall increase in total emissions.
In addition, a review of the MVACs, streaming agents, window AC units, ice makers, and small retail food end-uses
resulted in revisions to the Vintaging Model since the previous Inventory. Methodological recalculations were
applied to the entire time-series to ensure time-series consistency from 1990 through 2013.
For the MVAC light-duty vehicle (LDV) and light-duty trucks (LDT) end-uses, operational and servicing leak rates
were reduced based on a review of recent literature. For the small retail food and ice makers end-uses, revisions
were made to the overall stock, growth rates, assumed transition scenarios, and lifetimes based on research on
substitutes and growth in the market. For window air-conditioning, a review of air conditioner sales data from 2002
through 2012 increased the quantity of window air-conditioning equipment introduced into the market for 2002 and
2004 through 2008, while decreasing the quantity of equipment sold into the market for 2003 and 2009 through
2012. In the streaming agents end-use, the assumed transition scenarios were revised based on industry input.
Combined, these assumption changes and the use of AR4 GWPs increased GHG emissions on average by 7 percent
across the time series.
Industrial Processes and Product Use 4-101
-------
4.24 Electrical Transmission and Distribution
(IPCC Source Category 2G1)
The largest use of sulfur hexafluoride (SF6), both in the United States and internationally, is as an electrical insulator
and interrupter in equipment that transmits and distributes electricity (RAND 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. SF6 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 5.1 MMT CCh Eq. (0.2 kt) in 2013. This quantity represents an 80 percent decrease from the
estimate for 1990 (see Table 4-98 and Table 4-99). There are two potential causes for this decrease: a sharp increase
in the price of SF6 during the 1990s and a growing awareness of the magnitude and environmental impact of SF6
emissions through programs such as EPA's voluntary SF6 Emission Reduction Partnership for Electric Power
Systems (Partnership) and EPA's GHGRP. Utilities participating in the Partnership have lowered their emission
factor (kg SF6 emitted per kg of nameplate capacity) by more than 75 percent since the Partnership began in 1999. A
recent examination of the SF6 emissions reported by electric power systems to EPA's GHGRP revealed that SF6
emissions from reporters has decreased by 25 percent from 2011 to 2013, with much of the reduction seen from
utilities that are not participants in the Partnership. These utilities may be making relatively large reductions in
emissions as they take advantage of relatively large and/or inexpensive emission reduction opportunities (i.e., "low
hanging fruit," such as replacing major leaking circuit breakers) that Partners have already taken advantage of under
the voluntary program (Ottinger et al. 2014).
Table 4-98: SFe Emissions from Electric Power Systems and Electrical Equipment
Manufacturers (MMT COz Eq.)
Year
Electric Power
Systems
Electrical Equipment
Manufacturers
Total
1990
25.1
25.4
2009
2010
2011
2012
2013
6.7
6.2
5.7
4.6
4.2
0.6
0.9
1.1
1.1
0.9
7.3
7.0
6.8
5.7
5.1
Notes: Emissions values are presented in CCh equivalent mass units using
IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding.
Table 4-99: SFe Emissions from Electric Power Systems and Electrical Equipment
Manufacturers (kt)
Year
1990
2009
2010
2011
2012
Emissions
4-102 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
2013 0_2
Methodology
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.
1990 through 1998 Emissions from Electric Power Systems
Emissions from electric power systems from 1990 through 1998 were estimated based on (1) the emissions
estimated for this source category in!999, which, as discussed in the next section, were based on the emissions
reported during the first year of EPA's SF6 Emission Reduction Partnership for Electric Power Systems
(Partnership), and (2) the RAND survey of global SF6 emissions. 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
2006IPCC Guidelines (IPCC 2006).201 (Although Equation 7.3 of the 2006IPCC 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)202
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 SFe 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 14.3 MMT 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
201 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.
202 Nameplate capacity is defined as the amount of SFe within fully charged electrical equipment.
Industrial Processes and Product Use 4-103
-------
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).
1999 through 2013 Emissions from Electric Power Systems
Emissions from electric power systems from 1999 to 2013 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 covered by 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 2013, Partner utilities, which for inventory purposes are defined as utilities that either
currently are or previously have been part of the Partnership, represented between 42 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 began reporting their emissions (for 2011 and later years) through EPA's GHGRP
(discussed further below) rather than through the Partnership. In 2013, approximately 0.3 percent of the total
emissions attributed to Partner utilities were reported through Partnership reports. Approximately 91 percent of the
total emissions attributed to Partner utilities were reported and verified through EPA's GHGRP. Partners without
verified 2013 data accounted for approximately 9 percent of the total emissions attributed to Partner utilities.203
GHGRP-Only Reporters
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 CO2 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 (reporting emissions for 2011 and later years) 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
203 It should be noted that data reported through the GHGRP must go through a verification process; only data verified as of
September 1, 2014 could be used in the emission estimates for 2013. 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, 2014 was included in the emission estimates for
2011,2012, and2013.
4-104 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
(GHGRP-Only Reporters). GHGRP-Only Reporters accounted for 24 percent of U.S. transmission miles and 26
percent of estimated U.S. emissions from electric power system in 2013.204
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.205 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 set of reported 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.206 The availability of non-Partner emissions estimates allowed
the regression analysis to be modified for both large and non-large 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 - Present: 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. This was
repeated for 2013 using Partner and GHGRP-Only Reporter data for 2013.
o "Non-large" utilities (less than 10,000 transmission miles): The 2013 regression equation for "non-
large" utilities was developed based on the emissions reported by a subset of 89 Partner utilities and
GHGRP-Only utilities (representing approximately 47 percent of total U.S. transmission miles for
utilities with fewer than 10,000 transmission miles). The regression equation for 2013 is:
Emissions (kg) = 0.217 x Transmission Miles
o "Large" utilities (more than 10,000 transmission miles): The 2013 regression equation was developed
based on the emissions reported by a subset of 17 Partners and GHGRP-only utilities (representing
approximately 83 percent of total U.S. transmission miles for utilities with greater than 10,000
transmission miles). The regression equation for 2013 is:
Also, GHGRP-reported emissions from 17 facilities that had one or fewer transmission miles were included in the emission
estimates for 2011. Emissions from these facilities comprise approximately 1.2 percent of total reported and verified emissions.
In 2012,16 facilities had one or fewer transmission miles, comprising 1.4 percent of verified emissions and in 2013,16 facilities
had one or fewer transmission miles, comprising 3.2 percent of 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.
205 In the United States, SFe is contained primarily in transmission equipment rated above 34.5 kV.
Partners in EPA's SFe Emission Reduction Partnership reduced their emissions by approximately 77 percent from 1999 to
2013.
Industrial Processes and Product Use 4-105
-------
Emissions (kg) = 0.225 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),
and for 2011 through 2013 (the first three years with GHGRP reported data). The coefficients for non-large utilities
and large utilities both decreased slightly between 2012 and 2013.
Table 4-100: Transmission Mile Coverage and Regression Coefficients for Large and Non-
Large Utilities, Percent
Non-large
Percentage of Miles
Covered by Reporters
Regression Coefficient3
1999
31
0.89
2011
45
0.33
2012
44
0.23
2013
47
0.22
1999
86
0.58
Large
2011
97
0.27
2012
88
0.24
2013
83
0.22
a Regression coefficient is defined as emissions (in kg) divided by transmission miles.
Note: "Non-large" represents reporters with fewer than 10,000 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 yet declined by almost 4,000 miles between 2003 and 2006. Given these fluctuations,
periodic increases are assumed to occur gradually. Therefore, transmission mileage was assumed to increase at an
annual rate of 1.2 percent between 2000 and 2003 and decrease by -0.20 percent between 2003 and 2006. This
growth rate grew to 3 percent from 2006 to 2009 as transmission miles increased by more than 59,000 miles. The
annual growth rate for 2009 through 2012 was calculated to be 2.0 percent as transmission miles grew by
approximately 43,000 during this time period.
Total Industry Emissions
As a final step, total electric power system emissions from 1999 through 2013 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 2013 Emissions from Manufacture of Electrical Equipment
The 1990 to 2013 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 2013 were estimated using Partner reported data and the total industry SF6 nameplate
capacity estimate (198.2 MMT CO2 Eq. in 2013). 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 2013
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)
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.
4-106 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.207 Based on a
Monte Carlo analysis, the cumulative uncertainty of all GHGRP-Only reported data was estimated to be 5.8 percent.
There are two sources of uncertainty associated with the regression equations used to estimate emissions in 2013
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 Approach 2 quantitative uncertainty analysis are summarized in Table 4-101. Electrical
Transmission and Distribution SF6 emissions were estimated to be between 4.0 and 6.0 MMT CO2 Eq. at the 95
percent confidence level. This indicates a range of approximately 20 percent below and 19 percent above the
emission estimate of 5.1 MMT CO2 Eq.
Table 4-101: Approach 2 Quantitative Uncertainty Estimates for SFe Emissions from
Electrical Transmission and Distribution (MMT COz Eq. and Percent)
2013 Emission
Source Gas Estimate Uncertainty Range Relative to 2013 Emission Estimate3
(MMT C02 Eq.) (MMT CCh Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Electrical Transmission ^ 4Q 6Q _2Q%
and Distribution
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
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 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Uncertainty is assumed to be higher for the GHGRP-Only category, because 2011 is the first year that those utilities have
reported to EPA.
Industrial Processes and Product Use 4-107
-------
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh Eq. emissions from CH4, HFCs, and PFCs. The GWPs of N2O and SF6 have decreased, leading to a
decrease in CCh Eq. emissions for SF6. The AR4 GWPs have been applied across the entire time series for
consistency. For more information please see the Recalculations and Improvements Chapter.
Only taking this change into consideration, emissions estimates for each year from 1990 to 2012 would have slightly
decreased, relative to the emissions estimates in the previous Inventory report. However, other changes to the
historical calculations, as noted below, resulted in emission estimates fluctuating slightly (increasing for some years
and decreasing for other years) across the time series.
The historical emissions estimated for this source category have undergone several minor revisions. SF6 emission
estimates for the period 1990 through 2012 were updated relative to the previous report based on revisions to
interpolated and extrapolated non-reported Partner data as well as resubmissions of estimates through the GHGRP
for 2011 and 2012.208 The previously-described interpolation between 1999 and 2012 regression coefficients to
estimate emissions from non-reporting utilities were updated using revised GHGRP reports, which impacted
historical estimates for the period 2000 through 2012. Additionally, updated leak rates were calculated from
resubmitted Partner data through the GHGRP. These leak rates are used to estimate the nameplate capacity of non-
reporters during these years, and are interpolated back through 1999 to calculate Non-Reporter nameplate capacity
over the entire time series.209 Finally, revisions were made regarding the incorporation of transmission mile data
from the UDI database to remove instances of double counting transmission miles between parent and subsidiary
companies. Reductions in the total transmission miles reduced the total number of non-reporter transmission miles,
which reduced non-reporter emissions, and therefore total emissions.
As a result of the recalculations, SF6 emissions from electrical transmission and distribution decreased by 6 percent
for 2012 relative to the previous report. On average, the change in SF6 emission estimates for the entire time series is
approximately 0.5 percent per year.
Planned Improvements
EPA is exploring the use of OEM data that is reported under EPA's GHGRP to use for future Inventory reports
instead of estimating those emissions based on elements reported by utilities to the GHGRP and Partner data.
Specifically, using the GHGRP-reported OEM emissions and the estimated nameplate capacity increase estimated
for users of electrical equipment (available in the existing methodology), a leak rate would be calculated. This
approach would require estimating the portion of industry not reporting to the GHGRP program, which would
require market research. Once a new leak rate is established, leak rates could be interpolated for years between 2000
(at 10 percent) and 2011. 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.210
208 The earlier year estimates within the time series (i.e., 1990-1998) were updated based on revisions to the 1999 U.S. emission
estimate because emissions for 1990-1998 are estimated by multiplying a series of annual factors by the estimated U.S. emissions
of SFe from electric power systems in 1999 (see Methodology section).
209 Nameplate capacity estimates affect sector emissions because OEM emission estimation is calculated using total industry
nameplate capacity.
210 See.
4-108 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Box 4-2: Potential Emission Estimates of MFCs, PFCs, SFe, and NFs
Emissions of HFCs, PFCs, SF6, and NF3 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 2006IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) 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:
• By-product emissions. Some emissions do not result from the consumption or use of a chemical, but are
the unintended by-products of a process. For such emissions, which include emissions of CF4 and C2p6
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, SF6, and NF3 from semiconductor manufacture, and SF6 from magnesium production and
processing and electrical transmission and distribution.21! 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 2006 IPCC Guidelines (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 2006 IPCC Guidelines. Potential SF6 emissions
estimates for electrical transmission and distribution were developed using U.S. utility purchases of SF6 for
electrical equipment. From 1999 through 2013, estimates were obtained from reports submitted by participants in
EPA's SF6 Emission Reduction Partnership for Electric Power Systems as well as EPA's Greenhouse Gas Reporting
Program (GHGRP). 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: 2013 Potential and Actual Emissions of HFCs, PFCs, SF6, and NF3 from Selected
Sources (MMT COz Eq.)
Source
Substitution of Ozone Depleting Substances
Aluminum Production
HCFC-22 Production
Semiconductor Manufacture
Magnesium Production and Processing
Electrical Transmission and Distribution
Potential
306.9
NA
NA
43.7
1.5
33.3
Actual
158.6
3.0
4.1
4.0
1.5
5.1
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4
GWP values.
NA - Not applicable.
21! See Annex 5 for a discussion of sources of SFe emissions excluded from the actual emissions estimates in this report.
Industrial Processes and Product Use 4-109
-------
Under EPA's GHGRP, producers and larger importers and exporters212 of fluorinated greenhouse gases (F-GHG)
in bulk began annually reporting their production, destruction, imports, and exports in 2011 (for 2010 supplies), and
larger importers and exporters of F-GHGs inside of pre-charged equipment began reporting their imports and
exports in 2012 (for 2011 supplies). The collection of data from both emitters and suppliers of F-GHGs enables the
comparison of consumption that is implied by emissions (downstream estimation method) to the consumption that is
implied by balancing of production, destruction, imports, and exports (upstream estimation method). This type of
comparison ultimately supports and improves estimates of emissions, as noted in the 2006IPCC Guidelines:
"[W]hen considered along with estimates of actual emissions, the potential emissions approach can assist
in validation of completeness of sources covered and as a QC check by comparing total domestic
consumption as calculated in this 'potential emissions approach' per compound with the sum of all
activity data of the various uses (IPCC 2006)."
A comparison of upstream and downstream consumption estimates of SF6 was performed to help evaluate the
accuracy and completeness of the emissions inventory. This analysis revealed that the two potential emissions
estimates for 2012 (the upstream estimation and downstream estimation methods) differed with the supply-based,
upstream consumption estimate significantly larger than emitter-based, downstream consumption estimate (Ottinger
et al. 2014). This finding indicates that methods for determining national SF6 actual emission estimates by industry
sector are generating results that, when summed, do not fall within a close proximity to the overall total U.S. supply
of SF6 gas.
While multiple sources of uncertainty affect both data sets, Ottinger et al (2014) conclude that current SF6 emission
estimates likely do not account for all significant sources of SF6 in the United States. Additional research is
necessary to identify the other significant applications that consume and emit SF6
4.25 Nitrous Oxide from Product Uses (IPCC
Source Category 2G3)
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).
Importers and exporters report only if either their total imports or their total exports of F-GHGs are greater than or equal to
25,000 metric tons of CCh Eq. per year
4-110 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Production of N2O in 2013 was approximately 15 kt (Table 4-103).
Table 4-103: N2O Production (kt)
Year
kt
1990
2009
2010
2011
2012
2013
15
15
15
15
15
N2O emissions were 4.2 MMT CO2 Eq. (14 kt) in 2013 (Table 4-104). 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 4-104: NzO Emissions from NzO Product Usage (MMT COz Eq. and kt)
Year MMT CCh Eq. kt
1990 4.2 14
2009
2010
2011
2012
2013
4.2
4.2
4.2
4.2
4.2
14
14
14
14
14
Note: Emissions values are
presented in CCh equivalent
mass units using IPCC AR4
GWP values.
Methodology
Emissions from N2O product uses were estimated using the following equation:
where,
P
a
Sa
ERa
Sa x
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 2013, the medical/dental industry used an
estimated 86.5 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
Industrial Processes and Product Use 4-111
-------
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.
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 2013 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 N2O used by each
subcategory was kept the same as the 1996 number provided by SRI Consulting. The 1997 through 2001 share 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 2013 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 2013 N2O emission estimate from N2O product usage was calculated
using the 2006 IPCC Guidelines (2006) Approach 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 Approach 2 quantitative uncertainty analysis are summarized in Table 4-105. N2O emissions
from N2O product usage were estimated to be between 3.2 and 5.2 MMT 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 4.2 MMT
CO2 Eq.
4-112 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 4-105: Approach 2 Quantitative Uncertainty Estimates for NzO Emissions from NzO
Product Usage (MMT COz Eq. and Percent)
Source Gas 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
(MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
N2Q Product Use N2O 4.2 3.2 5.2 -24% +24%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Furthermore, methodological recalculations were applied to the entire time-series to ensure time-series consistency
from 1990 through 2013. Details on the emission trends through time-series are described in more detail in the
Methodology section, above.
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CO2-equivalent emissions from CH4, HFCs, and PFCs. The GWPs of N2O and SF6 have decreased,
leading to a decrease in CO2-equivalent emissions for N2O. The AR4 GWPs have been applied across the entire time
series for consistency. For more information please see the Recalculations and Improvements Chapter.
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 GHGRP 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.
4.26 Industrial Processes and Product Use
Sources of Indirect Greenhouse Gases
In addition to the main greenhouse gases addressed above, many industrial processes can result in emissions of
various ozone precursors (i.e., indirect greenhouse gases). 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. Non-CH4 volatile organic compounds (NMVOCs), commonly
referred to as "hydrocarbons," are the primary gases emitted from most processes employing organic or petroleum
based products, and can also result from the product storage and handling. Accidental releases of greenhouse gases
associated with product use and handling can constitute major emissions in this category. In the United States,
emissions from product use are primarily the result of solvent evaporation, whereby the lighter hydrocarbon
molecules in the solvents escape into the atmosphere. The major categories of product uses include: degreasing,
graphic arts, surface coating, other industrial uses of solvents (e.g., electronics), dry cleaning, and non-industrial
Industrial Processes and Product Use 4-113
-------
uses (e.g., uses of paint thinner). Product 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 this chapter.
Total emissions of nitrogen oxides (NOX), carbon monoxide (CO), and non-CH4 volatile organic compounds
(NMVOCs) from non-energy industrial processes and product use from 1990 to 2013 are reported in Table 4-106.
Table 4-106: NOX, CO, and NMVOC Emissions from Industrial Processes and Product Use (kt)
Gas/Source
1990
2005
2009 2010 2011 2012 2013
II
11,/ift
829 I
NOx 653 631
Industrial Processes
Other Industrial
Processes 378
Metals Processing 97
Chemical and Allied
Product Manufacturing 168 61
Storage and Transport 3 I 16
Miscellaneous* 6 I 2
Product Use
Surface Coating I I 3
Graphic Arts + I 0
Degreasing + I 0
Dry Cleaning + I 0
Other Industrial
Processes15 + I 0
Non-Industrial Processes0 + I 0
Other NA 0
CO 4,552 | 1,716
Industrial Processes
Metals Processing 2,640
Other Industrial
Processes 537 534
Chemical and Allied
Product Manufacturing 1,183 208
Miscellaneous* 111 36
Storage and Transport 76 107
Product Use
Surface Coating 1
Other Industrial
Processes15 4
Dry Cleaning +
Degreasing +
Graphic Arts +
Non-Industrial Processes0 +
Other NA
NMVOCs 8,419 6,448
Industrial Processes
Storage and Transport 1,490 1,442
Other Industrial
Processes 401 457
Chemical and Allied
Product Manufacturing 634 235
Metals Processing 122 49
Miscellaneous* 22 19
Product Use
Surface Coating 2,523 1,739
Non-Industrial Processes0 1,900 I 1,594
Degreasing 744 309
Dry Cleaning 215 254
544
395
76
54
13
2
0
0
0
1,467
815
397
178
51
21
351
86
36
28
521
374
73
53
16
2
498
353
71
498
353
71
0
0
0
1,411
791
367
173
53
24
0
0
0
1,355
766
337
167
56
27
766
337
167
56
27
340
85
35
29
329
83
34
30
329
83
34
30
1,285 1,218 1,152
1,177 1,116 1,055
228 217 205
187 178 168
498
353
71
51 51 51
20 20 20
333
3
0
0
0
2
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
0 0
0 0
0 0
1,355 1,355
766
337
167
56
27
00000
4,781 4,556 4,331 4,331 4,331
1,143 1,093 1,043 1,043 1,043
329
83
34
30
1,152 1,152
1,055 1,055
205 205
168 168
4-114 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Graphic Arts 274 213 158 149 141 141 141
Other Industrial
Processes'5 94 97 I 71 68 64 64 64
Other 0 I 39 29 28 26 26 26
a 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.
b Includes rubber and plastics manufacturing, and other miscellaneous applications.
c Includes cutback asphalt, pesticide application adhesives, consumer solvents, and other miscellaneous
applications.
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
Methodology
Emission estimates for 1990 through 2013 were obtained from data published on the National Emission Inventory
(NEI) Air Pollutant Emission Trends web site (EPA 2015), and disaggregated based on EPA (2003). Data were
collected for emissions of carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOC), and
sulfur dioxide (SCh) from metals processing, chemical manufacturing, other industrial processes, transport and
storage, and miscellaneous sources. Emission estimates for 2013 for non-EGU and non-mobile sources are held
constant from 2011 in EPA (2015). Emissions were calculated either for individual source categories or for many
categories combined, using basic activity data (e.g., the amount of raw material processed or the amount of solvent
purchased) 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.
Emissions for product use were calculated by aggregating product use data based on information relating to product
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 product-specific emission
factors to the amount of products used for surface coatings, an estimate of NMVOC emissions was obtained.
Emissions of CO and NOX under product use result primarily from thermal and catalytic incineration of solvent-
laden gas streams from painting booths, printing operations, and oven exhaust.
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 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Industrial Processes and Product Use 4-115
-------
5. 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 5-1). Carbon dioxide (CCh) 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 5-1: 2013 Agriculture Chapter Greenhouse Gas Emission Sources
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Agricultural Soil Management
Enteric Fermentation
Manure Management
Rice Cultivation
Field Burning of Agricultural Residues < 0.5
Agriculture as a Portion of
all Emissions
50 75
100 125 150 175 200 225 250 275 300
MMT CO2 Eq.
In 2013, the Agriculture sector was responsible for emissions of 515.7 MMT CCh Eq.,1 or 7.7 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 25.9
percent and 9.6 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 74.2 percent. Manure
management and field burning of agricultural residues were also small sources of N2O emissions.
1 Following the revised reporting requirements under the UNFCCC, this Inventory report presents CCh equivalent values based
on the IPCC Fourth Assessment Report (AR4) GWP values. See the Introduction chapter for more information.
Agriculture 5-1
-------
Table 5-1 and Table 5-2 present emission estimates for the Agriculture sector. Between 1990 and 2013, CH4
emissions from agricultural activities increased by 11.3 percent, while N2O emissions fluctuated from year to year,
but overall increased by 18.2 percent.
Table 5-1: Emissions from Agriculture (MMT COz Eq.)
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
Total
1990
210.8
164.2
37.2
9.2 1
0.3 1
237.9
224.0
13.8 1
0.1 •
448.7
2005
234.4
168.9
56.3
8.9 1
0.2 1
260.1
243.6
16.4 1
0.1 •
494.5
2009
242.1
172.7
59.7
9.4
0.3
281.2
264.1
17.0
0.1
523.3
2010
243.4
171.1
60.9
11.1
0.3
281.4
264.3
17.1
0.1
524.8
2011
238.9
168.7
61.4
8.5
0.3
283.2
265.8
17.3
0.1
522.1
2012
239.6
166.3
63.7
9.3
0.3
283.4
266.0
17.3
0.1
523.0
2013
234.5
164.5
61.4
8.3
0.3
281.1
263.7
17.3
0.1
515.7
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding.
Table 5-2: Emissions from Agriculture (kt)
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
1990
8,431
6,566
1,486
366
13 1
798 1
752 1
46 1
+ H
2005
9,375
6,755
2,254
358 1
9
873
817 1
55 1
+
2009
9,685
6,908
2,388
378
12
944
886
57
+
2010
9,736
6,844
2,437
444
11
944
887
57
+
2011
9,558
6,750
2,457
339
12
950
892
58
+
2012
9,585
6,653
2,548
372
12
951
892
58
+
2013
9,381
6,581
2,456
332
12
943
885
58
+
+ Less than 0.5 kt.
Note: Totals may not sum due to independent rounding.
5.1 Enteric Fermentation (IPCC Source
Category 3A)
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.
5-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.
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 5-3 and Table 5-4. Total livestock CH4
emissions in 2013 were 164.5 MMT CCh Eq. (6,581 kt). Beef cattle remain the largest contributor of CH4 emissions
from enteric fermentation, accounting for 71 percent in 2013. Emissions from dairy cattle in 2013 accounted for 25
percent, and the remaining emissions were from horses, sheep, swine, goats, American bison, mules and asses.
From 1990 to 2013, emissions from enteric fermentation have increased by 0.2 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 or minor decreases. For example, beef cattle emissions decreased 1.7 percent
from 1990 to 2013, while beef cattle populations actually declined by 7 percent and beef production increased 13
percent (USDA 2014), and while dairy emissions increased 5.7 percent over the entire time series, the population
has declined by 5 percent and milk production increased 36 percent (USDA 2014). This trend indicates that while
emission factors per head are increasing, emission factors per unit of product are going down. Generally, from 1990
to 1995 emissions from beef 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 an extensive literature
review and analysis of more than 350 dairy cow diets indicated a trend toward a decrease in feed digestibility for
those years. Total emissions decreased again from 2008 to 2013 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 increased 60 percent, with each annual increase ranging from about 2 to 9
percent between 1990 and 2007, followed by a 2 percent annual decline through 2013. Goat populations increased
by about 25 percent through 2007 but have since dropped back to 1990 numbers, while swine populations have
increased 22 percent during this timeframe. The population of American bison more than tripled, while mules and
asses have more than doubled.
Table 5-3: CH4 Emissions from Enteric Fermentation (MMT COz Eq.)
Livestock Type 1990 2005 2009 2010 2011 2012 2013
Beef Cattle
Dairy Cattle
Swine
Horses
Sheep
Goats
American Bison
Mules and Asses
119.1
39.4
%
11
0.1 1
+ H
125.2
37.6
2.3 1
1.7 1
1.2 1
0.4 1
0.4 1
0.1 •
125.5
41.0
2.5
1.7
1.1
0.4
0.4
0.1
124.4
40.7
2.4
1.7
1.1
0.4
0.4
0.1
121.7
41.1
2.5
1.7
1.1
0.3
0.3
0.1
118.7
41.7
2.5
1.6
1.1
0.3
0.3
0.1
117.1
41.6
2.5
1.6
1.1
0.3
0.3
0.1
Total li I J li i I 172.7 171.1 168.7 166.3 164.5
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.05 MMT CO2 Eq.
Table 5-4: CH4 Emissions from Enteric Fermentation (kt)
Livestock Type 1990 2005 2009 2010 2011 2012 2013
Beef Cattle
Dairy Cattle
Swine
Horses
4,763 1
1,574
81
40
5,007 1
1,503
92
70
5,022
1,639
199
70
4,976
1,626
97
68
4,867
1,643
98
67
4,747
1,669
100
65
4,684
1,664
99
64
Agriculture 5-3
-------
Sheep
Goats
American Bison
Mules and Asses
Total
91
13
1 •
6,566
149
14
17
2 •
6,755
146
15
15
3
6,908
45
14
15
3
6,844
44
14
14
3
6,750
43
13
13
3
6,653
43
13
13
3
6,581
Note: Totals may not sum due to independent rounding.
Methodology
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 3.10. 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 2014).
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.
5-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.2 Base year Ym values by region were estimated using Donovan (1999). A ruminant
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 3.10.
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 2013. Additionally, 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 USDA NASS (USDA 2014). Horse, goat and
mule and ass population data were available for 1987, 1992, 1997, 2002, 2007, and 2012 (USDA 1992, 1997, 2014);
the remaining years between 1990 and 2013 were interpolated and extrapolated from the available estimates (with
the exception of goat populations being held constant between 1990 and 1992). American bison population
estimates were available from USDA for 2002, 2007, and 2012 (USDA 2014) 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 extrapolation beyond 2012, as described in more
detail in Annex 3.10. 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
Due to inconsistencies in the 2003 literature values, the 2002 values were used for 2003, as well.
Agriculture 5-5
-------
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 3.10 for more detailed information on the methodology and data used to calculate CH4 emissions from
enteric fermentation.
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis for this source category was performed using the IPCC-recommended Approach
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 2013 emission estimates in this Inventory
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 5-5.
Based on this analysis, enteric fermentation CH4 emissions in2013 were estimated to be between 146.4 and 194.1
MMT CO2 Eq. at a 95 percent confidence level, which indicates a range of 11 percent below to 18 percent above the
2013 emission estimate of 164.5 MMT CCh 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 Food and Agricultural Organization of the United Nations (FAO) population estimates used for horses
at that time had a higher degree of uncertainty than for the USD A population estimates used for swine, goats, and
sheep. The horse populations are now from the same USD A 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 emission estimates at that time.
Table 5-5: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Enteric
Fermentation (MMT COz Eq. and Percent)
Source
Enteric Fermentation
Gas
CH4
2013 Emission
Estimate
(MMT CO2 Eq.)
164.5
Uncertainty Range Relative to Emission Estimate3' b> c
(MMT C02 Eq.) (%)
Lower Upper
Bound Bound
146.4 194.1
Lower Upper
Bound Bound
-11% +18%
a 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 2013 estimates.
c The overall uncertainty calculated in 2003, and applied to the 2013 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.
5-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section.
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. The recent addition of emission
estimates from calves to the enteric fermentation model and further separation of calves in beef and dairy
subcategories 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 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
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most Inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh-equivalent emissions from CH4. The GWPs of N2O and SF6 have decreased, leading to a decrease in
CCh-equivalent emissions for these greenhouse gases. The AR4 GWPs have been applied across the entire time
series for consistency. For more information please see the Recalculations Chapter. This resulted in no change in
CH4 emissions, but an increase of 19 percent in enteric emissions CO2 equivalent.
There were no modifications to the methodology that had an effect on emission estimates, therefore the only
recalculations were due to changes in activity data, including the following.
• Foraging animal types from 2007 through 2012 show minor revisions in emissions. The region designations for
the post 2006 foraging diets were offset by one from the states from Montana-onward (alphabetically).
• There was a transcription error in the CEFM that, when corrected, resulted in slight changes to the emissions
from feedlot cattle between 1992 and 2013. The overall impact is a slight decrease in enteric emissions from
cattle.
• The USDA published minor revisions in several categories that affected historical emissions estimated for cattle
in 2012, including dairy cow milk production for several states and beef cattle feedlot placement data. These
changes had an insignificant impact on the overall results.
• The 2012 USDA Census of Agriculture was released, providing updated 2012 population estimates for horses,
goats, American bison, and mules and asses. As a result, emissions between 2008 and 2012 increased an
average of 11 percent, 10 percent, 1.4 percent, and 25 percent, respectively.
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 5-7
-------
• 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;
• Further investigation on additional sources or methodologies for estimating DE for dairy, given the many
challenges in characterizing dairy diets;
• Further evaluation of the assumptions about weights and weight gains for beef cows, such that trends beyond
2007 are updated, rather than held constant;
• Further evaluation of the estimated weight for dairy cows (i.e., 1,500 Ibs) that is based solely on Holstein cows
as mature dairy cow weight is likely slightly overestimated, based on knowledge of the breeds of dairy cows in
the United States;
• Potentially updating to a Tier 2 methodology for other animal types (i.e., sheep, swine, goats, horses);
• Investigation of methodologies and emission factors for including enteric fermentation emission estimates from
poultry; and
• 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.
5.2 Manure Management (IPCC Source
Category 3B)
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.3 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
3 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.
5-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 2013 were 61.4 MMT CO2 Eq. (2,456 kt); in 1990, emissions were 37.2 MMT CO2
Eq. (1,486 kt). This represents a 65 percent increase in emissions from 1990. Emissions increased on average by
1.1 MMT CO2 Eq. (2.8 percent) annually over this period. The majority of this increase is due to swine and dairy
cow manure, where emissions increased 48 and 115 percent, respectively. From 2012 to 2013, there was a 3.6
percent decrease 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 2014a).
In 2013, total N2O emissions were estimated to be 17.3 MMT CO2 Eq. (58 kt); in 1990, emissions were 13.8 MMT
CO2 Eq. (46 kt). 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 2013 and a 0.1 percent decrease from 2012 through 2013. Overall shifts
toward liquid systems have driven down the emissions per unit of nitrogen excreted.
Table 5-6 and Table 5-7 provide estimates of CH4 and N2O emissions from manure management by animal
category.
Table 5-6: CH4 and NzO Emissions from Manure Management (MMT COz Eq.)
Gas/Animal Type 1990 2005 2009 2010 2011 2012 2013
CH4a 37.2 56.3 59.7 60.9 61.4 63.7 61.4
Dairy Cattle 14.7 26.4 30.4 30.4 31.2 32.6 31.8
Beef Cattle 3.11 3.sB 3.2 3.3 3.3 3.2 3.0
Swine 15.6 22.9 22.4 23.6 23.5 24.4 23.1
Sheep 0.2B O.ll 0.1 0.1 0.1 0.1 0.1
Goats +B +B + + + + +
Poultry 3.3i 3.2| 3.2 3.2 3.2 3.2 3.2
Horses 0.2^ 0.3^ 0.2 0.2 0.2 0.2 0.2
Agriculture 5-9
-------
American Bison
Mules and Asses
N20b
Dairy Cattle
Beef Cattle
Swine
Sheep
Goats
Poultry
Horses
American Bison
Mules and Asses
Total
13.8
5.1
"
'
4
NAl
+ |
51.0
16.4
54
'
'
NA
_ +1
72.8
17.0
5.6
7.5
1.9
0.3
+
1.5
0.1
NA
+
76.7
17.1
5.6
7.5
1.9
0.3
+
1.5
0.1
NA
+
78.0
17.3
5.7
7.7
1.9
0.3
+
1.5
0.1
NA
+_
78.7
17.3
5.8
7.6
1.9
0.3
+
1.6
0.1
NA
+
81.0
17.3
5.7
7.6
1.9
0.3
+
1.6
0.1
NA
+_
78.8
Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP values.
+ Less than 0.05 MMT CO2 Eq.
a Accounts for CELt reductions due to capture and destruction of CELt at facilities using anaerobic
digesters.
b 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 5-7: CH4 and NzO Emissions from Manure Management (kt)
Gas/Animal Type 1990 2005
2009 2010 2011 2012 2013
CH4a
Dairy Cattle
Beef Cattle
Swine
Sheep
Goats
Poultry
Horses
American Bison
Mules and Asses
N20b
Dairy Cattle
Beef Cattle
Swine
Sheep
Goats
Poultry
Horses
American Bison
Mules and Asses
+ Lessthan0.5kt.
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
5-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Methodology
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 2013 for all livestock types, except goats, horses, mules
and asses, and American bison were obtained fromUSDA National Agriculture Statistics Service (NASS).
For cattle, the USD A 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 5.1 and in more
detail in Annex 3.10. Goat population data for 1992, 1997, 2002, 2007, and 2012; horse and mule and ass
population data for 1987, 1992, 1997, 2002 2007, and 2012; and American bison population for 2002, 2007
and 2012 were obtained from the Census of Agriculture (USDA 2014a). American bison population data
for 1990 through 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' sAgricultur al 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 5.1.
• 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 2014a) 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 Fie Id Handbook (USDA 1996, 2008 and ERG 2010b and 2010c)
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 5-11
-------
• 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 CH4per 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 (EFnmoff/ieach);
• Fraction of N loss from volitalization of NH3 and NOX (Fracgas); and
• Fraction of N loss from runoff and leaching (FraCnmoff/ieach).
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
(Fracnmoff/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 3.11).
5-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 (EFrunoff/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 Approach 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 2013 emission estimates as there have not been significant changes in the methodology since that
time.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 5-8. Manure management
CH4 emissions in 2013 were estimated to be between 50.3 and 73.7 MMT CO2 Eq. at a 95 percent confidence level,
which indicates a range of 18 percent below to 20 percent above the actual 2013 emission estimate of 61.4 MMT
CO2 Eq. At the 95 percent confidence level, N2O emissions were estimated to be between 14.5 and 21.5 MMT CO2
Eq. (or approximately 16 percent below and 24 percent above the actual 2013 emission estimate of 17.3 MMT CO2
Eq.).
Table 5-8: Approach 2 Quantitative Uncertainty Estimates for CH4 and NzO (Direct and
Indirect) Emissions from Manure Management (MMT COz Eq. and Percent)
2013 Emission
Source Gas Estimate
(MMT CO2 Eq.)
Manure Management CH4 61.4
Manure Management N2O 17.3
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower
Bound
50.3
14.5
Upper
Bound
73.7
21.5
Lower
Bound
-18%
-16%
Upper
Bound
+20%
+24%
aRange 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 2013. Details on the emission trends through time are described in more detail in the Methodology section.
Agriculture 5-13
-------
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 IPCC 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 5-9 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 5-9: 2006 IPCC Implied Emission Factor Default Values Compared with Calculated
Values for CH4 from Manure Management (kg/head/year)
Animal Type
Dairy Cattle
Beef Cattle
Swine
Sheep
Goats
Poultry
Horses
Mules and Asses
American Bison
IPCC Default
CH4 Emission
Factors
fke/head/vear)
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
30.2
1.5
11.5
0.2
0.1
52.0
0.1
1.3
0.03 1
2005
59.4
1.6
15.0
Si
44.7
0.1
2.0
o.iH
2009
65.6
1.6
13.6
0.1
0.3
43.6
0.1
1.3
0.1
2010
66.6
1.6
14.6
0.1
0.3
45.4
0.1
1.3
0.1
2011
67.5
1.7
14.3
0.1
0.3
46.5
0.1
1.2
0.1
2012
70.2
1.7
14.7
0.1
0.3
48.9
0.1
1.2
0.1
2013
68.8
1.6
14.0
0.1
0.3
51.2
0.1
1.1
0.1
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
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most Inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
5-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CO2-equivalent emissions from CH4. The GWPs of N2O and SF6 have decreased, leading to a decrease in
CO2-equivalent emissions for N2O. The AR4 GWPs have been applied across the entire time series for
consistency. For more information please see the Recalculations Chapter.
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 5.1 contributed to changes in the
population, VS and Nex data used for calculating CH4 and N2O cattle emissions from manure management. In
addition, the manure management emission estimates included the following recalculations relative to the previous
Inventory:
• Calves were reported separately as dairy and beef calves. In previous Inventories, all calves were included
in the beef category. However, some calves are raised on dairy farms so the separation of calves into dairy
and beef categories improves the accuracy of the emission estimates.
• State animal populations were updated to reflect updated USDA NASS datasets, which resulted in
population changes for poultry and swine in 2012.
• Population changes also occurred for bison, goats, horses and mules and asses for 2008 through 2012 due
to incorporation of new state-level census data.
• Temperatures changed across all years due to a systematic recalculation of temperature data by NOAA
(Robel 2014). This caused a change in liquid system MCFs across all years and states, along with changes
in certain years and states for non-liquid systems due to changing climate zones.
Planned Improvements
The uncertainty analysis will be updated in future Inventories 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, updated AgSTAR anaerobic digester data will be incorporated. In addition, the 2012
Agricultural Census data will also be incorporated into the Inventory and will be used to update county-level animal
population and WMS estimates.
5.3 Rice Cultivation (IPCC Source Category 3C)
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 in the soil. Once
depleted, soil conditions become anaerobic, and CH4 is produced by the decomposition of soil organic matter by
anaerobic methanogenic bacteria. Most of the CH4 produced does not reach the atmosphere. Up to 60 to 90 percent
is oxidized by aerobic methanotrophic bacteria in the soil (some oxygen remains at the interfaces of soil and water,
and soil and root systems) (Holzapfel-Pschorn et al. 1985, Sass et al. 1990) and some is 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 systems used to cultivate rice are one of the most important factors affecting CH4 emissions.
Upland rice fields are not flooded, and therefore are not believed to produce much CH4. In deepwater rice fields
(i.e., fields with flooding depths greater than one meter), the lower stems and roots of the rice plants die, thus
blocking the primary CH4 transport pathway to the atmosphere. The quantities of CH4 released from deepwater
fields are therefore believed to be significantly less than rice fields with shallower flooding depths (Sass 2001).
Some flooded rice 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 due to soil
aeration. Aeration not only causes existing soil CH4 to oxidize, but also inhibits further CH4 production in soils. In
the United States, rice is grown under continuously flooded, shallow water conditions (USDA 2012) and mid-season
drainage does not occur except by accident (e.g., due to levee breach).
Agriculture 5-15
-------
Other factors that influence CH4 emissions from flooded rice fields include fertilization practices (i.e., the use of
urea and organic fertilizers), soil temperature, soil type, rice variety, and cultivation practices (e.g., tillage, seeding,
and weeding practices). Factors that influence the amount of organic material available for anaerobic decomposition
(i.e., fertilizer use, soil type, rice variety,4 and cultivation practices) are the most important variables influencing the
amount of CH4 emitted over the growing season. Soil temperature is an important factor regulating the activity of
methanogenic bacteria which in turn affects the rate of CH4 production. However, although temperature influences
the time required to convert organic material to CH4, the impact of soil temperature on CH4 emissions is minor over
the length of the growing season. The application of synthetic fertilizers also influences CH4 emissions; in
particular, both nitrate and sulfate fertilizers (e.g., ammonium nitrate and ammonium sulfate) appear to inhibit CH4
formation. Nitrate and sulfate fertilizers are not commonly used in rice cultivation in the United States.
Rice is currently cultivated in seven states: Arkansas, California, Florida, Louisiana, Mississippi, Missouri, and
Texas.5 Soil types, rice varieties, and cultivation practices for rice vary from state to state, and even from farm to
farm. Most rice farmers recycle crop residues from the previous rice or rotational crop, either by leaving them
standing, disking them, or rolling them into fields. Most farmers also apply synthetic fertilizer (usually urea) to their
fields. In addition, the climatic conditions of Arkansas, Florida, southwest Louisiana, and Texas often allow for a
second, or ratoon, rice crop. Ratoon crops are produced from regrowth of the stubble remaining after the harvest of
the first rice crop. Ratoon crops are infrequent to non-existent in California, Mississippi, and Missouri. In 2012,
Arkansas reported a larger-than-usual ratoon crop (10 percent) due to an early rice harvest followed by warm
weather and heavy rains (ideal conditions for secondary growth and ratoon crops) (Hardke 2014). CH4 emissions
from ratoon crops are considerably higher than those from the primary crops due to the lack of a delay between
cropping seasons (which would allow the stubble to decay aerobically) (Wang et al. 2013). Specifically, the amount
of organic material available for anaerobic decomposition during ratoon crop production is considerably higher than
the amount available with the first (i.e., primary) crop production.
Rice cultivation is a minor source of CH4 emissions in the United States (see Table 5-10 and Table 5-11). In 2013,
CH4 emissions from rice cultivation were 8.3 MMT CCh Eq. (332 kt). Annual emissions have fluctuated unevenly
between 1990 and 2013, 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, between 2006 and 2013 emission levels have increased by
8 percent due to increased ratooning and changes in production areas. California, Louisiana and Texas reported an
increase in rice crop area from 2012 to 2013. All other states reported a decrease in rice crop area from 2012 to
2013. 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 5-10: CH4 Emissions from Rice Cultivation (MMT COz Eq.)
State 1990 2005 2009 2010 2011 2012 2013
Primary 6.7 8.0 7.3 8.6 6.2 6.3 5.8
Arkansas 2.9 3.9 3.5 4.3 2.8 3.1 2.6
California 0.8 1.1 1.2 1.2 1.2 1.2 1.2
Florida + I + I + + + + +
Louisiana 1.3 1.3 1.1 1.3 1.0 1.0 1.0
Mississippi 0.6 0.6 0.6 0.7 0.4 0.3 0.3
Missouri 0.2 0.5 0.5 0.6 0.3 0.4 0.4
Oklahoma + I + I + + + + +
Texas 0.8 0.5 0.4 0.5 0.4 0.3 0.3
Ratoon 2.5 1.0 2.1 2.5 2.3 3.0 2.5
Arkansas + I + I + + + 1.0 0.4
Florida + I + I + + +0.1 +
Louisiana 1.3 0.5 1.3 1.7 1.2 1.3 1.2
Texas U 0.4 0.8 0.8 LI 0.6 0.8
Total 9.2 8.9 9.4 11.1 8.5 9.3 8.3
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.
5 Oklahoma has also historically produced rice. 2007 was the most recent production year reported (77 hectares).
5-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
+ Less than 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 5-11: CH4 Emissions from Rice Cultivation (kt)
State 1990 2005 2009 2010 2011 2012 2013
Primary 268 319 294 343 247 253 233
Arkansas 115 157 141 171 111 123 103
California 34 45 48 48 50 48 48
Florida I I I I 1 1 2 1 2
Louisiana 52 50 45 51 40 38 40
Mississippi 24 25 23 29 15 12 12
Missouri 8 I 21 19 24 12 17 15
Oklahoma + I + I + + + + +
Texas 34 19 16 18 17 13 14
Ratoon 98 39 84 101 92 119 99
Arkansas + I I I + + + 41 17
Florida 2! + I 2 2 2 2 2
Louisiana 52 22 51 68 46 50 50
Texas 45 17 31 32 44 26 31
Total 366 358 378 444 339 372 332
+ Less than 0.5 kt
Note: Totals may not sum due to independent rounding.
Methodology
IPCC (2006) recommends using harvested rice areas, and seasonally integrated emission factors (i.e., country
specific emission factors that have been developed from standardized field measurements (representing the mix of
different conditions that influence CH4 emissions in the area) for each commonly occurring rice production system).
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 are used.
Regional emission factors were derived based on a literature review of recent research on CH4 emissions from U.S.
rice production. In California, some rice fields are flooded during the winter to prepare the fields for the next
growing season, and to create waterfowl habitat (Young 2013). Winter flooded rice crops generate CH4 year round
due to the anaerobic conditions the winter flooding creates (Environmental Defense Fund 2011), and up to 50
percent of the CH4 emissions occur in the winter (Fitzgerald et al. 2000). 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 as almost all of the CH4 emissions occur during the growing season (Fitzgerald et al. 2000).
California-specific winter flooded and non-winter flooded emission factors were applied to rice area harvested in
California. Average U.S. seasonal emission factors were applied to Arkansas, Florida, Louisiana, Missouri,
Mississippi, and Texas as there was not sufficient data to develop state-specific, or daily emission factors, or both.
As described above, seasonal emissions are much higher for ratooned crops than for primary crops. Therefore,
emissions from ratooned and primary areas are estimated separately using the appropriate representative emission
factors. This approach is consistent with IPCC (2006).
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 non-
representative management practices (e.g., the application of nitrate or sulfate fertilizers, or other substances
believed to suppress CH4 formation, or floodwaters were drained mid-season), as well as experiments in which
measurements were not made over an entire flooding 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 starting with
the 1990-2012 Inventory. 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
Agriculture 5-17
-------
winter flooding (Bossio et al. 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 et al. 1999; Fitzgerald et al. 2000;
McMillan et al. 2007) were averaged to derive an annual emission factor for California's primary, winter-flooded
rice crop. Approximately 60 percent of California's rice crop is winter-flooded (Environmental Defense Fund
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
CHVhectare/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 2001). 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 CHVhectare/season, and the resultant emission factor for the ratoon crop is 780 kg CH4/hectare/season.
The harvested rice areas for the primary and ratoon crops in each state are presented in Table 5-12, and the ratooned
crop area as a percent of primary crop area is shown in Table 5-13. Primary crop areas for 1990 through 2013 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 2014). Source data for non-USDA
sources of primary and ratoon harvest areas are shown in Table 5-14. California, Mississippi, Missouri, and
Oklahoma have not ratooned rice over the period 1990 through 2013 (Anderson 2008 through 2014; Beighley 2011
through 2012; Buehring 2009 through 2011; Guethle 1999 through 2010; Lee 2003 through 2007; Mutters 2001
through 2005; Street 1999 through 2003; Walker 2005, 2007 through 2008).
Table 5-12: Rice Area Harvested (Hectare)
State/Crop
Arkansas
Primary
Ratoona
California
Florida
Primary
Ratoon
Louisiana
Primary
Ratoon
Mississippi
Missouri
Oklahoma
Lexas
Primary
Ratoon
Total Primary
Total Ratoon
Total
1990
485,633
159,854
4,978
2,489
220,558
66,168
101,174
32,376
617
142,857
57,143
1,148,047
125,799
1,273,847
2005
661,675
662
212,869
4,565
-
212,465
27,620
106,435
86,605
271
81,344
21,963
1,366,228
50,245
1,416,473
2009
594,901
6
225,010
5,664
2,266
187,778
65,722
98,341
80,939
68,798
39,903
1,261,431
107,897
1,369,328
2010
722,380
7
223,796
5,330
2,275
216,512
86,605
122,622
101,578
76,083
41,085
1,468,300
129,971
1,598,271
2011
467,017
5
234,723
8,212
2,311
169,162
59,207
63,537
51,801
72,845
56,091
1,067,298
117,613
1,184,911
2012
520,032
52,003
225,415
6,244
2,748
160,664
64,265
52,206
71,631
54,229
33,080
1,090,421
152,096
1,242,517
2013
433,023
21,651
227,034
6,739
2,159
167,139
63,513
50,182
63,132
58,276
39,628
1,005,525
126,951
1,132,476
a Arkansas ratooning occurred only in 1998,1999, and 2005 through 2013, with particularly high
ratoon rates in 2012 and 2013.
"-" No reported value
Note: Lotals may not sum due to independent rounding.
5-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 5-13: Ratooned Area as Percent of Primary Growth Area
State
1990
2005
2009 2010 2011 2012 2013
Arkansas
Florida
Louisiana
Texas
50%
30%
40%
0.1%
13%
27%
+ + + 10% 5%
40% 43% 28% 44% 32%
35% 40% 35% 40% 38%
58% 54% 77% 61% 68%
+ Indicates ratooning less than 0.05 percent of primary growth area.
Table 5-14: Non-USDA Data Sources for Rice Harvest Information
State/Crop
1990
2005
2009 2010 2011 2012 2013
Arkansas - Ratoona
Florida - Primaryb
Florida - Ratoonc
Louisiana - Ratoond
Oklahoma - Primary6
Texas - Ratoonf
Slaton
Schueneman
Schueneman
Linscombe
Lee
Klosterboer
Wilson
Gonzalez
Gonzalez
Linscombe
Lee
Hardke
Wilson (2009-2011) (2012-2013)
Gonzalez (2009-2013)
Gonzalez (2009-2013)
Linscombe (2009-2013)
Anderson (2009-2013)
Texas Agricultural Experiment Station (TABS)
(2009-2013)
a Arkansas: 1990 - 2000 (Slaton 1999 through 2001); 2001 - 2011 (Wilson 2002 through 2007, 2009
through 2012). 2012 - 2013 (Hardke 2013, 2014).
bFlorida - Primary: 1990 - 2000 (Schueneman 1997, 1999 through 2001); 2001 (Deren 2002); 2002 - 2004
(Kirstein 2003 through 2004,2006); 2005 - 2013 (Gonzalez 2007 through 2014)
cFlorida - Ratoon: 1990 - 2000 (Schueneman 1997, 1999 through 2001); 2001 (Deren 2002); 2002 - 2003
(Kirstein 2003 through 2004,2006); 2004 (Cantens 2004- 2005); 2005 - 2013 (Gonzalez 2007 through
2014)
dLouisiana: 1990 - 2013 (Linscombe 1999, 2001 through 2014).
e Oklahoma: 1990 - 2006 (Lee 2003 through 2007); 2007 - 2013 (Anderson 2008 through 2014).
•Texas: 1990 - 2002 (Klosterboer 1997, 1999 through 2003); 2003 - 2004 (Stansel 2004 through 2005);
2005 (Texas Agricultural Experiment Station 2006); 2006-2013 (Texas Agricultural Experiment Station
2007-2014).
Box 5-1: Comparison of the U.S. Inventory Seasonal Emission Factors and IPCC (1996) Default Emission Factors
Emissions from rice production were estimated using a Tier 2 methodology consistent with IPCC (2006).
Representative 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. Emissions are compared with the 1996 IPCC
Guidelines default U.S. seasonal emission factor, and not the more recent 2006IPCC Guidelines global daily
emission factor. The rationale for this comparison is that the evaluated studies were specific to the U.S., were
regional specific seasonal emission factors, and did not include daily emission factors or season length. As explained
above, four different emission factors were calculated: (1) a seasonal, California-specific factor without winter
flooding (133 kg CH4/hectare/season), (2) an annual, California specific-factor with winter flooding (266 kg
CHVhectare/year), (3) a seasonal, non-California primary crop factor (237 kg CHVhectare/season), and (4) a
seasonal, non-California ratoon crop factor (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 for both primary and ratoon crops is 250 kg CH4/hectare/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 2013 U.S. emissions from rice cultivation are 8.3 MMT CCh Eq. using the four U.S.-
specific emission factors for both primary and ratoon crops and 7.2 MMT CCh Eq. using the IPCC (1996) emission
factor.
Agriculture 5-19
-------
Table 5-15: Non-California Seasonal Emission Factors (kg ChU/hectare/season)
Primary
Minimum
Maximum
Mean
61
500
237
Ratoon
Minimum
Maximum
Mean
481
1490
780
Table 5-16: California Emission Factors (kg CH4/hectare/year or season)
Winter Flooded
(Annual)b
Minimum
Maximum
Mean
131
369
266
Non- Winter Flooded
(Seasonal)0
Minimum
Maximum
Mean
62
221
133
Note: See methodology text for why the emission factor is annual for winter flooded and
seasonal for non-winter flooded California rice production.
b Percentage of California rice crop winter flooded: 60 percent.
c Percentage of California rice crop not winter flooded: 40 percent.
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 CHVhectare/season
and ratoon emissions ranged from 481 to 1,490 kg CH4/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 crop system, but rather
skewed, with a tail trailing to the right of the mean. A lognormal statistical distribution was, therefore, applied in
the uncertainty 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 (Approach 2) uncertainty analysis
was performed using the information provided above. The results of the Approach 2 quantitative uncertainty
analysis are summarized in Table 5-17. Rice cultivation CH4 emissions in 2013 were estimated to be between 4.2
and 15.9 MMT CO2 Eq. at a 95 percent confidence level, which indicates a range of 50 percent below to 91 percent
above the actual 2013 emission estimate of 8.3 MMT CO2 Eq.
5-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 5-17: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Rice
Cultivation (MMT COz Eq. and Percent)
Source Gas
2013 Emission
Estimate Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (MMT CO2 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Rice Cultivation CH4
8.3 4.2 15.9 -50% 91%
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
a Range of emissions estimates predicted by Monte Carlo Stochastic Simulation for a 95% confidence interval
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2013. 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.
Recalculations Discussion
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most Inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh-equivalent emissions from CH4. The GWPs of N2O and SF6 have decreased, leading to a decrease in
CCh-equivalent emissions for these greenhouse gases. The AR4 GWPs have been applied across the entire time
series for consistency. For more information please see the Recalculations Chapter. As a result of the updated GWP
value for CH4, emissions estimates for each year from 1990 to 2012 increased by 19 percent relative to the
emissions estimates in previous Inventory reports.
Additionally, the 2012 emission estimates were updated to reflect an increase in previously-reported ratooning in
Arkansas. Rice was harvested early in 2012, after which a high percentage of "secondary growth" occurred.
Estimated percent ratooning of secondary growth in 2012 increased from 5 to 10 percent (Hardke 2014), resulting in
a 0.4 MMT CC>2 eq. (21 kt C) increase in emissions.
Planned Improvements
A planned improvement for the 1990 through 2014 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 area 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 area during the 1990 through 2002 time
period to capture the significant change in the percentage of rice area burned due to the California ARE Mandate.
Following 2002, the current methodology and emission factors will be applied.
Agriculture 5-21
-------
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 two to three years, because the
analyses needed for it are currently taking place.
5.4 Agricultural Soil Management (IPCC Source
Category 3D)
Nitrous oxide (N2O) is naturally produced in soils through the microbial processes of nitrification and denitrification
that is driven by the availability of mineral N (Firestone and Davidson 1989).6 Mineral N is made available in soils
through decomposition of soil organic matter and plant litter, as well as asymbiotic fixation of N from the
atmosphere.7 A number of agricultural activities directly or indirectly increase mineral nitrogen (N) availability in
soils, and therefore influence N2O emissions occurring through nitrification and denitrification (see Figure 5-2)
(Mosier et al. 1998). Direct N increases occur through a variety of management practices, 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 (i.e., soils with a high organic matter content, otherwise known as Histosols8) in croplands
and grasslands (IPCC 2006). Additionally, agricultural soil management activities, including irrigation, drainage,
tillage practices, and fallowing of land, can influence N mineralization by impacting moisture and temperature
regimes in soils. 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.9
Direct and indirect emissions from agricultural lands (i.e., cropland and grassland as defined in Chapter 6.1
Representation of the U.S. Land Base) are included in this section. As recommended by the IPCC (2006), N
mineralization from decomposition of soil organic matter and asymbiotic N fixation is also included in this section
for complete accounting of management impacts on greenhouse gas emission from managed land (see Methodology
section for more information).
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).
Asymbiotic N fixation is the fixation of atmospheric N2 by bacteria living in soils that do not have a direct relationship with
plants.
8 Drainage of organic soils in former wetlands enhances mineralization of N-rich organic matter, thereby increasing N2O
emissions from these soils.
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 particulate NH4+, nitric acid (HNOs), and NOX.
5-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 5-2: Sources and Pathways of N that Result in NzO Emissions from Agricultural Soil
Management
Sources and Pathways of N that Result in ^0 Emissions from Agricultural Soil Management
o
Synthetic N Fertilizers
Synthetic N fertilizer applied to soil
Organic
Amendments
Includes both commercial and
non-co,m merdsl fertilizers {i.e.,
sewage sludge, tankage, etc)
Urine and Dung from
Grazing Animals
Manure deposited on pasture,
and paddock
Crop Residues
Indudesabove-and belowground
residues for all crops(non-N and N-
fixing (and from perennial forage
crops and pastures followinq tencvja
Mineralization of
Soil Organic Matter
IndudesN convertedto mineral form
upon decomposition of soil organic
matter
Asymbiotic Fixation
Fixation of atmospheric N; by bacteria
living in soils that do not have a direct
relations hip 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.
N Volatilization
and Deposition
Agriculture 5-23
-------
Agricultural soils produce the majority of N2O emissions in the United States. Estimated emissions from this source
in 2013 were 263.7 MMT CO2 Eq. (885 kt) (see Table 5-18 and Table 5-19) Annual N2O emissions from
agricultural soils fluctuated between 1990 and 2013, although overall emissions were 17.7 percent higher in 2013
than in 1990. Year-to-year fluctuations are largely a reflection of annual variation in weather patterns, synthetic
fertilizer use, and crop production. From 1990 to 2013, on average cropland accounted for approximately 62
percent of total direct emissions, while grassland accounted for approximately 38 percent. The percentages for
indirect emissions on average are approximately 81 percent for croplands, 19 percent for grasslands. Estimated
direct and indirect N2O emissions by sub-source category are shown in Table 5-20 and Table 5-21.
Table 5-18: NzO Emissions from Agricultural Soils (MMT COz Eq.)
Activity
Direct
Cropland
Grassland
Indirect
Cropland
Grassland
Total
1990
190.8
117.1
73.7
33.2
26.4
6.8
224.0
2005
208.6
130.6
78.1
35.0 1
28.1
6.9 •
243.6
2009
225.3
136.0
89.4
38.8
31.8
6.9
264.1
2010
225.4
136.2
89.2
38.8
31.9
6.9
264.3
2011
226.3
137.2
89.1
39.5
32.6
6.9
265.8
2012
226.1
137.6
88.5
39.8
32.9
6.9
266.0
2013
224.7
135.7
89.0
39.0
32.1
6.9
263.7
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP
values.
Table 5-19: NzO Emissions from Agricultural Soils (kt)
Activity
Direct
Cropland
Grassland
Indirect
Cropland
Grassland
Total
1990
640
393
247
111
88
23
752
2005
700
438 1
262 1
117 1
94 1
23 •
817
2009
756
456
300
130
107
23
886
2010
757
457
299
130
107
23
887
2011
759
460
299
133
109
23
892
2012
759
462
297
134
110
23
892
2013
754
455
299
131
108
23
885
Table 5-20: Direct NzO Emissions from Agricultural Soils by Land Use Type and N Input Type
(MMT COz Eq.)
Activity
Cropland
Mineral Soils
Synthetic Fertilizer
Organic Amendment*
Residue Nb
Mineralization and
Asymbiotic Fixation
Organic Soils
Grassland
Mineral Soils
Synthetic Fertilizer
PRP Manure
Managed Manure0
Sewage Sludge
Residue Nd
Mineralization and
Asymbiotic Fixation
Organic Soils
Total
1990
117.1
114.4
49.4
11.2 1
6.2 1
47.7 1
2.7 1
73.7 1
71.4 1
1.9 1
16.5 1
0.2 1
0.2 1
18 1
50.7 1
2.3
190.8
2005
130.6
128.0
54.3
12.5 1
6.3 1
54.9 1
2.6 1
78.1 1
75.8 1
1.8 1
17.5
0.3 1
0.5 1
2.1 1
53.6 1
2.2
208.6
2009
136.0
133.5
57.8
13.1
6.3
56.3
2.5
89.4
87.2
1.9
18.2
0.3
0.5
2.3
64.0
2.1
225.3
2010
136.2
133.7
59.2
13.0
6.2
55.3
2.5
89.2
87.1
1.9
18.0
0.3
0.5
2.3
64.0
2.1
225.4
2011
137.2
134.7
60.2
13.2
6.0
55.3
2.5
89.1
86.9
2.0
17.6
0.3
0.5
2.3
64.1
2.1
226.3
2012
137.6
135.1
60.7
13.3
5.9
55.2
2.5
88.5
86.4
2.1
17.2
0.3
0.6
2.3
63.9
2.1
226.1
2013
135.7
133.2
58.8
13.2
6.2
54.9
2.5
89.0
86.9
2.2
17.2
0.3
0.6
2.3
64.3
2.1
224.7
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
a Organic amendment inputs include managed manure, daily spread manure, and commercial organic
fertilizers (i.e., dried blood, dried manure, tankage, compost, and other).
b Cropland residue N inputs include N in unharvested legumes as well as crop residue N.
5-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
0 Managed manure inputs include managed manure and daily spread manure amendments that are
applied to grassland soils.
d Grassland residue N inputs include N in ungrazed legumes as well as ungrazed grass residue N
Table 5-21: Indirect NzO Emissions from Agricultural Soils (MMT COz Eq.)
Activity 1990 2005 2009 2010 2011 2012 2013
Cropland 26.4 28.1 31.8 31.9 32.6 32.9 32.1
Volatilization & Atm.
Deposition 13.1 14.5 14.4 14.4 14.8 14.9 14.7
Surface Leaching & Run-Off 13.2 13.5 17.5 17.4 17.8 18.0 17.4
Grassland 6.8 6.9 6.9 6.9 6.9 6.9 6.9
Volatilization & Atm.
Deposition 4.2 4.5 4.5 4.5 4.5 4.5 4.4
Surface Leaching & Run-Off 2.7 2.4 2.5 2.5 2.5 2.5 2.5
Total 33.2 35.0 38.8 38.8 39.5 39.8 39.0
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Figure 5-3 and Figure 5-4 show regional patterns for direct N2O emissions for croplands and grasslands, and Figure
5-5 and Figure 5-6 show N losses from volatilization, leaching, and runoff that lead to indirect N2O emissions.
Annual emissions and N losses in 2013 are shown for the Tier 3 Approach only.
Direct N2O emissions from croplands tend to be high in the Corn Belt (Illinois, Iowa, Indiana, Ohio, southern and
western Minnesota, and eastern Nebraska), where a large portion of the land is used for growing highly fertilized
corn and N-fixing soybean crops (Figure 5-3). Kansas has high direct emissions associated with N management in
wheat production systems. Hay production in Missouri and irrigated cropping systems in California also contribute
relatively large amounts of direct N2O emissions, along with a combination of irrigated cropping in the west Texas
and hay production in east Texas. Direct emissions are low in many parts of the eastern United States because only
a small portion of land is cultivated and in many western states where rainfall and access to irrigation water are
limited.
Direct emissions from grasslands are highest in the central and western United States (Figure 5-4) where a high
proportion of the land is used for cattle grazing. In contrast, most areas in the Great Lake states, the Northeast, and
Southeast have moderate to low emissions due to less land dedicated to livestock grazing. However, emissions from
the Northeast and Great Lake states tend to be higher on a per unit area basis compared to other areas in the country.
This effect is likely due to a larger impact of freeze-thaw cycles in these regions, and possibly greater water-filled
pore space in the soil, which is key driver of N2O emissions (Kessavalou et al. 1998, Bateman and Baggs 2005).
Indirect emissions from croplands and grasslands (Figure 5-5 and Figure 5-6) show similar emission patterns to
those of direct emissions because the same driving variables (N inputs, weather patterns, soil characteristics) are
controlling both types of emissions. There are some exceptions to the similarity in patterns, however, because the
processes that contribute to indirect emissions (NOs" leaching, N volatilization) do not respond in exactly the same
manner to the driving variables as the processes that contribute to direct emissions (nitrification and denitrification).
Agriculture 5-25
-------
Figure 5-3: Crops, Annual Direct NzO Emissions Estimated Using the Tier 3 DAYCENT Model,
1990-2013 (MMT CO2 Eq./year)
5-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 5-4: Grasslands, Annual Direct NzO Emissions Estimated Using the Tier 3 DAYCENT
Model, 1990-2013 (MMT CO2 Eq./year)
Agriculture 5-27
-------
Figure 5-5: Crops, Average Annual N Losses Leading to Indirect NzO Emissions Estimated
Using the Tier 3 DAYCENT Model, 1990-2013 (kt N/year)
5-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 5-6: Grasslands, Average Annual N Losses Leading to Indirect NzO Emissions
Estimated Using the Tier 3 DAYCENT Model, 1990-2013 (kt N/year)
Methodology
The 2006IPCC Guidelines (IPCC 2006) divide emissions from the Agricultural Soil Management source category
into five components, including (1) direct emissions from N additions to cropland and grassland mineral soils from
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 manure deposited by livestock on PRP grasslands;
and (5) indirect emissions from soils and water from 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 in crop residues to indirect soil N2O emissions;
(2) adopting the revised emission factor for direct N2O emissions for Tier 1 methods 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 from harvest yield data; and (5) estimating emissions associated with land use and management
change (which can significantly change the N mineralization rates from soil organic matter).10 The Inventory also
reports on total emissions from all managed land, which is a proxy for anthropogenic impacts on greenhouse gas
emissions (IPCC 2006), including direct and indirect N2O emissions from asymbiotic fixation and mineralization of
10 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.
Agriculture 5-29
-------
soil organic matter and litter. One recommendation from IPCC (2006) that has not been completely adopted is the
accounting of emissions from pasture renewal, which involves occasional plowing to improve forage production in
pastures. The exception is pastures that are replanted occasionally in rotation with annual crops, this practice is
represented in the Inventory.
Direct
Emissions
The methodology used to estimate direct N2O emissions from agricultural soil management in the United States is
based on a combination of IPCC Tier 1 and 3 approaches (IPCC 2006, Del Grosso et al. 2010). A Tier 3 process-
based model (D AYCENT) was used to estimate direct emissions from a variety of crops that are grown 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 5-2 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 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 USD A 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.11 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 in 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, the annual data are currently available through 2010
(USDA-NRCS 2013) although this Inventory only uses NRI data through 2007 because newer data were not made
available in time to incorporate the additional years into this Inventory.
Box 5-2: Tier 1 vs. Tier 3 Approach for Estimating NzO 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.
11 NRI 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.
5-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
This is a simplifying assumption that is likely to create bias in estimated N2O emissions for a specific year. In
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.
DAYCENT was not used to estimate N2O emissions for all land areas. DAYCENT was used to estimate N2O
emissions associated with production of 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, but was not
applied to estimate N2O emissions from other crops or rotations with other crops12, such as sugarcane, some
vegetables, tobacco, and perennial/horticultural crops. Areas that are converted between agriculture (i.e., cropland
and grassland) and other land uses, such as forest land, wetland and settlements, were not simulated with
DAYCENT. DAYCENT was also not used to estimate emissions from land areas with very gravelly, cobbly, or
shaley soils (greater than 35 percent by volume), or to estimate emissions from organic soils (Histosols). The Tier 3
method has not been fully tested for estimating N2O emissions associated with these crops and rotations, land uses,
as well as organic soils or cobbly, gravelly, and shaley mineral soils. In addition, federal grassland areas were not
simulated with DAYCENT due to limited activity on land use histories. Consequently, the Tier 1IPCC (2006)
methodology was used to estimate (1) direct emissions from crops on mineral soils that are not simulated by
DAYCENT; (2) direct emissions from Pasture/Range/Paddock (PRP) 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 2009 NRI (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 in DAYCENT with a
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.13 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 3.12 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 (TVA 1991 through 1994; AAPFCO 1995 through 2011) 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
12 A small proportion of the major commodity crop production, such as corn and wheat, is included in the Tier 1 analysis because
these crops are rotated with other crops or land uses (e.g., forest lands) that are not simulated by DAYCENT.
See .
Agriculture 5-31
-------
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. N 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 5.2 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
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 (USDA ERS 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 2009 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 2013 were assumed to be similar to 2007. Annual data are currently available through 2010
(USDA-NRCS 2013). However, this Inventory only uses NRI data through 2007 because newer data were not made
available in time to incorporate the additional years into this Inventory.
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
5-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
The IPCC (2006) Tier 1 methodology was used to estimate direct N2O emissions for mineral cropland soils that are
not simulated by DAYCENT. 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.14 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
following sources were used to derive activity data:
• A process-of-elimination approach was used to estimate synthetic N fertilizer additions for crop areas not
simulated by DAYCENT. The total amount of fertilizer used on farms has been estimated at the county-
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 2013, state-level fertilizer for on-farm use is adjusted based on
annual fluctuations in total U.S. fertilizer sales (AAPFCO 1995 through 2007, AAPFCO 2008 through
2014).15 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. 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-NASS 2014), 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 2009 NRI (USDA-NRCS 2009) using soils data from the Soil Survey Geographic Database (SSURGO)
(Soil Survey Staff 2011). Temperature data from Daly 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 2013 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).
14 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.
I-5 Values were not available for 2013 so a "least squares line" statistical extrapolation using the previous 5 years of data is used
to arrive at an approximate value.
Agriculture 5-33
-------
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 PRP manure N additions for federal
grasslands, respectively. Grassland includes pasture and rangeland that produce grass forage primarily for livestock
grazing. Rangelands are typically extensive areas of native grassland that are not intensively managed, while
pastures are typically seeded grassland (possibly following tree removal) that may also have addition management,
such as irrigation or 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 (5.2 Manure Management (IPCC Source
Category 3B)) 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 PRP 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 0 for the conterminous United
States. Data were obtained from the U.S. Department of Agriculture NRI (Nusser and Goebel 1998) and the U.S.
Geological Survey (USGS) National Land Cover Dataset (Vogelman et al. 2001), which were reconciled with the
Forest Inventory and Analysis Data. The area data for pastures and rangeland were aggregated to the county level to
estimate non-federal and federal grassland areas.
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 2013 were assumed to be similar to 2007. The annual data
are currently available through 2010 (USDA-NRCS 2013). However, this Inventory only uses NRI data through
2007 because newer data were not made available in time to incorporate the additional years into this Inventory.
5-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Total Direct IVhO 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 5-18 and Table 5-19).
Indirect IVhO Emissions
This section describes the methods used for estimating indirect soil N2O emissions from croplands and grasslands.
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 and grasslands.
Indirect N2O Emissions from Atmospheric Deposition of Volatilized N
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. N 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). 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 5-21).
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, and
PRP manure N excreted on federal grasslands. 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 5-21).
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
Agriculture 5-35
-------
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) Approach 1 method, the proportion of
volatilization and leaching or runoff estimated with the IPCC (2006) Approach 1 method, and indirect N2O
emissions were estimated with a simple error propagation approach (IPCC 2006). Uncertainties from the Approach
1 and Approach 3 (i.e., DAYCENT) estimates were combined using simple error propagation (IPCC 2006).
Additional details on the uncertainty methods are provided in Annex 3.12. The combined uncertainty for direct soil
N2O emissions ranged from 16 percent below to 26 percent above the 2013 emissions estimate of 224.7 MMT CO2
Eq., and the combined uncertainty for indirect soil N2O emissions ranged from 46 percent below to 160 percent
above the 2013 estimate of 39.0 MMT CO2 Eq.
Table 5-22: Quantitative Uncertainty Estimates of NzO Emissions from Agricultural Soil
Management in 2013 (MMT COz Eq. and Percent)
2013 Emission
Source Gas Estimate Uncertainty Range Relative to Emission Estimate
(MMT CO2 Eq.) (MMT CCh Eq.) (%)
Direct Soil N2O Emissions N2O
Indirect Soil N2O Emissions N2O
Lower
Bound
224.7 189.2
39.0 21.2
Upper
Bound
282.4
101.6
Lower
Bound
-16%
-46%
Upper
Bound
26%
160%
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 the lack of an 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 2013. 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. N2O measurement data were available for 21 sites in the United States, 4 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 5-7).
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
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 3.12 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.
5-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 5-7: Comparison of Measured Emissions at Field Sites and Modeled Emissions Using
the DAYCENT Simulation Model and IPCC Tier 1 Approach.
9 n
8
t. 7 "
jp- 6 -
to 5 -
5,3 -
z 2 -
1 -
D Measured
1
_
ElDayCent
• IPCC
jrl |r|
-n
:j r
ki
c=tt!fl,WlS1Sn>1Scwn>
^ ^ [D In H [Q [Q t>— [Q ^ H l*—
ooQjnjmojQjgjQjQrBrg
u °"S w> M ~E ~E — •Ł ° ab —
Si ^LjZ^S-^C^ZC
m O ZcOo B
DO [fl OS W Łj OJ
•c •> Ł >• S -°
^ c ^ c o ^.
o 8 3 8 .a ^
u Q ? ra =
ui S i; ^
| Z
5
-
1
r
i i;
11
i i i i i i i i
oj 2 *^ o o " " QJ
-Q Mils'0 ° MttbŁ
8 1 J o K < 2
"S1 — 3
081-
0 1/1 5
Z
S
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
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most Inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh-equivalent emissions from CH4, HFCs, and PFCs. The GWPs of N2O and SF6 have decreased,
leading to a decrease in CCh-equivalent emissions for N2O. The AR4 GWPs have been applied across the entire time
series for consistency. For more information please see the Recalculations Chapter.
Methodological recalculations in the current Inventory were associated with the following improvements: 1) Driving
the DAYCENT simulations with updated input data for the excretion of C and N onto PRP and N additions from
managed manure based on national livestock population (note that revised total PRP N additions decreased from 4.4
to 4.1 MMT N on average and revised managed manure additions decreased from 2.9 to 2.7 MMT N on average); 2)
properly accounting for N inputs from residues for crops not simulated by DAYCENT; (3) modifying the number of
experimental study sites used to quantify model uncertainty for direct N2O emissions and bias correction; and (4)
reporting indirect N2O emissions from forest land and settlements in their respective sections, instead of the
agricultural soil management section. These changes resulted in a decrease in emissions of approximately 18 percent
on average relative to the previous Inventory and a decrease in the upper bound of the 95 percent confidence interval
Agriculture 5-37
-------
for direct N2O emissions from 29 to 26 percent. The differences are mainly due to changing the number of study
sites used to quantify model uncertainty and correct bias. Specifically, two sites were removed because they had a
relatively small number of daily N2O measurements, which tended to be anomalously high, so the validity of
extrapolating annual emission estimates was questionable for those data.
Planned Improvements
Several planned improvements are underway:
(1) Improvements to update the time series of land use and management data from the 2010 USDANRI 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
the latest changes in agricultural production for the United States;
(2) Improvements 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;
(3) Improvements 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;
(4) Improvements to simulate crop residue burning in the DAYCENT model 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 5.5). 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 5.5; and
(5) 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. A
planned improvement over the next two years is to add these states into the Inventory analysis.
5.5 Field Burning of Agricultural Residues (IPCC
Source Category 3F)
Crop production results in both harvested product(s) and large quantities of agricultural crop residues, which farmers
manage in a variety of ways. For example, crop 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 applied to
soils; landfilled; or, as discussed in this section, 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.
5-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
In the United States, field burning of agricultural residues commonly occurs in the southeastern states, the Great
Plains, and the Pacific Northwest (McCarty 2011). The primary crops whose residues may be burned are corn,
cotton, lentils, rice, soybeans, sugarcane, and wheat (McCarty 2009). Rice, sugarcane, and wheat residues account
for approximately 70 percent of all crop residue burning and emissions (McCarty 2011). In 2013, CH4 and N2O
emissions from Field Burning of Agricultural Residues were 0.3 MMT CO2 Eq. (12 kt) and 0.1 MMT. CO2 Eq. (0.3
kt), respectively. Annual emissions from this source from 1990 to 2013 have remained relatively constant,
averaging approximately 0.3 MMT CO2 Eq. (12 kt) of CH4 and 0.1 MMT CO2 Eq. (0.3 kt) of N2O (see Table 5-23
and Table 5-24).
Table 5-23: ChU and NzO Emissions from Field Burning of Agricultural Residues (MMT COz
Eq.)
Gas/Crop Type
1990
2005
2009 2010 2011
2012
2013
CH4
Corn
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
N2O
Corn
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
0.3
+
0.1
0.2
0.1
0.2
0.1
0.1
0.3
0.1
0.1
0.1
0.3
0.1
0.1
0.1
0.3
0.1
0.1
0.1
0.3
0.1
0.1
0.1
0.3
0.1
0.1
0.1
Total
0.4
0.3
0.4
0.3
0.4
0.4
0.4
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
+ Less than 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 5-24: CH4, NzO, CO, and NOX Emissions from Field Burning of Agricultural Residues
(kt)
Gas/Crop Type
1990
2005
2009
2010
2011
2012
2013
CH4
Corn
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
N20
Corn
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
CO
NOx
13
1
268
8
9
1
+
+
2
1
1
4
+
+
+
+
+
+
+
+
184
6
12
2
11
2
12
2
12
2
12
2
247
8
241
8
255
8
253
8
262
8
+ Lessthan0.5kt
Note: Totals may not sum due to independent rounding
Agriculture 5-39
-------
Methodology
A U.S.-specific Tier 2 method was used to estimate greenhouse gas emissions from Field Burning of Agricultural
Residues. The Tier 2 methodology used is consistent with the 2006IPCC Guidelines (for more details, see Box
5-3). In order to estimate the amounts of C and N released during burning, the following equation was used:
C or N released = Ł for all crop types and states
where,
AB
CAH x CP x RCR x DMF x BE x CE x (FC orFN)
Total area of crop burned, by state
Total area of crop harvested, by state
Annual production of crop in kt, by state
Amount of residue produced per unit of crop production
Amount of dry matter per unit of bio mass for a crop
Amount of C or N per unit of dry matter for a crop
The proportion of prefire fuel biomass consumed16
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 Crop Area Harvested were available by state and year from USD A (2014) 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
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)
where,
Emissions Ratio (ER)
Conversion Factor (CF)
= g CH4-C or CO-C/g C released, or g N2O-N or NOx-N/g N released
= 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)
Box 5-3: Comparison of Tier 2 U.S. Inventory Approach and IPCC (2006) Default Approach
Emissions from Field 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
rationale for using the IPCC/UNEP/OECD/IEA (1997) approach, and not the IPCC (2006) approach, is as follows:
(1) the equations from both guidelines rely on the same underlying variables (though the formats differ); (2) the
IPCC (2006) equation was developed to be broadly applicable to all types of biomass burning, and, thus, is not
specific to agricultural residues; and (3) the IPCC (2006) default factors are provided only for four crops (corn, rice,
sugarcane, and wheat) while this Inventory analyzes emissions from seven crops (corn, cotton, lentils, rice,
soybeans, sugarcane, and wheat).
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 2013 Inventory report to determine the difference in overall estimates
between the two approaches. To estimate greenhouse gas emissions from Field Burning of Agricultural Residue
using the IPCC (2006) methodology, the following equation—cf. IPCC (2006) Equation 2.27—was used:
Emissions (kt) = AB x (MBx Cf) x Gef x 10"
where,
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 x combustion efficiency).
5-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Area Burned (AB) = Total area of crop burned (ha)
Mass Burned (MB x Cf) = IPCC (2006) default fuel biomass consumption (metric tons dry matter burnt
ha"1)
Emission Factor (Gef) = IPCC (2006) emission factor (g kg"1 dry matter burnt)
The IPCC (2006) default approach resulted in 5 percent higher emissions of CH4 and 21 percent higher emissions of
N2O than the estimates in this Inventory (and are within the uncertainty percentage ranges estimated for this source
category). 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 2014). Rice production and area data for Florida and Oklahoma were estimated separately as they
are not collected by USDA. Average primary and ratoon rice crop yields for Florida (Schueneman and Deren 2002)
were applied to Florida acreages (Schueneman 1999, 2000, 2001; Deren 2002; Kirstein 2003, 2004; Cantens 2004,
2005; Gonzalez 2007 through 2014), and rice crop yields for Arkansas (USDA 2014) were applied to Oklahoma
acreages17 (Lee 2003 through 2007; Anderson 2008 through 2014). The production data for the crop types whose
residues are burned are presented in Table 5-25. 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 state18 from McCarty
(2010) for corn, cotton, lentils, rice, soybeans, sugarcane, and wheat.19 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
type for each state. The average fraction of area burned by crop type across all states is shown in Table 5-26. As
described above, all crop area harvested data were from USDA (2014), except for rice acreage in Florida and
Oklahoma, which is not measured by USDA (Schueneman 1999, 2000, 2001; Deren 2002; Kirstein 2003, 2004;
Cantens 2004, 2005; Gonzalez 2007 through 2014; Lee 2003 through 2007; Anderson 2008 through 2014). 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 five-year percent area burned, based on data
availability and inter-annual variability. This average was taken at the crop and state level. Table 5-26 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 area 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 Stutzle (1987).
The ratio for sugarcane is from Kinoshita (1988) and the ratio for cotton is 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 Stutzle (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 5-27. 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
17 Rice production yield data are not available for Oklahoma, so the Arkansas values are used as a proxy.
18 Alaska and Hawaii were excluded.
19 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 crops" categories.
Agriculture 5-41
-------
and conversion factors for all gases (see Table 5-28) were taken from the Revised 1996IPCC Guidelines
(IPCC/UNEP/OECD/IEA 1997).
Table 5-25: Agricultural Crop Production (kt of Product)
Crop
Corna
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
1990
1,534
3,376
7,114
52,416
25,525
74,292
2005
282,263
5,201
238 1
10,132
83,507
24,137
57,243
2009 2010 2011 2012
332,549 316
12,654 3
265
9,972 11
91,417 90
27,608 24
60,366 60
,165 313,949 273,832
,942 3,391 3,770
393 215 240
,027 8,389 9,048
,605 84,192 82,055
,821 26,512 29,193
,062 54,413 61,755
a Com for grain (i.e., excludes com for silage).
Table 5-26: U.S. Average Percent Crop Area Burned by Crop
State
Com
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
1990
+ 1
1% 1
3% 1
10% 1
+
59% 1
3% |
2005
+ 1
1%
+
6%
+
26%
2%
2009 2010
+ +
1% 1%
1% +
9% 8%
+ +
37% 38%
3% 3%
(Percent)
2013
353,715
2,811
228
8,613
89,507
27,906
57,961
2011 2012 2013
+
1%
1%
10%
+
+ +
1% 1%
1% 1%
9% 9%
+ +
40% 37% 38%
3%
3% 3%
+ Less than 0.5 percent
Table 5-27:
Residues
Crop
Com
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
Table 5-28:
Gas
CH4:C
CO:C
N20:N
NOX:N
Key Assumptions for Estimating Emissions from
Residue: Crop
Ratio
1.0
1.6
2.0
1.4
2.1
0.2
1.3
Greenhouse
Dry Matter C Fraction N 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 and
0.006
0.012
0.023
0.007
0.023
0.004
0.006
Field Burning of Agricultural
Burning
Efficiency
(Fraction)
0.93
0.93
0.93
0.93
0.93
0.81
0.93
Combustion
Efficiency
(Fraction)
0.88
0.88
0.88
0.88
0.88
0.68
0.88
Conversion Factors
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).
5-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Uncertainty and Time-Series Consistency
Due to data limitations, uncertainty resulting from the fact that emissions from burning of Kentucky bluegrass and
"other crop" residues are not included in the emissions estimates was not incorporated into the uncertainty analysis.
The results of the Approach 2 Monte Carlo uncertainty analysis are summarized in Table 5-29. CH4 emissions from
Field Burning of Agricultural Residues in 2013 were estimated to be between 0.2 and 0.4 MMT CO2 Eq. at a 95
percent confidence level. This indicates a range of 41 percent below and 42 percent above the 2013 emission
estimate of 0.3 MMT CO2 Eq.20 Also at the 95 percent confidence level, N2O emissions were estimated to be
between 0.07 and 0.14 MMT CO2 Eq., or approximately 30 percent below and 31 percent above the 2013 emission
estimate of 0.10 MMT CO2 Eq.
Table 5-29: Approach 2 Quantitative Uncertainty Estimates for CH4 and NzO Emissions from
Field Burning of Agricultural Residues (MMT COz Eq. and Percent)
2013 Emission
Source Gas Estimate
(MMT CO2 Eq.)
Field Burning of Agricultural „„ „,
-p. • j v_._n_4 U • J
Residues
Field Burning of Agricultural N „ „.
Residues
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower
Bound
0.2
0.1
Upper
Bound
0.4
0.1
Lower
Bound
-41%
-30%
Upper
Bound
42%
31%
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2013. 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 Lee (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
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous Inventories) which results in time-series recalculations
for most Inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CO2-equivalent emissions from CH4. The GWPs of N2O and SF6 have decreased, leading to a decrease
in CO2-equivalent emissions for N2O. The AR4 GWPs have been applied across the entire time series for
consistency. For more information please see the Recalculations Chapter. As a result of the updated GWP values,
emission estimates for each year in 1990 through 2012 increased by 19 percent for CH4 and decreased by 4 percent
for N2O relative to the emission estimates in previous Inventory reports. Rice cultivation data for Florida and
20 This value of 0.31 MMT CO2 is rounded and reported as 0.3 MMT CO2 in Table 6-21 and the text discussing Table 6-21. For
the uncertainty calculations, the value of 0.31 MMT CCh was used to allow for more precise uncertainty ranges.
Agriculture 5-43
-------
Oklahoma, which are not reported by USD A, were updated for 2013 through communications with state experts
(Gonzales 2014, Anderson 2014).
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 exceeds 100 percent. The
availability of useable area harvested and other data for Kentucky bluegrass and the "other crops" category in
McCarty (2010) will also be investigated in order to try to incorporate these emissions into past and future estimates.
More crop area burned data and new data to estimate crop-specific burning efficiency and consumption efficiency,
and emissions are becoming available—e.g., the combustion completeness and emission factors used for the EPA
National Emissions Inventory (NEI)21—and will be analyzed for incorporation into future Inventory reports.
21 More information on the NEI is available online at:
5-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
6. 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.1 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, Settlements, Wetlands (as
well as Other Land). The greenhouse gas flux from Forest Land Remaining Forest Land is reported using estimates
of changes in forest carbon (C) stocks, non-carbon dioxide (non-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 CO2 removals from harvested wood products and urban trees,
and CO2 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 harvested 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 one 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 CCh flux data. For the landfilled yard trimmings and food scraps source, historical annual solid
waste survey data were interpolated where annual data were missing 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 CCh flux.
Land use, land-use change, and forestry activities in 2013 resulted in a C sequestration (i.e., total sinks) of 881.7
MMT CO2 Eq.2 (240.5 MMT C).3 This represents an offset of approximately 13.2 percent of total (i.e., gross)
1 The term "flux" is used to describe the net emissions of greenhouse gases to the atmosphere accounting for both the emissions
of CO2 to and the removals of CCh from the atmosphere. Removal of CCh from the atmosphere is also referred to as "carbon
sequestration".
2 Following the revised reporting requirements under the UNFCCC, this Inventory report presents CCh equivalent values based
on the IPCC Fourth Assessment Report (AR4) GWP values. See the Introduction chapter for more information.
3 The total sinks value includes the positive C sequestration reported for Forest Land Remaining Forest Land, Cropland
Remaining Cropland, Land Converted to Grassland, Settlements Remaining Settlements, and Other Land plus the loss in C
sequestration reported for Land Converted to Cropland and Grassland Remaining Grassland.
Land Use, Land-Use Change, and Forestry 6-1
-------
greenhouse gas emissions in 2013. Emissions from land use, land-use change, and forestry activities in 2013
represent 0.3 percent of total greenhouse gas emissions.4
Total land use, land-use change, and forestry C sequestration increased by approximately 13.6 percent between 1990
and 2013. This increase was primarily due to an increase in the rate of net C accumulation in forest C stocks.5 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. Emissions from Land
Converted to Cropland and Wetlands Remaining Wetlands decreased. Emissions and removals for Land Use, Land-
Use Change, and Forestry are summarized in Table 6-1 by land-use and source category.
Table 6-1: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry by
Land-Use Change Category (MMT COz Eq.)
Land-Use/Source Category
Forest Land Remaining Forest Land
Changes in Forest Carbon Stocka
Forest Fires
Forest Soilsb
Cropland Remaining Cropland
Changes in Agricultural Soil Carbon Stock
Liming of Agricultural Soils
Urea Fertilization
Land Converted to Cropland
Changes in Agricultural Soil Carbon Stock
Grassland Remaining Grassland
Changes in Agricultural Soil Carbon Stock
Land Converted to Grassland
Changes in Agricultural Soil Carbon Stock
Settlements Remaining Settlements
Changes in Urban Tree Carbon Stock0
Settlement Soilsd
Wetlands Remaining Wetlands
Peatlands Remaining Peatlands
Other
Landfilled Yard Trimmings and Food
Scraps
1990
(635.2)
(639.4)
4.2
0.1 1
(58.1)
(65.2)
4.7
2.4 1
24.5 1
24.5
(1-9) 1
(1.9) 1
(7.4) 1
(7.4)
(59.0)
(60.4)
1.4
1.1 1
1.1
(26.0)
(26.0)
• 2005
(792.9)
(807.1)
13.8
0.5
(20.2)
(28.0)
4.3
3.5
19.8
19.8
4.2
4.2
(9.0)
(9.0)
(78.2)
(80.5)
2.3
1.1
1.1
(11.4)
(11.4)
2009
(754.7)
(764.9)
9.7
0.5
(20.2)
(27.5)
3.7
3.6
16.2
16.2
11.7
11.7
(8.9)
(8.9)
(82.8)
(85.0)
2.2
1.0
1.0
(12.5)
(12.5)
2010
(757.1)
(765.4)
7.9
0.5
(17.3)
(25.9)
4.8
3.8
16.2
16.2
11.7
11.7
(8.9)
(8.9)
(83.8)
(86.1)
2.4
1.0
1.0
(13.2)
(13.2)
2011
(749.2)
(773.8)
24.2
0.5
(17.8)
(25.8)
3.9
4.1
16.2
16.2
11.7
11.7
(8.9)
(8.9)
(84.8)
(87.3)
2.5
0.9
0.9
(13.2)
(13.2)
2012
(746.7)
(773.1)
26.0
0.5
(15.0)
(25.0)
5.8
4.2
16.1
16.1
11.5
11.5
(8.8)
(8.8)
(85.8)
(88.4)
2.5
0.8
0.8
(12.8)
(12.8)
2013
(765.5)
(775.7)
9.7
0.5
(13.5)
(23.4)
5.9
4.0
16.1
16.1
12.1
12.1
(8.8)
(8.8)
(87.1)
(89.5)
2.4
0.8
0.8
(12.6)
(12.6)
Total Fluxe
(762.1)
(886.4)
(850.2) (851.3) (844.9) (840.6) (858.5)
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
a Estimates include C stock changes on both Forest Land Remaining Forest Land and Land Converted to Forest Land.
b 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.
c Estimates include C stock changes on both Settlements Remaining Settlements and Land Converted to Settlements.
d Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements, and Land Converted to
Settlements, but not from land-use conversion.
e "Total Flux" is defined as the sum of positive emissions (i.e., sources) of greenhouse gases to the atmosphere plus removals of
CO2 (i.e., sinks or negative emissions) from the atmosphere.
Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
CO2 removals are presented in Table 6-2 along with CCh, CH4, and N2O emissions from Land use, Land-Use
Change, and Forestry source categories. Liming of agricultural soils and urea fertilization in 2013 resulted in CO2
emissions of 9.9 MMT CC>2 Eq. (9,936 kt). Lands undergoing peat extraction (i.e., Peatlands Remaining Peatlands)
The emissions value includes the CCh, CELi, andN2O emissions reported for Forest Fires, Forest Soils, Liming of Agricultural
Soils, Urea Fertilization, Settlement Soils, and Peatlands Remaining Peatlands.
5 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 or removal.
6-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
resulted in CO2 emissions of 0.8 MMT CO2 Eq. (770 kt), methane (CH4) emissions of less than 0.05 MMT CO2 Eq.,
and nitrous oxide (N2O) emissions of less than 0.05 MMT CO2 Eq. The application of synthetic fertilizers to forest
soils in2013 resulted in N2O emissions of 0.5 MMT CO2Eq. (2kt). 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, N2O emissions from fertilizer application to settlement soils in 2013 accounted for 2.4
MMTCO2Eq. (8kt). This represents an increase of 77 percent since 1990. Forest fires in 2013 resulted in CH4
emissions of 5.8 MMT CO2 Eq. (233 kt), and inN2O emissions of 3.8 MMT CO2 Eq. (13 kt). Emissions and
removals for Land Use, Land-Use Change, and Forestry are shown in Table 6-2 and Table 6-3.
Table 6-2: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry
(MMT COz Eq.)
Gas/Land-Use Category 1990 2005 2009 2010 2011 2012 2013
C02
Forest Land Remaining Forest Land:
Changes in Forest Carbon Stocka
Cropland Remaining Cropland:
Changes in Agricultural Soil Carbon
Stock
Cropland Remaining Cropland:
Liming of Agricultural Soils
Cropland Remaining Cropland:
Urea Fertilization
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Settlements Remaining Settlements:
Changes in Urban Tree Carbon Stockb
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands
Other:
Landfilled Yard Trimmings and Food
Scraps
CH4
Forest Land Remaining Forest Land:
Forest Fires
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands
N20
Forest Land Remaining Forest Land:
Forest Fires
Forest Land Remaining Forest Land:
Forest Soils0
Settlements Remaining Settlements:
Settlement Soils*
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands
(767.7) (903.0)
(639.4) (807.1)
(65.2)
4.7
2.4
24.5
(1.9)
(7.4)
(60.4)
1.1
(26.0)
2.5
2.5
3.1
1.7
0.1
1.4
(28.0)
4.3
3.5
19.8
4.2
(9.0)
(80.5)
1.1
(11.4)
8.3
8.3
8.3
5.5
0.5
2.3
(862.6) (862.0) (872.1) (869.6) (871.0)
(764.9) (765.4) (773.8) (773.1) (775.7)
(27.5)
3.7
3.6
16.2
11.7
(8.9)
(85.0)
1.0
(25.9)
4.8
3.8
16.2
11.7
(8.9)
(86.1)
1.0
(25.8)
3.9
4.1
16.2
11.7
(8.9)
(87.3)
0.9
(25.0)
5.8
4.2
16.1
11.5
(8.8)
(88.4)
0.8
5.8
6.5
3.8
0.5
2.2
4.7
6.0
3.1
0.5
2.4
14.6
12.6
9.6
0.5
2.5
15.7
13.3
10.3
0.5
2.5
(23.4)
5.9
4.0
16.1
12.1
(8.8)
(89.5)
0.8
(12.5) (13.2) (13.2) (12.8) (12.6)
5.8 4.8 14.6 15.7 5.8
5.8
6.7
3.8
0.5
2.4
Total Fluxe
(762.1)
(886.4)
(850.2) (851.3) (844.9) (840.6) (858.5)
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
+ Less than 0.05 MMT CO2 Eq.
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.
c 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.
d Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements, and Land Converted to
Settlements, but not from land-use conversion
e "Total Flux" is defined as the sum of positive emissions (i.e., sources) of greenhouse gases to the atmosphere plus removals
of CO2 (i.e., sinks or negative emissions) from the atmosphere.
Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
Land Use, Land-Use Change, and Forestry 6-3
-------
Table 6-3: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry
(kt)
Gas/Land-Use Category
1990
2005
2009
2010
2011
2012
2013
CO2
Forest Land Remaining Forest Land:
Changes in Forest Carbon Stocka
Cropland Remaining Cropland:
Changes in Agricultural Soil
Carbon Stock
Cropland Remaining Cropland:
Liming of Agricultural Soils
Cropland Remaining Cropland:
Urea Fertilization
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Settlements Remaining Settlements:
Changes in Urban Tree Carbon
Stockb
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands
Other:
Landfilled Yard Trimmings and
Food Scraps
CH4
Forest Land Remaining Forest Land:
Forest Fires
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands
N20
Forest Land Remaining Forest Land:
Forest Fires
Forest Land Remaining Forest Land:
Forest Soils0
Settlements Remaining Settlements:
Settlement Soilsd
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands
(767,697) (902,974) (862,631)
(639,432)
(65,196)
4,667
2,417
24,498
(1,913)
(7,410)
(60,408)
1,055
(25,975)
101
101
+
10
6
+
5
(807,075)
(28,035)
4,349
3,504
19,830
4,230
(8,995)
(80,523)
1,101
(11,360)
332
332
+
28
18
2
8
(764,871)
(27,473)
3,669
3,555
16,194
11,704
(8,917)
(85,008)
1,024
(12,508)
234
233
+
22
13
2
8
+ + +
(862,025)
(765,410)
(25,867)
4,784
3,778
16,194
11,694
(8,894)
(86,129)
1,022
(13,197)
190
190
+
20
11
2
8
+
(872,103)
(773,843)
(25,752)
3,871
4,099
16,194
11,680
(8,871)
(87,250)
926
(13,156)
584
584
+
42
32
2
8
+
(869,580)
(773,110)
(24,990)
5,776
4,225
16,095
11,532
(8,783)
(88,372)
812
(12,766)
627
626
+
45
35
2
8
+
(871,026)
(775,677)
(23,432)
5,925
4,011
16,125
12,083
(8,757)
(89,493)
770
(12,581)
233
233
+
23
13
2
8
+
+ Emissions are less than 0.5 kt
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.
c 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.
d Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements, and Land Converted to
Settlements, but not from land-use conversion.
Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
Box 6-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks
MIM MMIH MIM MM MIM
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
6-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
(IPCC).6 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.7 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. The manner that
emissions and sinks are provided in this Inventory is one of many ways U.S. emissions and sinks could be
examined; 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.
6.1 Representation of the U.S. 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 (GHG) fluxes over the
Inventory time series. This system should be consistent with IPCC (2006), such that all countries reporting on
national GHG 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 GHG fluxes on all managed lands. The IPCC (2006, Vol. IV, Chapter 1)
considers 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 GHG 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 U.S.
land area into the thirty-six IPCC land-use and land-use change categories (Table 6-5) (IPCC 2006). The primary
databases are the U.S. Department of Agriculture (USD A) National Resources Inventory (NRI)8 and the USD A
Forest Service (USFS) Forest Inventory and Analysis (FIA)9 Database. The Multi-Resolution Land Characteristics
Consortium (MRLC) National Land Cover Dataset (NLCD)10 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 U.S. Inventory is 936 million hectares across the 50 states.11 Approximately 890
million hectares of this land base is considered managed, which has not changed by much over the time series of the
6 See .
7 See .
8 NRI data is available at .
9 FIA data is available at .
I" NLCD data is available at and MRLC is a consortium of several U.S. government agencies.
The current land representation does not include areas from U.S. territories, but there are planned improvements to include
these regions in future reports.
Land Use, Land-Use Change, and Forestry 6-5
-------
Inventory (Table 6-5). In 2013, the United States had a total of 293 million hectares of managed Forest Land (1.3
percent increase since 1990), 159 million hectares of Cropland (6.6 percent decrease since 1990), 321 million
hectares of managed Grassland (1.1 percent decrease since 1990), 43 million hectares of managed Wetlands (3
percent decrease since 1990), 51 million hectares of Settlements (31 percent increase since 1990), and 24 million
hectares of managed Other Land (Table 6-5). 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 Inventory (e.g., Grassland Remaining Grassland).12>13
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 Inventory.
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 50 states (Table 6-4). 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 and Alaska. 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 6-4: Managed and Unmanaged Land Area by Land-Use Categories for All 50 States
(Thousands of Hectares)
Land-Use Categories
Managed Lands
Forest Land
Croplands
Grasslands
Settlements
Wetlands
Other Land
Unmanaged Lands
Forest Land
Croplands
Grasslands
Settlements
Wetlands
Other Land
Total Land Areas
Forest Land
Croplands
Grasslands
Settlements
Wetlands
Other Land
1990
890,018
288,964
170,448
324,327
38,602
44,453
23,225
46,212
9,634 •
o
25,782
1
10,796
936,230
298,598
170,448
350,109
38,602
44,453
34,021
2005
890,016
291,213 I
160,107 1
321,360 •
49,676 1
44,060
23,600 •
46,214
9,634
o
25,782
10,798
936,230
300,848 •
160,107 1
347, 142 •
49,676 1
44,060
34,397
2009
890,016
292,263
159,248
320,666
50,628
43,441
23,770
46,214
9,634
0
25,782
0
0
10,798
936,230
301,898
159,248
346,448
50,628
43,441
34,568
2010
890,017
292,399
159,243
320,657
50,624
43,330
23,765
46,213
9,634
0
25,782
0
0
10,797
936,230
302,033
159,243
346,439
50,624
43,330
34,562
2011
890,017
292,516
159,238
320,655
50,621
43,228
23,759
46,213
9,634
0
25,782
0
0
10,797
936,230
302,151
159,238
346,437
50,621
43,228
34,556
2012
890,017
292,634
159,234
320,652
50,617
43,126
23,754
46,214
9,634
0
25,782
0
0
10,797
936,230
302,268
159,234
346,434
50,617
43,126
34,551
2013
890,017
292,751
159,230
320,648
50,614
43,025
23,748
46,214
9,634
0
25,782
0
0
10,797
936,230
302,386
159,230
346,430
50,614
43,025
34,545
12 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.
13 These "managed area" discrepancies also occur in the Common Reporting Format (CRF) tables submitted to the UNFCCC.
6-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 6-5: Land Use and Land-Use Change for the U.S. Managed Land Base for All 50 States
(Thousands of Hectares)
Land-Use & Land-
Use Change
Categories3
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
1990
288,964
283,860
1,119
3,434 1
64
103
383
170,448
154,527
1,148
13,988 1
161
438
185 1
324,327
313,914
1,615 I
Q QQQ H
238 1
112
350 1
44,453
43,802
1431
132
343
1
38,602
34,060
1,787
l,344i
1,353 •
3|
55
23,225
22,175
182
345
454
67
2
890,018
2005
291,213
278,979 1
2,656
7,805 I
250
362 1
1,161 1
160,107
143,050
688 1
15,216
1991
692
262 •
321,360
301,823
3,022 1
14,986 1
409
274
846 1
44,060
42,545
397
365
698
10
44
49,676
35,269
6,112
3,633 I
4,433 |
_
23,600
21,372
538
645
903
121
21 •
890,016
2009
292,263
280,844
2,449
7,279
257
376
1,057
159,248
143,933
577
13,655
176
672
236
320,666
302,566
2,757
13,912
330
267
834
43,441
42,002
382
345
664
10
39
50,628
36,340
6,090
3,526
4,439
30
202
23,770
21,470
569
703
902
104
20
890,016
2010
292,399
280,977
2,450
7,280
257
376
1,059
159,243
143,928
576
13,655
176
672
236
320,657
302,594
2,755
13,878
329
267
834
43,330
41,892
381
345
664
10
39
50,624
36,337
6,090
3,526
4,439
30
202
23,765
21,466
569
703
902
104
20
890,017
2011
292,516
281,092
2,450
7,280
258
376
1,060
159,238
143,924
576
13,655
176
672
236
320,655
302,627
2,753
13,844
329
267
834
43,228
41,792
380
344
664
10
38
50,621
36,334
6,090
3,526
4,439
30
202
23,759
21,460
569
703
902
104
20
890,017
2012
292,634
281,207
2,450
7,281
258
377
1,062
159,234
143,920
576
13,655
175
672
236
320,652
302,660
2,752
13,810
329
267
834
43,126
41,691
379
344
664
10
38
50,617
36,330
6,090
3,526
4,439
30
202
23,754
21,455
570
703
901
104
20
890,017
2013
292,751
281,322
2,450
7,281
259
377
1,063
159,230
143,916
576
13,655
175
672
236
320,648
302,692
2,750
13,776
329
267
834
43,025
41,592
378
344
664
10
38
50,614
36,328
6,089
3,526
4,439
30
202
23,748
21,450
570
703
901
104
20
890,017
Land Use, Land-Use Change, and Forestry 6-7
-------
a The 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).
Note: 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 U.S. Land Representation Assessment includes both
managed and unmanaged lands. U.S. Territories have not been classified into land uses and are not included in the U.S. Land
Representation Assessment. See the Planned Improvements section 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 Inventory.
6-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 6-1: Percent of Total Land Area for Each State in the General Land-Use Categories for
2013
Croplands
Forest Lands
Grasslands
Other Lands
n 10 - 30
• 30 - 50
Settlements
Wetlands
Land Use, Land-Use Change, and Forestry 6-9
-------
Methodology
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, and Grassland to Cropland), using survey
samples or other forms of data, but does not provide location data on all parcels of land. Approach 3 extends
Approach 2 by providing location data on all 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 NPJ, 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. NPJ 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. NPJ 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-cover data that is used to
inform the classification of land use, and is therefore Approach 3 data. 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 definition of managed land is 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.14
• 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
14 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.
6-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.15
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,16 while definitions of Cropland, Grassland, and Settlements are based on the NRI.17 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 120 feet (36.6 meters) wide and at least one
acre (0.4 hectare) in size with at least 10 percent cover (or equivalent stocking) by live trees including land
that formerly had such tree cover and that will be naturally or artificially regenerated. Trees are woody
plants having a more or less erect perennial stem(s) capable of achieving at least 3 inches (7.6 cm) in
diameter at breast height, or 5 inches (12.7 cm) diameter at root collar, and a height of 16.4 feet (5 meters)
at maturity in situ. Forest Land includes all areas recently having such conditions and currently
regenerating or capable of attaining such condition in the near future. Forest Land also 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. Unimproved roads and trails,
streams, and clearings in forest areas are classified as forest if they are less than 120 feet (36.6 meters) wide
or an acre (0.4 hectare) in size. Forest Land does not include land that is predominantly under agricultural
or urban land use (Oswalt et al. 2014).
• 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.18 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 agroforestry, such as alley cropping and windbreaks,19 if the dominant use is crop production.
Lands in temporary fallow or enrolled in conservation reserve programs (i.e., set-asides20) are also
classified as Cropland, 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.21 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
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.
16 See.
17 See .
18 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.
19 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.
20 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.
21 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.
Land Use, Land-Use Change, and Forestry 6-11
-------
addition to tundra are considered Grassland.22 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, if the land is principally grasses, grass-like plants, forbs, and shrubs suitable
for grazing and browsing, and assuming the stand or woodlot does not meet the criteria for Forest Land.
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 cultivation and cranberry production), Grassland (drained wetlands dominated by grass cover),
and Forest Land (including drained or un-drained 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 Settlements 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 bio mass, litter and soil C pools.
Land-Use Data Sources: Description and Application to U.S.
Land Area Classification
U.S. Land-Use Data Sources
The three main sources for land-use data in the United States are the NRI, FIA, and the NLCD (Table 6-6). 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 6-6: Data Sources Used to Determine Land Use and Land Area for the Conterminous
United States, Hawaii, and Alaska
NRI FIA NLCD
Forest Land
Conterminous United
States
Non-Federal
Federal
22 IPCC (2006) guidelines do not include provisions to separate desert and tundra as land categories.
6-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Hawaii
Non-Federal
Federal
Alaska
Non-Federal
Federal
Croplands, Grasslands, Other Lands, Settlements, and Wetlands
Conterminous United
States
Non-Federal •
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 hectare] 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 five-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 five-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 Analysis
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 five 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 five years. See Annex
Land Use, Land-Use Change, and Forestry 6-13
-------
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 2012 through 2013; see Table A-246).
National Land Cover Dataset
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.23
Consequently, major gaps exist when the datasets are combined, such as federal grassland operated by Bureau of
Land Management (BLM), USD A, and National Park Service, as well as Alaska.24 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, 2006, and 2011 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, 2006, and 2011 were used in order to represent
both land use and land-use change for federal lands (Fry et al. 2011, Homer et al. 2007, Jin et al. 2013). 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.
As part of Quality Assurance and 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 U.S. 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 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 U.S. 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;25
• All Forest Land with active fire protection are considered managed;
23 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.
24 The FIA and NRI survey programs also do not include U.S. 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 U.S. Territories.
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 U.S. Land Representation Assessment.
6-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
• All Grassland is considered managed at a county scale if there are livestock in the county;26 other areas
are considered managed if accessible based on the proximity to roads and other transportation corridors,
and/or infrastructure;
• Protected lands maintained for recreational and conservation purposes are considered managed (managed
by public and private organizations);
• Lands with active and/or past resource extraction are considered managed; 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.
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, Jin et al. 2013). 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 National Agricultural Statistics Service livestock population data at the county scale (U.S. Department of
Agriculture 2011). Accessibility is evaluated based on a 10 -km buffer surrounding road and train transportation
networks using the ESRI Data and Maps product (ESRI 2008), and a 10-km buffer surrounding settlements using
NLCD. Lands maintained for recreational purposes are determined from analysis of the Protected Areas Database
(U.S. Geological Survey 2012). However, protected areas that are not accessible to human intervention, including
no suppression of disturbances or extraction of resources, are not included in the managed land base. Multiple data
sources are used to determine lands with active resource extraction: Alaska Oil and Gas Information System
(Alaska Oil and Gas Conservation Commission 2009), Alaska Resource Data File (U.S. Geological Survey 2012),
Active Mines and Mineral Processing Plants (U.S. Geological Survey 2005), and Coal Production and Preparation
Report (U.S. Energy Information Administration 2011). A buffer of 3,300 and 4.000 meters is assumed around
petroleum extraction and mine locations, respectively, to account for the footprint of operation and impacts of
activities on the surrounding landscape. 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). 27 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, another step in the analysis is to address the inconsistencies in the representation of the forest land among
the three databases. NRI and FIA have different criteria for classifying forest land in addition to different 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
26 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.
27 Definitions are provided in the previous section.
Land Use, Land-Use Change, and Forestry 6-15
-------
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).
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 6-6). The result is land use
and land-use change data for the conterminous United States, Hawaii, and Alaska.28
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 soil 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 soil 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 the Planned
Improvements section for details.
• Settlements: 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-use 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
28 Only one year of data are currently available for Alaska so there is no information on land-use change for this state.
6-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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,
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 then Other Land complete the list.
The assignment priority does not reflect the level of importance for reporting GHG emissions and removals on
managed land, but is intended to classify all areas into a discrete 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. The managed land base was further refined this year with the new
implementation criteria incorporating lands protected for recreation in addition to lands with mineral and petroleum
extraction. This change increased the managed land base in Alaska, but had limited impact on the managed land
base in the conterminous United States.
Planned Improvements
A key planned improvement is to fully incorporate area data by land-use type for U.S. Territories 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. Preliminary land-use area data by land-use category are
provided in Box 6-2: Preliminary Estimates of Land Use in U.S. Territories for the U.S. Territories.
Box 6-2: Preliminary Estimates of Land Use in U.S. Territories
Several programs have developed land cover maps for U.S. 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 National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program.
Land-cover data can be used to inform a land-use classification if there is a time series to evaluate the dominate
practices. For example, land that is principally used for timber production with tree cover over most of the time
series is classified as forest land even if there are a few years of grass dominance following timber harvest. These
products were reviewed and evaluated for use in the national Inventory as a step towards implementing a planned
improvement to include U.S. 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 and therefore will be continually updated. 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 U.S. Territories is 1.05
million hectares, representing 0.1 percent of the total land base for the United States.
Land Use, Land-Use Change, and Forestry 6-17
-------
Table 6-7: Total Land Area (Hectares) by Land-Use Category for U.S. Territories.
Cropland
Forest Land
Grasslands
Other Land
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
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, 2000, and 2010 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.
As adopted by the UNFCCC, new guidance in the 2013 Supplement to the 2006 Guidelines 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.
6.2 Forest Land Remaining Forest Land
Changes in Forest Carbon Stocks (IPCC Source Category 4A1)
For estimating carbon (C) stocks or stock change (flux), C in forest ecosystems can be divided into the following
five storage pools (IPCC 2006):
• 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 to account for when estimating C flux:
• Harvested wood products (HWP) in use.
6-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
• 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.
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 harvested biomass 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 COa
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, and these
emissions are reported for information purposes in the Energy Sector with the harvest (i.e., the associated reduction
in forest carbon stocks) and subsequent combustion implicitly accounted for under the Land Use, Land-Use Change
(LULUCF) Sector (i.e., the harvested timber does not enter the HWP pools). 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. These latter fluxes are also accounted for
under the LULUCF Sector.
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 C stock and stock-change relies on data from the extensive inventories of U.S.
forest lands, and improvements 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 for 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. Therefore, 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
(2006) recommends reporting changes in C stocks from forest lands 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 in the Forest
Land Remaining Forest Land Sector (see the Planned Improvements section for more details).
Forest C storage pools, and the flows between them via emissions, sequestration, and transfers, are shown in Figure
6-2. 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 five storage pools identified
in the 2006 IPCC Guidelines. Instead, the storage pools identified 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.
Land Use, Land-Use Change, and Forestry 6-19
-------
Figure 6-2: Forest Sector C Pools and Flows
Forest Sector Carbon Pools and Flows
Combustion from
forest fires (carbon
dioxide, methane)
Legend
Carbon Pool
Carbon transfer or flux
Combustion
Source: Heath et al. 2003
Approximately 34 percent of the U.S. land area is estimated to be forested (Oswalt et al. 2014). The most-recent
forest inventories from each of the conterminous 48 states (USD A Forest Service 2014a, 2014b, and see Annex
Table A-246) include an estimated 264 million hectares of forest land 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 Oswalt et al. (2014) and the forest land included
in this report, which is based on the USD A Forest Service (2014b) forest inventory. Survey data are not yet
available for Hawaii and interior Alaska, but estimates of these areas are included in Oswalt et al. (2014). 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 stocks 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 USD A Forest Service or the
NRI of the USD A Natural Resources Conservation Service (Perry et al. 2005).
An estimated 68 percent (211 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. Ten percent of Alaskan forests and 80 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 69 million hectares are lower productivity forest
lands (Oswalt et al. 2014). 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 8 million hectares over the period from the early 1960s to
the late 1980s. Since then, forest area has increased by about 14 million hectares (Oswalt et al. 2014). Current
trends in the managed forest area represented here increased by an average annual rate of 0.1 percent (see Annex
Table A-248). 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
6-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
forests that leads to an increased rate of growth may increase the eventual bio mass density of the forest, thereby
increasing the uptake and storage of C.29 Though harvesting forests removes much of the aboveground C, on
average the estimated volume of annual net growth nationwide is about double the volume of annual removals on
timberlands (Oswalt et al. 2014). The reversion of cropland or grassland to forest land increases C storage in
biomass, forest floor, and soils. Emerging research into forest ecosystem C stock change for forest remaining forest
versus land-use change transfers to the forest land use suggest that forest ecosystem C accretion continues at steady
rates in most regions of the United States (Figure 6-3) due to the aforementioned drivers. In concert with this trend,
conversion of croplands and grasslands to forest lands continues to facilitate net increases in forest C stocks over
time especially in northern and southern regions. 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.
Figure 6-3: Forest Ecosystem Carbon (All Pools) Stocks and Stock Change (1990-2013)
b 15.000-,
12.000
o
1-
9.000-
6.000
Pacific
. Coast
Rocky
. Mountain
1990 1995 2000 2005 2010
Year
• Forest remaining forest •——•New forest use through land use change
10-
, -20-
-30-
-80-1,
1990
Pacific Coast
1995 2000
North
2005 2010
1995 2000 2005 2010
Rocky Mountain
I90 1995 2000
2000 2005 2010
Year
Forest ecosystem C (all pools) stocks and stock change (1990-2013) analysis attributable to forest remaining forest
and land-use change transfers to forests: (a) Resource planning act assessment regions, (b) forest ecosystem stocks
by region, (c) annual stock change in forest ecosystem C by region decomposed into net transfers into the forest C
pool through land-use change and the net C accumulation in forests remaining forest (including disturbance related
mortality and growth) (for analytical techniques see Coulston et al. in review and Wear and Coulston 2014).
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
2013. The rate of forest clearing 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
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.
Land Use, Land-Use Change, and Forestry 6-21
-------
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 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 775.7
MMT CO2Eq. (211.5 MMT C) in 2013 (Table 6-8, Table 6-9, and Table 6-10). 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 55 to 66 T C/ha between 1990 and 2014 (see Annex 3.13 for estimated 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 2013 are the result of the sequences of new inventories for each state. Carbon 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.
Estimated annual net additions to HWP C stock increased slightly between 2012 and 2013. Estimated net 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. Estimated net additions to products in use for 2013 is about 20 percent of the level of net
additions to products in use in 2007—prior to the recession. Estimated additions to landfills have been relatively
stable over time.
Table 6-8: Estimated Net Annual Changes in C Stocks (MMT COz/yr) in Forest and Harvested
Wood Pools
Carbon Pool 1990 2005 2009 2010 2011 2012 2013
Forest (507.7) (704.4) (710.6) (704.9) (704.9) (704.9) (704.9)
Aboveground (324.6) (402.8) (433.8) (433.7) (433.7) (433.7) (433.7)
Belowground (63.2) I (79.3) I (87.3) (87.4) (87.4) (87.4) (87.4)
Dead Wood (45.9) I (66.8) I (94.2) (95.0) (95.0) (95.0) (95.0)
Litter (26.8) I (11.8) I (11.2) (10.9) (10.9) (10.9) (10.9)
Soil Organic C (47.2) I (143.8) I (84.1) (77.9) (77.9) (77.9) (77.9)
Harvested Wood (131.8) (102.7) I (54.3) (60.5) (68.9) (68.2) (70.8)
Products in Use (64.8) I (42.9) I 6.6 0.4 (7.3) (6.2) (8.4)
SWDS (67.0) (59.8) (60.9) (60.9) (61.6) (62.0) (62.3)
Total Net Flux (639.4) (807.1) (764.9) (765.4) (773.8) (773.1) (775.7)
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 6-9: Estimated Net Annual Changes in C Stocks (MMT 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
1990
(138.5)
(88.5)
(17.2)
(12.5)
(7.3)
(12.9)
(35.9)
(17.7)
(18.3)
1 2005
(192.1)
(109.9)
(21.6) 1
(18.2) 1
(3.2)
(39.2) 1
(28.0) 1
(11.7) 1
1 (16.3)
2009
(193.8)
(118.3)
(23.8)
(25.7)
(3.1)
(22.9)
(14.8)
1.8
(16.6)
2010
(192.2)
(118.3)
(23.8)
(25.9)
(3.0)
(21.2)
(16.5)
0.1
(16.6)
2011
(192.2)
(118.3)
(23.8)
(25.9)
(3.0)
(21.2)
(18.8)
(2.0)
(16.8)
2012
(192.2)
(118.3)
(23.8)
(25.9)
(3.0)
(21.2)
(18.6)
(1.7)
(16.9)
2013
(192.2)
(118.3)
(23.8)
(25.9)
(3.0)
(21.2)
(19.3)
(2.3)
(17.0)
6-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Total Net Flux
(174.4)
(220.1)
(208.6) (208.7) (211.0) (210.8) (211.5)
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 6-10. 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. Although not using the same pool delineations as this inventory
submission, recent research into imputing FIA plot data across the coterminous United States allows spatial
interpretation of forest C pools (Wilson etal. 2013). The imputed C density of individual forest ecosystem pools is
highly variable across the diverse ecosystems of the United States (see Figure 6-5) highlighting the technical hurdles
in refining C accounting across the matrix of changing land uses and ecosystem dynamics (e.g., temperate versus
subtropical forests).
Table 6-10: Estimated Forest area (1,000 ha) and C Stocks (MMT C) in Forest and Harvested
Wood Pools
Forest Area (1000 ha)
Carbon Pools (MMT C)
Forest
Aboveground Biomass
Belowground Biomass
Dead Wood
Litter
Soil Organic C
Harvested Wood
Products in Use
SWDS
Total C Stock
1990
265,938
36,309
12,266
2,430
2,138
2,749
16,726
1,859
1,231
628
38,168
2005
268,334
38,429
13,727
2,717
2,384
2,803
16,798
2,325
1,435
890
40,754
2009
269,396
39,214
14,188
2,809
2,470
2,816
16,931
2,431
1,473
958
41,645
2010
269,536
39,408
14,306
2,833
2,496
2,819
16,954
2,446
1,472
974
41,854
2011
269,661
39,600
14,425
2,857
2,522
2,822
16,975
2,462
1,471
991
42,062
2012
269,786
39
14
2
2
2
16
2
,792
,543
,881
,548
,825
,996
,481
1,473
1
42
,008
,273
2013
269,911
39,985
14,661
2,904
2,574
2,828
17,017
2,500
1,475
1,025
42,485
2014
270,035
40,177
14,780
2,928
2,600
2,831
17,038
2,520
1,478
1,042
42,697
Note: Forest area and carbon stock estimates include all forest land in the conterminous 48 states plus managed forests in coastal
Alaska (Figure 6-6), which is the current area encompassed by FIA survey data. A recent methodological change implemented to
address missing forest area data in coastal Alaska resulted in discrepancies between the coastal Alaska managed forest area of 1990
through 2014, as contributes to this table, and the areas presented in Section 6.1 "Representation of the United S Land
Base". Coastal Alaska managed forest lands contributing to this table changed linearly from 5.77 million hectares in 1990 to 5.86
million hectares in 2014. The estimates used for Section 6 changed linearly from 5.48 million hectares in 1990 to 5.95 million
hectares in 2014. This represents a change of 5.3 and-1.5 percent for 1990 and 2014 in coastal Alaska, respectively. This
discrepancy will be corrected in the 2016 submission. 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, agroforestry 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 etal. (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 2013 requires
estimates of C stocks for 2013 and 2014.
Land Use, Land-Use Change, and Forestry 6-23
-------
Figure 6-4: Estimates of Net Annual Changes in C Stocks for Major C Pools
25 n
Harvested Wood
Soil
Forest, Nonsoil
Total Net Change
6-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 6-5: Forest Ecosystem C Density Imputed from Forest Inventory Plots, Conterminous
United States, 2001-2009
Figure 6-5 shows: (A) Total forest ecosystem C, (B) aboveground live trees, (C) standing dead trees, (D) litter, and
(E) soil organic C (Wilson et al. 2013).
Land Use, Land-Use Change, and Forestry 6-25
-------
Box 6-3: COz 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
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. forest land 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 2013 were estimated to be 77.9 MMT CCh/yr. This amount is masked in the
estimate of net annual forest C stock change for 2013 because this net estimate accounts for the amount sequestered
minus any emissions.
Table 6-11: Estimates of COz (MMT/yr) Emissions from Forest Fires for the Lower 48 States
and Alaska
Year
CCh emitted from
Wildfires in Lower 48
States (MMT/yr)
CCh emitted from
Prescribed Fires in Lower
48 States (MMT/yr)
CCh emitted from
Wildfires in Alaska
(MMT/yr)
Total CO2
emitted
(MMT/yr)
1990
28.
33.7
2009
2010
2011
2012
2013
63.5
49.5
182.7
197.7
66.2
14.5
13.9
12.2
11.5
11.7
+ 77.9
+ 63.4
+ 194.9
+ 209.1
+ 77.9
+ Does not exceed 0.05 MMT CO2 Eq.
Note: 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 (2006). 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 and associated
models (e.g., live tree belowground biomass) in the United States (USDA Forest Service 2013b, 2013c). Carbon
conversion factors were applied at the disaggregated level of each inventory plot and then appropriately expanded to
6-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
population estimates. A combination of tiers as outlined by IPCC (2006) were used. The Tier 3 biomass C
estimates were calculated from forest inventory tree-level data. The Tier 2 dead organic and soil C estimates were
obtained from empirical or theoretical models using 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 2014b). Inventories include data collected on permanent inventory plots on forest lands
and were organized as separate datasets, each representing a complete inventory, or survey, of an individual state at
a specified time. Many of the more recent annual inventories reported for states are 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 2014d). Forest C calculations are organized according to these state surveys, and the
frequency of surveys varies by state. All available datasets are identified for each state starting with pre-1990 data,
and all unique surveys are 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 are 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 (2014b) as the
Forest Inventory and Analysis Database (FIADB) Version 6.0 (USDA Forest Service 2014c). 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—are used if available.
Second, Resources Planning Act Assessment (RPA) databases, which are periodic, plot-level only, summaries of
state inventories, are used to provide the data at or before 1990. Finally, the Integrated Database (IDE), which is a
compilation of periodic forest inventory data from the 1990s for California, Oregon, and Washington is used
(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 are included in this Inventory. See USDA Forest Service (2014a) 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.
Modifications to the use of some of the FIADB surveys or subsequent C conversions were initiated for this report.
First, the most-recent FIA population summary (known as an evaluation within the FIADB) was incorporated into
all states' stock-change calculations which stands in contrast to the approach in previous years where most of the
newest evaluations were already in use, but if the majority of the underlying plots in the most recent population were
also a part of the previous population (i.e., over 50 percent redundant plots) then the recent population was
considered insufficiently unique and not used for calculation. Second, modifications were conducted in coastal
Alaska for developing net annual change estimates (see Annex 3.13) and separating managed versus unmanaged
forest lands in order to exclude C stock and stock-change on unmanaged forest land (IPCC 2006, Ogle et al. in
preparation). This reduced the plots contributing to the Alaska forest C estimates by about 5 percent. A third
modification to the use of the FIADB-defined forest land, introduced this year, was applied to identify plots on
woodland forest types that do not meet the height requirement within the definition of forest land (Oswalt et al.
2014, Coulston et al. in preparation). These plots were identified as "other wooded lands" (i.e., not "forest" within
the FIA forest inventory) and provided as C density information to the grasslands land-use category as the plots
were not a complete inventory of the grassland land-use category in the United States. Finally, a new model
estimating plot level C density of litter was developed and incorporated into the C budget (Domke et al. in
preparation).
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, Woodall et al. 201 la,
Domke et al. 2011, Domke et al. 2012, Domke et al. in preparation), 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 (T C per hectare) for six forest ecosystem pools: Live trees, standing
dead trees, understory vegetation, downed 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
Land Use, Land-Use Change, and Forestry 6-27
-------
biomass, and standing dead trees and downed 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 2014 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
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 CPJVI 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 P^PA plot-level summaries. The C estimates for these plots were based on
average densities (T 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 214 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, downed dead wood, and
litter—with C stocks estimated from sample data or from models. 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 et al. 2011, Harmon et al. 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 (T C per hectare) obtained from plots of
more recent surveys with similar stand characteristics and location. This applied to less than 20 percent of the forest
land inventory-plot-to-C conversions within the 214 state-level surveys utilized here. Downed 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 etal. 2013). 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. 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 Domke et al. (in preparation).
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
6-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
described by Amichev and Galbraith (2004). Links to FIA inventory data were developed with the assistance of the
USD A 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
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, forthcoming).
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.
Land Use, Land-Use Change, and Forestry 6-29
-------
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 2013 net
annual change for forest C stocks was estimated to be between -972.9 and -575.9 MMT CCh Eq. at a 95 percent
confidence level. This includes a range of -900.7 to -505.9 MMT COa Eq. for forest ecosystems and -89.9 to -54.0
MMT CO2 Eq. for HWP.
Table 6-12: Approach 2 Quantitative Uncertainty Estimates for Net COz Flux from Forest
Land Remaining Forest Land: Changes in Forest C Stocks (MMT COz Eq. and Percent)
„ „ 2013 Flux Estimate Uncertainty Range Relative to Flux Estimate3
°UrCe aS (MMT CCh Eq.) (MMT CCh Eq.) (%)
Forest Ecosystem
Harvested Wood Products
Total Forest
CO2
CO2
CO2
(704.9)
(70.8)
(775.7)
Lower
Bound
(900.7)
(89.9)
(972.9)
Upper
Bound
(505.9)
(54.0)
(575.9)
Lower
Bound
-27.8
-27.0
-25.4
Upper
Bound
28.2
23.7
25.8
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 2013. 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 (USD A Forest Service 2014d).
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 6.0, which are available at an FIA internet site (USDA Forest Service 2014b).
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
of 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
6-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.
Recalculations Discussion
Forest ecosystem stock and stock-change estimates differ from the previous Inventory (EPA 2014) principally due to
some changes in data and methods (see discussion above in Methodology and in Annex 3.13). The net effect of the
modifications was to slightly reduce net C uptake (i.e., lower sequestration) and C stocks from 1990 to the present.
The influence of the individual modifications on stock and stock-change varied considerably; these were evaluated
to identify the relative sensitivity of totals to each. That is, the analysis identified where the estimates (as in Tables
Table 6-8 through Table 6-10) were most affected by the revised methods incorporated with this report. First, the
collective effects of selecting FIA population estimates and updates to the annual forest inventories for many states
had the effect of decreasing sequestration in early years while increasing after 2005 and had the greatest effect on
determining overall stock-change estimates for 2006 and 2007, but otherwise this modification was a minor
influence. Second, the application of a new managed land definition as part of the land representation analysis (see
Section 6.1) and the subsequent decrease in managed forest lands along coastal Alaska affected that individual
state's estimates but had minimal effect on C stock estimates for the United States as a whole. Third, the
reallocation of selected woodlands from forest land (i.e., these "other wooded lands" were then classified as
grasslands) had the greatest effect on annualized estimates of forest area throughout the time series. In addition, the
removal of these lands from forest had the greatest effect on total forest stock-change through the early 1990s, yet
the reclassification did tend to decrease sequestration throughout the entire time series. Finally, the revised litter C
estimates generally had a lower influence on stock-change relative to the woodland modification. However, the
revised litter estimates increased sequestration through the 1990s but decreased sequestration over more recent
years. In addition, the change in estimated litter C had the greatest effect on forest ecosystem stocks throughout the
time period.
The estimate of net annual change in HWP C stock and total C stock in HWP were revised upward by small
amounts. The increase in total net annual additions compared to estimates published in 2013 was 2 to 3 percent for
2010 through 2012. This increase 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 forthcoming).
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 Inventory
submissions. 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 are 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 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 1990 through 2012 Inventory report the downed dead tree C model was refined by
incorporation of a national field inventory of downed dead wood (Woodall et al. 2013, Domke etal. 2013). In the
current Inventory report, the litter C density model was refined with a nearly nationwide field inventory (Domke et
al. in preparation). The exact timing of future pool estimation refinements is dependent on the completion of current
research efforts. Research is underway to use a national inventory of SOC (Woodall et al. 20lib) to refine the
estimation of this pool. It is expected that improvements to SOC estimation will be incorporated into the 1990
through 2015 Inventory report. Components of other pools, such as C in belowground biomass (Russell et al. in
preparation) and understory vegetation (Russell et al. in press), are being explored but may require additional
investment in field inventories before improvements can be realized with Inventory submissions.
Land Use, Land-Use Change, and Forestry 6-31
-------
Despite the continuing accumulation of new data within the 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 more fully examine land-use change within the FIA inventory system (see Figure 6-3; Coulston et al. in
review, Wear and Coulston 2014) and bring 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 a few years for completion with subsequent time needed for application to
future Inventory submissions.
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 accuracy and precision of forest C estimates as new state surveys become
available (USDA Forest Service 2013b), particularly in western states. Hawaii and U.S. territories will be included
when appropriate forest C data are available (as of July 21, 2014, Hawaii is notyet reporting any data from the
annualized sampling design). In addition, the more intensive sampling of fine woody debris, litter, and SOC on a
subset of FIA plots continues and will substantially improve resolution of C pools (i.e., greater sample intensity;
Westfall et al. 2013) this information becomes available (Woodall et al. 2011b). Increased sample intensity of some
C pools and using annualized sampling data as it becomes available for those states currently not reporting are
planned for future submissions. The USDA Forest Service FIA Program's forest and wooded land inventories
extend beyond the forest land-use (e.g., woodlands and urban areas), and Inventory-relevant information for these
lands will likely become increasingly available in coming years.
Towards an Accounting of Managed Forest Carbon in Interior Alaska
Given the remote nature and vast expanse of forest across the state of Alaska, consistent inventories of all Alaskan
forest land have never been conducted. Figure 6-6 compares the vast expanse of Alaska to countries in Europe,
which in large part explains the lack of a consistent forest inventory and provides an indication of the extent of any
effort to include an area of this magnitude using the existing forest inventories for the United States. Starting in the
1990s, a forest inventory of south central and southeastern coastal (SCSE) Alaska was initiated following the same
approach applied in the conterminous United States (see Figure 6-7).
6-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 6-6: The Size of Alaska Compared to European Countries
The Size of Alaska
Compared to European Countries
TiF*Łrv'*"T'-» ic-
K IKi A _ '
«^-"»-
Land Use, Land-Use Change, and Forestry 6-33
-------
Figure 6-7: Delineations between Forest, Non-forest, Managed Land, and Inventoried Areas
of Alaska
Unmanaged Forest Land
Managed Forest Land
FIA Inventory Area
FIA Inventory Coastal
South Central Area Detail
FIA Inventory Coastal Southeast Area Deta
300 600 900 km
Forest land data was derived from the North American Land Change Monitoring
System (NALCMS) 2010 North American Land Cover data set. published by
Commission for Environmental Cooperation.
Establishment and data collection on these plots began in 1995 with the current inventory nearing completion of a
full re-measurement (i.e., one cycle of periodic inventory represented by the 2003 data and 90 percent of an annual
inventory cycle represented by the 2012 data). Forest C estimates for SCSE Alaska were first included in the
Inventory in 2008. The managed forest land in SCSE Alaska has been the only contribution to the Inventory since
2008 owing to the lack of a consistent inventory across the much larger interior portion of Alaska that generally
includes less productive forest lands.
Recognizing the need to inventory interior Alaskan forests for the Inventory and resource management, research is
being conducted towards these ends:
• A spatial model delineating managed and unmanaged lands for Alaska was developed in part to better align
greenhouse gas reporting with managed lands for Alaskan forests (Ogle et al. in preparation). In contrast to
Alaska, all forest lands in the conterminous 48 states are considered managed for purposes of greenhouse
gas reporting. The spatial model of managed lands for Alaska is applied to both the preliminary assessment
of interior Alaskan forest C provided here and the reported C of SCSE Alaska in order to align with the
practice of reporting of forest C on managed lands per IPCC (2006) Good Practice Guidelines.
• Research continues to better appraise the forest C stocks and their associated dynamics across the Alaskan
landscape that rely on remotely sensed imagery and limited in situ measurements. Based on this emerging
work the amount of managed forest land and ranges of C stocks will be estimated. This current work
(McGuire et al. in preparation, Genet et al. in preparation, Saatchi et al. in preparation) has identified 46-49
million hectares of managed forestland in interior Alaska. This represents 68 percent of total interior forest
land. Live biomass (e.g., vegetation) C stocks are estimated to range between 1,600 and 2,100 MMT C and
non-live biomass (e.g., soils, deadwood, litter) is estimated to range between 6,100 and 13,000 MMT C),
all with concomitant high levels of uncertainty.
6-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
• A joint USDA Forest Service-National Aeronautics and Space Administration research effort was
conducted in interior Alaska during the summer of 2014 where high-resolution airborne scanning laser,
hyperspectral, and thermal imagery were collected in a sampling mode over the entire Tanana valley
(135,000 km2). These remotely-sensed data will be combined with a limited number of in situ plot
measurements (100 FIA plots collected within the Tanana Valley State Forest and Tetlin National Wildlife
Refuge) to explore potential application across interior Alaska (NASA CMS 2014). Results from this
research study are expected within a few years.
As preliminary research results suggest that the managed forest C stock may be upwards of 15,000 MMT C or 37
percent of the United States' managed forest C stock in the current Inventory, care must be given to vet all emerging
research especially in regards to stock change. It is hoped that the managed forest land base in interior Alaska might
be included in future Inventories if: (a) adequate funding resources become available, and (b) research into
combining remotely sensed technologies with in situ measurements (especially of non-vegetation pools) is a success.
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 2013 were estimated to be 5.8 MMT CO2 Eq. of CH4 and 3.8 MMT CO2 Eq. of N2O, as shown in Table
6-13 and Table 6-14. 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 6-13: Estimated Non-COz Emissions from Forest Fires (MMT COz Eq.) for U.S. Forests
Gas
CH4
N20
Total
1990
2.5
1.7 •
4.2
2005
8.3
5.5
13.8
2009
5.8
3.8
9.7
2010
4.7
3.1
7.9
2011
14.6
9.6
24.2
2012
15.7
10.3
26.0
2013
5.8
3.8
9.7
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP
values.
Note: Calculated based on C emission estimates in Changes in Forest Carbon Stocks and
default factors in IPCC (2006).
Table 6-14: Estimated Non-COz Emissions from Forest Fires (kt) for U.S. Forests
Gas 1990 2005 2009 2010 2011 2012 2013
CH4 101 332 233 190 584 626 233
N2Q 6 18 13 11 32 35 13_
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 6-15) 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.
Land Use, Land-Use Change, and Forestry 6-35
-------
Estimates for C emitted from forest fires are the same estimates used to generate estimates of CO2 presented earlier
in Box 6-3. 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 6-15: Estimated C Released from Forest Fires for U.S. Forests (MMT/yr)
Year C Emitted (MMT/yr)
1990
2009
2010
2011
2012
2013
22.9
18.6
57.3
61.5
22.9
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 (Approach 2) uncertainty analysis was
performed using information about the uncertainty surrounding each of these variables. The results of the Approach
2 quantitative uncertainty analysis are summarized in Table 6-16.
Table 6-16: Approach 2 Quantitative Uncertainty Estimates of Non-COz Emissions from
Forest Fires in Forest Land Remaining Forest Land'(MMT COz Eq. and Percent)
Source
Gas
2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
(MMT CCh Eq.) (MMT CCh Eq.) (%)
Non-CCh Emissions from
Forest Fires
Non-CCh Emissions from
Forest Fires
CH4
N20
5.8
3.8
Lower Upper
Bound Bound
1.1 15.2
1.1 9.2
Lower
Bound
-80%
-71%
Upper
Bound
+161%
+139%
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 2013. 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. The QA/QC analysis did not reveal any inaccuracies or incorrect
input values.
Recalculations Discussion
The current Inventory estimates for 1990 through 2013 were developed according to the methodology used in the
previous Inventory report. However, the FIADB updates discussed in Changes in Forest Carbon Stocks affected
forest C stocks, C density of litter, and total forest area, including the forest area estimates for coastal Alaska, all of
6-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
which are used to calculate emissions estimates from forest fires. As a result of the FIADB updates, total non-CO2
emissions from forest fires decreased by an average of 14 percent relative to emission estimates in the previous
Inventory report.
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWP of CH4 has increased, leading to an overall increase in CO2-equivalent emissions from
CH4. The GWP of N2O has decreased, leading to a decrease in CO2-equivalent emissions for N2O. The AR4 GWPs
have been applied across the entire time series for consistency. For more information please see the Recalculations
and Improvements Chapter.
The combined effect of the FIADB updates and AR4 GWP values resulted in an average 7 percent decrease in total
non-CO2 emissions from wildfires and prescribed fires over the 1990 to 2012 time series.
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 through a portion of 2013. MTBS burn area data could be used to develop the
national area burned and resulting CO2 and non-CO2 emissions. Additional research is required to determine
appropriate uncertainty inputs for national area burned data derived from MTBS data.
N2O Fluxes from Forest Soils (IPCC Source Category 4A1)
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 cropland 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 midway through their life cycle). 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 annual application rate is quite low over the entire forestland area.
N additions to soils result in direct and indirect N2O emissions. Direct emissions occur on-site due to the N
additions. Indirect emissions result from fertilizer N that is transformed and transported to another location in a form
other than N2O (NH3 and NOX volatilization, NOs leaching and runoff), and later converted into N2O at the off-site
location. The indirect emissions are assigned to forest land because the management activity leading to the
emissions occurred in forest land.
Direct N2O emissions from forest soils in 2013 were 0.3 MMT CO2 Eq. (1 kt), and the indirect emission were 0.1
MMT CO2 Eq. (0.4 kt). Total emissions for 2013 were 0.5 MMT CO2 Eq. (2 kt) and have increased by 455 percent
from 1990 to 2013. Increasing emissions over the time series is a result of greater 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 6-17.
Table 6-17: NzO Fluxes from Soils in ForestLandRemaining ForestLand'(MMT COz Eq. and
kt NzO)
Direct N2O Fluxes from Soils
MMT CO2 Eq.
ktN20
Indirect N2O Fluxes from Soils
MMT C02 Eq.
1990
0.1
1
2005
0.3
1
2009
0.3
1
0.1
2010
0.3
1
0.1
2011
0.3
1
0.1
2012
0.3
1
0.1
2013
0.3
1
0.1
Land Use, Land-Use Change, and Forestry 6-37
-------
ktN2Q
Total
MMTCChEq. 0.1 • 0.5 • 0.5 0.5 0.5 0.5 0.5
ktN20 +• 2 22222
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP
values.
+ Does not exceed 0.05 MMT CO2 Eq. or 0.5 kt.
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-
2013, 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 (approximately 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.
Similar to the Southeast, data were not available for 2005 through 2013, so data from 2004 were used for these
years. 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.
For indirect emissions, the volatilization and leaching/runoff N fractions for forest land were calculated using the
IPCC default factors of 10 percent and 30 percent, respectively. The amount of N volatilized was multiplied by the
IPCC default factor of 1 percent for the portion of volatilized N that is converted to N2O off-site. The amount of N
leached/runoff was multiplied by the IPCC default factor of 0.075 percent for the portion of leached/runoff N that is
converted to N2O off-site The resulting estimates were summed to obtain total indirect emissions.
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
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 level30 of uncertainty at ±50 percent, and area receiving fertilizer was
assigned a ±20 percent according to expert knowledge (Binkley 2004). The uncertainty ranges around the 2005
activity data and emission factor input variables were directly applied to the 2013 emissions estimates. IPCC (2006)
provided estimates for the uncertainty associated with direct and indirect N2O emission factor for synthetic N
fertilizer application to soils.
30
Uncertainty is unknown for the fertilization rates so a conservative value of ±50 percent was used in the analysis.
6-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Quantitative uncertainty of this source category was estimated using simple error propagation methods (IPCC 2006).
The results of the quantitative uncertainty analysis are summarized in Table 6-18. Direct N2O fluxes from soils
were estimated to be between 0.1 and 1.1 MMT CO2 Eq. at a 95 percent confidence level. This indicates a range of
59 percent below and 211 percent above the 2013 emission estimate of 0.3 MMT €62 Eq. Indirect N2O emissions in
2013 were between 0.02 and 0.4 MMT CO2 Eq., ranging from 86 percent below to 238 percent above the 2013
emission estimate of 0.11 MMT CO2 Eq.
Table 6-18: Quantitative Uncertainty Estimates of NzO Fluxes from Soils in Forest Land
Remaining Forest Land'(MMT COz Eq. and Percent)
-------
6.4 Cropland Remaining Cropland (IPCC Source
Category 4B1)
Mineral and Organic Soil Carbon Stock Changes
Carbon (C) in cropland ecosystems occurs in biomass, dead biomass, and soils. However, C storage in biomass and
dead organic matter is relatively ephemeral, with the exception of C stored in perennial woody crop biomass, such
as citrus groves and apple orchards. Within soils, C is found in organic and inorganic forms of C, but soil organic C
(SOC) is the main source and sink for atmospheric CCh in most soils. IPCC (2006) recommends reporting changes
in SOC stocks due to agricultural land-use and management activities on both mineral and organic soils.31
Well-drained mineral soils typically contain from 1 to 6 percent organic C by weight, whereas mineral soils with
high water tables for substantial periods during the year may contain significantly more C (NRCS 1999).
Conversion of mineral soils from their native state to agricultural land uses can cause up to half of the SOC to be
lost to the atmosphere due to enhanced microbial decomposition. The rate and ultimate magnitude of C loss
depends on subsequent management practices, climate and soil type (Ogle et al. 2005). 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 (Parton
et al. 1987, Paustian et al. 1997a, Conant et al. 2001, Ogle et al. 2005). 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 (Paustian et al. 1997b).
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), and form under inundated conditions that results in minimal decomposition of plant residues.
When organic soils are prepared for crop production, they are drained and tilled, leading to aeration of the soil that
accelerates both the decomposition rate and CO2 emissions. Due to the depth and richness of the organic layers, C
loss from drained organic soils can continue over long periods of time, which varies depending on climate and
composition (i.e., decomposability) of the organic matter (Armentano and Menges 1986). Due to deeper drainage
and more intensive management practices, the use of organic soils for annual crop production leads to higher C loss
rates than drainage of organic soils in grassland or forests (IPCC 2006).
Cropland Remaining Cropland includes all cropland in an Inventory year that has been used as cropland for the
previous 20 years according to the 2007 USDA National Resources Inventory (NRI) land-use survey (USDA-NRCS
2009).32 The inventory includes all privately-owned croplands in the conterminous United States and Hawaii, but
does not include the 1 to 1.5 million hectares of Cropland Remaining Cropland (less than 1 percent of the total
cropland area in the United States) on federal lands between 1990 and 2013. In addition, approximately 28,700
hectares of cropland in Alaska are not included in this Inventory. This leads to a discrepancy between the total
amount of managed area in Cropland Remaining Cropland (see Section 6.1) and the cropland area included in the
Inventory. Improvements are underway to include croplands in Alaska and federal lands as part of future C
inventories.
CO2 emissions and removals33 due to changes in mineral soil C stocks are estimated using a Tier 3 approach for the
majority of annual crops (Ogle et al. 2010). A Tier 2 IPCC method is used for the remaining crops not included in
the Tier 3 method (i.e., vegetables, tobacco, perennial/horticultural crops, and rice) (Ogle et al. 2003, 2006). 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
31 CO2 emissions associated with liming are also estimated but are included in a separate section of the report.
32 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.
Note that removals occur through uptake of CCh into crop and forage biomass that is later incorporated into soil C pools.
6-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.
Land-use and land management of mineral soils was the largest contributor to total net C stock change, especially in
the early part of the time series (see Table 6-19 and Table 6-20). (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). In 2013, mineral
soils were estimated to remove 45.6 MMT CO2 Eq. (12.4 MMT C). This rate of C storage in mineral soils
represented about a 49 percent decrease in the rate since the initial reporting year of 1990. Emissions from organic
soils were 22.1 MMT CChEq. (6.0 MMT C) in 2013, which is an 8 percent decrease compared to 1990. In total,
United States agricultural soils in Cropland Remaining Cropland sequestered approximately 23.4 MMT CC>2 Eq.
(6.4 MMT C) in 2013.
Table 6-19: Net COz Flux from Soil C Stock Changes in Cropland Remaining Cropland (V\W\
COz Eq.)
Soil Type
Mineral Soils
Organic Soils
Total Net Flux
1990
(89.2) 1
24.0 •
(65.2)
2005
(50.4) 1
22.4
(28.0)
2009
(49.6)
22.1
(27.5)
2010
(48.0)
22.1
(25.9)
2011
(47.9)
22.1
(25.8)
2012
(47.1)
22.1
(25.0)
2013
(45.6)
22.1
(23.4)
Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
Note: Estimates after 2007 are based on NRI data from 2007 and therefore may not fully reflect
changes occurring in the latter part of the time series
Table 6-20: Net COz Flux from Soil C Stock Changes in Cropland Remaining Cropland (V\W\
C)
Soil Type
Mineral Soils
Organic Soils
Total Net Flux
1990
(24.3)
6.5
(17.8)
2005
(13.8) 1
6.1
(7.6)
2009
(13.5)
I 6.0
(7.5)
2010
(13.1)
6.0
(7.1)
2011
(13.1)
6.0
(7.0)
2012
(12.9)
6.0
(6.8)
2013
(12.4)
6.0
(6.4)
Note: Totals may not sum due to independent rounding. Parentheses indicate net
sequestration.
Note: Estimates after 2007 are based on NRI data from 2007 and therefore may not
fully reflect changes occurring in the latter part of the time series
The major cause of the reduction in soil C accumulation over the time series (i.e., 2013 is 49 percent less than 1990)
is the decline in annual cropland enrolled in the Conservation Reserve Program (CRP)34 which was initiated in 1985
(Jones et al., in prep). For example, over 2 million hectares of land in the CRP were returned to agricultural
production, during the last 5 years resulting in a loss of soil C. However, positive increases in C stocks continue on
the nearly 11 million hectares of land currently enrolled in the CRP, 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 2013 annual CC>2 flux is displayed in Figure 6-8 and Figure 6-9 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 region with the largest amounts of conservation tillage, with the next highest rates of
accumulation in the South-central and Northwest regions of the United States. The regions with the highest rates of
emissions from organic soils occur in the Southeastern Coastal Region (particularly Florida), upper Midwest and
34 The Conservation Reserve Program (CRP) is a land conservation program administered by the Farm Service Agency (FSA).
In exchange for a yearly rental payment, farmers enrolled in the program agree to remove environmentally sensitive land from
agricultural production and plant species that will improve environmental health and quality. Contracts for land enrolled in CRP
are 10-15 years in length. The long-term goal of the program is to re-establish valuable land cover to help improve water quality,
prevent soil erosion, and reduce loss of wildlife habitat.
Land Use, Land-Use Change, and Forestry 6-41
-------
Northeast surrounding the Great Lakes, and the Pacific Coast (particularly California), which coincides with largest
concentrations of organic soils in the United States that are used for agricultural production.
Figure 6-8: Total Net Annual COz Flux for Mineral Soils under Agricultural Management
within States, 2013, 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 computations. See methodology
for additional details.
MMTCO2Eq/yr
>0
-0.1 toO
-0.5 to-0.1
-1 to -0.5
-2 to -1
6-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 6-9: Total Net Annual COz Flux for Organic Soils under Agricultural Management
within States, 2013, Cropland Remaining Cropland
Note: Values greater than zero represent emissions.
MMTCO2Eq/yr
• >2
| 1 to 2
O 0.5 to 1
Q 0.1 to 0.5
GO to 0.1
G No organic soils
Methodology
The following section includes a description of the methodology used to estimate changes in soil C stocks for
Cropland Remaining Cropland, including (1) agricultural land-use and management activities on mineral soils; and
(2) agricultural land-use and management activities on organic soils.
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 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.35 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,
NRI points were classified as agricultural if under grassland or cropland management between 1990 and 2007.
Land Use, Land-Use Change, and Forestry 6-43
-------
and 1994-1997). In 1998, the NRI program began collecting annual data, and data are currently available through
2010 (USDA-NRCS, 2013) although this Inventory only uses NRI data through 2007 because newer data were not
made available in time to incorporate the additional years into this Inventory. 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.36 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 CRP (i.e., considered reserve cropland).
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
on the majority of land that is used to produce annual crops in the United States. These 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. 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 the
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 CRP 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 biogeochemical37 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 250 m. 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 (2006) because the
simulation model treats changes as continuous over time as opposed to the simplified discrete changes represented
in the default method (see Box 6-4 for additional information).
36 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.
Biogeochemical cycles are the flow of chemical elements and compounds between living organisms and the physical
environment
6-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Box 6-4: 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 on
mineral soils. This approach results in a more complete accounting of soil C stock changes and entails several
fundamental differences from the IPCC Tier 1 or 2 methods, as described below.
(1) The IPCC Tier 1 and 2 methods are simplified and classify land areas into discrete categories based on
highly aggregated information about climate (six regions), soil (seven types), and management (eleven
management systems) in the United States. In contrast, in the Tier 3 model, the same variables (i.e.
climate, soils, and management systems) are represented in considerably more detail both temporally and
spatially, and exhibit multi-dimensional interactions through the more complex model structure.
(2) The IPCC Tier 1 and 2 methods have a simplified spatial resolution, where, in the United States, data is
aggregated to climate and soil regions. In contrast, the Tier 3 model uses more than 300,000 individual NRI
point locations in individual fields.
(3) The IPCC Tier 1 and 2 methods use simplified equilibrium step changes for changes in carbon emissions.
In contrast, the Tier 3 approach simulates a continuous time period. More specifically, the DAYCENT
model (i.e., daily time-step version of the Century model) simulates soil C dynamics (and CC>2 emissions
and uptake) on a daily time step based on C emissions and removals from plant production and
decomposition processes. These changes in soil C stocks are influenced by multiple sources that affect
primary production and decomposition, including changes in land use and management, weather variability
and secondary feedbacks between management activities, climate, and soils.
Historical land-use patterns are simulated with DAYCENT based on the 2007 USDA NRI survey, in addition to
information on irrigation (USDA-NRCS 2009). 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 forfurther 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 5.2, 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 2005). The C 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 C 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 2013 were assumed to be similar to 2007 for
this Inventory due to a lack of activity data for these years. (Future Inventories will be updated with new activity
data and the time series will be recalculated; see Planned Improvements section).
Land Use, Land-Use Change, and Forestry 6-45
-------
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 (Ogle et al. 2003, 2006). 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 (2006). Soil
measurements under agricultural management are much more common and easily identified in the National Soil
Survey Characterization Database (NRCS 1997) than are soils under a native condition, and therefore cultivated
cropland provided a more robust sample for estimating the reference condition.
U. S.-specific stock change factors were derived from published literature to determine the impact of management
practices on SOC storage (Ogle et al. 2003, Ogle et al. 2006). The factors include changes in tillage, cropping
rotations, intensification, and land-use change between cultivated and uncultivated conditions. U.S. factors
associated with organic matter amendments were not estimated due to 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 2007 NRI
(USDA-NRCS 2009). 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 2013 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 2013 were adjusted to account for additional C stock
changes associated with gains or losses in soil C after 2007 due to changes in CRP enrollment (USDA-FSA 2013).
The change in enrollment relative to 2007 was based on data from USDA-FSA (2013) for 2008 through 2013. 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 3.12 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 (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 NRI (USDA-NRCS 2009). The annual emissions estimated for 2007 were applied to
6-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
2007 through 2013. (Future inventories will be updated with new activity data and the time series will be
recalculated; see Planned Improvements section).
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
6-21 for each subsource (mineral soil C stocks and organic soil C stocks) and the 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 152 percent below to 154 percent above the 2013 stock change estimate of -23.4 MMT CCh Eq.
Table 6-21: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes
occurring within Cropland Remaining Crop/and (MMJ COz Eq. and Percent)
Source
2013 Flux Estimate
(MMT CCh Eq.)
Uncertainty Range Relative to Flux Estimate3
(MMT CQ2 Eq.) (%)
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
Combined Uncertainty for Flux associated
with Agricultural Soil Carbon Stock
Change in Cropland Remaining Cropland
(49.3)
(2.8)
6.6
22.1
(23.4)
Lower
Bound
(83.7)
(5.1)
3.3
14.0
(59.0)
Upper
Bound
(14.9)
(0.9)
9.9
32.5
12.7
Lower
Bound
-70%
-80%
-50%
-37%
-152%
Upper
Bound
70%
68%
50%
47%
154%
a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Note: Parentheses indicate net sequestration.
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 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
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
Land Use, Land-Use Change, and Forestry 6-47
-------
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).
Recalculations Discussion
Methodological recalculations in the current Inventory were associated with the following improvements: 1) refining
parameters associated with simulating crop production and carbon inputs to the soil in the DAYCENT
biogeochemical model; 2) improving the model simulation of snow melt and water infiltration in soils; and 3)
driving the DAYCENT simulations with updated input data for managed manure based on national livestock
population. The change in SOC stocks increased by an average of 4.3 MMT CC>2 Eq. over the time series as a result
of the improvements to the Inventory.
Planned Improvements
Two major planned improvements are underway. The first is to update the time series of land use and management
data from the USD A NRI so that it is extended from 2008 through 2010 for both the Tier 2 and 3 methods (USDA-
NRCS 2013). 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 20 10 in order to use the EVI data to
drive crop production in DAYCENT. 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, 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 5.5). 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 2014 Inventory. However, the time
line may be extended if there are insufficient resources to fund all or part of these planned improvements.
Emissions from Agricultural Liming
IPCC (2006) recommends reporting CO2 emissions from lime additions (in the form of crushed limestone (CaCOs)
and dolomite (CaMg(CO3)2) to agricultural soils. Limestone and dolomite are added by land managers to increase
soil pH (i.e., to 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 of agricultural
soils have fluctuated over the past 23 years, ranging from 3.7 MMT CO2 Eq. to 5.9 MMT CO2 Eq. In 2013, liming
of agricultural soils in the United States resulted in emissions of 5.9 MMT CO2Eq. (1.6 MMT C), representing
about a 27 percent increase in emissions since 1990 (see Table 6-22 and Table 6-23). The trend is driven entirely by
the amount of lime and dolomite estimated to have been applied to soils over the time period.
Table 6-22: Emissions from Liming of Agricultural Soils (MMT COz Eq.)
Source
Limestone
Dolomite
Total3
1990
4.1
0.6 1
4.7
2005
3.9
0.4
4.3
2009
3.4
1 0.3
| 3.7
2010
4.3
0.5
4.8
2011
3.4
0.4
3.9
2012
4.3
1.5
5.8
2013
4.4
1.5
5.9
6-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.
Table 6-23: Emissions from Liming of Agricultural Soils (MMT C)
Source
Limestone
Dolomite
Total3
1990
1.1
0.2
1.3
2005
1.1
0.1
1.2
2009
0.9
1 0.1
| 1.0
2010
1.2
0.1
1.3
2011
0.9
0.1
1.1
2012
1.2
0.4
1.6
2013
1.2
0.4
1.6
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
6-24) 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 River basin, where the vast majority of all U.S. liming takes place (West 2008). U.S. liming that does
not occur in the Mississippi Paver 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, 2007b, 2009, 2010, 201 la, 20 lib, 2013aand
2014; USGS 2008 through 2014). 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 6-5: Comparison of the Tier 2 U.S. Inventory Approach and IPCC (2006) Default Approach
Emissions from liming of agricultural soils were estimated using a Tier 2 methodology based on liming emission
factors specific to the United States that are lower than the IPCC (2006) emission default factors, and 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 River basin, or in areas that have similar soil and rainfall regimes as the Mississippi
River 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 2013 U.S.
emissions from liming of agricultural soils are 5.9 MMT CO2 Eq. using the U.S.-specific, West and McBride (2005)
emission factors and 12.0 MMT CO2 Eq. using the IPCC (2006) emission factors.
Land Use, Land-Use Change, and Forestry 6-49
-------
The "unspecified" and "estimated" amounts of crashed 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" crashed 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" crashed limestone and dolomite that was applied to agricultural soils. In addition, data were not
available for 1990, 1992, and 2013 on the fractions of total crashed 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 crashed stone produced or used" reported for 1990 and 1992 in the 1994
Minerals Yearbook (Tepordei 1996). To estimate 2013 data, 2012 fractions were applied to a 2013 estimate of total
crashed stone presented in the USGS Mineral Industry Surveys: Crushed Stone and Sand and Gravel in the First
Quarter of 2014 (USGS 2014).
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 "Crashed Stone" chapter in the
Minerals Yearbook began rounding (to the nearest thousand metric tons) quantities for total crashed 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 Crop I and 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 6-24: Applied Minerals (MMT)
Mineral 1990 2005 2009 2010 2011 2012 2013
Limestone3 19.0 18.1 15.7 20.0 15.9 19.9 20.4
Dolomite8 2.4_^^ 1.9 1.9 6.3 6.4
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 2003, 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 (Approach 2) uncertainty analysis was applied to estimate the uncertainty of CC>2 emissions from
liming of agricultural soils. The results of the Approach 2 quantitative uncertainty analysis are summarized in Table
6-25. CO2 emissions from Liming of Agricultural Soils in 2013 were estimated to be between 0.7 and 12.1 MMT
CO2 Eq. at the 95 percent confidence level. This indicates a range of 88 percent below to 103 percent above the
2013 emission estimate of 5.9 MMT CO2 Eq.
6-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 6-25: Approach 2 Quantitative Uncertainty Estimates for COz Emissions from Liming of
Agricultural Soils (MMT COz Eq. and Percent)
„ „ 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
6 (MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Liming of Agricultural Soilsb CCh 5.9 0/7 12.1 -88% 103%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
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 2013. 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. In the previous Inventory, 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 Quarter of 2013 (USGS 2013). 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 2012. These values have replaced those used in the previous Inventory
to calculate the quantity of minerals applied to soil and the emissions from liming of agricultural soils. Compared to
the estimates used in the previous Inventory for 2012, the updated activity data for 2012 are approximately 3.8
MMT greater for limestone, and approximately 4.4 MMT greater for dolomite. As a result, the reported emissions
from liming of agricultural soils for 2012 increased by about 47 percent.
Emissions from Urea Fertilization
The use of urea (CO(NH2)2) as a fertilizer leads to CCh emissions through the release of CCh that was fixed during
the industrial production process. In the presence of water and urease enzymes, urea is converted into ammonium
(NH4+), hydroxyl ion (OH), and bicarbonate (HCOs"). The bicarbonate then evolves into CC>2 and water. Emissions
from urea fertilization in the United States totaled 4.0 MMT CO2 Eq. (1.1 MMT C) in 2013 (Table 6-26 and Table
6-27). Due to an increase in the use of urea as a fertilizer, emissions from urea have increased 66 percent between
1990 and 2013.
Table 6-26: COz Emissions from Urea Fertilization (MMT COz Eq.)
Source 1990 2005 2009 2010 2011 2012 2013
Urea Fertilization8 2.4 3.5 3.6 3.8 4.1 4.2 4.0
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 6-27: COz Emissions from Urea Fertilization (MMT C)
Source 1990 2005 2009 2010 2011 2012 2013
Urea Fertilization3 0.7 1.0 1.0 1.0 1.1 1.2 1.1
Land Use, Land-Use Change, and Forestry 6-51
-------
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
CO2 emissions from the application of urea to agricultural soils were estimated using the IPCC (2006) Tier 1
methodology. The annual amounts of urea applied to croplands (see Table 6-28) were derived from the state-level
fertilizer sales data provided in Commercial Fertilizers (TVA 1991, 1992, 1993, 1994; AAPFCO 1995 through
2014). These amounts were multiplied by the default IPCC (2006) emission factor (0.20 metric tons 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 previous year through June current year), a calculation was performed to convert the
data to calendar years (January through December). According to monthly fertilizer use data (TVA 1992b), 35
percent of total fertilizer used in any fertilizer year is applied between July and December of the previous calendar
year, and 65 percent is applied between January and June of the current calendar year. For example, for the 2000
fertilizer year, 35 percent of the fertilizer was applied in July through December 1999, and 65 percent was applied in
January through June 2000. Fertilizer sales data for the 2013 fertilizer year (i.e., July 2012 through June 2013) were
not available in time for publication. Accordingly, urea application in the 2013 fertilizer year was estimated using a
linear, least squares trend of consumption over the previous five years (2008 through 2012). A trend of five years
was chosen as opposed to a longer trend as it best captures the current inter-state and inter-annual variability in
consumption. First, January through June 2013 urea consumption was estimated using the approach described
above, after which the percentage change in use from the previous year (i.e., January through June 2012) was
determined. Next, the July through December 2012 data was multiplied by the same percent change to estimate the
July through December 2013 urea consumption (assuming a constant percentage change between 2012 and 2013).
State-level estimates of CCh 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 6-28: Applied Urea (MMT)
1990 2005 2009 2010 2011 2012 2013~
Urea Fertilizer8 3.3 48 4.8 5.2 5.6 5.8 5.5
a 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 6-29 for Urea Fertilization. An Approach 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 an
overestimate. 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
6-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 metric tons per year, 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. CCh emissions from urea fertilization of
agricultural soils in 2013 were estimated to be between 2.3 and 4.1 MMT CCh Eq. at the 95 percent confidence
level. This indicates a range of 42 percent below to 3 percent above the 2013 emission estimate of 4.0 MMT CCh
Eq.
Table 6-29: Quantitative Uncertainty Estimates for COz Emissions from Urea Fertilization
(MMT COz Eq. and Percent)
Source Gas 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
(MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Urea Fertilization CCh 40 2.3 41 -42% 3%
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 2013. 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, the 2011 and 2012 emissions estimates were updated to reflect the urea application reported
in the Commercial Fertilizers Report for the 2012 fertilizer year (July through December2011, January through
June, 2012). Specifically, the 2011 emissions estimates were revised to reflect the July to December 2011 urea
application data. This recalculation resulted in actual emissions that are 3 percent higher than the previously
estimated 2011 emissions. For 2012, the January through June, 2012 actual urea application rates were used to
replace the estimates from the previous year, and the July through December rates of application were estimated
using the methodology described above (i.e., the July through December, 2011 urea rates were multiplied by the
percentage change in rates from January through June, 2011 to January through June, 2012). The updated activity
data for 2012 are approximately 1,068 kt greater than the amount estimated for 2012 in the previous Inventory. As a
result, the reported emissions from urea for 2012 in the current Inventory are 23 percent higher than the estimated
emission reported for 2012 in 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.
Land Use, Land-Use Change, and Forestry 6-53
-------
6.5 Land Converted to Cropland (IPCC Source
Category 4B2)
Land Converted to Cropland includes all cropland in an Inventory year that had been in another land use(s) during
the previous 20 years38 (USDA-NRCS 2009). For example, grassland or forestland converted to cropland during the
past 20 years would be reported in this category. Recently-converted lands are retained in this category for 20 years
as recommended in the IPCC guidelines (IPCC 2006). This Inventory includes all privately-owned croplands in the
conterminous United States and Hawaii, but does not include the approximately 100,000 hectares of Land Converted
to Cropland on federal lands and a minor amount of Land Converted to Cropland in Alaska. Consequently there is
a discrepancy between the total amount of managed area in Land Converted to Cropland (see Section 6.1) and the
cropland area included in the Inventory. Improvements are underway to include federal croplands in future C
inventories.
Background on agricultural carbon (C) stock changes is provided in section 6.4 Cropland Remaining Cropland and
therefore will only be briefly summarized here. Soils are the largest pool of C in agricultural land, and also have the
greatest potential for long-term storage or release of C, because biomass and dead organic matter C pools are
relatively small and ephemeral compared with soils, with the exception of C stored in perennial woody crop
biomass. The IPCC (2006) guidelines recommend reporting changes in soil organic carbon (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.39
Land use and management of mineral soils in Land Converted to Cropland was the largest contributor to C loss
throughout the time series, accounting for approximately 70 percent of the emissions in the category (Table 6-30 and
Table 6-31). The conversion of grassland to cropland was the largest source of soil C loss (accounting for
approximately 65 percent of the emissions in the category), though losses declined over the time series. The net flux
of C from soil stock changes in 2013 was 16.1 MMT CO2 Eq. (4.4 MMT C) in 2013, including 11.3 MMT CO2 Eq.
(3.1 MMT C) from mineral soils and 4.8 MMT CO2 Eq. (1.3 MMT C) from drainage and cultivation of organic
soils.
Table 6-30: Net COz Flux from Soil C Stock Changes in Land Converted to Cropland^ Land
Use Change Category (MMT COz Eq.)
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
1990
20.0 1
1
1.5
(0.2)
0.3 |
0.6 1
•'
0.2
(0.2)
22.4
2005
14.0 1
1
0.3 1
0.3 1
0.1 1
+ 1
0.3 1
0.2 1
0.1 1
0.3
14.8
2009
10.6
4.0
0.3
0.2
0.1
+
0.3
0.2
0.1
0.4
11.4
2010
10.6
4.0
0.3
0.2
0.1
+
0.3
0.2
0.1
0.4
11.4
2011
10.6
4.0
0.3
0.2
0.1
+
0.3
0.2
0.1
0.4
11.4
2012
10.5
4.0
0.3
0.2
0.1
+
0.3
0.2
0.1
0.4
11.3
2013
10.6
4.0
0.3
0.2
0.1
+
0.3
0.2
0.1
0.4
11.3
38 The 2009 USDA National Resources Inventory (NRI) land-use survey points were classified according to land-use history
records starting in 1982 when the NRI survey began. Consequently the classifications from 1990 to 2001 were based on less than
20 years.
39 CO2 emissions associated with liming urea fertilization are also estimated but included in 7.4 Cropland Remaining Cropland.
6-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Total Organic Soil Flux 2.1 5.1 4.8 4.8 4.8 4.8 4.8
Total Net Flux 24.5 19.8 16.2 16.2 16.2 16.1 16.1
Note: Estimates after 2007 are based on NRI data from 2007 and therefore may not fully reflect changes occurring
in the latter part of the time series.
+ Does not exceed 0.05 MMT CO2 Eq.
Table 6-31: Net COz Flux from Soil C Stock Changes in Land Converted to Cropland (V\W\ C)
Soil Type 1990 2005 2009 2010 2011 2012 2013
Grassland Converted to Cropland
Mineral 5.4 3.8 2.9 2.9 2.9 2.9 2.9
Organic 0.7 1.2 1.1 1.1 1.1 1.1 1.1
Forest Converted to Cropland
Mineral 0.4 0.1 0.1 0.1 0.1 0.1 0.1
Organic (0.1) 0.1 0.1 0.1 0.1 0.1 0.1
Other Lands Converted Cropland
Mineral 0.1
Organic + I
Settlements Converted Cropland
Mineral
Organic
Wetlands Converted Cropland
Mineral
Organic
Total Mineral Soil Flux
Total Organic Soil Flux
Total Net Flux
.
(0.1) •
6.1
0.6
6.7
10.1
0.1
1
0.1
4.0
1.4
5.4
0.1
+
+
0.1
3.1
1.3
4.4
0.1
+
+
0.1
3.1
1.3
4.4
0.1
+
+
0.1
3.1
1.3
4.4
0.1
+
+
0.1
3.1
1.3
4.4
0.1
+
+
0.1
3.1
1.3
4.4
Note: Estimates after 2007 are based on NRI data from 2007 and therefore may not fully reflect changes
occurring in the latter part of the time series.
+ Does not exceed 0.05 MMT C
Parentheses indicate net sequestration.
The spatial variability in the 2013 annual flux in CC>2 from mineral soils is displayed in Figure 6-10 and from
organic soils in Figure 6-11. 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 C from
the soil pool. The regions with the highest rates of emissions from organic soils coincide with the largest
concentrations of organic soils used for agricultural production, including Southeastern Coastal Region (particularly
Florida), upper Midwest and Northeast surrounding the Great Lakes, and the Pacific Coast (particularly California).
Land Use, Land-Use Change, and Forestry 6-55
-------
Figure 6-10: Total Net Annual COz Flux for Mineral Soils under Agricultural Management
within States, 2013, Land Converted to 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 computations. See methodology
for additional details.
MMTCO2Eq/yr
-0.1 toO
-0.5to-0.1
6-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 6-11: Total Net Annual COz Flux for Organic Soils under Agricultural Management
within States, 2013, Land Converted to Cropland
Note: Values greater than zero represent emissions.
MMT CO2 Eq/yr
• >2
| 1 to 2
| 0.5 to 1
n 0.1 to 0.5
[] 0 to 0.1
'3H No organic soils
Methodology
The following section includes a description of the methodology used to estimate changes in soil C stocks for Land
Converted to Cropland, including (1) agricultural land-use and management activities on mineral soils; and (2)
agricultural land-use and management activities on organic soils Biomass and litter C stock changes associated with
conversion of forest to cropland are not explicitly included in this category, but 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
2007 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 began collecting annual data, and data are currently available through 2010 (USDA-NRCS 2013).
However, this Inventory only uses NRI data through 2007 because newer data were not made available in time to
incorporate the additional years into this Inventory. 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).
Land Use, Land-Use Change, and Forestry 6-57
-------
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
on the majority of land that is used to produce annual crops in the United States. These 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. 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.40
Tier 3 Approach
For the Tier 3 method, mineral SOC stocks and stock changes were estimated using the D AYCENT
biogeochemical41 model (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 NRI (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
2013 were assumed to be similar to 2007 due to a lack of activity data for these years. (Future inventories will be
updated with new activity data and the time series will be recalculated; See Planned Improvements section in
Cropland Remaining Cropland). 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.
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.
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 (2006), with U.S.-specific C loss rates (Ogle et al. 2003) as described in the Cropland
Remaining Cropland section for organic soils.
Uncertainty and Time-Series Consistency
Uncertainty analysis for mineral soil C stock changes using the Tier 3 and Tier 2 methodologies 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 Tier 2, as described in the Cropland
Remaining Cropland section.
Uncertainty estimates are presented in Table 6-32 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 72 percent below to 81 percent above the 2013 stock change estimate of 16.1 MMT CO2Eq.
40Federal land is not a land use, 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).
Biogeochemical cycles are the flow of chemical elements and compounds between living organisms and the physical
environment.
6-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 6-32: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes
occurring within Land Converted to Cropland (MWf COz Eq. and Percent)
Source
2013 Flux Estimate
(MMT CCh Eq.)
Uncertainty Range Relative to Flux Estimate3
(MMT 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
14.6
9.8
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
16.1
9.8
1.6
4.8
Lower
Bound
3.0
(1.3)
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
4.5
(1.3)
1.1
1.4
Upper
Bound
27.7
20.9
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
29.2
20.9
2.0
11.7
Lower
Bound
-80%
-114%
-49%
-83%
-53%
-49%
-100%
-49%
-49%
NA
-36%
-49%
-46%
-45%
-49%
-53%
-72%
-114%
-28%
-70%
Upper
Bound
90%
114%
54%
172%
123%
54%
258%
54%
54%
NA
41%
54%
63%
57%
54%
68%
81%
114%
31%
145%
Note: Parentheses indicate negative values or net sequestration.
NA: Other land by definition does not include organic soil (see Section 6.1—Representation of the U.S. Land Base).
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 Forest Land Remaining Forest Land section of this 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.
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2013. 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
parameters associated with simulating crop production and carbon inputs to the soil in the DAYCENT
biogeochemical model; 2) improving the model simulation of snow melt and water infiltration in soils; and 3)
driving the DAYCENT simulations with updated input data for the excretion of C and N onto
Pasture/Range/Paddock and N additions from managed manure based on national livestock population. Change in
SOC stocks declined by an average of 0.9 MMT CChEq. over the time series as a result of these improvements to
the Inventory.
Land Use, Land-Use Change, and Forestry 6-59
-------
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 section.
6.6 Grassland Remaining Grassland (IPCC
Source Category 4C1)
Grassland Remaining Grassland includes all grassland in an Inventory year that had been classified as grassland for
the previous 20 years42 (USDA-NRCS 2009). Grassland includes pasture and rangeland that are primarily used for
livestock grazing. Rangelands are typically extensive areas of native grassland that are not intensively managed,
while pastures are typically seeded grassland (possibly following tree removal) that may also have additional
management, such as irrigation or interseeding of legumes. This Inventory includes all privately-owned grasslands
in the conterminous United States and Hawaii, but does not include the 75 million hectares of Grassland Remaining
Grassland on federal lands or the 36 million hectares of Grassland Remaining Grassland in Alaska. This leads to a
discrepancy with the total amount of managed area in Grassland Remaining Grassland (see Section 6.1 —
Representation of the U.S. Land Base) and the grassland area included in the Grassland Remaining Grassland
(IPCC Source Category 4C1—Section 6.6).
Background on agricultural carbon (C) stock changes is provided in the section 6.4, Cropland Remaining Cropland,
and will only be summarized here. Soils are the largest pool of C in agricultural land, and also have the greatest
potential for longer-term storage or release of C, because biomass and dead organic matter C pools are relatively
small and ephemeral compared to the soil C pool, with the exception of C stored in tree and shrub biomass that
occurs in grasslands. The IPCC (2006) guidelines recommend reporting changes in soil organic C (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.43
In Grassland Remaining Grassland, there has been considerable variation in soil C flux between 1990 and 2013.
These changes are driven by variability in weather patterns and associated interaction with land management
activity. Even in the years with larger total changes in stocks, changes remain small on a per hectare rate. Land use
and management increased soil C in mineral soils of Grassland Remaining Grassland between 1990 and 2006, after
which the trend was reversed to small declines in soil C. In contrast, organic soils have lost relatively small amounts
of C annually from 1990 through 2013. While the overall trend was a gain in soil C in Grassland Remaining
Grassland from 1990 to 2003, the last decade has seen small losses in soil C during most years (Table 6-33 and
Table 6-34). Overall, from 1990 to 2013, the net change in soil C flux increased by 14.0 MMT CO2 Eq. (3.8 MMT
C). Current estimates for flux from soil C stock changes in 2013 are estimated at a total of 12.1 MMT CC^Eq. (3.3
The 2009 USDA National Resources Inventory (NRI) land-use survey points were classified according to land-use history
records starting in 1982 when the NRI survey began. Consequently the classifications from 1990 to 2001 were based on less than
20 years
CO2 emissions associated with liming and urea fertilization are also estimated but included in 6.4 Cropland Remaining
Cropland.
6-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
MMT C), with 9.1 MMT CO2 Eq. (2.5 MMT C) from mineral soils and 3.0 MMT CO2 Eq. (0.8 MMT C) from
organic soils.
Table 6-33: Net COi Flux from Soil C Stock Changes in Grassland Remaining Grassland (MWf
COz Eq.)
Soil Type
Mineral Soils
Organic Soils
Total Net Flux
1990
(6.5)
4.6
(1.9)
2005
1.2l
3.1
4.2
2009
8.7
3.0
11.7
2010
8.7
3.0
11.7
2011
8.7
3.0
11.7
2012
8.5
3.0
11.5
2013
9.1
3.0
12.1
Note: Totals may not sum due to independent rounding. Estimates after 2007 are based on NRI data
from 2007 and therefore may not fully reflect changes occurring in the latter part of the time series.
Parentheses indicate net sequestration.
Table 6-34: Net COz Flux from Soil C Stock Changes in Grassland Remaining Grassland (MWf
C)
Soil Type
Mineral Soils
Organic Soils
Total Net Flux
1990
(1.8)
1.3
(0.5)
2005
0.3l
0.8
1.2
2009
2.4
0.8
3.2
2010
2.4
0.8
3.2
2011
2.4
0.8
3.2
2012
2.3
0.8
3.1
2013
2.5
0.8
3.3
Note: Totals may not sum due to independent rounding. Estimates after 2007 are based on NRI data
from 2007 and therefore may not fully reflect changes occurring in the latter part of the time series.
Parentheses indicate net sequestration.
The spatial variability in the 2013 annual flux in CC>2 from mineral is displayed in Figure 6-12 and organic soils in
Figure 6-13. Although relatively small on a per-hectare basis, grassland gained soil C in several regions during
2013, including the Northeast, Southeast, portions of the Midwest, and Pacific Coastal Region. The regions with the
highest rates of emissions from organic soils coincide with the largest concentrations of organic soils used for
managed grassland, including the Southeastern Coastal Region (particularly Florida), upper Midwest and Northeast
surrounding the Great Lakes, and the Pacific Coast (particularly California).
Land Use, Land-Use Change, and Forestry 6-61
-------
Figure 6-12: Total Net Annual COz Flux for Mineral Soils under Agricultural Management
within States, 2013, Grassland Remaining 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.
MMTCO2Eq/yr
>o
-0.1 to 0
-0.5 to-0.1
-1 to -0.5
-2 to -1
6-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 6-13: Total Net Annual COz Flux for Organic Soils under Agricultural Management
within States, 2013, Grassland Remaining Grassland
Note: Values greater than zero represent emissions.
MMTCO2Eq/yr
>2
1 to 2
0.5 to 1
0.1 to 0.5
O to 0.1
No organic soils
Methodology
The following section includes a brief description of the methodology used to estimate changes in soil C stocks for
Grassland Remaining Grassland, including (1) agricultural land-use and management activities on mineral soils;
and (2) agricultural land-use and management activities on organic soils. 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 2007 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 2010
(USDA-NRCS 2013). However, this Inventory only uses NRI data through 2007 because newer data were not made
available in time to incorporate the additional years into this Inventory. 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.
Land Use, Land-Use Change, and Forestry 6-63
-------
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 DAY CENT
biogeochemical44 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
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 nitrogen
(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 Manure Management, Sections.2, and Annex3.ll. ManureN
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 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 2013 were assumed to be similar to 2007 due to a lack of activity
data for these years. (Future inventories will be updated with new activity data and the time series will be
recalculated; See Planned Improvements section in Cropland Remaining Cropland). The methods used for
Grassland remaining Grassland are the same as those described in the Tier 3 portion of Cropland Remaining
Cropland section 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.
Additional Mineral C Stock Change Calculations
A Tier 2 method was used to adjust annual C flux estimates for mineral soils between 1990 and 2013 to account for
additional C stock changes associated with sewage sludge amendments. 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 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 not likely to 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
Biogeochemical cycles are the flow of chemical elements and compounds between living organisms and the physical
environment.
6-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 (2006), which utilizes U.S.-specific C loss rates (Ogle et al. 2003) rather than default
IPCC rates. For more information, see the Cropland Remaining Cropland section for organic soils.
Uncertainty and Time-Series Consistency
Uncertainty estimates are presented in Table 6-35 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 291 percent below to 297 percent above the 2013 stock change estimate of 12.1 MMT CO2
Eq. The large relative uncertainty is due to the small net flux estimate in 2013.
Table 6-35: Approach 2 Quantitative Uncertainty Estimates for C Stock Changes Occurring
Within Grassland Remaining Grassland (MWT CQz Eq. and Percent)
Source
2013 Flux Estimate
(MMT CCh Eq.)
Uncertainty Range Relative to Flux Estimate3
(MMT CCh 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
10.3
0.1
(1.4)
3.0
Lower
Bound
(25.5)
0.0
(2.1)
1.6
Upper
Bound
46.2
0.2
(0.7)
4.9
Lower
Bound
-347%
-86%
-50%
-46%
Upper
Bound
347%
109%
50%
63%
Combined Uncertainty for Flux Associated
with Agricultural Soil Carbon Stock
Change in Grassland Remaining Grassland
12.1
(23.8)
48.0
-297%
297%
Note: Parentheses indicate negative values.
a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Uncertainty is also associated with a lack of reporting on agricultural biomass and litter C stock changes and non-
CO2 greenhouse gas emissions from burning. Biomass C stock changes may be significant for managed grasslands
with woody encroachment that has not attained enough tree cover to be considered forest lands. Grassland burning
is not as common in the United States as in other regions of the world, but fires do occur through both natural
ignition sources and prescribed burning. 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 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Land Use, Land-Use Change, and Forestry 6-65
-------
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. In the previous Inventory, D AYCENT was used to simulate the PRP manure
N input with automated routines, but errors occurred leading to a mismatch between the amount of manure N
excreted according to the Manure Management data, relative to the amount simulated in DAY CENT. This error
appears to be corrected based on internal checks, and should provide 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).
Recalculations Discussion
Methodological recalculations in the current Inventory were associated with the following improvements, including
1) improving the model simulation of snow melt and water infiltration in soils; and 2) driving the DAYCENT
simulations with updated input data for the excretion of C and N onto Pasture/Range/Paddock and N additions from
managed manure based on national livestock population. As a result of these improvements to the Inventory,
changes in SOC stocks declined by an average of 1.76 MMT €62 eq. annually over the time series.
Planned Improvements
One of the key planned improvements for 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
likely have minimal changes in land management and C stocks, improvements are underway to include these
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 1990-2014 Inventory. Another key
planned improvement is to estimate non-CCh greenhouse gas emissions from burning of grasslands. For
information about other improvements, see the Planned Improvements section in Cropland Remaining Cropland.
6.7 Land Converted to Grassland (IPCC Source
Category 4C2)
Land Converted to Grassland includes all grassland in an Inventory year that had been in another land use(s) during
the previous 20 years45 (USDA-NRCS 2009). For example, cropland or forestland converted to grassland during
the past 20 years would be reported in this category. Recently-converted lands are retained in this category for 20
years as recommended by IPCC (2006). Grassland includes pasture and rangeland that are used primarily for
livestock grazing. Rangelands are typically extensive areas of native grassland that are not intensively managed,
while pastures are typically seeded grassland (possibly following tree removal) that may also have additional
management, such as irrigation or interseeding of legumes. This Inventory includes all privately-owned grasslands
in the conterminous United States and Hawaii, but does not but does not include the 800,000 to 850,000 hectares of
Land Converted to Grassland on federal lands or Land Converted to Grassland in Alaska. Consequently there is a
discrepancy between the total amount of managed area for Land Converted to Grassland (see Section 6.1—
Representation of the U.S. Land Base) and the grassland area included in Land Converted to Grassland (IPCC
Source Category 4C2—Section 6.7).
45 The 2009 USDA National Resources Inventory (NRI) land-use survey points were classified according to land-use history
records starting in 1982 when the NRI survey began. Consequently the classifications from 1990 to 2001 were based on less than
20 years.
6-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Background on agricultural carbon (C) stock changes is provided in Cropland Remaining Cropland and therefore
will only be briefly summarized here. Soils are the largest pool of C in agricultural land, and also have the greatest
potential for long-term storage or release of C, because biomass and dead organic matter C pools are relatively small
and ephemeral compared with soils, with the exception of C stored in tree and shrub biomass that occurs in
grasslands. IPCC (2006) recommend reporting changes in soil organic C (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.46
Land use and management of mineral soils in Land Converted to Grassland led to an increase in soil C stocks
between 1990 and 2013 (see Table 6-36 and Table 6-37). The net C flux from soil C stock changes for mineral soils
between 1990 and 2013 led to a decrease of 1.7 MMT CO2 Eq. (0.5 MMT C) in the atmosphere. In contrast, over
the same period, drainage of organic soils for grassland management led to an increase in C emissions to the
atmosphere of 0.3 MMT CO2 Eq. (0.1 MMT C). The flux associated with soil C stock changes in 2013 is estimated
at a net uptake of 8.8 MMT CO2 Eq. (-2.4 MMT C) from the atmosphere.
Table 6-36: Net COz Flux from Soil C Stock Changes for Land Converted to Grass/and (MMJ
COz Eq.)
Soil Type 1990 2005 2009 2010 2011 2012 2013
Cropland Converted to Grassland
Mineral (6.4) (9.0) (8.8) (8.8) (8.7) (8.6) (8.6)
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
(8.2)
0.8
(7.4)
(0.1)
0.1 •
(10.3)
1.3
(9.0)
(0.1)
1 0.1
(10.0)
1 1.1
(8.9)
(0.1)
0.1
(10.0)
1.1
(8.9)
(0.1)
0.1
(10.0)
1.1
(8.9)
(0.1)
0.1
(9.9)
1.1
(8.8)
(0.1)
0.1
(9.9)
1.1
(8.8)
Note: Estimates after 2007 are based on NRI data from 2007 and therefore may not fully reflect changes
occurring in the latter part of the time series. Parentheses indicate net sequestration.
+ Does not exceed 0.05 MMT CO2 Eq.
Table 6-37: Net COz Flux from Soil C Stock Changes for Land Converted to Grass/and (MMJ
C)
Soil Type 1990 2005 2009 2010 2011 2012 2013
Cropland Converted to Grassland
Mineral (1.7) (2.5) (2.4) (2.4) (2.4) (2.4) (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) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0)
Organic + I + I + + + + +
Settlements Converted Grassland
Mineral (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)
46 CO2 emissions associated with liming are also estimated but included in 6.4 Cropland Remaining Cropland.
Land Use, Land-Use Change, and Forestry 6-67
-------
Organic + + + + + + +
Wetlands Converted Grassland
Mineral + + + + + + +
Organic + + + + + + +
Total Mineral Soil Flux
Total Organic Soil Flux
Total Net Flux
(2.2)
0.2
(2.0)
(2.8)
0.3
(2.5)
(2.7)
0.3
(2.4)
(2.7)
0.3
(2.4)
(2.7)
0.3
(2.4)
(2.7)
0.3
(2.4)
(2.7)
0.3
(2.4)
Note: Estimates after 2007 are based on NRI data from 2007 and therefore may not fully reflect changes
occurring in the latter part of the time series.
Parentheses indicate net sequestration.
+ Does not exceed 0.05 MMT CO2 Eq.
The spatial variability in the 2013 annual flux in COa from mineral soils is displayed in Figure 6-14 and from
organic soils in Figure 6-15. The soil C stock increased in most states for Land Converted to Grassland, which was
driven by conversion of annual cropland into continuous pasture. The largest gains were in the Southeastern region,
Northeast, South-Central, Midwest, and northern Great Plains. The regions with the highest rates of emissions from
organic soils coincide with the largest concentrations of organic soils used for managed grasslands, including
Southeastern Coastal Region (particularly Florida), upper Midwest and Northeast surrounding the Great Lakes, and
the Pacific Coast (particularly California).
6-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Figure 6-14: Total Net Annual COz Flux for Mineral Soils under Agricultural Management
within States, 2013, 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.
MMTC02Eq/yr
-0.1 to o
-0.5 to -0.1
-1 to -0.5
Land Use, Land-Use Change, and Forestry 6-69
-------
Figure 6-15: Total Net Annual COz Flux for Organic Soils under Agricultural Management
within States, 2013, Land Converted to Grassland
Note: Values greater than zero represent emissions.
MMT CO2 Eq/yr
• >2
| 1 to 2
| 0.5 to 1
QO.1 to 0.5
DO to 0.1
LJ No organic soils
Methodology
The following section includes a description of the methodology used to estimate changes in soil C stocks for Land
Converted to Grassland, including (1) agricultural land-use and management activities on mineral soils; and (2)
agricultural land-use and management activities on organic soils. Biomass and litter C stock changes associated
with conversion of forest to grassland are not explicitly included in this category, but 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 Grassland according to land-use histories recorded in
the 2009 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 and data are currently available through 2010
(USDA-NRCS 2013). However, this Inventory only uses NRI data through 2007 because newer data were not made
available in time to incorporate the additional years into this Inventory. 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 classified
as another use during the previous 20 years.
6-70 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.47
Tier 3 Approach
Mineral SOC stocks and stock changes were estimated using the DAYCENT biogeochemical48 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 (USDA-NCRS 2009), 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 the
Cropland Remaining Cropland section for additional discussion of the Tier 3 methodology for mineral soils.
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 Grassland as described in the Tier 2 portion of the Cropland Remaining Cropland section for
mineral soils.
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 (2006), with U.S.-specific C loss rates (Ogle et al. 2003) as described in the Cropland
Remaining Cropland section for organic soils.
Uncertainty and Time-Series Consistency
Uncertainty estimates are presented in Table 6-38 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 107 percent below to 107 percent above the 2013 stock change estimate of -8.8 MMT CO2
Eq. The large relative uncertainty is due to the small net flux estimate in 2013.
Federal land 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.
Biogeochemical cycles are the flow of chemical elements and compounds between living organisms and the physical
environment.
Land Use, Land-Use Change, and Forestry 6-71
-------
Table 6-38: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes
occurring within Land Converted to Grassland(MMT COz Eq. and Percent)
Source
2013 Flux Estimate
(MMT CCh Eq.)
Uncertainty Range Relative to Flux Estimate3
(MMT 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
(7.7)
(7.3)
(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
(8.8)
(7.3)
(2.5)
1.1
Lower
Bound
(17.1)
(16.7)
(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
(18.1)
(16.7)
(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%
-127%
-45%
-63%
-62%
-48%
-100%
-48%
-48%
NA
-51%
-48%
-86%
-108%
-48%
-58%
-107%
-127%
-27%
-52%
Upper
Bound
123%
127%
45%
98%
72%
44%
231%
44%
44%
NA
47%
44%
160%
108%
44%
81%
107%
127%
26%
81%
Note: Parentheses indicate negative values.
NA: Other land by definition does not include organic soil (see Section 6.1— of the U.S. Land Base). 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 Forest Land Remaining Forest Land section of the
report. Biomass C stock changes may be significant for managed grasslands with woody encroachment that has not
attained enough tree cover to be considered forest lands. Changes in litter C stocks are assumed to be negligible in
grasslands over annual time frames, although there are likely 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 2013. Details on the emission trends through time are described in more detail in the above Methodology
section.
QA/QC and Verification
See the QA/QC and Verification section in Grassland Remaining Grassland.
Recalculations Discussion
Methodological recalculations in the current Inventory were associated with the following improvements: 1) refining
parameters associated with simulating crop production and carbon inputs to the soil in the DAYCENT
biogeochemical model; 2) improving the model simulation of snow melt and water infiltration in soils; and 3)
driving the DAYCENT simulations with updated input data for the excretion of C and nitrogen (N) onto
Pasture/Range/Paddock and N additions from managed manure based on national livestock population. As a result
of these improvements to the Inventory, changes in SOC stocks increased by an average of 0.2 MMT CO2 eq.
annually over the time series.
6-72 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 to 850,000 hectares of Federal grasslands in the western United States. Grasslands in Alaska will also be
evaluated. For information about other improvements, see the Planned Improvements section in Cropland
Remaining Cropland and Grassland Remaining Grassland.
6.8 Wetlands Remaining Wetlands (IPCC
Source Category 4D1)
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 GHG 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 the 2006 IPCC Guidelines). Drained land surface and ditch networks contribute to the CH4 flux in
peatlands managed for peat extraction. CH4 emissions were considered insignificant under IPCC Tier 1
methodology (IPCC 2006), but are included in the emissions estimates for Peatlands Remaining Peatlands
consistent with the 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories:
Wetlands (IPCC 2013). N2O emissions from managed peatlands depend on site fertility. In addition, abandoned
and restored peatlands continue to release GHG emissions, and at present no methodology is provided by IPCC
(2006) to estimate greenhouse gas emissions or removals from restored peatlands; although methodologies are
provided for rewetted organic soils (which includes rewetted/restored peatlands) in IPCC (2013) guidelines. This
Inventory estimates CO2, N2O, and CH4 emissions from peatlands managed for peat extraction in accordance with
IPCC (2006 and 2013) guidelines.
CCh, N2O, and CH4 Emissions from Peatlands Remaining Peatlands
IPCC (2013) recommends reporting CO2, N2O, and CH4 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 (e.g., Minnesota) and wetlands instates
further south (e.g., Florida). 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.
Land Use, Land-Use Change, and Forestry 6-73
-------
IPCC (2006 and 2013) recommend 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
and CH4 emissions, since off-site N2O estimates are complicated by the risk of double-counting emissions from
nitrogen fertilizers added to horticultural peat, and off-site CH4 emissions are not relevant given the non-energy uses
of peat, so methodologies are not provided in IPCC (2013) guidelines. 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, and
contributes to the activity of methanogens and methanotrophs (Blodau 2002; Treat et al. 2007 as cited in IPCC
2013). Drainage ditches, which are constructed as land is drained in preparation for peat extraction, also contribute
to the flux of CH4 through in situ production and lateral transfer of CH4 from the organic soil matrix (IPCC 2013).
Off-site CO2 emissions from managed peatlands occur from waterborne carbon losses and the horticultural and
landscaping use of peat. As drainage waters in peatlands accumulate, dissolved organic carbon reacts within aquatic
ecosystems and is converted to CO2, then emitted to the atmosphere (Billet et al. 2004 as cited in IPCC 2013).
During 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 the aforementioned horticultural and landscaping purposes.
Total emissions from Peatlands Remaining Peatlands were estimated to be 0.8 MMT CO2 Eq. in 2013 (see Table
6-39) comprising 0.8 MMT CO2 Eq. (770 kt) of CO2, 0.001 MMT CO2 Eq. (0.002 kt) of N2O, and 0.004 MMT CO2
Eq. (0.16 kt) of CH4. Total emissions in 2013 were about 5 percent smaller than total emissions in 2012. Peat
production in Alaska in 2013 was not reported in Alaska's Mineral Industry 2013 report. However, peat production
reported in the lower 48 states in 2013 was 5 percent lower than in 2012, resulting in smaller total 48 states plus
Alaska emissions from Peatlands Remaining Peatlands in 2013 compared to 2012.
Total emissions from Peatlands Remaining Peatlands have fluctuated between 0.8 and 1.3 MMT CO2Eq. 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 2013
represent a decline from emissions in 2012. CO2 emissions from Peatlands Remaining Peatlands have fluctuated
between 0.8 and 1.3 MMT CO2 across the time series, and these emissions drive the trends in total emissions. CH4
and N2O emissions remained close to zero across the time series. N2O emissions showed 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 between 2011 and 2013. CH4 emissions decreased
from 1990 until 1995, followed by an increasing trend through 2000, a period of fluctuation through 2010, then a
decline between 2011 and 2013.
Table 6-39: Emissions from Peatlands Remaining Peatlands (MMT COz Eq.)
Gas
C02
Off-site
On-site
N2O (On-site)
CH4 (On-site)
Total
1990
1.1
1.0 1
0.1 1
+ 1
+
1.1
2005
1.1
1.0 1
0.1 1
+ 1
+
1.1
2009
1.0
1.0
0.1
+
+
1.0
2010
1.0
1.0
0.1
+
+
1.0
2011
0.9
0.9
0.1
+
+
0.9
2012
0.8
0.8
0.1
+
+
0.8
2013
0.8
0.7
+
+
+
0.8
Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP values.
+ Less than 0.05 MMT 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). Guidance for estimating off-site CH4 emissions is not included in
IPCC (2013). Totals may not sum due to independent rounding.
6-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 6-40: Emissions from Peatlands Remaining Peatlands(kt)
Gas 1990 2005 2009 2010 2011 2012 2013"
CO2 1,055 1,101 1,024 1,022 926 812 770
Off-site 985 1,030 957 956 866 760 720
On-site 70 71 67 66 60 53 50
N2O (On-site) + I + I + + + + +
CH4 (On-site) + I + + + + + +
+ Less than 0.5 kt
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). Guidance for estimating off-site CELi emissions is not included in IPCC
(2013). Totals may not sum due to independent rounding.
Methodology
Off-site CO2 Emissions
CO2 emissions from domestic peat production were estimated using a Tier 1 methodology consistent with IPCC
(2006). Off-site CCh emissions from Peatlands Remaining Peatlands were calculated by apportioning the annual
weight of peat produced in the United States (Table 6-41) 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 1995-2014a;
USGS 2014b). 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 domestic peat producers. On average, about 75 percent of the
peat operations respond to the survey; and USGS estimates data for non-respondents on the basis of prior-year
production levels (Apodaca 2011).
The Alaska estimates rely on reported peat production from the annual Alaska's Mineral Industry reports (DGGS
1993-2014). Similar to the U.S. Geological Survey, the Alaska Department of Natural Resources, Division of
Geological & Geophysical Surveys (DGGS) solicits voluntary reporting of peat production from producers for the
Alaska's Mineral Industry report. 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. 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 6-42). 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).49 Peat production was not reported for 2013 m Alaska's Mineral Industry 2013 report (DGGS
2014); therefore Alaska's peat production in 2013 (reported in cubic yards) was assumed to be equal to its peat
production in 2012.
Consistent with IPCC (2013) guidelines, off-site CO2 emissions from dissolved organic carbon were estimated based
on the total area of peatlands managed for peat extraction, which is calculated from production data using the
methodology described in the On-Site CO2 Emissions section below. CO2 emissions from dissolved organic C were
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 6-75
-------
estimated by multiplying the area of peatlands by the default emissions factor for dissolved organic C provided in
IPCC(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 CCh emissions from the use of peat not produced within the United States are not included in the
Inventory. The United States has largely imported peat from Canada for horticultural purposes; from 2010 to 2013,
imports of sphagnum moss (nutrient-poor) peat from Canada represented 63 percent of total U.S. peat imports
(USGS 2015). 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 CCh emissions from apparent consumption
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 6-41: Peat Production of Lower 48 States (kt)
Type of Deposit
Nutrient-Rich
Nutrient-Poor
Total Production
1990
595.1
55.4
692.0
2005
657.6 1
27.4
685.0
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
2013
418.5
46.5
465.0
Sources: United States Geological Survey (USGS) (\99\-2Q\4a)Minerals Yearbook: Peat (1994-2013);
United States Geological Survey (USGS) (2014b)Mwera/ Commodity Summaries: Peat (2013).
Table 6-42: Peat Production of Alaska (Thousand Cubic Meters)
1990 2005 2009 2010 2011 2012 2013"
Total Production 49.7 47.8 183.9 59.8 61.5 93.1 93.1
Sources: Division of Geological & Geophysical Surveys (DGGS), Alaska Department of Natural Resources
(1997-2014) Alaska's Mineral Industry Report (1997-2013).
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).50 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 IPCC (2013) 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
50 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).
6-76 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 and 2013).
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 (2013).
On-site CH4 Emissions
IPCC (2013) also suggests basing the calculation of on-site CH4 emission estimates on the total area of peatlands
managed for peat extraction. Area data is derived using the calculation from production data described in the On-
site CO2 Emissions section above. In order to estimate CH4 emissions from drained land surface, the area of
Peatlands Remaining Peatlands was multiplied by the emission factor for direct CH4 emissions taken from IPCC
(2013). In order to estimate CH4 emissions from drainage ditches, the total area of peatland was multiplied by the
default fraction of peatland area that contains drainage ditches, and the appropriate emission factor taken from IPCC
(2013).
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 reported
production data for Alaska was assumed to be the same as for the lower 48 states, or ± 25 percent with a normal
distribution. It should be noted that the DGGS 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 and 2013) 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. The uncertainty values associated with the fraction of peatland
covered by ditches was assumed to be ± 100 percent with a normal distribution based on the assumption that greater
than 10 percent coverage, the upper uncertainty bound, is not typical of drained organic soils outside of The
Netherlands (IPCC 2013). Based on these values and distributions, a Monte Carlo (Approach 2) uncertainty
analysis was applied to estimate the uncertainty of COa, CH4, and N2O emissions from Peatlands Remaining
Peatlands. The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 6-43. CCh
emissions from Peatlands Remaining Peatlands in 2013 were estimated to be between 0.5 and 1.0 MMT COa Eq. at
the 95 percent confidence level. This indicates a range of 29 percent below to 32 percent above the 2013 emission
estimate of 0.8 MMT CChEq. N2O emissions from Peatlands Remaining Peatlands in 2013 were estimated to be
between 0.0003 and 0.0010 MMT CO2 Eq. at the 95 percent confidence level. This indicates a range of 55 percent
below to 62 percent above the 2013 emission estimate of 0.0006 MMT CO2 Eq. CH4 emissions from Peatlands
Remaining Peatlands in 2013 were estimated to be between 0.002 and 0.007 MMT COa Eq. This indicates a range
of 60 percent below to 85 percent above the 2013 emission estimate of 0.004 MMT CChEq.
Table 6-43: Approach 2 Quantitative Uncertainty Estimates for COz, Cm, and NzO Emissions
from Peatlands Remaining Peatlands (WWCQi Eq. and Percent)
2013 Emission
Source Gas Estimate Uncertainty Range Relative to Emission Estimate3
(MMT CCh Eq.) (MMT CCh Eq.) (%)
Land Use, Land-Use Change, and Forestry 6-77
-------
Peatlands Remaining Peatlands
Peatlands Remaining Peatlands
Peatlands Remaining Peatlands
CO2 0.8
CH4 +
N2O +
Lower
Bound
0.5
Upper
Bound
1.0
+
+
Lower
Bound
-29%
-60%
-55%
Upper
Bound
32%
85%
62%
Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
+ Does not exceed 0.05 MMT CO2 eq.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2013. 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 revealed an incorrect emission factor for off-site CO2 emissions from dissolved organic carbon. The
emission factor for a boreal climate zone was replaced with the emission factor for a temperate climate zone, which
is more representative of the climate zone for the majority of peat producing areas in the United States.
The QA/QC analysis also revealed that revised production estimates for peat were published in the 2013 Minerals
Yearbook: Peat (USGS 2014a). The estimates for the U.S. production of peat and the percentage of sphagnum moss
(nutrient-poor peat) reported in the 2013 Mineral Commodity Summaries: Peat (USGS 2014b) were replaced with
the estimates reported in the 2013 Minerals Yearbook: Peat (USGS 2014a). As a result, the estimate for peat
production decreased by 3 percent and the percentage of sphagnum moss decreased by 6 percent.
Recalculations Discussion
The emissions estimates for Peatlands Remaining Peatlands were updated to reflect the 2013 Supplement to the
2006IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands (IPCC 2013). IPCC (2013)
methodologies include off-site CO2 emissions from dissolved organic carbon, on-site CH4 emissions from drainage
ditches and drained land surface, and updated emissions factors for off-site CO2, on-site CO2, and on-site N2O
emissions estimates. As a result of the methodological changes listed above, CO2 emissions over the entire time
series increased by an average of approximately 1 percent and N2O emissions over the entire time series decreased
by an average of approximately 500 percent. Total emissions from Peatlands Remaining Peatlands increased by an
average of approximately 1 percent over the entire time series relative to the previous emissions estimates using the
IPCC (2006) guidelines.
The current Inventory estimates for 2011 and 2012 were also updated to incorporate information on the volume of
peat production in Alaska fmmAlaska 's Mineral Industry 2012 report (DGGS 2013); and the historical estimate for
2004 was updated to incorporate more recent information on the volume of peat product in Alaska in 2004 from
Alaska's Mineral Industry 2006 report (DGGS 2007). In the previous Inventory report, peat production in Alaska in
2011 and 2012 was assumed to equal the values reported for 2011 and 2012 in the 2012 Minerals Yearbook: Peat
(USGS 2013). As a result of the updated production estimates, emissions decreased by 0.005 percent in 2011,
increased by 0.001 percent in 2012, and increased by 10 percent in 2004. Since no peat production was reported in
Alaska's Mineral Industry 2013 report, peat production in Alaska in 2013 was assumed to equal the value reported
for 2012 in Alaska's Mineral Industry 2012 report; this will result in a recalculation in the next Inventory report if
the production value is updated.
In addition, for the current Inventory, emission estimates have been revised to reflect the GWPs provided in the
IPCC Fourth Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the
IPCC Second Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series
recalculations for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries
are required to report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of
each greenhouse gas. The GWP of CH4 has increased, leading to an overall increase in CO2-equivalent emissions
from CH4. The GWP of N2O has decreased, leading to a decrease in CO2-equivalent emissions for N2O. The AR4
GWPs have been applied across the entire time series for consistency. For more information please see the
Recalculations and Improvements Chapter. As a result of the updated GWP value for N2O, N2O emissions estimates
6-78 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
for each year from 1990 to 2012 decreased by 4 percent relative to the N2O emissions estimates in previous
Inventory reports.
Planned Improvements
In order to further improve estimates of CO2, N2O, and CEU 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.
6.9 Settlements Remaining Settlements
Changes in Carbon Stocks in Urban Trees (IPCC Source
Category 4E1)
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 75.8 MMT CCh Eq. (20.7 MMT C) over the period
from 1990 through 2013. Net C flux from urban trees in 2013 was estimated to be -89.5 MMT CO2 Eq. (-24.4
MMT C). Annual estimates of CO2 flux (Table 6-44) 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 2013 time series—i.e., the Census urban area is a subset of the Settlements area.
In 2013, 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 48 percent between 1990 and 2013 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 6-44: Net C Flux from Urban Trees (MMT COz Eq. and MMT C)
Year MMT CCh Eq. MMT C
1990 (60.4) (16.5)
2009
2010
(85.0)
(86.1)
(23.2)
(23.5)
Land Use, Land-Use Change, and Forestry 6-79
-------
2011 (87.3) (23.8)
2012 (88.4) (24.1)
2013 (89.5) (24.4)
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, 2007b, 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 6-45, 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 et al. (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. 2007).
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
6-80 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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. (2007), 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 6-45)
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). U.S. 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 from Nowak et al.
Land Use, Land-Use Change, and Forestry 6-81
-------
(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 6-45: 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
Vermont
Virginia
Washington
West Virginia
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
46,111
822,286
560,055
249,592
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
34,122
608,492
414,440
184,698
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
53.0
39.8
34.6
61.0
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
0.213
0.293
0.258
0.241
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
0.158
0.217
0.191
0.178
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
0.74
0.74
0.74
0.74
6-82 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Wisconsin 356,405 263,739 31.8 0.225 0.167 0.74
Wyoming 18,726 13,857 19.9 0.182 0.135 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 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.
A Monte Carlo (Approach 2) uncertainty analysis was applied to estimate the overall uncertainty of the
sequestration estimate. The results of the Approach 2 quantitative uncertainty analysis are summarized in Table
6-46. The net C flux from changes in C stocks in urban trees in 2013 was estimated to be between -133.1 and -47.0
MMT CO2 Eq. at a 95 percent confidence level. This indicates a range of 49 percent more sequestration to 48
percent less sequestration than the 2013 flux estimate of-89.5 MMT CCh Eq.
Table 6-46: Approach 2 Quantitative Uncertainty Estimates for Net C Flux from Changes in C
Stocks in Urban Trees (MMT COz Eq. and Percent)
2013 Flux Estimate Uncertainty Range Relative to Flux Estimate3
Source Gas (MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Changes in C Stocks in 49% _48%
Urban Trees
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 2013. 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
necessary. The net C flux resulting from urban trees was predominately calculated using state-specific estimates of
gross and net C sequestration estimates for urban trees and urban tree coverage area published in the literature.
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 USD A Forest Service to
Land Use, Land-Use Change, and Forestry 6-83
-------
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 USD A 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 MMT 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.
N2O Fluxes from Settlement Soils (IPCC Source Category 4E1)
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.
N additions to soils result in direct and indirect N2O emissions. Direct emissions occur on-site due to the N
additions. Indirect emissions result from fertilizer and sludge N that is transformed and transported to another
location in a form other than N2O (NH3 and NOX volatilization, NOs leaching and runoff), and later converted into
N2O at the off-site location. The indirect emissions are assigned to settlements because the management activity
leading to the emissions occurred in settlements.
In 2013, total N2O emissions from settlement soils were 2.4 MMT CO2 Eq. (8 kt). There was an overall increase of
77 percent over the period from 1990 through 2013 due to a general increase in the application of synthetic N
fertilizers on 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 6-47.
Table 6-47: NzO Fluxes from Soils in Settlements Remaining Settlements ($Wt\ COz Eq. and
kt NzO)
Direct N2O Fluxes from Soils
MMT CO2 Eq.
ktN2O
Indirect N2O Fluxes from Soils
MMT CO2 Eq.
ktN20
Total
MMT CO2 Eq.
ktN2O
1990
1.0
0.4
!_•
1.4
5
2005
1.8
1
0.6
2
2.3
sH
2009
1.7
6
0.6
2
2.2
8
2010
1.8
6
0.6
2
2.4
8
2011
1.9
6
0.6
2
2.5
8
2012
1.9
6
0.6
2
2.5
8
2013
1.8
6
0.6
2
2.4
8
Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP values.
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 (see Annex 3.12 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 2013 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
6-84 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 3.12 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).
For indirect emissions, the total N applied from fertilizer and sludge was multiplied by the IPCC default factors of
10 percent for volatilization and 30 percent for leaching/runoff to calculate the amount of N volatilized and the
amount of N leached/runoff. The amount of N volatilized was multiplied by the IPCC default factor of 1 percent for
the portion of volatilized N that is converted to N2O off-site and the amount of N leached/runoff was multiplied by
the IPCC default factor of 0.075 percent for the portion of leached/runoff N that is converted to N2O off-site. The
resulting estimates were summed to obtain total indirect emissions.
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.51 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. The uncertainty ranges around 2005 activity data and emission factor input variables were directly
applied to the 2013 emission estimates. Uncertainty in the direct and indirect emission factors was provided by the
IPCC (2006).
Quantitative uncertainty of this source category was estimated using simple error propagation methods (IPCC 2006).
The results of the quantitative uncertainty analysis are summarized in Table 6-48. Direct N2O emissions from soils
in Settlements Remaining Settlements in 2013 were estimated to be between 0.9 and4.8MMT CO2Eq. at a 95
percent confidence level. This indicates a range of 49 percent below to 163 percent above the 2013 emission
estimate of 1.8 MMT CO2Eq. Indirect N2O emissions in 2013 were between 0.1 and 1.9MMT CO2Eq., ranging
from a -85 percent to 212 percent around the estimate of 0.6 MMT CO2 Eq.
Table 6-48: Quantitative Uncertainty Estimates of NzO Emissions from Soils in Settlements
Remaining Settlements (MMT COz Eq. and Percent)
Source
Settlements Remaining
Settlements:
Direct N2O Fluxes from Soils
Indirect N2O Fluxes from Soils
_ 2013 Emissions Uncertainty Range Relative to Emission Estimate
(MMTCChEq.) (MMT CCh Eq.) (%)
N20
N2O
1.8
0.6
Lower
Bound
0.9
0.1
Upper
Bound
4.8
1.9
Lower
Bound
-49%
-85%
Upper
Bound
163%
212%
Note: These estimates include direct and indirect N2O emissions from N fertilizer additions to both Settlements Remaining
Settlements and from Land Converted to Settlements.
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
No uncertainty is provided with the USGS fertilizer consumption data (Ruddy et al. 2006) so a conservative ±50 percent was
used in the analysis.
Land Use, Land-Use Change, and Forestry 6-85
-------
QA/QC and Verification
The spreadsheet containing fertilizer and sewage sludge applied to settlements and calculations for N2O and
uncertainty ranges were checked and corrections were made. Linkage errors in the uncertainty calculation for 2013
were found and corrected. The reported emissions in the Inventory were also adjusted accordingly.
Recalculations Discussion
Indirect emissions from settlements were previously reported in Agricultural Soil Management, but are now
included in this source category. Including indirect emissions resulted in a 66 percent increase.
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous Inventories) which results in time-series recalculations
for most Inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWP of N2O decreased, leading to a decrease in CO2-equivalent emissions for N2O. The AR4
GWPs have been applied across the entire time series for consistency. For more information please see the
Recalculations and Improvements Chapter.
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.
6.10 Land Converted to Settlements (IPCC
Source Category 4E2)
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 CO2 or N2O
fluxes on Land Converted to Settlements from fluxes on Settlements Remaining Settlements at this time.
6.11 Other (IPCC Source Category 4H)
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 (C) 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.
6-86 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 2014a). 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 2013. 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 2013, the tonnage disposed of 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, and the net result is a decrease in annual
landfill C storage from 26.0 MMT CO2 Eq. (7.1 MMT C) in 1990 to 12.6 MMT CO2 Eq. (3.4 MMT C) in 2013
(Table 6-49 and Table 6-50).
Table 6-49: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills
(MMT COz Eq.)
Carbon Pool 1990 2005 2009 2010 2011 2012 2013
Yard Trimmings (21.0) (7.4) (8.5) (9.3) (9.4) (9.3) (9.3)
Grass (1.8) (0.6) (0.8) (0.9) (0.9) (0.9) (0.9)
Leaves (9.0) (3.4) (3.9) (4.2) (4.3) (4.3) (4.3)
Branches (10.2) I (3.4) (3.8) (4.1) (4.2) (4.2) (4.2)
Food Scraps (5.0) (4.0) M (4.0) (3.9) (3.8) (3.4) (3.3)
Total Net Flux (26.0) (11.4) (12.5) (13.2) (13.2) (12.8) (12.6)
Note: Parentheses indicate net sequestration.
Table 6-50: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills
(MMT C)
Carbon Pool
Yard Trimmings
Grass
Leaves
Branches
Food Scraps
Total Net Flux
1990
(5.7)
(0.5) 1
(2.5) 1
(2.8) 1
(1.4)
(7.1)
2005
(2.0)
(0.2)
(0.9)
(0.9)
(1.1)
(3.1)
2009
(2.3)
(0.2)
(1.1)
(1.0)
(1.1)
(3.4)
2010
(2.5)
(0.3)
(1.1)
(1.1)
(1.1)
(3.6)
2011
(2.6)
(0.3)
(1.2)
(1.1)
(1.0)
(3.6)
2012
(2.5)
(0.2)
(1.2)
(1.1)
(0.9)
(3.5)
2013
(2.5)
(0.2)
(1.2)
(1.1)
(0.9)
(3.4)
Note: Parentheses indicate net sequestration.
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
Land Use, Land-Use Change, and Forestry 6-87
-------
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
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 2014a), 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 historical
data tables that EPA developed for 1960 through 2012 (EPA 2014b). Remaining years in the time series for which
data were not provided were estimated using linear interpolation. Data for 2013 are not yet available, so they were
set equal to 2012 values. The EPA (2014a) report and historical data tables (EPA 2014b) do not subdivide the
discards (i.e., total generated minus composted) of individual materials into masses landfilled and combusted,
although it provides a mass of overall waste stream discards managed in landfills52 and combustors with energy
recovery (i.e., ranging from 67 percent and 33 percent, respectively, in 1960 to 92 percent and 8 percent,
respectively, in 1985); 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 6-51).
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 6-51).
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 CC>2. (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 6-51.
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
fromEPA'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.
52 EPA (2014) 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.
6-88 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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, k=0.02) and bioreactor landfill conditions (moisture is
controlled for rapid decomposition, k=0.l2). 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,
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 year1 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 6-51.
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:
t
LFd,t= I Wi,n x (1 - MG) x ICCx {[CSjX.ICG\ + [(1 - (C&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),
LFd,t = 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 <«
-------
Table 6-51: Moisture Contents, C Storage Factors (Proportions of Initial C Sequestered),
Initial C Contents, and Decay Rates for Yard Trimmings and Food Scraps in Landfills
Variable
Moisture Content (% H2O)
C Storage Factor, Proportion of Initial C
Stored (%)
Initial C Content (%)
Decay Rate (year"1)
Yard Trimmings
Grass
70
53
45
0.323
Leaves Branches
30 10
85 77
46 49
0.185 0.016
Food Scraps
70
16
51
0.156
Table 6-52: C Stocks in Yard Trimmings and Food Scraps in Landfills (MMT C)
Carbon Pool
Yard Trimmings
Branches
Leaves
Grass
Food Scraps
Total Carbon Stocks
1990
155.8
14.5
66.7
74.6
17.6
173.5
2005
202.9
18.1
87.3 1
97.5 1
32.8
235.6
2009
211.0
18.8
91.1
101.2
36.9
248.0
2010
213.
19,
99
102,
38.
251.
6
.0
2011
216.
19,
1
.3
.2 93.4
.3
0
6
103.
39.
255.
,5
0
1
2012
218.7
19.5
94.5
104.6
39.9
258.6
2013
221.
19,
95
105.
,2
.8
7
,7
40.8
262.
1
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 (Approach 2) uncertainty analysis was applied to estimate the overall uncertainty of the
sequestration estimate. The results of the Approach 2 quantitative uncertainty analysis are summarized in Table
6-53. Total yard trimmings and food scraps CC>2 flux in 2013 was estimated to be between -19.3 and -4.9 MMT
CO2 Eq. at a 95 percent confidence level (or 19 of 20 Monte Carlo stochastic simulations). This indicates a range of
53 percent below to 61 percent above the 2013 flux estimate of -12.6 MMT CO2 Eq. More information on the
uncertainty estimates for Yard Trimmings and Food Scraps in Landfills is contained within the Uncertainty Annex.
Table 6-53: Approach 2 Quantitative Uncertainty Estimates for COz Flux from Yard
Trimmings and Food Scraps in Landfills (MMT COz Eq. and Percent)
Source
2013 Flux
Estimate
Gas (MMT CCh Eq.)
Uncertainty Range Relative to Flux Estimate3
(MMT C02 Eq.) (%)
Lower Upper
Bound Bound
Lower Upper
Bound Bound
Yard Trimmings and Food ^ (u fi) (J93) (4 9) _53% +61%
ocraps
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 2013. Details on the emission trends through time are described in more detail in the Methodology section,
above.
6-90 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 has been revised relative to the previous report. Generation and recovery data for yard
trimmings and food scraps was not previously provided for every year from 1960 in the Municipal Solid Waste
Generation, Recycling, and Disposal in the United States: Facts and Figures report. EPA has since released
historical data, which included data for each year from 1960 through 2012. The recalculations based on these
historical data resulted in changes ranging from a 17 percent increase in sequestration in 1996 to a 5 percent
decrease in sequestration in 2005, and an average 4 percent increase in sequestration across the 1990-2012 time
series compared to the previous Inventory.
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.
Land Use, Land-Use Change, and Forestry 6-91
-------
7. Waste
Waste management and treatment activities are sources of greenhouse gas emissions (see Figure 7-1). Landfills
accounted for approximately 18.0 percent of total U.S. anthropogenic methane (CH4) emissions in 2013, the third
largest contribution of any CH4 source in the United States. Additionally, wastewater treatment and composting of
organic waste accounted for approximately 2.4 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 7-1 and Table 7-2.
Figure 7-1: 2013 Waste Chapter Greenhouse Gas Sources
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Landfills
Wastewater Treatment
Composting
Waste as a Portion of all
Emissions
2.1%
25
50 75
MMT CO2 Eq.
100
125
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 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 2006).l Additionally, the calculated emissions and sinks in a given year for the United
1 See .
Waste 7-1
-------
States are presented in a common manner in line with the UNFCCC reporting guidelines for the reporting of
inventories under this international agreement.2 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. The manner that emissions and sinks are provided in this Inventory is one of many ways U.S. emissions
and sinks could be examined; this Inventory report presents emissions and sinks in a common format consistent with
how countries are to report inventories under the UNFCCC. Emissions and sinks provided in the current Inventory
do not preclude alternative examinations,3 but rather 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 2013, waste activities generated emissions of 138.3 MMT CC>2 Eq.,4 or just over 2 percent of total U.S.
greenhouse gas emissions.
Table 7-1: Emissions from Waste (MMT COz Eq.)
Gas/Source
CH4
Landfills
Wastewater Treatment
Composting
N2O
Domestic Wastewater
Treatment
Composting
Total
1990
202.3
186.2
15.7 1
0.4
::
0.3
206.0
2005
183.2
165.5
15.91
1.9l
6.0
4.3
1.7
189.2
2009
175.5
158.1
15.6
1.9
6.3
4.6
1.7
181.8
2010
139.1
121.8
15.5
1.8
6.4
4.7
1.6
145.5
2011
138.4
121.3
15.3
1.9
6.5
4.8
1.7
144.9
2012
132.4
115.3
15.2
1.9
6.6
4.9
1.7
138.9
2013
131.6
114.6
15.0
2.0
6.7
4.9
1.8
138.3
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding.
Table 7-2: Emissions from Waste (kt)
Gas/Source
1990
2005
2009 2010
2011 2012
2013
CH4
Landfills
Wastewater Treatment
Composting
N20
Domestic Wastewater
Treatment
Composting
5,536
4,851
610
75
22
16
6
5,294
4,611
606
77
22
16
6
5,265
4,585
601
79
22
17
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
1 See.
3 For example, see .
4 Following the revised reporting requirements under the UNFCCC, this Inventory report presents CCh equivalent values based
on the IPCC Fourth Assessment Report (AR4) GWP values. See the Introduction chapter for more information.
7-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 2013 resulted in 10.4 MMT CO2 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.
The UNFCCC incorporated the 2006IPCC Guidelines for National Greenhouse Gas Inventories as the standard for
Annex I countries at the Nineteenth Conference of the Parties (Warsaw, November 11-23, 2013). This chapter
presents emission estimates calculated in accordance with the methodological guidance provided in these guidelines.
Box 7-2: 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 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.
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.5
EPA presents the data collected by EPA's GHGRP through a data publication tool6 that allows data to be viewed
in several formats including maps, tables, charts and graphs for individual facilities or groups of facilities.
7.1 Landfills (IPCC Source Category 5A1)
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 7-1 and Box 7-2. 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 7-3. 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 time
frame of 1990 to 2013. 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.
5 See
.
6 See .
Waste T
-------
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 under the Land Use/Land Use Change and Forestry (LULUCF) sector (see
Box 7-4). 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 within the first year after the 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 2013, landfill CH4 emissions were approximately 114.6 MMT CO2 Eq. (4,585 kt), 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 63 percent of the total solid waste generated in the United States (Shin 2014),
accounted for approximately 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 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 2014c; 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). Only 176 facilities with
industrial waste landfills reported under subpart TT (Industrial Waste Landfills) of EPA's Greenhouse Gas
Reporting Program (GHGRP) since reporting began in 2011, 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
MMT in 1990 to 259 MMT in2013 (see Annex 3.14). The annual amount of waste generated and subsequently
7-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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, but the percentage of waste landfilled may decline
due to increased recycling and composting practices. The estimated quantity of waste placed in industrial waste
landfills has remained relatively steady since 1990, ranging from 9.7 MMT in 1990 to 10.7 MMT in 2013.
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 2013, net CH4 emissions from landfills
decreased by approximately 38 percent, from 7.4 MMT to 4.6 MMT (see Table 7-3). 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.7 For instance, in 1990, approximately 491 kt of CH4 were recovered and
combusted from landfills, while in 2013, approximately 8,970 kt of CH4 were recovered and combusted,
representing an average annual increase in the quantity of CH4 recovered and combusted at MSW landfills from
1990 to 2013 of 13 percent (see Annex 3.14). Landfill gas collection and control is not accounted for at industrial
waste landfills in this chapter (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 2013, an
estimated 16 new landfill gas-to-energy (LFGTE) projects (EPA 2014a) and 3 new flares began operation. 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.
Table 7-3: CH4 Emissions from Landfills (MMT COz Eq.)
Activity
MSW Landfills
Industrial Landfills
Recovered
Gas-to-Energy
Flared
Oxidized*
Total
1990
205.4 1
13.8 1
(8.0)
(4.2)
(20.7)
186.2
2005
287.4 1
18.3
(56.4) 1
(65.4) 1
(18.4)
165.5
2009
316.4
18.8
(81.7)
(78.0)
(17.6)
158.1
2010
321.5
18.9
(170.2)
(34.8)
(13.5)
121.8
2011
325.7
18.9
(174.8)
(35.1)
(13.5)
121.3
2012
329.1
19.0
(184.4)
(35.6)
(12.8)
115.3
2013
332.6
19.1
(188.9)
(35.3)
(12.7)
114.6
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Lotals may not sum due to independent rounding. Parentheses indicate negative values.
a Includes oxidation at municipal and industrial landfills.
Table 7-4: ChU Emissions from Landfills (kt)
Activity
MSW Landfills
Industrial Landfills
Recovered
Gas-to-Energy
Flared
Oxidized*
Total
1990
8,215
553
(321)
(170)
(828)
7,450
2005
11,498
732
(2,256)
(2,618)
(736)
6,620
2009
12,657
753
(3,266)
(3,119)
(703)
6,324
2010
12,860
756
(6,809)
(1,393)
(539)
4,873
2011
13,030
758
(6,991)
(1,406)
(539)
4,851
2012
13,166
760
(7,377)
(1,426)
(521)
4,611
2013
13,303
763
(7,557)
(1,414)
(509)
4,585
Note: Lotals may not sum due to independent rounding. Parentheses indicate negative values.
a Includes oxidation at municipal and industrial landfills.
7 Due to a lack of data specific to industrial waste landfills, landfill gas recovery is only estimated for MSW landfills.
Waste 7-5
-------
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, solid 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.
States and local municipalities across the United States do not consistently track and report quantities of generated
or 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 secondary data, specifically the State of Garbage surveys,
published approximately every two years, with the most recent publication date of 2014. 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 nationwide 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; Shin 2014). 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 have typically been 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. In the most recent survey, state
agencies were asked to provide already filtered, MSW-only data. Where this was not possible, they were asked to
provide comments to better understand the data being reported. All state disposal data are adjusted for imports and
exports across state lines where imported waste is included in a particular state's total while exported waste is not.
Methodological changes have occurred over the time frame the SOG survey has been published, and this has
affected the fluctuating trends observed in the data (RTI 2013).
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 by multiplying the waste per capita
from a previous SOG survey by that particular state's population. If that particular state did not report any waste
generation data in the previous SOG survey, the average nationwide waste per capita rate for the current SOG
survey 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,
7-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
2008, and 2010). The most recent SOG survey provides data for 2011 (Shin 2014). State-specific landfill waste
generation data for the years in-between the SOG surveys (e.g., 2001, 2003, 2005, 2007, 2009, 2010, 2012, and
2013) were either interpolated or extrapolated based on the SOG data and the U.S. Census population data. Because
the most recent SOG survey was published in 2014 for the 2011 year, the annual quantities of waste generated for
the years 2012 and 2013 were extrapolated based on the 2011 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 (2014) and national per capita solid waste generation from the SOG survey (Shin 2014).
Estimates of the quantity of waste landfilled from 1989 to 2013 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 fromEPA'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 the current
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 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.
The estimated landfill gas recovered per year (R) at MSW landfills was based on a combination of four databases
and grouped into recovery from flares and recovery from landfill gas-to-energy (LFGTE) projects:
• the flare vendor database (contains updated sales data collected from vendors of flaring
equipment)
• a database of LFGTE projects compiled by LMOP (EPA 2014a)
• a database developed by the Energy Information Administration (EIA) for the voluntary reporting
of greenhouse gases (EIA 2007), and
• EPA's GHGRP dataset for MSW landfills (EPA 2014b).
EPA's GHGRP MSW landfills database was first introduced as a data source for the current Inventory (i.e., the
1990-2013 Inventory report). EPA's GHGRP MSW landfills database contains facility-reported data that undergoes
rigorous verification, thus it is considered to contain the least uncertain data of the four databases. However, this
database is unique in that it only contains a portion of the landfills in the United States (although, presumably the
highest emitters since only those landfills that meet a certain CH4 generation threshold must report) and only
contains data for 2010 and later.
The total amount of CH4 recovered and destroyed was estimated using the four databases listed above. To avoid
double- or triple-counting CH4 recovery, the landfills across each database were compared and duplicates identified.
A hierarchy of recovery data is used based on the certainty of the data in each database as described below.
Forthe years 2010 to 2013, if a landfill in EPA's GHGRP MSW landfills database was also in the EIA, LMOP,
and/or flare vendor database, the avoided emissions were based on EPA's GHGRP MSW landfills database. For the
years 1990 to 2009, 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 directly reported the amount of
CH4 recovered based on measurements of gas flow and concentration, and the reporting accounted for changes over
time. However, as the EIA database only includes data through 2006, the amount of CH4 recovered from 2007 to
Waste 7-7
-------
2013 for projects included in the EIA database were assumed to be the same as in 2006. This quantity likely
underestimates flaring because the EIA database does not have information on all flares in operation. If both flare
data and LMOP recovery data were available for any of the remaining landfills (i.e., not in the EIA or GHGRP
databases), then the avoided emissions were 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 vendor
database, on the other hand, estimates CH4 combusted by flares using the midpoint of a flare's reported capacity.
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.
The amount of landfill gas recovered and combusted is also presented in terms of avoided emissions by flaring and
avoided emissions by LFGTE. The amount combusted by flaring was directly determined using information
provided by the EIA and flare vendor databases and indirectly determined using information in EPA's GHGRP
dataset for MSW landfills. Information provided by the EIA and LMOP databases were used to directly estimate
methane combusted in LFGTE projects over the time series. EPA's GHGRP MSW landfills database provides a
total amount of CH4 recovered at the facility-level and was indirectly used to estimate methane combusted in
LFGTE projects. Unlike the three other databases, EPA's GHGRP dataset does not identify whether the amount of
CH4 recovered is combusted by a flare versus an LFGTE project. Therefore, a mapping exercise was performed
between EPA's GHGRP MSW landfills database and the three other databases to make a distinction between
landfills contained in both EPA's GHGRP MSW landfills database and one or more of the other databases. The CH4
recovered by landfills matched to the EIA (and marked as LFGTE) and LMOP databases was allocated as CH4
recovered and combusted by LFGTE projects. The remaining CH4 recovered from EPA's GHGRP dataset was
allocated as CH4 recovered and combusted by flares.
The destruction efficiencies reported through EPA's GHGRP were applied to the landfills in EPA's GHGRP MSW
landfills database. The median value of the reported destruction efficiencies was 99 percent for all reporting years
(2010 through 2013). 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) in the EIA, LMOP, and flare
vendor databases. The 99 percent 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- CH4 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 2014), 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.
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
7-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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. EPA's GHGRP does allow facilities to report annual quantities of waste disposed by
composition, but very few do so. Additionally, some MSW landfills have conducted detailed waste composition
studies, but because landfills in the United States are not required to perform these types of studies, the data are
scarce over the time series and across the country.
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 also 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. Until the current Inventory, three separate databases containing recovery
information were used to determine the total amount of CH4 recovered and there are uncertainties associated with
each. For the current Inventory, EPA's GHGRP MSW landfills database was added as a fourth recovery database.
Relying on multiple databases for a complete picture introduces uncertainty because the coverage of each database
differs, which increases the chance of double counting avoided emissions. Additionally, the methodology and
assumptions that go into each database differ. For example, the flare database assumes the midpoint of each flare
capacity at the time it is sold and installed at a landfill; in reality, the flare may be achieving a higher capacity, in
which case the flare database would underestimate the amount of CH4 recovered.
The LMOP database and the flare vendor databases are updated annually. The EIA database has not been updated
since 2005 and, for the most part, was replaced by EPA's GHGRP MSW landfills database for the portion of
landfills reporting under EPA's GHGRP (i.e., those meeting the GHGRP thresholds) that were also included in the
EIA database. 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 four databases. EPA's GHGRP data are given precedence because CH4
recovery is directly reported by landfills and undergoes a rigorous verification process; the EIA data are given
second priority because facility data were directly reported; the LMOP data are given third priority because CH4
recovery is estimated from facility-reported LFGTE system characteristics; and the flare data are given fourth
priority because this database contains minimal information about the flare and no site-specific operating
characteristics (Bronstein et al. 2012). The coverage provided across the databases most likely represents the
complete universe of landfill CH4 gas recovery, however the number of unique landfills between the four databases
does differ.
The IPCC default value of 10 percent for uncertainty in recovery estimates was used for 2 of the 4 recovery
databases in the uncertainty analysis where 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 the EIA and LMOP databases. A lower
Waste 7^9
-------
uncertainty value (5 percent) was applied to the GHGRP MSW landfills dataset as a result of the supporting
information provided and verification process. For flaring without metered recovery data (the flare database), a
much higher uncertainty value of approximately 50 percent was used. The compounding uncertainties associated
with the 4 databases in addition to the uncertainties associated with the first order decay model and annual waste
disposal quantities leads to the large upper and lower bounds for MSW landfills presented in Table 7-5. Industrial
waste landfills are shown with a lower range of uncertainty due to the smaller number of data sources and associated
uncertainty involved. For example, 3 data sources are used to generate the annual quantities of MSW waste disposed
over the 1940 to current year, while industrial waste landfills rely on 2 data sources.
The results of the 2006IPCC Guidelines Approach 2 quantitative uncertainty analysis are summarized in Table 7-5.
In 2013, landfill CH4 emissions were estimated to be between 60.7 and 217.4 MMT CO2 Eq., which indicates a
range of 47 percent below to 90 percent above the 2013 emission estimate of 114.6 MMT CCh Eq.
Table 7-5: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Landfills
(MMT COz Eq. and Percent)
Source
Landfills
MSW
Industrial
2013 Emission
Gas Estimate
(MMT CO2 Eq.)
CH4
CH4
CH4
114.6
97.5
17.2
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower
Bound
60.7
45.0
12.2
Upper
Bound
217.4
201.0
21.3
Lower
Bound
-47%
-54%
-29%
Upper
Bound
+90%
+106%
+24%
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 2013. Details on the emission trends through time-series 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
performed for the transcription of the published data set used to populate the Inventory data set, including the SOG
survey data and the published LMOP database, but are not performed on the data itself against primary data used. 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 used to ensure that emission avoidance from each landfill was calculated only once. 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.
Recalculations Discussion
Three major methodological recalculations were performed for the current Inventory. First, a new SOG survey was
published allowing for the update of the annual quantities of waste generated and disposed and the amount of CH4
generated for the years 2009 through 2012. Second, the percent of the U.S. population within the three precipitation
ranges were updated for the year 2010 (see Table A-3 in Annex 3.14), which impacted the distribution for the years
2001 through 2013 in the waste model. Third, the EPA's GHGRP CH4 recovery and destruction efficiency data were
incorporated. Further discussion on the recalculations made are discussed below.
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 (CO2 Eq.) are required to calculate and report
their greenhouse gas emissions to EPA through its GHGRP. The data reported in one year represent the GHGs that
the landfill generated and emitted in the previous calendar year. As a result EPA now has data from 2010 through
2013 for MSW landfills. The MSW landfill source category of EPA's GHGRP consists of the landfill, landfill gas
collection systems, and landfill gas destruction devices, including flares. For the current Inventory year, the annual
7-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
quantity of CH4 recovered and the destruction efficiency of the flare and/or LFGTE system at each facility were
incorporated as a fourth CH4 recovery database (i.e., the GHGRP MSW landfills database). The GHGRP data
undergo an extensive series of verification steps, are more reliable and accurate than the data currently used in the
three other CH4 recovery databases (Bronstein et al. 2012). A significant effort was made to compare the unique
landfills in each database to ensure the hierarchy of recovery was maintained (i.e., GHGRP > EIA > LMOP > flare
database) and that double, or triple counting was not encountered.
Facility-level reporting data from EPA's GHGRP are not available for the entire time series reported in the current
Inventory; therefore, particular attention was made to ensure time series consistency while incorporating data from
EPA's GHGRP. 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 was relied upon.8 However, after
incorporating the GHGRP MSW landfills data, a significant drop in net CH4 emissions from 2009 to 2010 was
observed (see Table 7-3 and Table 7-4). The underlying reason(s) for the large increase in the CH4 recovered and the
large decrease in net emissions is being investigated and may most likely result from the flare database
underestimating the amount of CH4 recovered as a result of the midpoint in each flare's reported capacity being used
in the recovery calculations.
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh-equivalent emissions from CH4. The GWPs of N2O and SF6 have decreased, leading to a decrease in
CCh-equivalent emissions for these greenhouse gases. The AR4 GWPs have been applied across the entire time
series for consistency. For more information please see the Recalculations and Improvements Chapter.
Planned Improvements
Improvements being examined include incorporating additional data from recent peer-reviewed literature to modify
the default oxidation factor applied to MSW and industrial waste landfills (currently 10 percent), and to either
modify the bulk waste degradable organic carbon (DOC) value or estimate emissions using a waste-specific
approach in the first order decay model using data from the GHGRP and peer-reviewed literature.
A standard CH4 oxidation factor of 10 percent has been used for both industrial and MSW landfills in prior
Inventory reports 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. As a
first step toward revising this oxidation factor, a literature review was conducted in 2011 (RTI2011). In addition,
facilities reporting under EPA's GHGRP have the option to use an oxidation factor other than 10 percent (e.g., 0, 25,
or 35 percent) if the calculated result of methane flux calculations warrants it. Various options are being investigated
to incorporate this facility-specific data for landfills reporting under EPA's GHGRP and or the remaining facilities.
The standard 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
' See: .
Waste 7-11
-------
March 2013. After incorporating EPA's 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.
Other potential improvements to the methodology may be made in the future using other portions of EPA's GHGRP
dataset, 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 7-2). 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.
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 are being reported.
Any additions or improvements to the Inventory using reported GHGRP data will be made for the industrial waste
landfill source category. One potential improvement includes a revision to the waste disposal factor currently used
by the Inventory for the pulp and paper sector using production data from pulp and paper facilities that reported
annual production and annual disposal data under EPA's GHGRP. Another 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 EPA's GHGRP data set for industrial waste landfills is
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 industrial waste landfills.
Box 7-3: Nationwide Municipal Solid Waste Data Sources
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 amount of 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 across state lines 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
the imports and exports of produced materials to other countries. MSW that is not recycled, composted, or
combusted is assumed to be landfilled. The data presented in the report are nationwide totals.
7-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 (WTE) 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 7-4: Overview of the Waste Sector
As shown in Figure 7-2 and Figure 7-3, 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 and
recycled would have normally been disposed of in a landfill.
Figure 7-2: Management of Municipal Solid Waste in the United States, 2011
Composted
6%
MSW to WTE
8%
Source: Shin 2014
Waste 7-13
-------
Figure 7-3: MSW Management Trends from 1990 to 2012
Landfilling
100
80
60
•10
20
Recycling
Combustion with
Energy Recovery
Comporting
Q\
'
-------
Table 7-6: Materials Discarded in the Municipal Waste Stream by Waste Type (Percent)
Waste Type
Paper and Paperboard
Glass
Metals
Plastics
Rubber and Leather
Textiles
Wood
Other1
Food Scrapsb
Yard Trimmings0
Miscellaneous Inorganic
Wastes
1990
30,
,0%
6.0%
7.2%
9.6%
3.1%
2.9%
6.9%
1.4%
13.6%
17.6%
1
,7%
2005
24,
,5%
5.7%
7.7%
15.7%
3.5%
5.5%
7.4%
1.8%
17.9%
7.0%
2,
.1%
2009
14
.8%
5.0%
8.0%
15.8%
3.7%
6.3%
7.7%
1.9%
19.1%
7.6%
2
.2%
2010
16.2%
5.1%
8.8%
17.4%
3.7%
6.7%
8.1%
2.0%
21.0%
8.6%
2.3%
2011
14
.8%
5.1%
8.9%
17.8%
3.8%
6.8%
8.2%
2.0%
21.4%
8.8%
2
.4%
2012
14.8%
5.1%
9.0%
17.6%
3.8%
7.4%
8.2%
2.0%
21.1%
8.7%
2.4%
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 2014c.
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 2014c.
c Data for yard trimmings were estimated using sampling studies, population data, and published
sources documenting legislation affecting yard trimmings disposal in landfills. Source: EPA 2014c.
Figure 7-4: Percent of Recovered Degradable Materials from 1990 to 2012 (Percent)
80%
70%
60%
50%
40%
30%
20%
10%
0%
Paper and
Paperboard
Food Scraps
Ya-d
Trimmings
Source: EPA 2014c
Box 7-5: 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.
Additionally, many new landfills collect and destroy landfill gas through flares or landfill gas-to-energy projects.
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)
Waste 7-15
-------
• 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.9
7.2 Wastewater Treatment (IPCC Source
Category 5D)
Wastewater treatment processes can produce anthropogenic CH4 and N2O emissions. Wastewater from domestic10
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 (NOs) 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). Other more recent research suggests that N2O may also result from
other types of wastewater treatment operations (Chandran 2012).
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
9 For more information regarding federal MSW landfill regulations, see
.
Throughout the inventory, emissions from domestic wastewater also include any commercial and industrial wastewater
collected and co-treated with domestic wastewater.
7-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 2013, CH4 emissions from domestic wastewater treatment were 9.2 MMT CO2 Eq. (368 kt CEU). 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
treatment systems (EPA 1992, 1996, 2000, and 2004, U.S. Census 2011). In 2013, CH4 emissions from industrial
wastewater treatment were estimated to be 5.8 MMT CO2 Eq. (233 kt 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 7-7 and
Table 7-8 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 2013 emissions of N2O from centralized
wastewater treatment processes and from effluent were estimated to be 0.3 MMT CO2 Eq. (1 kt N2O) and 4.6 MMT
CO2 Eq. (15 kt N2O), respectively. Total N2O emissions from domestic wastewater were estimated to be 4.9 MMT
CO2 Eq. (17 kt 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 7-7: CH4 and NzO Emissions from Domestic and Industrial Wastewater Treatment
(MMT COz Eq.)
Activity
CH4
Domestic
Industrial*
N20
Domestic
Total
1990
15.7
10.5
5.1
3.4
3.4
19.1
2005
15.9
10.0 1
58I
4.3
4.3 I
20.2
2009
15.6
9.8
5.8
4.6
4.6
20.2
2010
15.5
9.6
5.9
4.7
4.7
20.2
2011
15.3
9.4
5.9
4.8
4.8
20.1
2012
15.2
9.3
5.8
4.9
4.9
20.1
2013
15.0
9.2
5.8
4.9
4.9
19.9
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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 7-8: ChU and NzO Emissions from Domestic and Industrial Wastewater Treatment (kt)
Activity
CH4
Domestic
Industrial*
N2O
Domestic
1990
626
421 1
206 1
11
11
2005
635
401 1
234 1
15
15
2009
623
392
231
16
16
2010
619
384
235
16
16
2011
610
375
235
16
16
2012
606
373
233
16
16
2013
601
368
233
17
17
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
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.
Waste 7-17
-------
Methodology
Domestic Wastewater ChU 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 CHVcapita/day), and then converting the result to kt/year. CH4emissions 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
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. CH4emissions 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
BOD5 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 1/10A9
(FRAC_CH4) x (365.25) x (density
Total CH4 Emissions (kt) = 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
= 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)
7-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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
FRAC_CH4
density of CH4
1/10A9
= 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 kt
= 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)
= Wastewater flow to POTW per person per day (100 gal/person/day)
= Conversion factor, ft3 to m3 (0.0283)
= Proportion CH4 inbiogas (0.65)
= 662 (g CH4/m3 CH4)
= Conversion factor, g to kt
U.S. population data were taken from the U.S. Census Bureau International Database (U.S. Census 2014) and
include the populations of the United States, American Samoa, Guam, Northern Mariana Islands, Puerto Rico, and
the Virgin Islands. Table 7-9 presents U.S. population and total BOD5 produced for 1990 through 2013, while Table
7-10 presents domestic wastewater CH4 emissions for both septic and centralized systems in 2013. 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, and 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
2013 forecasted using 1990-2012 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 2013 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 maximum CH4-producing capacity (0.6 kg CHVkg BOD5) and both MCFs used for
centralized treatment systems were taken from IPCC (2006), while the CH4 emission factor (10.7 g CHVcapita/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, along with data from CAR
(2011), Sullivan (2007), Sullivan (2010), and UNFCCC (2012). 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 7-9: U.S. Population (Millions) and Domestic Wastewater BODs Produced (kt)
Year Population
BODs
1990
253
8,333
2009
2010
2011
2012
2013
311
313
316
318
320
10,220
10,303
10,377
10,452
10,534
Waste 7-19
-------
Sources: U.S. Census Bureau (2014);
Metcalf& Eddy (2003).
Table 7-10: Domestic Wastewater CH4 Emissions from Septic and Centralized Systems
(2013)
CH4 Emissions (MMT CCh Eg.) % of Domestic Wastewater CH4
Septic Systems
Centralized Systems (including anaerobic
sludge digestion)
6.0
3.2
65.5%
34.5%
Total
9.2
100%
Note: Emission values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding.
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 2013 are displayed in
Table 7-11 below. Table 7-12 contains production data for these industries.
Table 7-11: Industrial Wastewater CH4 Emissions by Sector (2013)
CH4 Emissions (MMT CCh Eq.) % of Industrial Wastewater CH4
Meat & Poultry
Pulp & Paper
Fruit & Vegetables
Petroleum Refineries
4.4
1.1
0.1
0.1
75%
18%
2%
2%
Ethanol Refineries
0.1
2%
Total
5.8
100%
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding.
Table 7-12: U.S. Pulp and Paper, Meat, Poultry/ Vegetables, Fruits and Juices, Ethanol, and
Petroleum Refining Production (MMT)
Year
Meat Poultry Vegetables,
Pulp and (Live Weight (Live Weight Fruits and
Paper3 Killed) Killed) Juices
Ethanol
Petroleum
Refining
2009
2010
2011
2012
2013
120.4
128.6
127.5
127.0
131.5
33.8
33.7
33.8
33.8
33.6
25.2
25.9
26.2
26.1
26.5
46.5
43.2
44.3
45.3
43.9
32.7
39.7
41.6
39.5
39.8
822.4
848.6
858.8
856.1
875.9
aPulp and paper production is the sum of woodpulp production plus paper and paperboard production.
Sources: Lockwood-Post (2002); FAO (2014); USDA (2014a); RFA (2014); EIA (2014).
7-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
CH4 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 CHVkg 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 values used in the
%TA calculations are presented in Table 7-13 below.
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]
%TAP = [%Plants0 x %WWa,p x %CODP]
%TAs = [%Plantsa x %WWa,s x %CODS] + [%Plantst x %WWa,t x %CODS]
where,
CH4 (industrial wastewater) = Total CH4 emissions from industrial wastewater (kg/year)
P = Industry output (metric tons/year)
W = Wastewater generated (m3/metric ton of product)
COD = Organics loading in wastewater (kg/m3)
%TAP = Percent of wastewater treated anaerobically on site in primary treatment
%TAs = Percent of wastewater treated anaerobically on site in secondary treatment
%Plants0 = Percent of plants with onsite treatment
%WWa,p = Percent of wastewater treated anaerobically in primary treatment
%CODP = Percent of COD entering primary treatment
%Plantsa = Percent of plants with anaerobic secondary treatment
%Plantst = Percent of plants with other secondary treatment
%WWa,s = Percent of wastewater treated anaerobically in anaerobic secondary treatment
%WWa,t = Percent of wastewater treated anaerobically in other secondary treatment
%CODS = Percent of COD entering secondary treatment
Bo = Maximum CH4 producing potential of industrial wastewater (default value of
0.25 kg CHVkg COD)
MCF = 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 x %WWas x %CODs]+[%Plantst x %WWat x CODS]
%TAat = [%Plantsat x %WWas x %CODS]
where,
%TAa = Percent of wastewater treated anaerobically on site in secondary treatment
"/oTAat = Percent of wastewater treated in aerobic systems with anaerobic portions on
site in secondary treatment
%Plantsa = Percent of plants with anaerobic secondary treatment
%Plantsa,t = Percent of plants with partially anaerobic secondary treatment
%WWa,s = Percent of wastewater treated anaerobically in anaerobic secondary treatment
%WWa t = Percent of wastewater treated anaerobically in other secondary treatment
Waste 7-21
-------
%CODS
= Percent of COD entering secondary treatment
As described below, the values presented in Table 7-13 were used in the emission calculations and are described in
detail in ERG (2008), ERG (2013a), and ERG (2013b).
Table 7-13: Variables Used to Calculate Percent Wastewater Treated Anaerobically by
Industry (percent)
Variable
%TAP
%TAS
%TAa
%TAa,t
%Plants0
%PlantSa
%Plantsa,t
%Plantst
%WWa,p
%WWa,s
%WWa,t
%CODP
%CODS
Pulp
and
Paper
0
0
2.2
11.8
0
5
28
35
0
100
0
100
42
Meat
Processing
0
33
0
0
100
33
0
67
0
100
0
100
100
Poultry
Processing
0
25
0
0
100
25
0
75
0
100
0
100
100
Industry
Fruit/
Vegetable
Processing
0
4.2
0
0
11
5.5
0
5.5
0
100
0
100
77
Ethanol
Production
-Wet Mill
0
33.3
0
0
100
33.3
0
66.7
0
100
0
100
100
Ethanol
Production
- Dry Mill
0
75
0
0
100
75
0
25
0
100
0
100
100
Petroleum
Refining
0
23.6
0
0
100
23.6
0
0
0
100
0
100
100
Sources: ERG (2008); ERG (2013a); and ERG (2013b).
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'sOAQPS Pulp and Paper Sector Survey, 5.3
percent of pulp and paper mills reported using anaerobic secondary treatment for wastewater and/or pulp
condensates (ERG 2013a). Twenty-eight percent (28 percent) 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 2013 (FAO 2014). The overall wastewater outflow
varies based on a time series outlined in ERG (2013a) 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).
7-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 CHVkg 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 2014a). 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 2014a) provided
production data for potatoes, other vegetables, citrus fruit, non-citrus fruit, and grapes processed for wine. Outflow
and BOD data, presented in Table 7-14, 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 7-14: Wastewater Flow (m3/ton) and BOD Production (g/L) for U.S. Vegetables, Fruits,
and Juices Production
Commodity Wastewater Outflow (mVton) 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
Sources: EPA 1974, EPA 1975.
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 (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 dry milling process is cheaper to implement, and has become more
efficient in recent years (Rendleman and Shapouri 2007). 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
Waste 7^23"
-------
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.
Biomethanators are anaerobic reactors that use microorganisms under anaerobic conditions to reduce COD and
organic acids and recover biogas from wastewater (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 %CODJ) x B0 x MCF x (% Recovered) x (l-
DE)] x 1/10A9
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
%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 CH4/g 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 kt
A time series of CH4 emissions for 1990 through 2013 was developed based on production data from the Renewable
Fuels Association (RFA 2014).
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.u 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 (ERG 2013b). In addition, the wastewater generation rate was determined to be 26.4 gallons
per barrel of finished product (ERG 2013b). 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
Available online at .
7-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 2013 was developed based on production data from the Energy
Information Association (EIA 2014).
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.
• 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 = N2ONIT/DENIT + N2OwOUT MT/DENIT
N2ONIT/DENIT= [(USpQPND) x EF2 X FlND-COM] x 1/10A9
N2OwOUTNIT/DENIT = {[(USpOP X WWTP) - USpOPM)] x FlND-COM x EFl} X 1/10A9
N2OEFFLUENT = {[(((USpop x WWTP) - (0.9 x USpopND)) x Protein x FNPR x FNON-CON x FIND-COM) - NSLUDGE] x EF3 x
44/28} x 1/10A6
where,
N2OioTAL = Annual emissions of N2O (kt)
N2OpLANi = N2O emissions from centralized wastewater treatment plants (kt)
N2ONiT/DENii = N2O emissions from centralized wastewater treatment plants with
nitrification/denitrification (kt)
N2Owour NIT/DENIT = N2O emissions from centralized wastewater treatment plants without
nitrification/denitrification (kt)
N2OEFFLUENi = N2O emissions from wastewater effluent discharged to aquatic environments (kt)
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)
Waste 7-25
-------
FNON-CON
FIND-COM
NSLUDGE
EF3
0.9
44/28
= Factor for non-consumed protein added to wastewater (1.4)
= Factor for industrial and commercial co-discharged protein into the sewer system
(1.25)
= N removed with sludge, kg N/yr
= Emission factor (0.005 kg N2O -N/kg sewage-N produced) - from effluent
= Amount of nitrogen removed by denitrification systems
= Molecular weight ratio of N2O to N2
U.S. population data were taken from the U.S. Census Bureau International Database (U.S. Census 2014) 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 and 2013 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 2014b). Protein consumption data for 2010 through 2013 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 (IPCC 2006). The fraction of N in protein (0.16 kg N/kg protein) was also obtained from IPCC (2006).
The factor for non-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. The amount of nitrogen removed by denitrification systems was taken
from EPA (2008). 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 (US EPA 1993b, Beecher et al. 2007, McFarland 2001, US EPA 1999). In 2013, 286 kt N
was removed with sludge. Table 7-15 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 7-15: U.S. Population (Millions), Population Served by Biological Denitrification
(Millions), Fraction of Population Served by Wastewater Treatment (percent), Available
Protein (kg/person-year), Protein Consumed (kg/person-year), and Nitrogen Removed with
Sludge (kt-N/year)
Year Population Populations WWTP Population Available Protein Protein Consumed N Removed
1990
253
75.6
38.4
29.5
214.1
2009
2010
2011
2012
2013
311
313
316
318
320
2.9
3.0
3.0
3.0
3.1
79.3
80.0
80.6
80.4
80.7
40.9
41.0
41.1
41.2
41.3
31.5
31.6
31.7
31.8
31.9
273.4
276.4
279.5
282.6
285.6
Sources: Beecher et al. 2007, McFarland 2001, U.S. Census 2011, U.S. Census 2014, USDA 2014b, US EPA 1992, US EPA
1993b, US EPA 1996, US EPA 1999, US EPA 2000, US EPA 2004.
Uncertainty and Time-Series Consistency
The overall uncertainty associated with both the 2013 CH4 and N2O emission estimates from wastewater treatment
and discharge was calculated using the 2006IPCC Guidelines Approach 2 methodology (2006). 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
7-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 Approach 2 quantitative uncertainty analysis are summarized in Table 7-16. Methane emissions
from wastewater treatment were estimated to be between 9.2 and 15.3 MMT CC>2 Eq. at the 95 percent confidence
level (or in 19 out of 20 Monte Carlo Stochastic Simulations). This indicates a range of approximately 39 percent
below to 2 percent above the 2013 emissions estimate of 15.0 MMT CO2 Eq. N2O emissions from wastewater
treatment were estimated to be between 1.2 and 10.2 MMT CC>2 Eq., which indicates a range of approximately 76
percent below to 107 percent above the 2013 emissions estimate of 4.9 MMT €62 Eq.
Table 7-16: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from
Wastewater Treatment (MMT COz Eq. and Percent)
Source
Wastewater Treatment
Domestic
Industrial
Wastewater Treatment
2013 Emission Estimate Uncertainty Range Relative to Emission Estimate3
(MMTCChEq.) (MMT CCh Eq.) (%)
CH4
CH4
CH4
N20
15.0
9.2
5.8
4.9
Lower
Bound
9.2
5.7
2.4
1.2
Upper
Bound
15.3
9.9
6.9
10.2
Lower
Bound
-39%
-38%
-59%
-76%
Upper
Bound
+2%
+7%
+18%
+107%
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 2013. 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 USDA NASS datasets. In addition, the most recent USDA ERS data
were used to update percent protein values from 1990 through 2010. The updated ERS data also resulted in small
changes in forecasted values from 2011. The factor for sewage sludge production change per year was updated to
include all available data. This change resulted in updated 1990 through 1995 values for total N in sludge along with
a change in forecasted values from 2005 through 2012.
Waste 7-27
-------
Workbooks were also updated to show emissions in kilotons and MMT CO2 Eq. In addition, global warming
potentials for N2O and CH4 were updated with the AR4 100-year values (IPCC 2007).
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CCh-equivalent emissions from CH4. The GWPs of N2O and SF6 have decreased, leading to a decrease in
CO2-equivalent emissions for N2O. The AR4 GWPs have been applied across the entire time series for consistency.
For more information please see the Recalculations and Improvements Chapter.
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. While NEBRA is no longer involved in the project, the Water Environment Federation (WEF)
now hosts and manages the dataset which has been relocated to www.wef.org/biosolids. WEF will complete the
second phase of their data collection and by late fall. They are currently collecting additional data on a Region by
Region basis which should add to the quality of the database by decreasing uncertainty and data gaps (ERG 2014a).
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 Greenhouse Gas Reporting Program Subpart II, Industrial Wastewater Treatment
(GHGRP) is being investigated for use in improving the emission estimates for the industrial wastewater category.
Ensuring time series consistency has been the focus, as the reporting data from EPA's GHGRP are not available for
all inventory years. Whether EPA's GHGRP reporters sufficiently represent U.S. emissions is being investigated to
determine if moving to a facility-level implementation of GHGRP data is warranted, or whether the GHGRP data
will allow update of activity data for certain industry sectors, such as use of biogas recovery systems or update of
waste characterization data. Since EPA's GHGRP only includes reporters that have met a certain threshold and
because EPA is unable to review whether the reporters represent the majority of U.S. production, GHGRP data are
not believed to be sufficiently representative to move toward facility-level estimates in the Inventory. However, the
GHGRP data continues to be evaluated for improvements to activity data, and in verifying methodologies currently
in use in the Inventory to estimate emissions (ERG 2014b). In implementing any improvements and integration of
data from EPA's GHGRP, EPA will follow the latest guidance from the IPCC on the use of facility-level data in
national inventories.12
For industrial wastewater emissions, EPA is also working with the National Council of Air and Stream Improvement
(NCASI) to determine if there are sufficient data available to update the estimates of organic loading in pulp and
paper wastewaters treated on site. These data include the estimates of wastewater generated per unit of production,
the BOD and/or COD concentration of these wastewaters, and the industry-level production basis used in the
Inventory. EPA has received data on the industry-level production basis to date and intends to incorporate these data
once a full evaluation of the production basis is made in relation to the wastewater generation rate and the organic
12 See:
7-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
content of the wastewater. In this way, EPA plans to make a coordinated update to the three values that are used to
estimate the total organic industry load to wastewater treatment plants, rather than multiple changes over time.
In addition to this investigation, any reports based on international research will be investigated to inform potential
updates to the Inventory. The Global Water Research Coalition report has been evaluated, regarding wastewater
collection and treatment systems (GWRC 2011). The report included results of studies from Australia, France, the
Netherlands, and the United States. Since each dataset was taken from a variety of wastewater treatment plant types
using different methodologies and protocols, it was determined that it was not representative enough to include in
the Inventory at this time (ERG 2014a). In addition, wastewater inventory submissions from other countries have
been investigated to determine if there are any emission factors, specific methodologies, or additional industries that
could be used to inform the U.S. inventory calculations. Although no comparable data have been found, other
countries' submissions will continue to be investigated for potential improvements to the inventory.
IPCC's 2013 wetlands supplement has also been investigated regarding the inclusion of constructed and semi-
natural treatment wetlands in Inventory calculations (IPCC 2014). Methodologies are presented for estimating both
CH4 and N2O. Next, the use of CWNS treatment system data will be investigated to determine if these data can be
used to estimate the amount of wastewater treated in constructed wetlands for potential implementation in future
Inventory reports.
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 which was reviewed (Willis et al. 2013). It was determined that the emissions were too
variable and the sample size too small to include in the Inventory at this time. In addition, information on flare
efficiencies was reviewed and it was determined that they were not suitable for use in updating the Inventory
because the flares used in the study are likely not comparable to those used at wastewater treatment plants (ERG
2014a). The status of this and similar research will continue to be monitored for potential inclusion in the Inventory
in the future.
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. The Phase I report from WERF on N2O emissions was recently reviewed and EPA
concluded, along with the author, that there were not enough data to create an emission factor for N2O (Chandran
2012). While the authors suggested a facility-level approach, there are not enough data available to estimate N2O
emissions on a facility-level for the more than 16,000 POTWs in the United States (ERG 2014a). 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 (ERG 2011). 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.
There is the potential for N2O emissions associated with on-site industrial wastewater treatment operations;
however, the methodology provided in IPCC (2006) only addresses N2O emissions associated with domestic
wastewater treatment. A literature review was initiated to assess other Annex I countries' wastewater inventory
submissions for additional data and methodologies that could be used to inform the U.S. wastewater inventory
calculations, in particular to determine if any countries have developed industrial wastewater N2O emission
estimates (ERG 2014a). Currently, there are insufficient data to develop a country-specific methodology; however,
available data will continue to be reviewed, and will consider if indirect N2O emissions associated with on-site
industrial wastewater treatment using the IPCC default factor for domestic wastewater (0.005 kg N2O-N/kg N)
would be appropriate.
Previously, a new measurement data from WERF was used to develop a U.S.-specific emission factor for CH4
emissions from septic systems, and these were incorporated 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.
Waste 7^29
-------
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. Unfortunately,
NACWA's suggestion of using National Pollution Discharge Elimination System (NPDES) permit data to estimate
nitrogen loading rates is not feasible as influent concentration are not available. EPA is also evaluating whether
available effluent nitrogen concentrations reported under POTW NPDES permits would support a more robust
analysis of nitrogen contributing to indirect nitrous oxide emissions. Not every POTW is required to measure for
effluent nitrogen so the database is not a complete source. Often, only those POTWs that are required to reduce
nutrients are monitoring effluent nitrogen, so the database may reflect lower N effluent loadings than that typical
throughout the United States. However, EPA is continuing to evaluate the utility of these data in future inventories.
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
investigated. In addition, based on UNFCCC review comments, the transparency of the fate of sludge produced in
wastewater treatment will continue to be improved.
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. wastewater 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. Analyses of breweries and
dairy products processing facilities have been performed. While the amount of COD present in brewery wastewater
is substantial, it is likely that the majority of the industry utilizes aerobic treatment or anaerobic treatment with
biogas recovery. As a result, breweries will not be included in the Inventory at this time. There are currently limited
data available on the wastewater characteristics and treatment of dairy processing wastewater, but EPA will continue
to investigate this and other industries as necessary for inclusion in future years of the Inventory.
7.3 Composting (IPCC Source Category 5B1)
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, stabilization of the waste, and
destruction of pathogens in the waste. 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, which are
created when there is excessive moisture or inadequate aeration (or mixing) of the compost pile. This CH4 is then
oxidized to a large extent in the aerobic sections of the compost. 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.
7-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
From 1990 to 2013, the amount of waste composted in the United States has increased from 3,810 ktto 19,633 kt, an
increase of approximately 415 percent. From 2000 to 2013, the amount of material composted in the United States
has increased by approximately 32 percent. Emissions of CH4 and N2O from composting have increased by the
same percentage. In 2013, CH4 emissions from composting (see Table 7-17 and Table 7-18) were 2.0 MMT CO2
Eq. (79 kt), and N2O emissions from composting were 1.8 MMT CO2 Eq. (6 kt). 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 1990's (US Composting Council 2010). By 2010, 25 states,
representing about 50 percent of the nation's population, had enacted such legislation (BioCycle 2010). An
additional 16 states are known to have commercial-scale composting facilities (Shin 2014). Despite these factors, the
total amount of waste composted exhibited a downward trend after peaking in 2008 (see Table 7-17). The amount of
waste composted has been increasing slightly since 2010 however.
Table 7-17: CH4 and NzO Emissions from Composting (MMT COz Eq.)
Activity
CH4
N20
Total
1990
0.4
0.3 1
0.7
2005
1.9
1 1.7 1
3.6
2009
1.9
1 1.7
| 3.6
2010
1.8
1.6
3.5
2011
1.9
1.7
3.5
2012
1.9
1.7
3.7
2013
2.0
1.8
3.7
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Table 7-18: CH4 and NzO Emissions from Composting (kt)
Activity 1990 2005 2009 2010 2011 2012 2013
73 75 77 79
5666
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 7-17 and Table 7-18 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):
where,
Ei = CH4 or N2O emissions from composting, kt CH4 or N2O,
M = mass of organic waste composted in kt,
EFi = emission factor for composting, 4 t CHVkt of waste treated (wet basis) and 0.3
t N2O/kt 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 7-19. Estimates of the quantity composted
for 1 990, 2005 and 2007 through 2009 were taken from Municipal Solid Waste in the United States: 2010 Facts and
Figures (EPA 201 1); 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 20 1 1 through
2013 were taken from EPA's Municipal Solid Waste In The United States: 2012 Facts and Figures (EPA 2014);
Waste 7-31
-------
estimates of the quantity composted for 2013 were calculated using the 2012 quantity composted and a ratio of the
U.S. population in 2012 and 2013 (U.S. Census Bureau 2014).
Table 7-19: U.S. Waste Composted (kt)
Activity 1990 2005 2009 2010 2011 2012 2013
Waste Composted 3,810 18,643 18,824 18,298 18,661 19,351 19,633
Uncertainty and Time-Series Consistency
The estimated uncertainty from the 2006IPCC Guidelines is ±50 percent for the Approach 1 methodology.
Emissions from composting in 2013 were estimated to be between 1.9 and 5.6 MMT CC>2 Eq., which indicates a
range of 50 percent below to 50 percent above the actual 2013 emission estimate of 3.7 MMT €62 Eq. (see Table
7-20).
Table 7-20: Approach 1 Quantitative Uncertainty Estimates for Emissions from Composting
(MMT COz Eq. and Percent)
o (-. 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate
(MMT CCh Eq.) (MMT CCh Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Composting CH4, N2O 3/7 1.9 5.6 -50% +50%
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2013. Details on the emission trends through time-series 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
EPA Municipal Solid Waste In The United States: Facts and Figures report.
Recalculations Discussion
The estimated amount of waste composted in 2010 through 2012 was updated based on new data contained in
EPA's Municipal Solid Waste In The United States: 2012 Facts andFigures (EPA 2014). The amounts of CH4 and
N2O emissions estimates presented in Table 7-17 and Table 7-18 were revised accordingly.
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second
Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations
for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to
report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each
greenhouse gas. The GWPs of CH4 and most fluorinated greenhouse gases have increased, leading to an overall
increase in CO2-equivalent emissions from CH4. The GWPs of N2O and SF6 have decreased, leading to a decrease in
CO2-equivalent emissions for N2O. The AR4 GWPs have been applied across the entire time series for consistency.
For more information please see the Recalculations and Improvements Chapter.
7-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Planned Improvements
For future Inventories, 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.
7.4 Waste Incineration (IPCC Source Category
5C1)
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 2013 resulted in 10.4 MMT CO2 Eq. emissions, over half of which (5.7 MMT CO2 Eq.) 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 emission estimates are not provided. An analysis of the likely level of emissions was conducted based on a 2009
study of hospital/ medical/ infectious waste incinerator (HMIWI) facilities in the United States (RTI 2009). Based
on that study' s information of waste throughput and an analysis of the fossil-based composition of the waste, it was
determined that annual greenhouse gas emissions for medical waste incineration would be below 500 kt CO2 Eq. per
year and considered insignificant for the purposes of Inventory reporting under the UNFCCC. More information on
this analysis is provided in Annex 5.
7.5 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 2013 are provided in Table 7-21.
Waste 7-33
-------
Table 7-21: Emissions of NOX, CO, and NMVOC from Waste (kt)
Gas/Source 1990 2005 2009 2010 2011 2012 2013
NOx Tl 3~| 2 2 1 I T
Landfills + I 3 I 2 2 1 1 1
Wastewater Treatment + I 0 I 0 0 000
Miscellaneous3 + I 0 I 0 0 000
CO l|7J65555
Landfills I I 7 I 6 5 555
Wastewater Treatment + I + I + + + + +
Miscellaneous3 + I 0 I 0 0 000
NMVOCs 742 126 54 48 42 42 42
Wastewater Treatment 63 54 23 21 18 18 18
Miscellaneous3 614 48 20 18 16 16 16
Landfills 64_^__24_^__10 9 888
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 kt.
Methodology
Emission estimates for 1990 through 2013 were obtained from data published on the National Emission Inventory
(NEI) Air Pollutant Emission Trends web site (EPA 2015), and disaggregated based on EPA (2003). Emission
estimates for 2013 for non-EGU and non-mobile sources are held constant from 2011 in EPA (2015). 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 categories
from various agencies. Depending on the 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 2013. Details on the
emission trends through time are described in more detail in the Methodology section, above.
7-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
8. Other
The United States does not report any greenhouse gas emissions under the Intergovernmental Panel on Climate
Change (IPCC) "Other" sector.
Other 8-1
-------
9. 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 the 2006IPCC
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 the current Inventory 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 9-2 summarizes the quantitative effect of these changes on U.S. greenhouse
gas emissions and sinks and Table 9-3 summarizes the quantitative effect on annual net CCh fluxes, both relative to
the previously published U.S. Inventory (i.e., the 1990 through 2012 report). These tables present the magnitude of
these changes in units of million metric tons of carbon dioxide equivalent (MMT CC>2 Eq.).
The Recalculations Discussion section of each source's description in the respective chapter of this Inventory
presents the details of each recalculation. In general, when methodological changes have been implemented, the
entire time series (i.e., 1990 through 2012) 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.
For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth
Assessment Report (AR4) (IPCC 2007). Revised UNFCCC reporting guidelines for national inventories now require
the use of GWP values from AR4 (IPCC 2007),298 which reflect an updated understanding of the atmospheric
properties of each greenhouse gas. AR4 GWP values differ from those presented in the IPCC Second Assessment
Report (SAR) (IPCC 1996) and used in the previous inventories as required by earlier UNFCCC reporting
guidelines. The use of AR4 GWP values in this Inventory results in time-series recalculations for most inventory
sources. In Table 9-1 below, recalculations are presented including both the quantitative effect of the data and
methodological changes as well as the quantitative effect of the change in using the AR4 GWP.
The following ten emission sources and sinks, which are listed in absolute decending order of the average change in
emissions or sequestration between 1990 and 2012, underwent some of the most significant methodological and
historical data changes. These emission sources consider only methodological and historical data changes. A brief
summary of the recalculations and/or improvements undertaken is provided for each of the ten sources.
• Forest Land Remaining Forest Land (CO2 sink). Forest ecosystem stock and stock-change estimates differ from
the previous Inventory (EPA 2014) principally due to some changes in data and methods. The net effect of the
modifications was to slightly reduce net C uptake (i.e., lower sequestration) and C stocks from 1990 to the
present. The estimate of net annual change in HWP C stock and total C stock in HWP were revised upward by
small amounts. The increase in total net annual additions compared to estimates published in 2013 was 2 to 3
298See.
Recalculations and Improvements 9-1
-------
percent for 2010 through 2012. This increase 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 forthcoming). These changes
resulted in an average annual increase of 76.7 MMT CC>2 Eq. relative to the previous Inventory.
• Agricultural Soil Management (NjO). Methodological recalculations in the current Inventory were associated
with the following improvements: 1) Driving the DAYCENT simulations with updated input data for the
excretion of C and N onto PRP and N additions from managed manure based on national livestock population
(note that revised total PRP N additions decreased from 4.4 to 4.1 MMT N on average and revised managed
manure additions decreased from 2.9 to 2.7 MMT N on average); 2) properly accounting for N inputs from
residues for crops not simulated by DAYCENT; (3) modifying the number of experimental study sites used to
quantify model uncertainty for direct N2O emissions and bias correction; and (4) reporting indirect N2O
emissions from forestland and settlements in their respective sections, instead of the agricultural soil
management section. These changes resulted in an average annual decrease of 43.6 MMT CCh Eq. relative to
the previous Inventory.
• Petrochemical Production (CO2). Emission information from EPA's GHGRP was used to update estimates.
Average country-specific CCh emission factors were derived from the 2010 through 2013 GHGRP data for
carbon black, ethylene, ethylene dichloride, and ethylene oxide. Annual production and CCh emission factor
data were obtained from EPA's GHGRP for 2010 through 2013, and were used to estimate emissions for 2010
through 2013. An average COa emission factor was calculated from the 2010 through 2013 GHGRP data and
was used to estimate emissions for 1990 through 2009 for carbon black, ethylene, ethylene dichloride, and
ethylene oxide using historic production data compiled for 1990 through 2009 (ACC 2014a; ACC 2014b). Note,
ethylene oxide is included in the IPCC petrochemical production source category but had not been included in
previous versions of this Inventory due to lack of publicly-available data. Similarly, acrylonitrile is included in
the IPCC Petrochemical Production source category but had not been included in the previous Inventory due to
lack of publicly-available data. Annual acrylonitrile production data for 1990 through 2013 was obtained from
ACC (ACC 2014b). These changes resulted in an average annual increase of 23.5 MMT CCh Eq. relative to the
previous Inventory.
• Landfills (CH4). Three major methodological recalculations were performed for the current Inventory. First, a
new SOG survey was published allowing for the update of the annual quantities of waste generated and
disposed and the amount of CH4 generated for the years 2009 through 2012. Second, the percent of the U.S.
population within the three precipitation ranges were updated for the year 2010 (see Table A-3 in Annex 3.14),
which impacted the distribution for the years 2001 through 2013 in the waste model. Third, the EPA's GHGRP
CH4 recovery and destruction efficiency data were incorporated. These changes resulted in an average annual
increase of 18.9 MMT CCh Eq. relative to the previous Inventory.
• Petroleum Systems (CH.4). For the current Inventory, EPA received information and data related to the emission
estimates through the Inventory preparation process, previous Inventories' formal public notice periods, the
latest GHGRP data, and new studies. EPA carefully evaluated relevant information available, and made several
updates, such as updates to offshore platforms, pneumatic controllers, refineries, and well count data. In
addition, revisions to use the latest activity data resulted in changes to emissions for several sources. The
decrease in calculated emissions from this source is largely due to the recalculation for offshore platforms.
The net impact of the changes (comparing 2012 estimate from previous (2014) Inventory and current (2015)
Inventory) is a decrease in CH4 emissions of around 14.5 MMT CC>2 Eq., or 38 percent. Recalculations in the
offshore petroleum platforms estimates resulted in a large decrease in the 2012 CH4 emission estimate from this
source in the production segment, from 15.2 MMT CChEq. in the previous (2014) Inventory, to 4.7 MMT CCh
Eq. in the current (2015) Inventory. Recalculations to the onshore petroleum production emissions estimates
resulted in a small decrease in the 2012 CH4 emission estimate for onshore sources, from 22.0 MMT CO2 Eq. in
the 2014 Inventory, to 19.5 MMT COa Eq. in the 2015 Inventory. Methane emission estimates for other
segments (i.e., refining and transport) changed by around 0.5 percent.
9-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Across the 1990 through 2012 time series, compared to the previous (2014) Inventory, in the current (2015)
Inventory, the CH4 emission estimate decreased by 11.8 MMT CO2 Eq. on average.299
• Fossil Fuel Combustion (CO2) The Energy Information Administration (EIA 2015) updated energy
consumption statistics across the time series relative to the previous Inventory. One such revision is the
historical petroleum consumption in the residential sector in 2011 and 2012. These revisions primarily impacted
the previous emission estimates from 2010 to 2012; 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. In addition, EIA revised the heat contents of motor gasoline, distillate fuel, and
petroleum coke.
For motor gasoline, heating values were previously based on the relative volumes of conventional and
reformulated gasoline in the total motor gasoline product supplied to the United States. The revised heating
values (first occurring in the January 2015 publication of the Monthly Energy Review) incorporated inputs of
ethanol, methyl tert-butyl ether (MTBE) through April 2006, other oxygenates through 2006, and a single
national hydrocarbon gasoline blend-stock from 1993 through 2013.
Changes to the heat content of distillate fuel resulted in an annual average decrease of approximately 0.1
percent between 1994 through 2012. This decrease was a result of EIA's heat content revision from a constant
sulfur content across the time series, to a weighted sulfur content. Additionally, in 2009, EIA began subtracting
inputs of renewable diesel fuel from petroleum consumption before converting to energy units.
Petroleum coke consumption decreased by an annual average of approximately 0.1 percent from 2004 to 2012.
This decrease was a result of a similar heat content revision in which the EIA recalculated the historically
constant petroleum coke heat content to include weighted petroleum coke heat contents (by the two categories
of petroleum coke, catalyst and marketable) starting in 2004.
Overall, these changes resulted in an average annual decrease of 9.6 MMT CO2 Eq. (less than 0.2 percent) in
CO2 emissions from fossil fuel combustion for the period 1990 through 2012, relative to the previous report.
• Nitric Acid Production (NŁ)). GHGRP data from subpart V of regulation 40 CFR Part 98 were used to
recalculate emissions from nitric acid production over the entire time series (EPA 2014), and used directly for
emission estimates for 2010 through 2013. Nitric acid production and N2O emissions data were available for
2010 through 2013 fromEPA's GHGRP, given nearly all nitric acid production facilities, with the exception of
the strong acid facility, in the United States are required to report annual data under subpart V. Country-specific
N2O emission factors were developed using the 2010 GHGRP emissions and production data for nitric acid
production with abatement and without abatement. Due to differences in operational efficiencies and recent
installation of abatement technology at some U.S. facilities, 2010 GHGRP production data were used for
recalculating time series emissions (1990 through 2009) instead of average factors developed from 2010
through 2013 GHGRP data. As per the 2010 GHGRP data, 70.7 percent of total domestic nitric acid production
was estimated to be produced without any abatement.
Time series emissions for 1990 through 2009 were recalculated, and the revised emission estimates are
approximately 30 percent lower than the prior estimates. Throughout the whole time series, these changes
resulted in an average annual decrease of 5.3 MMT CO2 Eq. relative to the previous Inventory.
• Natural Gas Systems (CH^). For the current Inventory, EPA received information and data related to the
emission estimates through the Inventory preparation process, previous Inventories' formal public notice
periods, GHGRP data, and new studies. EPA carefully evaluated relevant information available, and made
several updates, including revisions to offshore platforms, pneumatic controllers, well counts data, and
hydraulically fractured gas well completions and workovers.
In addition, revisions to activity data resulted in changes to emission estimates for several sources. For example,
the 2014 Inventory used 2011 data as a proxy for condensate production for 2012. The 2015 Inventory was
Additional information on recent changes to the Inventory can be found at:
.
Recalculations and Improvements 9-3
-------
updated to use the most recent data on condensate production. Large increases in production in the Rocky
Mountain and Gulf Coast regions resulted in an increase in calculated 2012 CH4 emissions from condensate
tanks of 0.6 MMT €62 Eq., or 15 percent.
The combined impact of all revisions on 2012 natural gas production segment emissions compared to the
previous (2014) Inventory, is a decrease in CH4 emissions of approximately 0.2 MMT CCh Eq. Recalculations
in the offshore gas platforms estimates resulted in a large decrease in the 2012 CH4 emission estimate from this
source in the production segment, from 7.2 MMT COa Eq. in the previous (2014) Inventory, to 3.8 MMT CCh
Eq. in the current (2015) Inventory. Recalculations to the onshore gas production emissions estimates resulted
in an increase in the 2012 CH4 emission estimate for onshore sources, from 42.6 MMT CO2 Eq. in the previous
(2014) Inventory, to 46.0 MMT CO2 Eq. in the current (2015) Inventory. Methane emission estimates for other
segments (i.e. processing, transmission and storage, and distribution) changed by less than 0.5 percent.
Across the 1990-2012 time series, compared to the previous (2014) Inventory, in the current (2015) Inventory,
the total CH4 emission estimate decreased by 5.2 MMT CCh Eq. on average (or 3 percent), with the largest
decreases in the estimate occurring in early years of the time series.300
• Petroleum Systems (CO2). EPA received information and data related to the emission estimates through the
Inventory preparation process, previous Inventories' formal public notice periods, the latest GHGRP data, and
new studies. EPA carefully evaluated relevant information available, and made several updates, such as updates
to offshore platforms, pneumatic controllers, refineries, and well count data. In addition, revisions to use the
latest activity data resulted in changes to emissions for several sources.
The net impact of the changes (comparing 2012 estimate from previous (2014) Inventory and current (2015)
Inventory) is an increase in €62 emissions of around 6 MMT €62, or 1,400 percent. The increase in the €62
emission estimates is due to the update to the petroleum refineries calculations.
Across the 1990-2012 time series, compared to the previous (2014) Inventory, in the current (2015) Inventory,
the CO2 emissions estimate increased by 4.4 MMT €62 Eq. on average (or around 1,300 percent).301
• Cropland Remaining Cropland (CO2 sink). Recalculations for the cropland remaining cropland source is
divided up into three components: Refining parameters associated with simulating crop production and carbon
inputs to the soil in the DAYCENT biogeochemical model; improving the model simulation of snow melt and
water infiltration in soils; and driving the DAYCENT simulations with updated input data for managed manure
based on national livestock population. These changes resulted in an average annual decrease of 4.3 MMT €62
Eq. relative to the previous Inventory.
300 Additional information on recent changes to the Inventory can be found at:
Additional information on recent changes to the Inventory can be found at:
http://www.epa.gov/climatechange/ghgemissions/usinventoryreport/natural-gas-systems.html.
9-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 9-1: Revisions to U.S. Greenhouse Gas Emissions, Including Quantitative Change
Related to Use of AR4 GWP values (MMT COz Eq.)
Gas/Source
1990
2005
2009 2010 2011 2012
Average
Annual
Change
CO2 15.0
Fossil Fuel Combustion (4.4)
Electricity Generation NC
Transportation (0.2)
Industrial (2.6)
Residential NC
Commercial (1.6)
U.S. Territories NC
Non-Energy Use of Fuels (3.2)
Natural Gas Systems (0.1)
Cement Production NC
Lime Production 0.3
Other Process Uses of Carbonates NC
Glass Production NC
Soda Ash Production and Consumption NC
Carbon Dioxide Consumption 0.1
Incineration of Waste NC
Titanium Dioxide Production NC
Aluminum Production NC
Iron and Steel Production & Metallurgical Coke
Production NC
Ferroalloy Production NC
Ammonia Production NC
Urea Consumption for Non-Agricultural Purposes NC
Phosphoric Acid Production +1
Petrochemical Production 18.2
Silicon Carbide Production and Consumption NC
Lead Production NC
Zinc Production NC
Liming of Agricultural Soils NC
Peatlands Remaining Peatlands +1
Petroleum Systems 4.1
Magnesium Production and Processing NC *
Urea Fertilization NC
Land Use, Land-Use Change, and Forestry (Sink)" 55.3
Biomass - Wood" NC
International Bunker Fuels" NC
Biomass - Ethanol" NC
CH4 109.8
Stationary Combustion 1.0|
Mobile Combustion 1.1
Coal Mining 15.4
Abandoned Underground Coal Mines 1.2
Natural Gas Systems 22.7
Petroleum Systems (4.2)
Petrochemical Production (2.0)
Silicon Carbide Production and Consumption +1
Iron and Steel Production & Metallurgical Coke
Production 0.2
Ferroalloy Production
21.7
(5.2)|
(1.3)
(3.9)
0.2|
(0.1)
(0.1)
+
(2.1)
NCJ
o.el
NC|
NC!
NC!
O.ll
NC!
NC!
NC
NC!
NC
NC!
NC!
+
23.8
NCI
NCJ
NCJ
NC!
J
NC*I
NCI
118.8\
NC\
NC\
NC\
122. ll
o.sl
0.6J
10.51
1.1
24.3
(5.4)1
(3.0)1
+1
O.ll
VJ.OH
II
(5.5)
(28.7)
(0.8)
(27.4)
0.2
+
(0.4)
(0.3)
(2.1)
NC
0.5
NC
NC
NC
NC
NC
NC
NC
NC
(17.8)
(37.8)
(0.8)
(33.1)
0.1
(0.1)
(0.5)
(3.4)
(6.3)
NC
0.5
NC
NC
NC
+ (1.0)
(0.4) (1.0)
NC
NC
NC
NC
NC
(23.3)
(39.8)
(0.8)
(36.3)
5.4
2.3
(0.5)
(9.8)
(9.0)
0.5
NC
0.5
NC
NC
NC
(1.0)
(1.6)
NC
NC
NC
0.1
(0.1)
(24.9)
(46.3)
(0.5)
(38.8)
10.1
(5.8)
(0.3)
(11.0)
(5.4)
(0.5)
NC
0.4
NC
(1.0)
(1.8)
(0.2)
NC
0.2
20.9
NC
NC
NC
NC
(0.1)
4.3
NC*
NC
90.7
NC
NC
NC
113.0
0.7
0.5
12.8
1.2
25.1
(7.6)
(2.8)
23.9
NC
NC
NC
NC
+
3.8
NC*
+
96.4
NC
NC
NC
81.6
0.7
0.5
13.2
1.6
24.9
(8.2)
(3.0)
22.9
NC
NC
+
NC
+
4.1
NC*
0.1
99.3
NC
NC
NC
82.6
0.7
0.5
11.4
1.6
26.1
(8.6)
(3.1)
23.0
NC
NC
0.1
1.8
+
4.7
NC*
0.8
98.9
0.9
NC
NC
80.4
0.9
0.5
10.6
1.5
24.5
(8.5)
(3.0)
15.3
(9.6)
(0.4)
(8.2)
0.5
(0.1)
(0.3)
(1.1)
(3.2)
+
NC
0.5
NC
(0.1)
(0.2)
+
NC
0.1
+
0.1
0.1
+
0.1
23.5
NC
NC
+
0.1
+
4.4
NC*
+
72.2
+
NC
NC
111.7
0.9
0.8
11.9
1.3
23.9
(5.8)
(2.8)
+
0.2
Recalculations and Improvements 9-5
-------
Enteric Fermentation
Manure Management
Rice Cultivation
Field Burning of Agricultural Residues
Forest Fires
Peatlands Remaining Peatlands
Landfills
Wastewater Treatment
Composting
Incineration of Waste
International Bunker Fuels"
N20
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
Settlement Soils
Forest Fires
Forest Soils
Composting
Peatlands Remaining Peatlands
Semiconductor Manufacture
International Bunker Fuels"
26.3
5.7
1.5
0.1
+
NC
38.5J
25J
(111
(68.7)
(0.3)1
(2.8)1
(0.6)1
(6.0)1
(0.6)1
(58.1)
(0.1)1
(0.2)1
+1
O.J
(0.4)1
+1
NC*
26.41
8.8
0.2
NC|
53.41
25
(59.9)
(0.4)
1.2
(0.3)
(5.6)
(0.7)
(53.7)
+
(0.1)
(0.2)
0.9|
(1.1)
0.1
(0.1)
NC*I
26.6
9.2
1.5
+
0.1
NC
42.8
2.5
0.3
26.2 25.8
9.1 9.4
1.8 1.4
NC
11.9
2.5
0.3
0.6
NC
13.9
2.4
0.3
25.3
10.8
1.9
+
0.4
NC
12.4
2.4
0.3
(56.1)
(0.3)
1.9
(0.1)
(4.4)
(0.7)
(52.3)
(49.3)
(0.4)
3.0
(0.2)
(5.2)
(0.7)
(45.8)
(45.3)
(0.3)
4.0
(0.4)
(5.0)
(0.7)
(42.0)
(44.5)
(0.6)
3.7
(0.2)
(4.8)
(0.7)
(40.6)
(0.2)
(0.2)
+
0.8
(0.9)
0.1
(0.1)
(0.2)
(0.2)
+
0.9
(0.7)
0.1
(0.1)
(0.2)
(0.2)
(0.1)
1.0
(1.8)
0.1
(0.1)
(0.2)
(0.2)
(0.1)
1.1
(2.1)
0.1
+
NC* NC* NC* NC*
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
NC (No Change)
+ Absolute value does not exceed 0.05 MMT CCh Eq. or 0.05 percent
* Indicates a new source for the current Inventory year
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.
26.7
7.9
1.5
+
0.2
NC
42.2
2.6
0.2
(63.4)
(0.4)
(0.4)
(0.3)
(6.0)
(0.6)
(55.2)
+
(0.1)
(0.2)
+
0.8
(1.0)
0.1
NC*
HFCs
Substitution of Ozone Depleting Substances
HCFC-22 Production
Semiconductor Manufacture
Magnesium Production and Processing
PFCs
Aluminum Production
Semiconductor Manufacture
SF6
Electrical Transmission and Distribution
Semiconductor Manufacture
Magnesium Production and Processing
NF3
Semiconductor Manufacture
Net Change in Total Emissions'"
Percent Change
9.7
+
9.7
+
NC*
3.6
3.0
0.6
(1.6)
(1.3)
+
(0.3)
NC*
NC*
67.8
1.1%
11.6
+•
1(0.1)1
NC*
NC*
96.4
1.3%B
7.8
6.4
1.4
+
NC*
0.6
0.3
0.3
(0.3)
(0.2)
+
(0.1)
NC*
NC*
59.9
0.9%
8.6
6.9
1.7
+
NC*
0.7
0.3
0.4
(0.3)
(0.2)
+
(0.1)
NC*
NC*
24.1
0.4%
8.8
6.9
1.8
+
NC*
0.9
0.5
0.4
(0.8)
(0.4)
(0.3)
(0.1)
NC*
NC*
23.6
0.4%
8.0
6.8
1.1
+
NC*
0.6
0.4
0.1
(0.7)
(0.3)
(0.3)
(0.1)
NC*
NC*
19.5
0.3%
11.9
5.9
6.0
0.1
NC*
2.0
1.2
0.8
(0.9)
(0.7)
(0.1)
(0.2)
NC*
NC*
9-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 9-2: Revisions to U.S. Greenhouse Gas Emissions due only to Methodology and Data
Changes, with the AR4 GWP values applied across the time series (MMT COz Eq.)
Gas/Source
1990
2005
2009 2010 2011 2012
Average
Annual
Change
CO2 15.0 21.7
Fossil Fuel Combustion (4.4)H (5.2)
Electricity Generation NcB (1.3)
Transportation (0.2)B (3.9)
Industrial (2.6)B 0.2
Residential NcB (0.1)
Commercial (1.6) (0.1)
U.S. Territories NcF
Non-Energy Use of Fuels (3-2)H (2.1)
Natural Gas Systems (0.1 )l +1
Cement Production NcB NC
Lime Production O.sH 0.6l
Other Process Uses of Carbonates NcB NC
Glass Production NcB NC
Soda Ash Production and Consumption NcB NC
Carbon Dioxide Consumption 0.11 0.1
Incineration of Waste NcB NC
Titanium Dioxide Production NcB NC
Aluminum Production NcB NC
Iron and Steel Production & Metallurgical Coke
Production NCB NC
Ferroalloy Production NcB NC
Ammonia Production NcB NC
Urea Consumption for Non-Agricultural Purposes NcB NC
Phosphoric Acid Production +1 4
Petrochemical Production 18.2B 23.8
Silicon Carbide Production and Consumption NcH NC
Lead Production NcB NC
Zinc Production NcB NC
Liming of Agricultural Soils NcB NC
Peatlands Remaining Peatlands +1 +1
Petroleum Systems 4.11 4.6
Magnesium Production and Processing NC*H NC*
Urea Fertilization NcB NC
Land Use, Land-Use Change, and Forestry (Sink)" 55.3U 118.8
Biomass - Wood" NCU NC
International Bunker Fuels" jVCH NC
Biomass - Ethanol" jVCH NC
CH4 (11.3) 10.5
Stationary Combustion (0.4)
Mobile Combustion 0.2
Coal Mining NC
Abandoned Underground Coal Mines NC
Natural Gas Systems (7.1)
Petroleum Systems (11.1 )|
Petrochemical Production (2.5) (3.6)
Silicon Carbide Production and Consumption NC NC
Iron and Steel Production & Metallurgical Coke
Production NcH NC
Ferroalloy Production NC NC
i \y-->jm
(5.5)
(28.7)
(0.8)
(27.4)
0.2
+
(0.4)
(0.3)
(2.1)
NC
0.5
NC
NC
NC
NC
NC
NC
NC
NC
(17.8)
(37.8)
(0.8)
(33.1)
0.1
(0.1)
(0.5)
(3.4)
(6.3)
NC
0.5
NC
NC
NC
+ (1.0)
(0.4) (1.0)
NC
NC
(23.3)
(39.8)
(0.8)
(36.3)
5.4
2.3
(0.5)
(9.8)
(9.0)
0.5
NC
0.5
NC
NC
NC
(1.0)
(1.6)
NC
NC
NC NC
NC 0.1
NC (0.1)
(24.9)
(46.3)
(0.5)
(38.8)
10.1
(5.8)
(0.3)
(11.0)
(5.4)
(0.5)
NC
0.4
NC
(1.0)
(1.8)
(0.2)
NC
0.2
20.9
NC
NC
NC
NC
(0.1)
4.3
NC*
NC
90.7
NC
NC
NC
(0.7)
(0.5)
0.1
NC
0.3
(2.1)
(13.2)
(3.4)
NC
23.9
NC
NC
NC
NC
+
3.8
NC*
+
96.4
NC
NC
NC
(29.9)
(0.5)
0.2
NC
0.6
(0.8)
(13.8)
(3.6)
NC
22.9
NC
NC
+
NC
+
4.1
NC*
0.1
99.3
NC
NC
NC
(27.5)
(0.5)
0.2
NC
0.7
0.7
(14.4)
(3.7)
NC
23.0
NC
NC
0.1
1.8
+
4.7
NC*
0.8
98.9
0.9
NC
NC
(27.7)
(0.2)
0.2
NC
0.6
(0.2)
(14.5)
(3.6)
NC
15.3
(9.6)
(0.4)
(8.2)
0.5
(0.1)
(0.3)
(1.1)
(3.2)
+
NC
0.5
NC
(0.1)
(0.2)
+
NC
23.5
NC
NC
+
0.1
+
4.4
NC*
+
NC
+
NC
NC
(3.7)
(0.4)
0.2
+
0.1
(5.2)
(11.8)
(3.4)
NC
NC
NC
NC
NC
NC
+
NC
+
NC
+
Recalculations and Improvements 9-7
-------
Enteric Fermentation
Manure Management
Rice Cultivation
Field Burning of Agricultural Residues
Forest Fires
Peatlands Remaining Peatlands
Landfills
Wastewater Treatment
Composting
Incineration of Waste
International Bunker Fuels"
N20
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
Settlement Soils
Forest Fires
Forest Soils
Composting
Peatlands Remaining Peatlands
Semiconductor Manufacture
International Bunker Fuels"
HFCs
Substitution of Ozone Depleting Substances
HCFC-22 Production
Semiconductor Manufacture
Magnesium Production and Processing
PFCs
Aluminum Production
Semiconductor Manufacture
SF6
Electrical Transmission and Distribution
Semiconductor Manufacture
Magnesium Production and Processing
NF3
Semiconductor Manufacture
Net Change in Total Emissions'"
Percent Change
NC
(0.3)
NC
NC
(0.5)
NC
10.3
+
NC
NC
NC
(53.3)1
o.iB
(i.i)
NC
(5.3)
NC
(47.2)1
NCM
+
NC
NC
0.4
(0.3)
0.1
NC
+
NC*
wcl
NC
NC
NC*
NC
(0.1)
(0.1)
NC
NC
NC*
NC*
(49.6)
-0.8%
(0.7)
JNUH
+
Ncl
Ncl
0.9l
(0.9)1
Ncl
+
INC*
&
NCI
NC*
(0.5)
NCBI
NC*
(13.1)
-0.2%
(1.2)
(0.4)
NC
NC
(1.0)
NC
20.8
+
NC
+
NC
(40.2)
0.5
2.8
NC
(3.8)
+
(40.0)
NC
+
NC
+
0.9
(0.7)
0.1
NC
+
NC*
NC
(5.5)
(5.5)
NC
+
NC*
(0.5)
NC
(0.5)
0.1
0.2
NC*
NC*
(51.8)
-0.8%
(1.4)
(0.7)
NC
NC
(0.9)
NC
(9.0)
+
NC
+
NC
(33.4)
0.5
3.8
NC
(4.5)
+
(33.8)
NC
+
NC
+
0.9
(0.6)
0.1
NC
+
NC*
NC
(6.3)
(6.3)
NC
+
NC*
(0.5)
NC
(0.5)
0.1
0.1
NC*
NC*
(87.3)
-1.2%
(1.5)
(0.5)
NC
NC
(2.0)
NC
(6.5)
+
+
+
NC
(29.2)
0.5
4.7
NC
(4.3)
+
(30.0)
NC
+
NC
+
1.0
(1.3)
0.1
+
+
NC*
NC
(7.1)
(7.1)
NC
+
NC*
(0.7)
NC
(0.7)
(0.3)
(0.2)
NC*
NC*
(87.4)
-1.3%
(1.5)
0.7
0.5
NC
(2.5)
NC
(7.2)
+
+
+
NC
(28.6)
0.2
4.3
+
(4.2)
+
(28.8)
NC
+
NC
+
1.1
(1.7)
0.1
+
+
NC*
NC
(8.3)
(8.3)
NC
+
NC*
(0.9)
NC
(0.9)
(0.3)
(0.2)
NC*
NC*
(90.1)
-1.4%
(0.7)
(0.3)
+
NC
(1.2)
NC
18.9
+
+
+
NC
(47.1)
0.3
1.2
+
(5.3)
+
(43.6)
NC
+
NC
+
0.8
(0.8)
0.1
+
+
NC*
NC
(0.9)
(0.9)
NC
+
NC*
(0.3)
NC
(0.3)
NC*
NC*
Note: Emissions values are presented in CCh equivalent mass units using IPCC AR4 GWP values.
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
+ Absolute value does not exceed 0.05 MMT CCh Eq. or 0.05 percent
NC (No Change)
* Indicates a new source for the current Inventory year
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.
9-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Table 9-3: Revisions to Annual Sinks (C Sequestration) from Land Use, Land-Use Change,
and Forestry (MMT COz Eq.)
Average
Component: Sinks from Land Use, Annual
Land-Use Change, and Forestry3 1990 2005 2009 2010 2011 2012 Change
Forest Land Remaining Forest Land:
Changes in Forest Carbon Stock 65.1 120.2 84.5 90.2 93.2 93.4 76.7
Cropland Remaining Cropland:
Changes in Agricultural Soil Carbon
Stock (13.3) l.ll 1.8 1.8 1.8 1.5 (4.3)
Land Converted to Cropland (2.4) (1.0) (0.6) (0.6) (0.6) (0.7) (1.0)
Grassland Remaining Grassland 7.6U (1.4) 4.9 4.9 4.9 4.8 1.8
Land Converted to Grassland (0.1) (0.7) (0.3) (0.3) (0.3) (0.2) (0.2)
Settlements Remaining Settlements:
Changes in Urban Tree Carbon
Stock NCB NCB NC NC NC NC NC
Other (Landfilled Yard Trimmings and
Food Scraps) (1.8) 0.6 0.4 0.4 0.3 0.3 (0.7)
Net Change in Sinks3 55.3 118.8 90.7 96.4 99.3 98.9
Percent Change 6.7% 11.5% 9.4% 10.0% 10.1% 10.1%
NC (No Change)
Note: Numbers in parentheses indicate an increase in C sequestration.
a The sinks value includes the positive C sequestration reported for Forest Land Remaining Forest
Land, Cropland Remaining Cropland, Land Converted to Grassland, Settlements Remaining
Settlements, and Other Land plus the loss in C sequestration reported for Land Converted to
Cropland and Grassland Remaining Grassland.
Note: Totals may not sum due to independent rounding.
Recalculations and Improvements 9-9
-------
10. References
Executive Summary
BEA (2014) 2014 Comprehensive Revision of the National Income and Product Accounts: Current-dollar and
"real" GDP, 1929-2013. Bureau of Economic Analysis (BEA), U.S. Department of Commerce, Washington, D.C.
Last Modified November 2014. Available online at .
EIA (2015a) Electricity Generation. Monthly Energy Review, February 2015. Energy Information Administration,
U.S. Department of Energy, Washington, D.C. DOE/EIA-0035(2015/02).
EIA (2015b) Electricity in the United States. Electricity Explained. Energy Information Administration, U.S.
Department of Energy, Washington, D.C. Available online at
.
EIA (2013) International Energy Statistics 2013. Energy Information Administration (EIA), U.S. Department of
Energy. Washington, D.C. Available online at
EIA (2014). Accessed on 30 November 2014.
EPA (2015) "1970 - 2014 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. Last Modified
March 2015. Available online at .
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.
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 (2001) Climate Change 2001: The Scientific Basis. Intergovernmental Panel on Climate Change. J.T.
Houghton, Y. Ding, D. J. Griggs, M. Noguer, PJ. van der Linden, X. Dai, C. A. Johnson, and K. Maskell (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.
NOAA/ESRL (2015) "Trends in Atmospheric Carbon Dioxide." Available online at
. 4 February 2015.
References 10-1
-------
UNFCCC (2014) Report of the Conference of the Parties on its nineteenth session, held in Warsaw from 11 to 23
November 2013. United Nations Framework Convention on Climate Change, Warsaw. (FCCC/CP/2013/10/Add.3).
January 31, 2014. Available online at .
U.S. Census Bureau (2014) U.S. Census Bureau International Database (IDE). November 2014. Available online at
.
Introduction
CDIAC (2014) Recent Greenhouse Gas Concentrations. T.J. Biasing; DOI: 10.3334/CDIAC/atg.032. Available
online at . 11 November 2014.
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 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.
IPCC (2001) 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 (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 (1999) Aviation and the Global Atmosphere. Intergovernmental Panel on Climate Change. J.E. Penner, et al.
(eds.). Cambridge University Press. Cambridge, United Kingdom.
IPCC/TEAP (2005) Special Report: Safeguarding the Ozone Layer and the Global Climate System, Chapter 4:
Refrigeration. 2005. Available online .
Jacobson, M.Z. (2001) "Strong Radiative Heating Due to the Mixing State of Black Carbon in Atmospheric
Aerosols." Nature, 409:695-697.
NOAA (2014) Vital Signs of the Planet. Available online at . 12 December 2014.
NOAA/ESRL (2015) Trends in Atmospheric Carbon Dioxide. Available online at
. 6 February 2015.
UNEP/WMO (1999) Information Unit on Climate Change. Framework Convention on Climate Change. Available
online at .
UNFCCC (2014) Report of the Conference of the Parties on its nineteenth session, held in Warsaw from 11 to 23
November 2013. United Nations Framework Convention on Climate Change, Warsaw. (FCCC/CP/2013/10/Add.3).
January 31, 2014. Available online at < http://unfccc.int/resource/docs/2013/copl9/eng/10a03.pdf>
10-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Trends in Greenhouse Gas Emissions
BEA (2014) 2013 Comprehensive Revision of the National Income and Product Accounts: Current-dollar and
"real" GDP, 1929-2013. Bureau of Economic Analysis (BEA), U.S. Department of Commerce, Washington, D.C.
Available online at .
Duffield, J. (2006) Personal communication. Jim Duffield, Office of Energy Policy and New Uses, USD A and
Lauren Flinn, ICF International. December 2006.
EIA (2015) Monthly Energy Review, February 2015. Energy Information Administration, U.S. Department of
Energy, Washington, D.C. DOE/EIA-0035(2015/02).
EPA (2015) "1970 - 2014 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, March 2015.
Available online at .
EPA (2014) Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy Trends: 1975 -
2014. Office of Transportation and Air Quality, U.S. Environmental Protection Agency. Available online at <
http://www.epa.gov/otaq/fetrends-complete.htm>.
FRB (2014) Industrial Production and Capacity Utilization. Federal Reserve Statistical Release, G. 17, Federal
Reserve Board. Available online at . March 28, 2014.
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 (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.
U.S. Census Bureau (2014) U.S. Census Bureau International Database (IDE). Available online at
.
Energy
EIA (2013) Indicators: CO2 Emissions. International Energy Statistics 2013. Energy Information Administration,
U.S. Department of Energy. Washington, D.C. Available at
.
Carbon Dioxide Emissions from Fossil Fuel Combustion
AAR (2008 through 2013) Railroad Facts. Policy and Economics Department, Association of American Railroads,
Washington, D.C. Obtained from Clyde Crimmel at AAR.
AISI (2004 through 2013) Annual Statistical Report, American Iron and Steel Institute, Washington, D.C.
APTA (2007 through 2013) Public Transportation Fact Book. American Public Transportation Association,
Washington, D.C. Available online at .
APTA (2006) Commuter Rail National Totals. American Public Transportation Association, Washington, D.C.
Available online at .
BEA (2014) Table 1.1.6. Real Gross Domestic Product, Chained 2009 Dollars. Bureau of Economic Analysis
(BEA), U.S. Department of Commerce, Washington, D.C. November, 2014. Available online at
.
References 10-3
-------
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 (2014) Nitrogen Fertilizer Operations. Available online at
.
Dakota Gasification Company (2006) CO2 Pipeline Route and Designation Information. Bismarck, ND. Available
online at .
DHS (2008) Email Communication. Elissa Kay, Department of Homeland Security and Joe Aamidor, ICF
International. January 11, 2008.
DLA Energy (2014) Unpublished data from the Fuels Automated System (FAS). Defense Logistics Agency Energy,
U.S. Department of Defense. Washington, D.C.
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, D.C.
DOE (1993 through 2014) Transportation Energy Data Book. Office of Transportation Technologies, Centerfor
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, D.C. 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 (2015) Monthly Energy Review, February 2015, Energy Information Administration, U.S. Department of
Energy, Washington, DC. DOE/EIA-0035(2015/2).
EIA (2014a) Natural Gas Annual 2013. Energy Information Administration, U.S. Department of Energy.
Washington, D.C. DOE/EIA-0131(06).
EIA (2014b) Quarterly Coal Report: January - March 2014. Energy Information Administration, U.S. Department
of Energy. Washington, D.C. DOE/EIA-0121.
EIA (2014c) U.S. Energy-Related Carbon Dioxide Emissions, 2013. Energy Information Administration, U.S.
Department of Energy. Washington, D.C. October 2014. Available online at
.
EIA (1991 through 2014) Fuel Oil and Kerosene Sales. Energy Information Administration, U.S. Department of
Energy. Washington, D.C. Available online at: .
EIA (2013) Indicators: CO2 Emissions. International Energy Statistics 2013. Energy Information Administration,
U.S. Department of Energy. Washington, D.C. Available at:
.
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, D.C. DOE-EIA-
0573(2009).
EIA (2009b) Manufacturing Consumption of Energy 2006. Energy Information Administration, U.S. Department of
Energy. Washington, D.C. Released July, 2009.
EIA (2008) Historical Natural Gas Annual, 1930 - 2008. Energy Information Administration, U.S. Department of
Energy. Washington, D.C.
10-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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, D.C. Available online at .
EIA (2001) U.S. Coal, Domestic and International Issues. Energy Information Administration, U.S. Department of
Energy. Washington, D.C. March 2001.
EPA (2014a) Acid Rain Program Dataset 1996-2013. Office of Air and Radiation, Office of Atmospheric Programs,
U.S. Environmental Protection Agency, Washington, D.C.
EPA (2014b/ Motor Vehicle Emissions Simulator (Moves) 2014. Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Available online at < http://www.epa.gov/otaq/models/moves/index.htm>.
EPA (2014c; NONROAD 2008a Model. Office of Transportation and Air Quality, U.S. Environmental Protection
Agency. Available online at .
EPA (2014d) Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy Trends: 1975 -
2014. Office of Transportation and Air Quality, U.S. Environmental Protection Agency. Available online at <
http://www.epa.gov/otaq/fetrends-complete.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 .
F AA (2015) Personal Communication between F AA 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, D.C. Report FHWA-PL-96-023-annual. Available online at
.
Fitzpatrick, E. (2002) The Weyburn Project: A Model for International Collaboration. Available online at
.
FRB (2014) Industrial Production and Capacity Utilization. Federal Reserve Statistical Release, G.17, Federal
Reserve Board. Available online at . March 28, 2014.
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.
Jacobs, G. (2010) Personal communication. Gwendolyn Jacobs, Energy Information Administration and Rubaab
Bhangu, ICF International. U.S. Territories Fossil Fuel Consumption, 1990-2013. Unpublished. U.S. Energy
Information Administration. Washington, D.C.
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 (SAIC) for Office of Integrated Analysis and
Forecasting, Energy Information Administration, U.S. Department of Energy. Washington, D.C. June 22, 2001.
U.S. Census Bureau (2011) Current Industrial Reports Fertilizer Materials and Related Products: 2010 Summary.
Available online at .
References 10-5
-------
USAA(2014) U.S. Primary Aluminum Production 2013. U.S. Aluminum Association, Washington, D.C. January,
2014.
USAF (1998) Fuel Logistics Planning. U.S. Air Force: AFPAM23-221. May 1, 1998.
United States Geological Survey (USGS) (1994 through 201 1) Minerals Yearbook: Lead Annual Report. U.S.
Geological Survey, Reston, VA.
USGS (1991 through 20 ll)Minerals Yearbook: Manufactured Abrasives Annual Report. U.S. Geological Survey,
Reston, VA.
USGS (2011) 2010 Mineral Yearbook; Aluminum [Advanced Release] . U.S. Geological Survey, Reston, VA.
USGS (1991 through 20 10a) Miner als Yearbook: Silicon Annual Report. U.S. Geological Survey, Reston, VA.
USGS (1991 through 201 Ob) Miner al 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) 2006 Mineral 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 2013) Personal communication, Class II and III Rail energy consumption, American
Short Line and Regional Railroad Association.
Stationary Combustion (excluding CO2)
EIA (2015) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, February 2015, Energy
Information Administration, U.S. Department of Energy, Washington, D.C. DOE/EIA -0035(2015/02).
EIA (2014) Electricity in the United States. Electricity Explained. Energy Information Administration, U.S.
Department of Energy, Washington, D.C. Available online at
.
EPA (2015) "1970 - 2014 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 (2014; NONROAD 2008a 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) 2006 1PCC 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.
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, D.C.
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, D.C. June 22, 2001.
Mobile Combustion (excluding CO2)
AAR (2008 through 201 3) Railroad Facts. Policy and Economics Department, Association of American Railroads,
Washington, D.C. Obtained from Clyde Crimmel at AAR.
10-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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, D.C. Available online at .
APTA (2006) Commuter Rail National Totals. American Public Transportation Association, Washington, D.C.
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, D.C.
Browning, L. (2015) "Methodology for Highway Vehicle Alternative Fuel GHG Estimates". Technical Memo,
March 2015.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. Elissa Kay, Department of Homeland Security and Joe Aamidor, ICF
International. January 11, 2008.
DLA Energy (2014) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense
Energy Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, D.C.
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, D.C.
DOE (1993 through 2014) Transportation Energy Data Book. Office of Transportation Technologies, Centerfor
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, D.C. DAI-10. Available online at: .
EIA (2015). Monthly Energy Review, February 2015, Energy Information Administration, U.S. Department of
Energy, Washington, D.C. DOE/EIA-0035(2015/02).
EIA (1991 through 2014) Fuel Oil and Kerosene Sales. Energy Information Administration, U.S. Department of
Energy. Washington, D.C. Available at: http://www.eia.gov/petroleum/fueloilkerosene/
EIA (2007 through 2012) Natural Gas Annual. Energy Information Administration, U.S. Department of Energy,
Washington, D.C. DOE/EIA-0131(11).
EIA (2011) Annual Energy Review 2010. Energy Information Administration, U.S. Department of Energy,
Washington, D.C. 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, D.C. 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, D.C. Available online at .
EPA (2014a) Annual Certification Test Results Report. Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Available online at .
References 10-7
-------
EPA (2014b) Confidential Engine Family Sales Data Submitted To EPA By Manufacturers. Office of
Transportation and Air Quality, U.S. Environmental Protection Agency.
EPA (2Ql4c). Motor Vehicle Emissions Simulator (Moves) 2014. Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Available online at .
EPA (2014d; NONROAD 2008a Model. Office of Transportation and Air Quality, U.S. Environmental Protection
Agency. Available online at .
EPA (2015) "1970 - 2014 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 (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.
F AA (2015) Personal Communication between F AA 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, D.C. 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.
10-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 (2011)
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, D.C. 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 2013) 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 (2014a) "U.S. Resin Production & Sales: 2013 vs. 2012," American Chemistry Council. Available online at:
.
ACC (2014b) "Guide to the Business of Chemistry, 2014" American Chemistry Council.
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 (2014) Financial Markets Department Year Average of Exchange Rates. Available online at
.
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
.
EIA (2015) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, February 2015. Energy
Information Administration, U.S. Department of Energy, Washington, D.C. DOE/EIA-0035(2015/02).
EIA (2013) EIA Manufacturing Consumption of Energy (MECS) 2010. U.S. Department of Energy, Energy
Information Administration, Washington, D.C.
EIA (2010) EIA Manufacturing Consumption of Energy (MECS) 2006. U.S. Department of Energy, Energy
Information Administration, Washington, D.C.
EIA (2005) EIA Manufacturing Consumption of Energy (MECS) 2002. U.S. Department of Energy, Energy
Information Administration, Washington, D.C.
EIA (2001) EIA Manufacturing Consumption of Energy (MECS) 1998. U.S. Department of Energy, Energy
Information Administration, Washington, D.C.
EIA (1997) EIA Manufacturing Consumption of Energy (MECS) 1994. U.S. Department of Energy, Energy
Information Administration, Washington, D.C.
EIA (1994) EIA Manufacturing Consumption of Energy (MECS) 1991. U.S. Department of Energy, Energy
Information Administration, Washington, D.C.
EPA (2015a) "1970 - 2014 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, March 2015.
Available online at .
References 10-9
-------
EPA (2015b). Resource Conservation and Recovery Act (RCRA) Info, Biennial Report, GM Form (Section 2-
Onsite Management) and WR Form.
EPA (2014a) Municipal Solid Waste in the United States: 2012 Facts and Figures. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency, Washington, D.C. Available online at
.
EPA (2014b) Chemical Data Access Tool (CDAT). U.S. Environmental Protection Agency, June 2014. Available
online at . Accessed January 2015.
EPA (2013a) Municipal Solid Waste in the United States: 2011 Facts and Figures. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency, Washington, D.C. Available online at
.
EPA (2013b). 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, D.C. 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, D.C. 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, D.C. 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 .
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 Chemical Society, 2 July.
Available online at .
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 .
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 .
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 .
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 .
10-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 .
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 .
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 .
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) Production 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, The Intergovernmental Panel on Climate Change. H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
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 (2014) 2013 U.S. Scrap Tire Management Summary. Rubber Manufacturers Association, Washington, D.C.
November 2014.
RMA (2011) U.S. Scrap Tire Management Summary: 2005-2009. Rubber Manufacturers Association, Washington,
D.C. 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.
U.S. Census Bureau (2014) 2012 Economic Census. Available online at:
. Accessed November 2014.
U.S. Census Bureau (2009) Soap and Other Detergent Manufacturing: 2007. Available online at
.
U.S. Census Bureau (2004) Soap and Other Detergent Manufacturing: 2002, Issued December 2004, EC02-3II-
325611 (RV). Available online at .
U.S. Census Bureau (1999) Soap and Other Detergent Manufacturing: 1997, Available online at
.
References 10-11
-------
U.S. International Trade Commission (1990-2014) "Interactive Tariff and Trade DataWeb: Quick Query." Available
online at . Accessed November 2014.
Incineration of Waste
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, 2014) Municipal Solid Waste in the United States: Facts and Figures. Office of Solid
Waste and Emergency Response, U.S. Environmental Protection Agency. Washington, D.C. 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, D.C.
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, D.C. 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 (2014) "2013 U.S. Scrap Tire Management Summary." Rubber Manufacturers Association. November 2014.
Available online at: .
RMA (2012a) "Rubber FAQs." Rubber Manufacturers Association. Available online at . Accessed 19 November 2014.
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: .
RTI (2009) Updated Hospital/Medical/Infectious Waste Incinerator (HMIWI) Inventory Database. Memo dated July
6, 2009. Available online at: .
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.
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.
10-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Coal Mining
AAPG (1984) Coalbed Methane Resources of the United States. AAPG Studies in Geology Series #17.
CAR (2014) Project Database. Climate Action Reserve. Available at .
Consol (2014) Ruby Canyon Summary 2013. CONSOL Energy Inc. excel spreadsheet
Creedy, D.P. (1993) Chemosphere. Vol. 26, pp. 419-440.
EIA (2014) Annual Coal Report 1991-2013 (Formerly called Coal Industry Annual). Table 1. Energy Information
Administration, U.S. Department of Energy, Washington, D.C.
EPA (2014) Greenhouse Gas Reporting Program (GHGRP): 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.
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.
GSA (2014) Well Records Database. Geological Survey of Alabama State Oil and Gas Board. Retrieved from
.
IEA (2014) 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: EgglestonH.S., Srivastava N., Tanabe K., Baasansuren J., Fukuda M., Pub. IGES, Japan 2011.
JWR (2014) Wells Intercepted 2013. Jim Walter Resources excel spreadsheet.
JWR (2010) No. 4 & 7 Mines General Area Maps. Walter Energy: Jim Walter Resources.
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 (2014) Data Transparency atMSHA. Mine Safety and Health Administration. Retrieved from
.
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) (2014) Oil & Gas Production Data. Retrieved from
.
Abandoned Underground Coal Mines
EPA (2004) Methane Emissions Estimates & Methodology for Abandoned Coal Mines in the U.S. Draft Final
Report. Washington, D.C. April 2004.
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 (2014) Data Retrieval System. Available online at
.
References 10-13
-------
Petroleum Systems
Allen et al. (2014) Methane Emissions from Process Equipment at Natural Gas Production Sites in the United
States: Pneumatic Controllers. ES&T. December 9, 2014. 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.
BOEM (2014) Year 2011 Gulfwide Emission Inventory Study. Bureau of Ocean Energy Management, U.S.
Department of Interior. OCS Study BOEM 2014-666. Available online at
Drillinglnfo (2014) December 2014 Download. DI Desktop® Drillinglnfo, Inc.
EIA (1990 through 2014) Refinery Capacity Report. Energy Information Administration, U.S. Department of
Energy. Washington, DC. Available online at < http://www.eia.gov/petroleum/refmerycapacity/ >.
EIA (1995 through 2014a) 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 2014b) 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 2014c) Petroleum Supply Annual. Volume 1. U.S Department of Energy Washington, DC.
Available online at: < http://www.eia.gov/petroleum/supply/annual/volumel/>.
EPA (2015a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Data Source for Well
Counts. Available at http://www.epa.gov/climatechange/ghgemissions/usinventoryreport/natural-gas-systems.html.
EPA (2015b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Offshore Oil and Gas
Platforms Emissions Estimate. Available at
http://www.epa.gov/climatechange/ghgemissions/usinventoryreport/natural-gas-systems.html.
EPA (2015c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Refineries Emissions
Estimate. Available at .
EPA (2015d) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Potential Updates to Pneumatic
Controller Emissions Estimate. Available at
.
EPA (2014) Greenhouse Gas Reporting Program. Environmental Protection Agency. Data reported as of August 18,
2014.
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 (1999a) 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.
10-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
EPA (1999b) 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, V12: 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, The Intergovernmental Panel on Climate Change. H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2007) Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change. Pachauri, R.K and Reisinger, A. eds.; IPCC,
Geneva, Switzerland.
OGJ (2014a) Oil and Gas Journal 1990-2013. Pipeline Economics Issue, September 2014.
OGJ (2013b) Oil and Gas Journal 1990-2013. Worldwide Refining Issue, January 2013.
United States Army Corps of Engineers (1995 through 2012) Waterborne Commerce of the United States, Part 5:
National Summaries. U.S. Army Corps of Engineers. Washington, DC.
Natural Gas Systems
AGA (1991 through 1998) Gas Facts. American Gas Association. Washington, DC.
Alabama (2014) Alabama State Oil and Gas Board. Available online at .
Allen et al. (2014a) Methane Emissions from Process Equipment at Natural Gas Production Sites in the United
States: Liquids Unloading. ES&T. December 9, 2014. Available online at:
.
Allen et al. (2014b) Methane Emissions from Process Equipment at Natural Gas Production Sites in the United
States: Pneumatic Controllers. ES&T. December 9, 2014. Available online at:
.
Allen etal. (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
.
API/ANGA (2012) Characterizing Pivotal Sources of Methane Emissions from Natural Gas Production - Summary
and Analysis of API andANGA 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 (20 lib) Pacific OCS Region Offshore Information. Bureau of Ocean Energy Management, Regulation
and Enforcement, U.S. Department of Interior.
References 10-15
-------
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) December 2014 Download. DI Desktop® Drillinglnfo, Inc.
EIA (2014a) "Table 1— Summary of natural gas supply and disposition in the United States, 2009-2014." Natural
Gas Monthly, Energy Information Administration, U.S. Department of Energy, Washington, DC. Available online at
.
EIA (2014b) "Table 2—Natural Gas Consumption in the United States, 2009-2014." Natural Gas Monthly, Energy
Information Administration, U.S. Department of Energy, Washington, DC. Available online at
.
EIA (2014c) "Table 7 - Marketed production of natural gas in selected states and the Federal Gulf of Mexico, 2009-
2014." Natural Gas Monthly, Energy Information Administration, U.S. Department of Energy, Washington, DC.
Available online at .
EIA (2014d) U.S. Natural Gas Imports by Country. Energy Information Administration, U.S. Department of Energy,
Washington, DC. Available online at .
EIA (2014e) Natural Gas Gross Withdrawals and Production. Energy Information Administration, U.S. Department
of Energy, Washington, DC. Available online at .
EIA (2012a) Formation crosswalk. Energy Information Administration, U.S. Department of Energy, Washington,
DC. Provided July 7.
EIA (2012b) Lease Condensate Production, 1979-2012, Natural Gas Navigator. 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.
EIA (2001) "Documentation of the Oil and Gas Supply Module (OGSM)." Energy Information Administration, U.S.
Department of Energy, Washington, DC.
EPA (2015a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Data Source for Well
Counts. Available at .
EPA (2015b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Offshore Oil and Gas
Platform Emission Estimates. Available at
.
EPA (2015c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Hydraulically Fractured
Gas Well Completions and Workover Estimate. Available at
.
EPA (2015d) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Potential Updates to Pneumatic
Controller Emissions Estimate. Available at
.
EPA (2015e) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Potential Updates to Liquids
Unloading Emissions Estimate. Available at
.
EPA (2014) Greenhouse Gas Reporting Program- Subpart W-Petroleum andNatural Gas Systems. Environmental
Protection Agency. Data reported as of August 18, 2014.
10-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
EPA (2013 a) 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 Gas STAR Reductions 1990-2012. Natural Gas STAR Program. September 2013.
EPA (2013d) Updating GHG Inventory Estimate for Hydraulically Fractured Gas Well Completions and
Workovers. Available online at .
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 (2014) 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, The Intergovernmental Panel on Climate Change. H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Jackson etal., (2014) Natural Gas Pipeline Leaks Across Washington, D.C., 48 Environ. Science Technology 2051-
2058, January 16, 2014. Available online at . March 24, 2014.
McGeehan et al., (2014) Beneath Cities, a Decaying Tangle of Gas Pipes, N.Y. Times, March 24, 2014. Available
online at .
Miller et al. (2013) Anthropogenic emissions of methane in the United States. November 25, 2013, doi:
10.1073/pnas.l314392110. Available online at
.
OGJ (1997-2013) "Worldwide Gas Processing." Oil & Gas Journal, PennWell Corporation, Tulsa, OK. Available
online at .
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 Available online at .
References 10-17
-------
PHMSA (2013a) Transmission Annuals Data. Pipeline and Hazardous Materials Safety Administration, U.S.
Department of Transportation, Washington, DC. Available online at .
PHMSA (2013b) Gas Distribution Annual Data. Pipeline and Hazardous Materials Safety Administration, U.S.
Department of Transportation, Washington, DC. Available online at .
Wyoming (2013) Wyoming Oil and Gas Conservation Commission. Available online at
.
Energy Sources of Indirect Greenhouse Gases
EPA (2015) "1970 - 2014 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, March 2015.
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 .
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.
DLA Energy (2014) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense
Energy Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, D.C.
DOC (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, D.C.
DOT (1991 through 2013) Fuel Cost and Consumption. Federal Aviation Administration, Bureau of Transportation
Statistics, U.S. Department of Transportation. Washington, D.C. DAI-10.
EIA(2Ql5)Monthly Energy Review, February 2015, Energy Information Administration, U.S. Department of
Energy, Washington, D.C. DOE/EIA-0035(2015/2).
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.
USAF (1998) Fuel Logistics Planning. U.S. Air Force pamphlet AFPAM23-221, May 1, 1998.
10-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Wood Biomass and Ethanol Consumption
EIA (2015) Monthly Energy Review, February 2015. Energy Information Administration, U.S. Department of
Energy. Washington, D.C. DOE/EIA-0035(2015/2).
EPA (2014) Acid Rain Program Dataset 1996-2013. 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.
Lindstrom, P. (2006) Personal Communication. Perry Lindstrom, Energy Information Administration and Jean Kim,
ICF International.
Industrial Processes and Product Use
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: EgglestonH.S., Srivastava N., Tanabe K., Baasansuren J., Fukuda M., Pub. IGES, Japan 2011.
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, D.C.
United States Geological Survey (USGS) (2014) Mineral Industry Survey: Cement in June 2014. U.S. Geological
Survey, Reston, VA. August, 2014.
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.
Van Oss (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
Corathers (2014) Personal communication, Michael Miller, U.S. Geological Survey and Gopi Manne, Eastern
Research Group, Inc. September 23, 2014.
EPA (2014) Greenhouse Gas Reporting Program (GHGRP). Aggregation of reported facility level data under
Subpart S -National Lime production for calendar years 2010-2013. Office of Air and Radiation, Office of
Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
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.
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.
References 10-19
-------
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.
United States Geological Survey (USGS) (1992 through 2013) Minerals Yearbook: Lime. U.S. Geological Survey,
Reston, VA.
Glass 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.
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, D.C.
U.S. Bureau of Mines (1991 and 1993a) Minerals Yearbook: Crushed Stone Annual Report. U.S. Department of the
Interior. Washington, D.C.
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, D.C.
United States Geological Survey (USGS) (1995 through 20I4a) Minerals Yearbook: Crushed Stone Annual Report.
U.S. Geological Survey, Reston, VA.
USGS (2014b) Minerals Industry Surveys; SodaAsh in August 2015. U.S. Geological Survey, Reston, VA.
USGS (1995 through 20 l3b)Miner als Yearbook: Soda Ash Annual Report. U.S. Geological Survey, Reston, VA.
Willett (2014) Personal communication, Jason Christopher Willett, U.S. Geological Survey and Gopi Manne,
Eastern Research Group, Inc. September 25, 2014.
Willett (2013) Personal communication., Jason Christopher Willett, U.S. Geological Survey and Gopi Manne,
Eastern Research Group, Inc. October 29, 2013.
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, D.C.
U.S. Bureau of Mines (1990 through 1993b) Minerals Yearbook: Magnesium and Magnesium Compounds Annual
Report. U.S. Department of the Interior. Washington, D.C.
United States Geological Survey (USGS) (2013a) Magnesium Metal Mineral Commodity Summary for 2013. U.S.
Geological Survey, Reston, VA.
USGS (1995 through 2014) Minerals Yearbook: Crushed Stone Annual Report. U.S. Geological Survey, Reston,
VA.
USGS (1995 through 2012) Minerals Yearbook: Magnesium Annual Report. U.S. Geological Survey, Reston, VA.
Willett (2014) Personal communication, Jason Christopher Willett, U.S. Geological Survey and Gopi Manne,
Eastern Research Group, Inc. September 25, 2014.
10-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Ammonia Production
ACC (2014b) Business of Chemistry (Annual Data). American Chemistry Council, Arlington, VA.
Bark (2004) CoffeyvilleNitrogen Plant Available online at
. 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 (2014) CVR Energy, Inc. 2013 Annual Report. Available online at .
CVR (2012) CVR Energy, Inc. 2012 Annual Report. Available online at .
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. Census Bureau (2011) Current Industrial Reports Fertilizer Materials and Related Products: 2010 Summary.
Available online at .
U.S. Census Bureau (2010) Current Industrial Reports Fertilizer Materials and Related Products: 2009 Summary.
Available online at .
U.S. Census Bureau (2009) Current Industrial Reports Fertilizer Materials and Related Products: 2008 Summary.
Available online at .
U.S. Census Bureau (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 .
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 .
References 10-21
-------
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 (200 la) 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, D.C.
United States Geological Survey (USGS) (1994 through 2009) Minerals Yearbook: Nitrogen. Available online at
.
USGS (2014) 2012 Minerals Yearbook: Nitrogen [Advance Release]. September 2014. Available online at
. August 2002.
U.S. Census Bureau (2011) Current Industrial Reports Fertilizer Materials and Related Products: 2010 Summary.
Available online at .
U.S. Census Bureau (2010) Current Industrial Reports Fertilizer Materials and Related Products: 2009 Summary.
Available online at .
U.S. Census Bureau (2009) Current Industrial Reports Fertilizer Materials and Related Products: 2008 Summary.
Available online at .
U.S. Census Bureau (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 .
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 .
10-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
U.S. Census Bureau (200 la) 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 .
U.S. ITC (2002) United States International Trade Commission Interactive Tariff and Trade DataWeb, Version
2.5.0. Available online at . August 2002.
United States Geological Survey (USGS) (2014) 2012 Minerals Yearbook: Nitrogen [Advance Release]. September
2014. Available online at .
USGS (1994 through 2009) Minerals Yearbook: Nitrogen. Available online at
.
Nitric Acid Production
Climate Action Reserve (CAR) (2013), Project Report,
. Accessed on January 18, 2013.
Desai (2012) Personal communication. Mausami Desai, U.S. Environmental Protection Agency, January 25, 2012.
EPA (2014) Greenhouse Gas Reporting Program (GHGRP). Aggregation of reported facility level data under
Subpart V -National Nitric Acid production for calendar years 2010 -2013. Office of Air and Radiation, Office of
Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
EPA (2013a) Personal communication, Mausami Desai, U.S. Environmental Protection Agency, January 23, 2013.
Includes file "NitricAcidProduction_1990-2011 (EPA).xls."
EPA(2010a, 2Ql3b) 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.
EPA (201Gb) Available and Emerging Technologies for Reducing Greenhouse Gas Emissions from the Nitric Acid
Production Industry. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency.
Research Triangle Park, NC. December 2010. Available online at:
.
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.
IPCC (2007) Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, J. Lean, D.C.
Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz and R. Van Dorland, 2007: Changes in Atmospheric
Constituents and in Radiative Forcing. In: Climate Change 2007: The Physical Science Basis. Contribution of
Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S.,
D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA.
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.
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.
References 10-23
-------
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 .
United States Geological Survey (USGS) (2012) 2011 Minerals Yearbook: Nitrogen [Advance Release]. December,
2012. U.S. Geological Survey, Reston, VA.
Adipic Acid Production
ACC (2014) Business of Chemistry (Annual Data). American Chemistry Council, Arlington, VA.
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 & Engineering 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 (201 la) Personal communication. Mausami Desai, U.S. Environmental Protection Agency and Roy Nobel,
Ascend Performance Materials, October 18, 2011.
Desai (201 Ib) Personal communication. Mausami Desai, U.S. Environmental Protection Agency with Steve Zuiss of
Invista, November 18, 2011.
Desai (2010) Personal communication. Mausami Desai, U.S. Environmental Protection Agency with Steve Zuiss of
Invista, October 15, 2010.
EPA (2014) Greenhouse Gas Reporting Program. 2013, 2012, 2011 and 2010 Detailed Data for Additional Industry
Types (Adipic Acid Tab). Office of Air and Radiation, Office of Atmospheric Programs, U.S. Environmental
Protection Agency, Washington, D.C. Accessed 11/18/2014, Available online at:
.
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
. 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 (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.
10-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.
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-CCh 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, Virginia Department of Environmental Quality and
Joseph Herr, ICF International. March 12, 2010.
VA DEQ (2009) Personal communication. Stanley Faggert, Virginia 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 (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.
U.S. Census Bureau (2005 through 2014) U.S. International Trade Commission (USITC) Trade DataWeb.
Available online at .
United States Geological Survey (USGS) (2014) Minerals Industry Surveys: Abrasives (Manufactured) in Fourth
Quarter of 2013. U.S. Geological Survey, Reston, VA. December 2013. Available online at < http://
http://minerals.usgs.gov/minerals/pubs/commodity/abrasives/myb 1 -2012-abras.pdf>.
USGS (1991a through 2013a) Minerals Yearbook: Manufactured Abrasives Annual Report. U.S. Geological
Survey, Reston, VA. Available online at .
USGS (1991b through 20 lib, 2012c, and 2013b) Minerals Yearbook: Silicon Annual Report. U.S. Geological
Survey, Reston, VA. Available online at .
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) 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.
United States Geological Survey (USGS) (USGS 1991 through 20Ha)Minerals Yearbook: Titanium. U.S.
Geological Survey, Reston, VA.
USGS (2014b) Mineral Commodity Summary: Titanium and Titanium Dioxide 2013. U.S. Geological Survey,
Reston, VA.
References 10-25
-------
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.
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.
United States Geological Survey (USGS) (2014) Mineral Industry Surveys: Soda Ash in August 2014. U.S.
Geological Survey, Reston, VA.
USGS (1994 through 2013) Minerals Yearbook: Soda Ash Annual Report. 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.
Petrochemical Production
ACC (2014a) U.S. Chemical Industry Statistical Handbook. American Chemistry Council, Arlington, VA.
ACC (2014b) Business of Chemistry (Annual Data). American Chemistry Council, Arlington, VA.
ACC (2002, 2003, 2005 through 2011) Guide to the Business of Chemistry. American Chemistry Council,
Arlington, VA.
AN (2014) About Aery lonitrile: Production. AN Group, Washington, D.C. Available online at:
Argus JJ&A (2014). U.S. Methanol Production data for 2009-2013. Argus Media Inc., Houston, TX. Obtained via
personal communication between Mausami Desai (EPA) and Argus Media Inc. Email received 01/30/2015.
EPA Greenhouse Gas Reporting Program (2014). Aggregation of reported facility level data under Subpart X -
National Petrochemical production for calendar years 2010-2013. Office of Air and Radiation, Office of
Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
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 (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.
Jordan, J. (2011) Personal communication, Jim Jordan of Jordan Associates on behalf of the Methanol Institute and
PierLaFarge, ICF International. October 18, 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.
10-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.
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.
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 (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.
References 10-27
-------
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 (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.
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.
UNFCCC (2014) Report of the Conference of the Parties on its nineteenth session, held in Warsaw from 11 to 23
November 2013. United Nations Framework Convention on Climate Change, Warsaw. (FCCC/CP/2013/10/Add.3).
January 31, 2014. Available online at .
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 2010) 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, D.C. 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, D.C. 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 (1999 through 2014) Monthly CO2 Produced by County. Available online at
. Accessed October
2014.
Denbury Resources Inc. (2002 through 2010) Annual Report: 2001 through 2009, Form 10-K. Available online at
.
Accessed September 2014.
EPA (2014) Greenhouse Gas Reporting Program (GHGRP). Aggregation of Reported Facility Level Information on
Greenhouse Gases Tool (FLIGHT) on Suppliers of CO2. Office of Air and Radiation, Office of Atmospheric
Programs, U.S. Environmental Protection Agency, Washington, D.C. Available online at
. Accessed October 2014.
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
.
10-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.
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
. Accessed on January 25, 2013.
United States Geological Survey (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 (2014) Mineral Commodity Summaries: Phosphate Rock. February 2014. U.S. Geological Survey, Reston,
VA. Available online at: .
Iron and Steel Production and Metallurgical Coke Production
AISI (2004 through 2014a) Annual Statistical Report, American Iron and Steel Institute, Washington, D.C.
AISI (2006 through 2014b) Personal communication, Mausami Desai, U.S. EPA, and American Iron and Steel
Institute, December 8, 2014.
AISI (2008c) Personal communication, Mausami Desai, U.S. EPA, and Bruce Steiner, Technical Consultant with
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 2014) Quarterly Coal Report: October-December, Energy Information Administration, U.S.
Department of Energy. Washington, D.C. DOE/EIA-0121.
EIA (20l2a) Annual Energy Review 2011, Energy Information Administration, U.S. Department of Energy.
Washington, D.C. DOE/EIA-0384(2011).
EIA (2012b) Natural Gas Annual 2011, Energy Information Administration, U.S. Department of Energy.
Washington, D.C. 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, D.C. 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, 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.
Fenton (2014) Personal communication. Michael Fenton, Commodity Specialist, U.S. Geological Survey and Marty
Wolf, Eastern Research Group. December 19, 2014.
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.
United States Geological Survey (USGS) (1991 through 2013) USGSMinerals Yearbook - Iron and Steel Scrap.
U.S. Geological Survey, Reston, VA.
References 10-29
-------
Ferroalloy Production
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.
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.
United States Geological Survey (USGS) (2014) Mineral Industry Surveys: Silicon in September 2014. U.S.
Geological Survey, Reston, VA.
USGS (1996 through 2013) Minerals Yearbook: Silicon. U.S. Geological Survey, Reston, VA.
Aluminum Production
EPA (2014) Greenhouse Gas Reporting Program (GHGRP). Envirofacts, Subpart: F Aluminum Production.
Available online at . Accessed on: November 13, 2014.
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 (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.
USAA(2014) U.S. Primary Aluminum Production 2013. U.S. Aluminum Association, Washington, D.C. October,
2014.
USAA(2013) U.S. Primary Aluminum Production 2012. U.S. Aluminum Association, Washington, D.C. January,
2013.
USAA(2012) U.S. Primary Aluminum Production 2011. U.S. Aluminum Association, Washington, D.C. January,
2012.
USAA (2011) U.S. Primary Aluminum Production 2010. U.S. Aluminum Association, Washington, D.C.
USAA (2010) U.S. Primary Aluminum Production 2009. U.S. Aluminum Association, Washington, D.C.
USAA (2008, 2009) U.S. Primary Aluminum Production. U.S. Aluminum Association, Washington, D.C.
USAA (2004, 2005, 2006) Primary Aluminum Statistics. U.S. Aluminum Association, Washington, D.C.
USGS (2014) 2014Mineral 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.
10-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.
EPA (2014) Envirofacts. Greenhouse Gas Reporting Program (GHGRP), Subpart T: Magnesium Production and
Processing. Available online at . Accessed on: November,
2014.
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 SF6 by End-Use
Applications" Katie D. Smythe. International Conference on SFg and the Environment: Emission Reduction
Strategies. San Diego, CA. November 21-22, 2002.
United States Geological Survey (USGS) (2002, 2003, 2005 through 2008, 201 Ib, 2012, and 2013) Minerals
Yearbook: Magnesium Annual Report. U.S. Geological Survey, Reston, VA. Available online at
.
USGS (2QWa) Mineral Commodity Summaries: Magnesium Metal. U.S. Geological Survey, Reston, VA. Available
online at .
Lead Production
Dutrizac, J.E., V. Ramachandran, and J.A. Gonzalez (2000) Lead-Zinc 2000. The Minerals, Metals, and Materials
Society.
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.
Morris, D., F.R. Steward, and P. Evans (1983) Energy Efficiency of a Lead Smelter. Energy 8(5):337-349.
Sjardin, M. (2003) CC>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.
United States Geological Survey (USGS) (2014a) 2014Mineral Commodity Summary, Lead. U.S. Geological
Survey, Reston, VA.
USGS (2014b) Mineral Industry Surveys: Lead in June 2014. U.S. Geological Survey, Reston, VA.
USGS (1995 through 2013) Minerals Yearbook: Lead Annual Report. U.S. Geological Survey, Reston, VA.
Zinc Production
Horsehead Corp. (2014) Form 10-k, Annual Report for the Fiscal Year Ended December 31, 2013. Available at:
. Submitted
March 13, 2014.
Horsehead Corp. (2013) Form 10-k, Annual Report for the Fiscal Year Ended December 31, 2012. Available at:
.
Submitted March 18, 2013.
References 10-31
-------
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) 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.
PIZO (2014) Available at . Accessed December 9, 2014.
PIZO (2012) Available at . Accessed October 10, 2012.
Rowland (2014) Personal communication. Art Rowland, Plant Manager, Steel Dust Recycling LLC and Gopi
Manne, Eastern Research Group, Inc. December 9, 2014.
Rowland (2012) Personal communication. Art Rowland, Plant Manager, Steel Dust Recycling LLC and Gopi
Manne, 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 . Accessed October 29, 2013.
United States Geological Survey (USGS) (2014b) 2014 Mineral Commodity Summary: Zinc. U.S. Geological
Survey, Reston, VA.
USGS (1995 through 2014a) Miner als Yearbook: Zinc Annual Report. U.S. Geological Survey, Reston, VA.
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.
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.
Doering, R. and Nishi, Y (2000) "Handbook of Semiconductor Manufacturing Technology", Marcel Dekker, New
York, USA, 2000.
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.
ISMI (2009) Analysis of Nitrous Oxide Survey Data. Walter Worth. June 8, 2009. Available online at
10-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
ITRS (2007, 2008, 2011, 2013) International Technology Roadmap for Semiconductors: 2006 Update, January
2007; International Technology Roadmap for Semiconductors: 2007 Edition, January 2008; International
Technology Roadmap for Semiconductors: 2011, January 2012; Update, International Technology Roadmap for
Semiconductors: 2013 Edition, Available online at . These
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.
Semiconductor Industry Association (SIA) (2009) STATS: SICAS Capacity and Utilization Rates Ql -Q4 2008, Ql-
Q4 2009, Q1-Q4 2010, Q1-Q4 2011. Available online at <
http://www.semiconductors.org/industry_statistics/semiconductor_capacity_utilization_sicas_reports/>.
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.
Substitution of Ozone Depleting Substances
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.
Electrical Transmission and Distribution
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 (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.
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,Plaits.
UDI (2010) 2010 UDI Directory of Electric Power Producers and Distributors, 118th Edition, Plaits.
References 10-33
-------
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.
UNFCCC (2014) Report of the Conference of the Parties on its nineteenth session, held in Warsaw from 11 to 23
November 2013. United Nations Framework Convention on Climate Change, Warsaw. (FCCC/CP/2013/10/Add.3).
January 31, 2014. Available online at .
Nitrous Oxide from Product Use
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.
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.
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.
Industrial Processes and Product Use Sources of Indirect
Greenhouse Gases
EPA (2015) "1970 - 2014 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, March 2015.
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.
10-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 (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.
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 Beef NRC: Appendix Table 22. National Research Council.
References 10-35
-------
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: .
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,
USDA (2014) Quick Stats: Agricultural Statistics Database. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. Available online at . Accessed
August 13, 2014.
USDA (2007) Census of Agriculture: 2007 Census Report. United States Department of Agriculture. Available
online at: .
USDA (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.
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, D.C. 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.
USDA: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.
USDA:APHIS:VS (2002) Reference of 2002 Dairy Management Practices. USDA-APHIS-VS, CEAH. Fort
Collins, CO. Available online at .
USD A: APHIS: VS (1998) Beef '97, Parts I-IV. USDA-APHIS-VS, CEAH. Fort Collins, CO. Available online at
USDA:APHIS:VS (1996) Reference of 1996 Dairy Management Practices. USDA-APHIS-VS, CEAH. Fort
Collins, CO. Available online at .
USD A: APHIS: VS (1994) Beef Cow/Calf Health and Productivity Audit. USDA-APHIS-VS, CEAH. Fort Collins,
CO. Available online at .
USD A: 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.
Manure Management
Anderson, S. (2000) Personal Communication. Steve Anderson, Agricultural Statistician, National Agriculture
Statistics Service, U.S. Department of Agriculture and Lee-Ann Tracy, ERG. Washington, D.C. May 31, 2000.
10-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
ASAE (1998) ASAE Standards 1998, 45th Edition. American Society of Agricultural Engineers. St. Joseph, MI.
Bryant, M.P., V.H. Varel, R.A. Frobish, and H.R. Isaacson (1976) InH.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, D.C.
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, D.C. April 22, 2005. Available online at
. Retrieved
August 2007.
EPA (2003) AgSTAR Digest. Office of Air and Radiation, U.S. Environmental Protection Agency. Washington, D.C.
Winter 2003. Available online at . Retrieved July 2006.
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, D.C.
Spring 2000. Available online at: .
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 Itle of ERG Concerning Updated VS and
Nex Rates. August 8, 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.
References 10-37
-------
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)." Agricultural 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 oftheASAE,
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.
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, D.C. 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. (2014) 1990-2013 Volatile Solids and Nitrogen Excretion Rates. Dataset to EPA from
ICF International. August 2014.
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 (2014) National Climate Data Center (NCDC). Available online at
(for all states except Alaska and Hawaii) and
. (for Alaska and Hawaii). September 2014.
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.
Robel, J. (2014) Personal Communication. Jeff Robel, Physical Scientist, National Climate Data Center and Sara
Matasci, ERG. September 30, 2014.
10-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 (2014a) 1987, 1992, 1997, 2002, 2007, and 2012 Census of Agriculture. National Agriculture Statistics
Service, U.S. Department of Agriculture. Washington, D.C. Available online at
. May 2014.
USDA (2014b) Quick Stats: Agricultural Statistics Database. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. Available online at .
USDA (2014c) Chicken and Eggs 2013 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. February 2014. Available online at
.
USDA (2014d) Poultry - Production and Value 2013 Summary. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. April 2014. Available online at
.
USDA (2013a) Chicken and Eggs 2012 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. February 2013. Available online at
.
USDA (2013b) Poultry - Production and Value 2012 Summary. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. April 2013. Available online at
.
USDA (2012a) Chicken and Eggs 2011 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. February 2012. Available online at
.
USDA (2012b) Poultry - Production and Value 2011 Summary. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. April 2012. Available online at
.
USDA (201 la) Chicken and Eggs 2010 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. February 2011. Available online at
.
USDA (201 Ib) Poultry - Production and Value 2010 Summary. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. April 2011. Available online at
.
USDA (2010a) Chicken and Eggs 2009 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. February 2010. Available online at
.
USDA (2010b) Poultry - Production and Value 2009 Summary. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. April 2010. Available online at
.
References 10-39
-------
USD A (2009a) Chicken and Eggs 2008 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. February 2009. Available online at
.
USDA (2009b) Poultry - Production and Value 2008 Summary. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. April 2009. Available online at
.
USDA (2009c) Chicken and Eggs - Final Estimates 2003-2007. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. March 2009. Available online at
.
USDA (2009d) Poultry Production and Value—Final Estimates 2003-2007. National Agriculture Statistics Service,
U.S. Department of Agriculture. Washington, D.C. 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, D.C. 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, D.C. 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, D.C. March 1999. Available online at
.
USDA (1998) Chicken and Eggs—Final Estimates 1994-97. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. 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.
USDA (1995a) Poultry Production and Value—Final Estimates 1988-1993. National Agriculture Statistics Service,
U.S. Department of Agriculture. Washington, D.C. March 1995. Available online at
.
USDA (1995b) Chicken and Eggs—Final Estimates 1988-1993. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. December 1995. Available online at
.
USDA (1994) Sheep and Goats—Final Estimates 1989-1993. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. January 31, 1994. Available online at
.
USDA, APHIS (2003) Sheep 2001, Part I Reference of Sheep Management in the United States, 2001 and Part
IV:Baseline Reference of 2001 Sheep Feedlot Health and Management. USDA-APHIS-VS. Fort Collins, CO.
#N356.0702. .
USDA, APHIS (2000) Layers '99—Part II: References of 1999 Table Egg Layer Management in the U.S. USDA-
APHIS-VS. Fort Collins, CO.
.
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.
.
Wright, P. (2000) Personal Communication. Lee-Ann Tracy, ERG and Peter Wright, Cornell University, College of
Agriculture and Life Sciences. June 23, 2000.
10-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Rice Cultivation
Anderson, M. (2008 through 2014) 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, P. (2013). The Birds and Rice Connection. Bird Watcher's Digest. Available online at
.
Beighley, D. (2011 through 2012) Email correspondence. Bonn Beighley, Southeast Missouri State University,
Department of Agriculture and 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.
Environmental Defense Fund (2011) Creating and Quantifying Carbon Credits from Voluntary Practices on Rice.
Available online at .
Fife, L. (2011) Email correspondence. Les Fife, Sacramento Valley Agricultural Burning 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 2014) Email correspondence. Rene Gonzalez, Plant Manager, Sem-Chi Rice Company
and ICF International.
Hardke, J. (2014) Personal Communication. Dr. Jarrod Hardke, Rice Extension Agronomist at the University of
Arkansas Rice Research and Extension Center and Kirsten Jaglo, ICF International. September 11, 2014.
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 (2007) Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth
Assessment Report (AR4) of the IPCC. The Intergovernmental Panel on Climate Change, R.K. Pachauri, A. Resinger
(eds.). Geneva, Switzerland.
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/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.
References 10-41
-------
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, M. (2005). Effect of Plant Residue and Water Management Practices on Soil Redox Chemistry,
Methane Emission, and Rice Productivity. LSU PhD Thesis.
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 2014) Email correspondence. Steve Linscombe, Professor with the Rice
Research Station at Louisiana State University Agriculture Center and ICF International.
McMillan, A., M. L.Goulden, and S.C. Tyler. (2007) Stoichiometry of CH4 and CCh 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.
Rogers, C.W., K. R. Brye, R.J. Norman, T. Gasnier, D. Frizzell, and J. Branson. (2011). Methane Emissions from a
Silt-Loam Soil Under Direct-Seeded, Delayed-Flood Rice Managements.R. Wells Rice Research Studies 2011.
306-315.
Sass, R. 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., J.A. Andrews, A. Ding and P.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.
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.
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.
Street, J. (1999 through 2003) Personal Communication. Joe Street, Rice Specialist, Mississippi State University,
Delta Research Center and ICF International.
Texas Agricultural Experiment Station (2007 through 2014) Texas Rice Acreage by Variety. Agricultural Research
and Extension Center, Texas Agricultural Experiment Station, Texas A&M University System. Available online at
.
10-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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
.
USD A (2005 through 2014) Crop Production Summary. National Agricultural Statistics Service, Agricultural
Statistics Board, U.S. Department of Agriculture, Washington, D.C. 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, D.C. Available online at
. September 2013.
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, D.C. 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, D.C. 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, D.C. 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, J.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 (2008 through 2014) 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.
Bateman, E. J. and E. M. Baggs (2005) "Contributions of nitrification and denitrification to N2O emissions from
soils at different water-filled pore space." Biology and Fertility of Soils 41(6): 379-388.
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 at
.
References 10-43
-------
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 FJ. Breidt (2010) "Estimating Uncertainty in N2O Emissions from
U.S. 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.
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, D.C. 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.
Firestone, M. K., and E.A. Davidson, Ed. (1989). Microbiological basis of NO and N2O production and
consumption in soil. Exchange of trace gases between terrestrial ecosystems and the atmosphere. New York, John
Wiley & Sons.
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.
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.
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.
Kessavalou, A., A.R. Mosier, J.W. Doran, R.A. Drijber, D.J. Lyon, and O. Heinemeyer (1998). "Fluxes of carbon
dioxide, nitrogen oxide, and methane in grass sod and winter wheat -fallow tillage management." Journal of
Environmental Quality 27: 1094-1104.
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.
10-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Mesinger, F., G. DiMego, E. Kalnay, K. Mitchell, P. C. Shafran, W. Ebisuzaki, D. Jovic, J. Woollen, E. Rogers, E.
Mosier, A. R., J.M. Duxbury, J.R. Freney, O. Heinemeyer, K. Minami (1998) "Assessing and mitigating N2O
emissions from agricultural soils." Climatic Change 40: 7-38.
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
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.
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, and D.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. U.S Department of the
Interior.
Scheer, C., S.J. Del Grosso, W.J. 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, .
Soil Survey Staff (2011) State Soil Geographic (STATSGO) Database for State. Natural Resources Conservation
Service, United States Department of Agriculture. 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: .
USDA-ERS (1997) Cropping Practices Survey Data—1995. Economic Research Service, United States Department
of Agriculture. Available online at .
References 10-45
-------
USDA-NASS (2014) Quick Stats. National Agricultural Statistics Service, United States Department of
Agriculture, Washington, D.C. .
USDA-NRCS (2013) Summary Report: 2010 National Resources Inventory, Natural Resources Conservation
Service, Washington, D.C, and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa.
USDA-NRCS (2009) Summary Report: 2007 National Resources Inventory, Natural Resources Conservation
Service, Washington, D.C, and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa,
.
Vogelman, J.E., S.M. Howard, L. Yang, C. R. Larson, B. K. Wylie, and J. N. VanDriel (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 2014) Email correspondence. Monte Anderson, Oklahoma Farm Service Agency and
ICF International. August 21, 2014.
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.
Deren, C. (2002) Personal communication. Dr. Chris Deren, Everglades Research and Education Centre at the
University of Florida and CarenMintz, 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, D.C.
Gonzalez, R. (2007 through 2014) Email correspondence. Rene Gonzalez, Plant Manager, Sem-Chi Rice Company
and ICF International.
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/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.
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. (2011) "Remote Sensing-Based Estimates of Annual and Seasonal Emissions from Crop Residue
Burning in the Contiguous United States." Journal of the Air & Waste Management Association, 61:1,22-34, DOI:
10.3155/1047-3289.61.1.22.
10-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 NPJ 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.
Murphy, WJ. (1993). "Tables for weights and measurement: crops". Extension publications. (University of Missouri
Extension) .
Schueneman, T. (1999 through 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.
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 bio mass burning: Wind tunnel derived source profiles for
herbaceous and wood fuels." Journal of Geophysical Research 102(D3):3683-3699.
USDA (2014) Quick Stats: U.S. & All States Data; Crops; Production and Area Harvested; 1990 - 2013. National
Agricultural Statistics Service, U.S. Department of Agriculture. Washington, D.C. U.S. Department of Agriculture,
National Agricultural Statistics Service. Washington, D.C., Available online at .
Land Use, Land-Use Change, and Forestry
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.
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 .
Alaska Oil and Gas Conservation Commission (2009) Oil and Gas Information System. Available online at
.
EIA (2011) Coal Production and Preparation Report Shapefile. 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.
References 10-47
-------
IPCC (2010) Revisiting the use of managed land as a proxy for estimating national anthropogenic emissions and
removals. Eggleston HS, Srivastava N, Tanabe K, Baasansuren J, (eds.).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.
Jin, S., L. Yang, P. Danielson, C. Homer, J. Fry, and G. Xian. (2013) A comprehensive change detection method for
updating the National Land Cover Database to circa 2011. Remote Sensing of Environment, 132: 159-175.
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 .
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, D.C.
U.S. Census Bureau (2010) Topologically Integrated Geographic Encoding and Referencing (TIGER) system
shapefiles. U.S. Census Bureau, Washington, D.C. Available online at .
U.S. Department of Agriculture (2011) County Data - Livestock, 1990-2011. U.S. Department of Agriculture,
National Agriculture Statistics Service, Washington, D.C.
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 D.C. Available online at
.
United States Geological Survey (USGS), Gap Analysis Program (2012) Protected Areas Database of the United
States (PADUS), version 1.3 Combined Feature Class. November 2012.
USGS (2012) Alaska Resource Data File. Available online at .
USGS (2005) Active Mines and Mineral Processing Plants in the United States in 2003. U.S. Geological Survey,
Reston, VA.
Forest Land Remaining Forest Land: Changes in Forest Carbon
Stocks
AF&PA (2006a and earlier) Statistical roundup. (Monthly). Washington, D.C. American Forest & Paper
Association.
AF&PA (2006b and earlier) Statistics of paper, paperboard and wood pulp. Washington, D.C. 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." Global Biogeochemical 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: U.S. 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, D.C., 1-26 and 261-379 (appendices 262 and 263).
10-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Birdsey, R., and L.S. Heath (2001) "Forest Inventory Data, Models, and Assumptions for Monitoring Carbon Flux."
In Soil Carbon Sequestration and the Greenhouse Effect. Soil Science Society of America. Madison, WI, 125-135.
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.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, (eds.). 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
Qua! 35: 1461-1469.
Coulston, J.W., Woodall, C.W., Domke, G.M., and Walters, B.F. (in preparation) Refined Delineation between
Woodlands and Forests with Implications for U.S. National Greenhouse Gas Inventory of Forests. Climatic Change.
Coulston, J.W., Wear, D.N., and Vose, J.M. (in review) Complex forest dynamics indicate potential for slowing
carbon accumulation in the southeastern United States. Scientific Reports.
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., Perry, C.H., Walters, B.F., Woodall, C.W., and Smith, J.E. (in preparation) Estimation of forest floor
carbon using the national forest inventory of the United States. Intended outlet: Geoderma. 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." Em. Sci. Tech. 31(3):911-917.
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.
EPA (2012) Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2010. EPA, Office of Atmospheric
Programs. Washington, D.C.
EPA (2006) Municipal solid waste in the United States: 2005 Facts and figures. Office of Solid Waste, U.S.
Environmental Protection Agency. Washington, D.C. (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, D.C.
Genet et al. (in preparation) Synthesis of the role of dynamic driving factors (climate, fire, permafrost dynamics, and
forest management) on the historical and projected vegetation and soil organic carbon dynamics in upland
ecosystems of Alaska. Intended outlet: Ecological Applications.
Hair, D. (1958) "Historical forestry statistics of the United States." Statistical Bull. 228. U.S. Department of
Agriculture Forest Service, Washington, D.C.
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, D.C.
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.
References 10-49
-------
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 Smith, J.E. (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., Smith, J.E., and Birdsey, R.A. (2003) Carbon Trends inU. 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., 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., Smith, J.E., Woodall, J.E., Azuma, D.L., and Waddell, K.L. (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. (forthcoming) U.S. timber production, trade, consumption, and price statistics 1965 to 2013. Res.
Pap. FPL-RP-XXX. Madison, WI: USDA, Forest Service, Forest Products Laboratory.
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 (2007) Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth
Assessment Report (AR4) of the IPCC. The Intergovernmental Panel on Climate Change, R.K. Pachauri, A. Resinger
(eds.). Geneva, Switzerland.
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. The Intergovernmental
Panel on Climate Change, National Greenhouse Gas Inventories Programme, J. Penman, et al. (eds.). August 13,
2004. Available online at .
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.
McGuire et al. (in preparation) A synthesis of terrestrial carbon balance of Alaska and projected changes in the 21st
Century: Implications for climate policy and carbon management at local, regional, national, and international
scales. Intended for: Ecological Applications.
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 &Biodegradation. 39(2-3): 145-158.
NASA CMS (2014) NASA Carbon Monitoring System, .
10-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.
Ogle, S.M., Woodall, C.W., Swan, A., Smith, J.E., and Wirth. T. (in preparation) Determining the Managed Land
Base for Delineating Carbon Sources and Sinks in the United States. Environmental Science and Policy.
Oswalt, S.N., W.B. Smith, P.O. Miles, and S.A. Pugh (in preparation/2014) Forest Resources of the United States,
2012. Gen. Tech. Rep. WO-XXX. Washington, D.C. U.S. Department of Agriculture, Forest Service, Washington
Office. XXX p.
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. andFolliott, 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.B., A.W. D'Amato, B.K. Schulz, C.W. Woodall, G.M. Domke, and J.B. Bradford (in press) Quantifying
understory vegetation in the U.S. Lake States: a proposed framework to inform regional forest carbon stocks.
Forestry.
Russell, M.B., Domke, G.M., Woodall, C.W., and D'Amato, A.W (in preparation) Comparisons of allometric and
climate-derived estimates of tree coarse root carbon in forests of the United States: implications for the National
Greenhouse Gas Inventory. Climatic Change.
Saatchi et al. (in preparation) Distribution of Carbon Stocks in Managed and Unmanaged Forests of Alaska.
Intended for: Journal of Carbon Balance 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., 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.
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.
References 10-51
-------
Steer, Henry B. (1948) Lumber production in the United States. Misc. Pub. 669, U.S. Department of Agriculture
Forest Service. Washington, D.C.
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, D.C.
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, D.C, 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 (2014a) Forest Inventory and Analysis National Program: Program Features. U.S. Department
of Agriculture Forest Service. Washington, D.C. Available online at .
Accessed 17 September 2014.
USDA Forest Service. (2014b) Forest Inventory and Analysis National Program: FIA Data Mart. U.S. Department
of Agriculture Forest Service. Washington, D.C. Available online at . Accessed 17 September 2014.
USDA Forest Service. (2014c) Forest Inventory and Analysis National Program, FIA library: Field Guides, Methods
and Procedures. U.S. Department of Agriculture Forest Service. Washington, D.C. Available online at
. Accessed 17 September 2014.
USDA Forest Service (2014d) Forest Inventory and Analysis National Program, FIA library: Database
Documentation. U.S. Department of Agriculture, Forest Service, Washington Office. Available online at
. Accessed 17 September 2014.
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
. 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.
U.S. Census Bureau (1976) Historical Statistics of the United States, Colonial Times to 1970, Vol. 1. Washington,
D.C.
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.
Wear, D. and J. Coulston 2014. Projecting changes in U.S. forest carbon pools and net forest C sequestration.
Working Paper. Center for Integrated Forest Science, 5227 Jordan Hall, North Carolina State University, Raleigh,
North Carolina.
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, and Will-Wolf, S. (201 la) "Status and future of the forest health indicators program of
the United States." Environmental Monitoring and Assessment, 177:419-436.
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.
10-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Woodall, C.W., B.L. Conkling, M.C. Amacher, J.W. Coulston, S. Jovan, C.H. Peny, B. Schulz, G.C. Smith, S. Will
Wolf. (2010). The Forest Inventory and Analysis Database Version 4.0: Database Description and User's 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., 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 (201 la) 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., Heath, L.S., and Smith, J.E. (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
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.
Frelich, L.E. (1992) Predicting Dimensional Relationships for Twin Cities Shade Trees. University of Minnesota,
Department of Forest Resources, St. Paul, MN, p. 33.
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.
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. (2009) E-mail correspondence regarding new data for Chicago's urban forest. David Nowak, USDA
Forest Service to Nikhil Nadkarni, ICF International. October 7, 2009.
Nowak, D.J. (2007a) "New York City's Urban Forest." USDA Forest Service. Newtown Square, PA, February
2007.
Nowak, D.J. (2007b) 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.
References 10-53
-------
Nowak, DJ. (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, DJ. Nowak, and R.A.
Rowntree (eds.). General Technical Report NE-186. U.S. Department of Agriculture Forest Service, Radnor, PA.
pp. 83-94.
Nowak, DJ. (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., Buckelew-Cumming, A., Twardus, D., Hoehn, R., and Mielke, M. (2007). National Forest Health
Monitoring Program, Monitoring Urban Forests in Indiana: Pilot Study 2002, Part 2: Statewide Estimates Using the
UFORE Model. Northeastern Area Report. NA-FR-01e07, p. 13.
Nowak, D J. and D.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., 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., EJ. 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, DJ., 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.
U.S. Census Bureau (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." U.S. Census Bureau, Geography
Division.
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.
USD A Forest Service (2001) U.S. Forest Facts and Historical Trends. FS-696. U.S. Department of Agriculture
Forest Service, Washington, D.C. Available online at .
10-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Cropland Remaining Cropland: Mineral and Organic Soil
Carbon Stock Changes
Armentano, T. V., and E.S. Menges (1986). Patterns of change in the carbon balance of organic soil-wetlands of the
temperate zone. Journal of Ecology 74: 755-774.
Brady, N.C. and R.R. Weil (1999) The Nature and Properties of Soils. Prentice Hall. Upper Saddle River, NJ, 881.
Conant, R. T., K. Paustian, and E.T. Elliott (2001). "Grassland management and conversion into grassland: effects
on soil carbon." Ecological Applications 11: 343-355.
CTIC (2004) National Crop Residue Management Survey: 1989-2004. Conservation Technology Information
Center, Purdue University, Available at
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.
Del Grosso, S.J., W.J. Parton, A.R. Mosier, M.D. Hartman, J. Brenner, D.S. Ojima, andD.S. Schimel (2001)
"Simulated Interaction of Carbon Dynamics and Nitrogen Trace Gas Fluxes Using the DAYCENT Model." In
Modeling Carbon and Nitrogen Dynamics for Soil Management, Schaffer, M., L. Ma, S. Hansen, (eds.). CRC Press,
Boca Raton, Florida, pp. 303-332.
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.ch001. In: Understanding Greenhouse Gas
Emissions from Agricultural Management, L. Guo, A. Gunasekara, L. McConnell (eds.). American Chemical
Society, Washington, D.C.
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
.
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.
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.
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 .
Jones, C., R. Claassen, E. Marx and S.M. Ogle (forthcoming) GHG mitigation and the CRP: the role of post-contract
land use change.
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.
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.
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.
References 10-55
-------
NASS (2004) Agricultural Chemical Usage: 2003 Field Crops Summary. Report AgChl(04)a. National Agricultural
Statistics Service, U.S. Department of Agriculture, Washington, D.C. 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, D.C. Available online at
.
NRCS (1999) Soil 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, D.C.
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.
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 U.S. croplands using a process-based model." Global Change
Biology 16:810-820.
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., et al. (2005) "Agricultural management impacts on soil organic carbon storage under moist and dry
climatic conditions of temperate and tropical regions." Biogeochemistry 72: 87-121.
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.
Ogle, S., M. Eve, M. Sperrow, F.J. 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., 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., 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.
Paustian, K., et al. (1997a). "Agricultural soils as a sink to mitigate CO2 emissions." Soil Use and Management 13:
230-244.
Paustian, K., et al. (1997b). Management controls on soil carbon. In Soil organic matter in temperate
agroecosystems: long-term experiments in North America (Paul E.A., K. Paustian, and C.V. Cole, eds). Boca Raton,
CRC Press, pp. 15-49.
10-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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
fromMODIS satellite data and ecosystem modeling. Earth Interactions 11, Article No. 13, DOI 10.1175/EI228.1.
Soil Survey Staff (2005) State Soil Geographic (STATSGO) Database for State. Natural Resources Conservation
Service, United States Department of Agriculture. 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.
USDA-ERS (2011) Agricultural Resource Management Survey (ARMS) Farm Financial and Crop Production
Practices: Tailored Reports. Online at: .
USDA-ERS (1997) Cropping Practices Survey Data—1995. Economic Research Service, United States Department
of Agriculture. Available online at .
USD A-FSA (2013) Conservation Reserve Program Monthly Summary- September 2013. U.S. Department of
Agriculture, Farm Service Agency, Washington, D.C. 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.
USDA-NRCS (2013) Summary Report: 2010 National Resources Inventory, Natural Resources Conservation
Service, Washington, D.C., and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa.
.
USDA-NRCS (2009) Summary Report: 2007 National Resources Inventory, Natural Resources Conservation
Service, Washington, D.C., and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa,
.
Cropland Remaining Cropland: Liming of Agriculture Soils
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.
Tepordei, V.V. (1997 through 2006) "Crushed Stone," In Minerals Yearbook. U.S. Department of the Interior/U.S.
Geological Survey. Washington, D.C. Available online at
.
Tepordei, V.V. (2003b) Personal communication. Valentin Tepordei, U.S. Geological Survey and ICF Consulting,
August 18, 2003.
Tepordei, V.V. (1996) "Crushed Stone," In Minerals Yearbook 1994. U.S. Department of the Interior/Bureau of
Mines, Washington, D.C. Available online at
. Accessed August 2000.
Tepordei, V.V. (1995) "Crushed Stone," InMinerals Yearbook 1993. U.S. Department of the Interior/Bureau of
Mines, Washington, D.C. pp. 1107-1147.
Tepordei, V V (1994) "Crushed Stone," InMinerals Yearbook 1992. U.S. Department of the Interior/Bureau of
Mines, Washington, D.C. pp. 1279-1303.
Tepordei, V.V. (1993) "Crushed Stone," InMinerals Yearbook 1991. U.S. Department of the Interior/Bureau of
Mines, Washington, D.C. pp. 1469-1511.
References 10-57
-------
United States Geological Survey (USGS) (2014) Mineral Industry Surveys: Crushed Stone and Sand and Gravel in
the First Quarter of 2014, U.S. Geological Survey, Reston, VA. Available online at
.
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, T.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.
Willett, J.C. (2014) "Crushed Stone," In Minerals Yearbook 2012. U.S. Department of the Interior/U.S. Geological
Survey, Washington, D.C. Available online at
. Accessed September 2014.
Willett, J.C. (2013a) "Crushed Stone," InMinerals Yearbook 2011. U.S. Department of the Interior/U.S. Geological
Survey, Washington, D.C. Available online at
. Accessed May 2013.
Willett, J.C. (2013b) Personal Communication. Jason Willet, U.S. Geological Survey and ICF International.
September 9, 2013.
Willett, J.C. (201 la) "Crushed Stone," InMinerals Yearbook 2009. U.S. Department of the Interior/U.S. Geological
Survey, Washington, D.C. Available online at
. Accessed August 2011.
Willett, J.C. (201 Ib) "Crushed Stone," InMinerals Yearbook 2010. U.S. Department of the Interior/U.S. Geological
Survey, Washington, D.C. Available online at
. Accessed September 2012.
Willett, J.C. (2010) "Crushed Stone," In Minerals Yearbook 2008. U.S. Department of the Interior/U.S. Geological
Survey, Washington, D.C. Available online at
. Accessed August 2010.
10-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Willett, J.C. (2009) "Crashed Stone," InMinerals Yearbook 2007. U.S. Department of the Interior/U.S. Geological
Survey, Washington, D.C. Available online at
. Accessed August 2009.
Willett, J.C. (2007a) "Crashed Stone," InMinerals Yearbook 2005. U.S. Department of the Interior/U.S. Geological
Survey, Washington, D.C. 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, D.C. Available online at
. Accessed August 2008.
Cropland Remaining Cropland: Urea Fertilization
AAPFCO (2008 through 2014) 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.
EPA (2000) Preliminary Data Summary: Airport Deicing Operations (Revised). EPA-821-R-00-016. August 2000.
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.
Me, C. (2009) Email correspondence. Cortney Itle, ERG and Tom Wirth, U.S. Environmental Protection Agency on
the amount of urea used in aircraft deicing. January 7, 2009.
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 (1992b) Fertilizer Summary Data 1992. Tennessee Valley Authority, Muscle Shoals, AL.
Land Converted to Cropland
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
Modeling Carbon and Nitrogen Dynamics for Soil Management, Schaffer, M., L. Ma, S. Hansen, (eds.). CRC Press,
Boca Raton, Florida, pp. 303-332.
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.ch001. In: Understanding Greenhouse Gas
Emissions from Agricultural Management (L. Guo, A. Gunasekara, L. McConnell. Eds.), American Chemical
Society, Washington, D.C.
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.
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.
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 U.S. croplands using a process-based model." Global Change
Biology 16:810-820.
References 10-59
-------
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., 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., 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.
Grassland Remaining Grassland
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
Modeling Carbon and Nitrogen Dynamics for Soil Management, Schaffer, M., L. Ma, S. Hansen, (eds.). CRC Press,
Boca Raton, Florida, pp. 303-332.
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.ch001. In: Understanding Greenhouse Gas
Emissions from Agricultural Management (L. Guo, A. Gunasekara, L. McConnell. Eds.), American Chemical
Society, Washington, D.C.
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 .
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.
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, D.C. Publication number npsOO-0579.
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, D.C. 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, D.C. 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, D.C. Available online at
.
NEBRA (2007) A National Biosolids Regulation, Quality, End Use & Disposal Survey. North East Biosolids and
Residuals Association. July 21, 2007.
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 U.S. croplands using a process-based model." Global Change
Biology 16:810-820.
10-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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. 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.
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.
USDA-ERS (2011) Agricultural Resource Management Survey (ARMS) Farm Financial and Crop Production
Practices: Tailored Reports. Online at: .
USDA-ERS (1997) Cropping Practices Survey Data—1995. Economic Research Service, United States Department
of Agriculture. Available online at .
USDA-NRCS (2013) Summary Report: 2010 National Resources Inventory, Natural Resources Conservation
Service, Washington, D.C., and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa.
.
USDA-NRCS (2009) Summary Report: 2007 National Resources Inventory, Natural Resources Conservation
Service, Washington, D.C., and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa.
.
Land Converted to Grassland
Del Grosso, S.J., S.M. Ogle, WJ. Parton. (2011). Soil organic matter cycling and greenhouse gas accounting
methodologies, Chapter 1, pp 3-13 DOI: 10.1021/bk-2011-1072.ch001. In: Understanding Greenhouse Gas
Emissions from Agricultural Management (L. Guo, A. Gunasekara, L. McConnell. Eds.), American Chemical
Society, Washington, D.C.
Del Grosso, S.J., WJ. 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
Modeling Carbon and Nitrogen Dynamics for Soil Management (Schaffer, M., L. Ma, S. Hansen, (eds.). CRC
Press, Boca Raton, Florida, pp. 303-332.
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.
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, D.C. 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, D.C. 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, D.C. Available online at
.
References 10-61
-------
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 U.S. croplands using a process-based model." Global Change
Biology 16:810-820.
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. 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.
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.
USDA-ERS (2011) Agricultural Resource Management Survey (ARMS) Farm Financial and Crop Production
Practices: Tailored Reports. Online at: .
USDA-ERS (1997) Cropping Practices Survey Data—1995. Economic Research Service, United States Department
of Agriculture. Available online at .
USDA-NRCS (2013) Summary Report: 2010 National Resources Inventory, Natural Resources Conservation
Service, Washington, D.C. and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa.
.
USDA-NRCS (2009) Summary Report: 2007 National Resources Inventory, Natural Resources Conservation
Service, Washington, D.C. and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa,
.
Wetlands Remaining Wetlands: CO2, CH4, 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, J., 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 (1997-2014)
Alaska's Mineral Industry Report (1997-2013). Alaska Department of Natural Resources, Fairbanks, AK.
Available online at .
IPCC (2013) 2013 Supplement to the 2006IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands.
Hiraishi, T., Krug, T., Tanabe, K., Srivastava, N., Baasansuren, J., Fukuda, M. and Troxler, T.G. (eds.). Published:
IPCC, Switzerland.
IPCC (2007) Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth
Assessment Report (AR4) of the IPCC. The Intergovernmental Panel on Climate Change, R.K. Pachauri, A. Resinger
(eds.). Geneva, Switzerland.
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.
10-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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 18, 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 17, 2008.
United States Geological Survey (USGS) (2014b) Mineral Commodity Summaries: Peat (2013). United States
Geological Survey, Reston, VA. Available online at
.
USGS (2015) Mineral Commodity Summaries: Peat (2014). United States Geological Survey, Reston, VA.
Available online at .
USGS (1991-2014a) Minerals Yearbook: Peat (1994-2013). United States Geological Survey, Reston, VA.
Available online at .
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.
Frelich, L.E. (1992) Predicting Dimensional Relationships for Twin Cities Shade Trees. University of Minnesota,
Department of Forest Resources, St. Paul, MN, p. 33.
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.
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. (2009) E-mail correspondence regarding new data for Chicago's urban forest. David Nowak, USDA
Forest Service to Nikhil Nadkarni, ICF International. October 7, 2009.
Nowak, D.J. (2007a) "New York City's Urban Forest." USDA Forest Service. Newtown Square, PA, February
2007.
Nowak, D.J. (2007b) 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. (1994) "Atmospheric Carbon Dioxide Reductionby 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., Buckelew-Cumming, A., Twardus, D., Hoehn, R., and Mielke, M. (2007). National Forest Health
Monitoring Program, Monitoring Urban Forests in Indiana: Pilot Study 2002, Part 2: Statewide Estimates Using the
UFORE Model. Northeastern Area Report. NA-FR-01e07, p. 13.
References 10-63
-------
Nowak, D. J. and D.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., 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., EJ. 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., 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.
U.S. Census Bureau (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." U.S. Census Bureau, Geography
Division.
Settlements Remaining Settlements: N2O Fluxes from Soils
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 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. U.S. Department of the
Interior.
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.
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 Biode gradation of Municipal Solid Waste Components in Laboratory-
Scale Landfills." Global Biogeochemical Cycles 12:373-380.
De la Cruz, F.B. and M.A. Barlaz (2010) "Estimation of Waste Component Specific Landfill Decay Rates Using
Laboratory-Scale Decomposition Data" Environmental Science & Technology 44:4722- 4728.
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 (2014a) Municipal Solid Waste Generation, Recycling, and Disposal in the United States: 2012 Facts and
Figures. U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington,
D.C. Available online at .
EPA (2014b) Municipal Solid Waste Generation, Recycling, and Disposal in the United States: 2012 Historical
(summary) Data Tables. U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response,
Washington, D.C. Available online at
.
10-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
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.
Tchobanoglous, G., H. Theisen, and S.A. Vigil (1993) Integrated Solid Waste Management, 1st edition. McGraw-
Hill, NY. Cited by Barlaz (1998) "Carbon Storage during Biodegradation of Municipal Solid Waste Components in
Laboratory-Scale Landfills." Global Biogeochemical Cycles 12:373-380.
Waste
Landfills
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
.
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. VanHaaren, 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. VanHaaren, 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., andR. 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 .
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 El A Form 1605B (Reporting Year 2006). Available online at
.
EPA (2014a) Landfill Gas-to-Energy Project Database. Landfill Methane and Outreach Program. January 2014.
EPA (2014b) Greenhouse Gas Reporting Program (GHGRP). 2014 Envirofacts. Subpart HH: Municipal Solid Waste
Landfills. Available online at .
EPA (2014c) Municipal Solid Waste Generation, Recycling, and Disposal in the United States Detailed Tables and
Figures for 2012. February 2014. Available online at <
http://epa.gov/epawaste/nonhaz/municipal/pubs/2012_msw_dat_tbls.pdf >.
EPA (2008) Compilation of Air Pollution Emission Factors, Publication AP-42, Draft Section 2.4 Municipal Solid
Waste Landfills. October 2008.
References 10-65
-------
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, D.C. EPA/430-R-93-003. April 1993.
EPA (1988) National Survey of Solid Waste (Municipal) Landfill Facilities, U.S. Environmental Protection Agency.
Washington, D.C. EPA/530-SW-88-011. September 1988.
ERG (2014) Draft Production Data Supplied by ERG for 1990-2013 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.
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.
Shin, D. (2014). Generation and Disposition of Municipal Solid Waste (MSW) in the United States - A National
Survey. Master of Science thesis submitted to the Department of Earth and Environmental Engineering Fu
Foundation School of Engineering and Applied Science, Columbia University. January 3, 2014. Available online at
.
Solid Waste Association of North America (SWAN A) (1998) Comparison of Models for Predicting Landfill
Methane Recovery. Publication No. GR-LG0075. March 1998.
U.S. Census Bureau (2014). Annual Population Estimates, Vintage 2012 April 1, 2010 to July 1, 2013. Available
online at .
Waste Business Journal (WBJ) (2010). Directory of Waste Processing & Disposal Sites 2010.
Wastewater Treatment
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.
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.
Climate Action Reserve (CAR) (2011) Landfill Project Protocol V4.0, June 2011. Available online at
.
10-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Chandran, K. (2012) Greenhouse Nitrogen Emissions from Wastewater Treatment Operation Phase I: Molecular
Level Through Whole Reactor Level Characterization. WERF Report U4R07.
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 (2014) Energy Information Administration. U.S. Refinery and Blender Net Production of Crude Oil and
Petroleum Products (Thousand Barrels). Available online at: .
Accessed June 2014.
EPA (2008) US Environmental Protection Agency. Municipal Nutrient Removal Technologies Reference
Document: Volume 2 - Appendices. U.S. Environmental Protection Agency, Office of Wastewater Management.
Washington, D.C.
EPA (2004) U.S. Environmental Protection Agency. Clean Watersheds Needs Survey 2004 - Report to Congress.
U.S. Environmental Protection Agency, Office of Wastewater Management. Washington, D.C.
EPA (2002) U.S. Environmental Protection Agency. Development Document for the Proposed Effluent Limitations
Guidelines and Standards for the Meat and Poultry Products Industry Point Source Category (40 CFR 432). EPA-
821-B-01-007. Office of Water, U.S. Environmental Protection Agency. Washington, D.C. January 2002.
EPA (2000) U.S. Environmental Protection Agency. Clean Watersheds Needs Survey 2000 - Report to Congress.
Office of Wastewater Management, U.S. Environmental Protection Agency. Washington, D.C. Available online at
. Accessed July 2007.
EPA (1999) U.S. Environmental Protection Agency. Biosolids Generation, Use and Disposal in the United States.
Office of Solid Waste and Emergency Response, U.S. Environmental Protection Agency. Washington, D.C.
EPA530-R-99-009. September 1999.
EPA (1998) U.S. Environmental Protection Agency. "AP-42 Compilation of Air Pollutant Emission Factors."
Chapter 2.4, Table 2.4-3, page 2.4-13. Available online at
.
EPA (1997a) U.S. Environmental Protection Agency. 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, D.C. September 1997.
EPA (1997b) U.S. Environmental Protection Agency. Supplemental Technical Development Document for Effluent
Guidelines and Standards (Subparts B & E). EPA-821-R-97-011. Office of Water, U.S. Environmental Protection
Agency. Washington, D.C. October 1997.
EPA (1996) U.S. Environmental Protection Agency. 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, D.C. Available online at
. Accessed July 2007.
EPA (1993) U.S. Environmental Protection Agency. 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, D.C. October 1993.
EPA (1992) U.S. Environmental Protection Agency. Clean Watersheds Needs Survey 1992 - Report to Congress.
Office of Wastewater Management, U.S. Environmental Protection Agency. Washington, D.C.
EPA (1975) U.S. Environmental Protection Agency. 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 D.C. October 1975.
EPA (1974) U.S. Environmental Protection Agency. Development Document for Effluent Limitations Guidelines
and New Source Performance Standards for the Apple, Citrus, and Potato Processing Segment of the Canned and
References 10-67
-------
Preserved Fruits and Vegetables Point Source Category. Office of Water, U.S. Environmental Protection Agency,
Washington, D.C. EPA-440/l-74-027-a. March 1974.
ERG (2014a) Recommended Improvements to the 1990-2013 Wastewater Greenhouse Gas Inventory. September
2014.
ERG (2014b) Recommended Improvements to the 1990-2013 Wastewater Greenhouse Gas Inventory Using the
GHGRP Data. September 2014.
ERG (2013a) Revisions to Pulp and Paper Wastewater Inventory. October 2013.
ERG (2013b) Revisions to the Petroleum Wastewater Inventory. October 2013.
ERG (2011) Review of Current Research on Nitrous Oxide Emissions from Wastewater Treatment. April 2011.
ERG (2008) Planned Revisions of the Industrial Wastewater Inventory Emission Estimates for the 1990-2007
Inventory. August 10, 2008.
ERG (2006) Memorandum: Assessment of Greenhouse Gas Emissions from Wastewater Treatment of U.S. Ethanol
Production Wastewaters. Prepared for Melissa Weitz, EPA. 10 October 2006.
FAO (2014) FAOSTAT-Forestry Database. Available online at
Accessed July 2013.
Global Water Research Coalition (GWRC) (2011) N2O and CH4 Emission from Wastewater Collection and
Treatment Systems - technical Report. GWRC Report 2011-30.
Great Lakes-Upper Mississippi River Board of State and Provincial Public Health and Environmental Managers.
(2004) Recommended Standards for Wastewater Facilities (Ten-State Standards).
IPCC (2014) 2013 Supplement to the 2006 IP CC Guidelines for National Greenhouse Gas Inventories: Wetlands.
Hiraishi, T., Krug, T., Tanabe, K., Srivastava, N., Baasansuren, I, Fukuda, M. and Troxler, T.G. (eds.). Published:
IPCC, Switzerland.
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.
Leverenz, H.L., G. Tchobanoglous, and J.L. Darby (2010) "Evaluation of Greenhouse Gas Emissions from Septic
Systems". Water Environment Research Foundation. Alexandria, VA.
Lockwood-Post (2002) Lockwood-Post's Directory of Pulp, Paper and Allied Trades, Miller-Freeman Publications.
San Francisco, CA.
McFarland (2001) Biosolids Engineering, New York: McGraw-Hill, p. 2.12.
Merrick (1998) Wastewater Treatment Options for the Biomass-to-Ethanol Process. Report presented to National
Renewable Energy Laboratory (NREL). Merrick & Company. Subcontract No. AXE-8-18020-01. October 22,
1998.
Metcarf & Eddy, Inc. (2003) Wastewater Engineering: Treatment, Disposal and Reuse, 4th ed. McGrawHill
Publishing.
Nemerow, N.L. and A. Dasgupta (1991) Industrial and Hazardous Waste Treatment. Van Nostrand Reinhold. NY.
ISBN 0-442-31934-7.
NRBP (2001) Northeast Regional Biomass Program. An Ethanol Production Guidebook for Northeast States.
Washington, D.C. (May 3). Available online at . Accessed October 2006.
Rendleman, C.M. and Shapouri, H. (2007) New Technologies in Ethanol Production. USDA Agricultural Economic
Report Number 842.
10-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
RFA (2014) Renewable Fuels Association. Historic U.S. Fuel Ethanol Production. Available online at
. Accessed October 2012.
Ruocco (2006a) Email correspondence. Dr. Joe Ruocco, Phoenix Bio-Systems to Sarah Holman, ERG. "Capacity of
Bio-Methanators (Dry Milling)." October 6, 2006.
Ruocco (2006b) Email correspondence. Dr. Joe Ruocco, Phoenix Bio-Systems to Sarah Holman, ERG. "Capacity
of Bio-Methanators (Wet Milling)." October 16, 2006.
Scheehle, E.A., and Doom, M.R. (2001) "Improvements to the U.S. Wastewater Methane and Nitrous Oxide
Emissions Estimate." July 2001.
Sullivan (SCS Engineers) (2010) The Importance of Landfill Gas Capture and Utilization in the U.S. Presented to
SWICS, April 6, 2010. Available online at
.
Sullivan (SCS Engineers) (2007) Current MSW Industry Position and State of the Practice on Methane Destruction
Efficiency in Flares, Turbines, and Engines. Presented to Solid Waste Industry for Climate Solutions (SWICS). July
2007. Available online at
.
UNFCCC (2012) COM Methodological tool, Project emissions from flaring (Version 02.0.0). EB 68 Report. Annex
15. Available online at .
U.S. Census Bureau (2014) International Database. Available online at and
. Accessed August 2014.
U.S. Census Bureau (2011) "American Housing Survey." Table 1A-4: Selected Equipment and Plumbing-All
Housing Units. From 1989, 1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005, 2007, 2009 and 2011 reports.
Available online at . Accessed October 2012.
U.S. DOE (2013) U.S. Department of Energy Bioenergy Technologies Office. Biofuels Basics. Available online at
. Accessed September 2013.
USD A (2014a) U.S. Department of Agriculture. National Agricultural Statistics Service. Washington, D.C.
Available online at and
. Accessed June 2014.
USD A (2014b) U.S. Department of Agriculture. Economic Research Service. Nutrient Availability. Washington
D.C. Available online at
.
Accessed August 2014.
USPoultry (2006) Email correspondence. John Starkey, USPOULTRY to D. Bartram, ERG. 30 August 2006.
White and Johnson (2003) White, P.J. and Johnson, L.A. Editors. Corn: Chemistry and Technology. 2nd ed.
AACC Monograph Series. American Association of Cereal Chemists. St. Paul, MN.
Willis et al. (2013) Methane Evolution from Lagoons and Ponds. Prepared for the Water Environment Research
Foundation under contract U2R08c.
World Bank (1999) Pollution Prevention and Abatement Handbook 1998, Toward Cleaner Production. The
International Bank for Reconstruction and Development, The World Bank, Washington, D.C. ISBN 0-8213-3638-
X.
Composting
BioCycle (2010) The State of Garbage in America. Prepared by Rob vanHaaren, Nickolas Themelis and Nora
Goldstein. Available online at .
References 10-69
-------
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, D.C. 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, D.C. 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, D.C. 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 .
Shin, D. (2014). Generation and Disposition of Municipal Solid Waste (MSW) in the United States - A National
Survey. Table 3. Master of Science thesis, Department of Earth and Environmental Engineering, Fu Foundation
School of Engineering and Applied Science, Columbia University. Available online at
.
U.S. Census Bureau (2014) Population Estimates: National Totals: Vintage 2013. Available online at
.
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 (2015) "1970 - 2014 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, March 2015.
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.
Recalculations and Improvements
ACC (2014a) U.S. Chemical Industry Statistical Handbook. American Chemistry Council, Arlington, VA.
ACC (2014b) Business of Chemistry (Annual Data). American Chemistry Council, Arlington, VA.
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 .
EIA (2015) Monthly Energy Review, February 2015, Energy Information Administration, U.S. Department of
Energy, Washington, DC. DOE/EIA-0035(2015/2).
EPA (2014) Greenhouse Gas Reporting Program. Aggregation of reported facility level data under Subpart V -
National Nitric Acid production for calendar years 2010-2013. Office of Air and Radiation, Office of Atmospheric
Programs, U.S. Environmental Protection Agency, Washington, D.C.
10-70 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013
-------
Howard, J. L. (forthcoming) U.S. timber production, trade, consumption, and price statistics 1965 to 2013. Res.
Pap. FPL-RP-XXX. Madison, WI: USD A, Forest Service, Forest Products Laboratory.
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.
References 10-71
------- |